Course Highlights & Why Data Science Course in Chennai at FITA Academy?
Upcoming Batches
05-10-2023 | Weekdays | Thursday (Monday - Friday) | |
07-10-2023 | Weekend | Saturday (Saturday - Sunday) | |
09-10-2023 | Weekdays | Monday (Monday - Friday) | |
14-10-2023 | Weekend | Saturday (Saturday - Sunday) |
Classroom Training
- Get trained by Industry Experts via Classroom Training at any of the FITA Academy branches near you
- Why Wait? Jump Start your Career by taking Data Science Course in Chennai!
Instructor-Led Live Online Training
- Take-up Instructor-led Live Online Training. Get the Recorded Videos of each session.
- Travelling is a Constraint? Jump Start your Career by taking the Data Science Training Online!
Syllabus
- Understanding Data Science
- The Data Science Life Cycle
- Understanding Artificial Intelligence (AI)
- Overview of Implementation of Artificial Intelligence
- Machine Learning
- Deep Learning
- Artificial Neural Networks (ANN)
- Natural Language Processing (NLP)
- How R connected to Machine Learning
- R - as a tool for Machine Learning Implementation
- What is Python and history of Python
- Python-2 and Python-3 differences
- Install Python and Environment Setup
- Python Identifiers, Keywords and Indentation
- Comments and document interlude in Python
- Command line arguments and Getting User Input
- Python Basic Data Types and Variables
- Understanding Lists in Python
- Understanding Iterators
- Generators, Comprehensions and Lambda Expressions
- Understanding and using Ranges
- Introduction to the section
- Python Dictionaries and More on Dictionaries
- Sets and Python Sets Examples
- Reading and writing text files
- Appending to Files
- Writing Binary Files Manually and using Pickle Module
- Python user defined functions
- Python packages functions
- The anonymous Functions
- Loops and statement in Python
- Python Modules & Packages
- What is Exception?
- Handling an exception
- try….except…else
- try-finally clause
- Argument of an Exception
- Python Standard Exceptions
- Raising an exceptions
- User-Defined Exceptions
- What are regular expressions?
- The match Function and the Search Function
- Matching vs Searching
- Search and Replace
- Extended Regular Expressions and Wildcard
- Collections – named tuples, default dicts
- Debugging and breakpoints, Using IDEs
- Understanding different types of Data
- Understanding Data Extraction
- Managing Raw and Processed Data
- Wrangling Data using Python
- Using Mean, Median and Mode
- Variation and Standard Deviation
- Probability Density and Mass Functions
- Understanding Conditional Probability
- Exploratory Data Analysis (EDA)
- Working with Numpy, Scipy and Pandas
- Understand what is a Machine Learning Model
- Various Machine Learning Models
- Choosing the Right Model
- Training and Evaluating the Model
- Improving the Performance of the Model
- Understanding Predictive Model
- Working with Linear Regression
- Working with Polynomial Regression
- Understanding Multi Level Models
- Selecting the Right Model or Model Selection
- Need for selecting the Right Model
- Understanding Algorithm Boosting
- Various Types of Algorithm Boosting
- Understanding Adaptive Boosting
- Understanding the Machine Learning Algorithms
- Importance of Algorithms in Machine Learning
- Exploring different types of Machine Learning Algorithms
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Understanding the Supervised Learning Algorithm
- Understanding Classifications
- Working with different types of Classifications
- Learning and Implementing Classifications
- Logistic Regression
- Naïve Bayes Classifier
- Nearest Neighbour
- Support Vector Machines (SVM)
- Decision Trees
- Boosted Trees
- Random Forest
- Time Series Analysis (TSA)
- Understanding Time Series Analysis
- Advantages of using TSA
- Understanding various components of TSA
- AR and MA Models
- Understanding Stationarity
- Implementing Forecasting using TSA
- Understanding Unsupervised Learning
- Understanding Clustering and its uses
- Exploring K-means
- What is K-means Clustering
- How K-means Clustering Algorithm Works
- Implementing K-means Clustering
- Exploring Hierarchical Clustering
- Understanding Hierarchical Clustering
- Implementing Hierarchical Clustering
- Understanding Dimensionality Reduction
- Importance of Dimensions
- Purpose and advantages of Dimensionality Reduction
- Understanding Principal Component Analysis (PCA)
- Understanding Linear Discriminant Analysis (LDA)
Understanding Hypothesis Testing
- What is Hypothesis Testing in Machine Learning
- Advantages of using Hypothesis Testing
- Basics of Hypothesis
- Normalization
- Standard Normalization
- Parameters of Hypothesis Testing
- Null Hypothesis
- Alternative Hypothesis
- The P-Value
- Types of Tests
- T Test
- Z Test
- ANOVA Test
- Chi-Square Test
- Understanding Reinforcement Learning Algorithm
- Advantages of Reinforcement Learning Algorithm
- Components of Reinforcement Learning Algorithm
- Exploration Vs Exploitation tradeoff
- What is R?
- History and Features of R
- Introduction to R Studio
- Installing R and Environment Setup
- Command Prompt
- Understanding R programming Syntax
- Understanding R Script Files
- Data types in R
- Creating and Managing Variables
- Understanding Operators
- Assignment Operators
- Arithmetic Operators
- Relational and Logical Operators
- Other Operators
- Understanding and using Decision Making Statements
- The IF Statement
- The IF…ELSE statement
- Switch Statement
- Understanding Loops and Loop Control
- Repeat Loop
- While Loop
- For Loop
- Controlling Loops with Break and Next Statements
More on Data Types
- Understanding the Vector Data type
- Introduction to Vector Data type
- Types of Vectors
- Creating Vectors and Vectors with Multiple Elements
- Accessing Vector Elements
- Understanding Arrays in R
- Introduction to Arrays in R
- Creating Arrays
- Naming the Array Rows and Columns
- Accessing and manipulating Array Elements
- Understanding the Matrices in R
- Introduction to Matrices in R
- Creating Matrices
- Accessing Elements of Matrices
- Performing various computations using Matrices
- Understanding the List in R
- Understanding and Creating List
- Naming the Elements of a List
- Accessing the List Elements
- Merging different Lists
- Manipulating the List Elements
- Converting Lists to Vectors
- Understanding and Working with Factors
- Creating Factors
- Data frame and Factors
- Generating Factor Levels
- Changing the Order of Levels
- Understanding Data Frames
- Creating Data Frames
- Matrix Vs Data Frames
- Sub setting data from a Data Frame
- Manipulating Data from a Data Frame
- Joining Columns and Rows in a Data Frame
- Merging Data Frames
- Converting Data Types using Various Functions
- Checking the Data Type using Various Functions
- Understanding Functions in R
- Definition of a Function and its Components
- Understanding Built in Functions
- Character/String Functions
- Numerical and Statistical Functions
- Date and Time Functions
- Understanding User Defined Functions (UDF)
- Creating a User Defined Function
- Calling a Function
- Understanding Lazy Evaluation of Functions
- Understanding External Data
- Understanding R Data Interfaces
- Working with Text Files
- Working with CSV Files
- Understanding Verify and Load for Excel Files
- Using WriteBin() and ReadBin() to manipulate Binary Files
- Understanding the RMySQL Package to Connect and Manage MySQL Databases
- What is Data Visualization
- Understanding R Libraries for Charts and Graphs
- Using Charts and Graphs for Data Visualizations
- Exploring Various Chart and Graph Types
- Pie Charts and Bar Charts
- Box Plots and Scatter Plots
- Histograms and Line Graphs
- Understanding the Basics of Statistical Analysis
- Uses and Advantages of Statistical Analysis
- Understanding and using Mean, Median and Mode
- Understanding and using Linear, Multiple and Logical Regressions
- Generating Normal and Binomial Distributions
- Understanding Inferential Statistics
- Understanding Descriptive Statistics and Measure of Central Tendency
- Understanding Packages
- Installing and Loading Packages
- Managing Packages
- Understand what is a Machine Learning Model
- Various Machine Learning Models
- Choosing the Right Model
- Training and Evaluating the Model
- Improving the Performance of the Model
- Understanding Predictive Model
- Working with Linear Regression
- Working with Polynomial Regression
- Understanding Multi Level Models
- Selecting the Right Model or Model Selection
- Need for selecting the Right Model
- Understanding Algorithm Boosting
- Various Types of Algorithm Boosting
- Understanding Adaptive Boosting
- Understanding the Machine Learning Algorithms
- Importance of Algorithms in Machine Learning
- Exploring different types of Machine Learning Algorithms
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Understanding the Supervised Learning Algorithm
- Understanding Classifications
- Working with different types of Classifications
- Learning and Implementing Classifications
- Logistic Regression
- Naïve Bayes Classifier
- Nearest Neighbor
- Support Vector Machines (SVM)
- Decision Trees
- Boosted Trees
- Random Forest
- Time Series Analysis (TSA)
- Understanding Time Series Analysis
- Advantages of using TSA
- Understanding various components of TSA
- AR and MA Models
- Understanding Stationarity
- Implementing Forecasting using TSA
- Understanding Unsupervised Learning
- Understanding Clustering and its uses
- Exploring K-means
- What is K-means Clustering
- How K-means Clustering Algorithm Works
- Implementing K-means Clustering
- Exploring Hierarchical Clustering
- Understanding Hierarchical Clustering
- Implementing Hierarchical Clustering
- Understanding Dimensionality Reduction
- Importance of Dimensions
- Purpose and advantages of Dimensionality Reduction
- Understanding Principal Component Analysis (PCA)
- Understanding Linear Discriminant Analysis (LDA)
- What is Hypothesis Testing in Machine Learning
- Advantages of using Hypothesis Testing
- Basics of Hypothesis
- Normalization
- Standard Normalization
- Parameters of Hypothesis Testing
- Null Hypothesis
- Alternative Hypothesis
- The P-Value
- Types of Tests
- T Test
- Z Test
- ANOVA Test
- Chi-Square Test
- Understanding Reinforcement Learning Algorithm
- Advantages of Reinforcement Learning Algorithm
- Components of Reinforcement Learning Algorithm
- Exploration Vs Exploitation tradeoff
Have Queries? Talk to our Career Counselor
for more Guidance on picking the right Career for you! .
Trainer Profile
- Trainers at FITA Academy are the best in the field and have 8+ years of experience in the field of Data Science.
- The trainers have extensive experience working on projects that are related to real-life situations.
- As working professionals in multinational companies, they are highly qualified and experienced.
- They are certified professionals in our institute with extensive practical and theoretical knowledge of data science concepts.
- In order to gain industry experience, the trainers provide detailed hands-on training and have the students work on real-time projects during training.
- Students are trained by the instructors on how to make use of the latest algorithms and tools used in data science, as well as the methods.
- The trainers provide students with the individual attention they need and assist them in achieving their career goals.
- At FITA Academy, trainers guide students with the necessary interview tips & support in building up a successful resume as part of their training.
- Students are guided by trainers in enhancing their technical skills in Data Science so that they can excel in the field.
Features
Real-Time Experts as Trainers
At FITA Academy, You will Learn from the Experts from industry who are Passionate in sharing their Knowledge with Learners. Get Personally Mentored by the Experts.
LIVE Project
Get an Opportunity to work in Real-time Projects that will give you a Deep Experience. Showcase your Project Experience & Increase your chance of getting Hired!
Certification
Get Certified by FITA Academy. Also, get Equipped to Clear Global Certifications. 72% FITA Academy Students appear for Global Certifications and 100% of them Clear it.
Affordable Fees
At FITA Academy, Course Fee is not only Affordable, but you have the option to pay it in Installments. Quality Training at an Affordable Price is our Motto.
Flexibility
At FITA Academy, you get Ultimate Flexibility. Classroom or Online Training? Early morning or Late evenings? Weekdays or Weekends? Regular Pace or Fast Track? - Pick whatever suits you the Best.
Placement Support
Tie-up & MOU with more than 1500+ Small & Medium Companies to Support you with Opportunities to Kick-Start & Step-up your Career.
Data Science Certification Training in Chennai
About Data Science Certification Training in Chennai
at FITA Academy
Data Science Certification Training in Chennai
Data Science course certification is a professional qualification that shows the ability of the candidate to acquire total subject knowledge in addition to learning all the basic tools and algorithms that are utilized by Data Science professionals. Having this certification will help the student get the best job opportunities in MNCs. With this certification, you will be equipped with the necessary skills to begin your career in the Data Science field. With this certification, you can make a positive impression on the interviewer and get the job easily on the spot.
By gaining a thorough understanding of the major services in this Data science field, you will be able to make informed decisions. This is a great opportunity for those who are looking for a kickstart in their career in Data Science. They can join the FITA Academy’s Data Science Course in Chennai to get their career off on the right foot. This course will lead them to a successful career path in Data Science.
To become a master of this field, you need to have some formal training. That’s where FITA Academy comes in, we offer a Data Science Certification in Chennai that will teach you everything from foundational concepts to advanced techniques. In fact, our courses are so comprehensive and intensive that they can equip you with the skills needed not only for getting started in data science but also for advancing your career as a data scientist.
This is the perfect time to be considered as there are many job opportunities in the area of data science. You can also get a good pay package with more value-added skills with this certification. So if you are planning to move into the world of analytics and want to make your career brighter and better, then take our Data Science Course in Chennai.
The best part is that it doesn’t matter whether you come from any background or not. However, we recommend starting from scratch by enrolling in trial classes. Once you have completed the data science course in Chennai successfully, we will provide you with comprehensive support till you get certified. We help our students through every step of getting certified. Students are given ample practice sessions in order to learn the concepts well and understand them easily.
Get ready for success by joining FITA Academy today! It is the right choice as our institute provides quality education on Data Science without making any compromises on teaching methodology. If you wish to gain knowledge and expertise in Data Science, join today!.
The course curriculum covers all topics related to Machine Learning, Statistics, Programming languages like Python, Big Data Analytics, R, Cloud Computing, etc.
Benefits of getting Data Science Certification in Chennai at FITA Academy
In today’s competitive job market, having a data science certification can give you an edge. Our course will teach you the fundamentals of data science, giving you the skills you need to analyze and interpret data. Here are some of the benefits of taking our Data Science Training in Chennai:
- Career Opportunities: Many companies are now hiring data scientists, and this is a very good opportunity for those who want to make their career as data scientists.
- Flexible Timings: As per the availability of candidates, you can choose your timings. The training will be conducted according to your convenience at weekdays, weekends and fast track batches.
- Real-time Project Work: During the course, we will provide real-time projects that help students get hands-on experience.
- Comprehensive Knowledge: We have designed this data science course in Chennai based on industry standards that cover all the aspects related to data science.
- 100% Placement Assistance: We always believe in providing quality education, and there is no compromise on that front. So after completion of the course, you will receive 100% placement assistance.
- Get Hands-On Experience: Our curriculum is designed in such a way that it provides the best exposure to the topics covered by each module.
- Fast Track Learning Methodology: Our learning methodology enables you to grasp things easily without having any problems.
- No Prerequisites: Are you new to data science? We have covered topics like basic data analysis using the R programming language. If you don’t know any programming, then you can join us without any prerequisites.
- Guaranteed Results: Your success is our success. Because we take care of your future by ensuring that you achieve great results.
The benefits of taking our Data Science Course in Chennai are many. To name a few, students will gain the skills and knowledge necessary to enter the data analytics field, increase their productivity, and develop a stronger foundation in statistics. We hope that this training will help you achieve your career goals.
Have Queries? Talk to our Career Counselor
for more Guidance on picking the right Career for you! .
Job Opportunities After Completing Data Science Course in Chennai
With the development of applications incorporating Big data and artificial intelligence, the demand for data science is growing at an unprecedented rate. To determine which series to produce in the future, P&G generates time series models of the demand for their products using data science. In contrast, Netflix uses data science to understand the viewing patterns of the audience in order to determine which shows to produce in the future.
However, supply does not keep up with demand. The time is perfect for becoming a data scientist. Employers are increasingly interested in hiring data scientists. Managing the large amounts of data flowing into social media and e-commerce sites requires the expertise of data scientists. The majority of companies also consider data scientists to be the right path to embracing Artificial Intelligence.
Despite the fact that most of the major companies are preparing to invest in data mining operations, there are also a number of smaller companies that are ready to do the same.
The combination of all these factors is projected to lead to an increase in the number of data science jobs in the next year that is about 30% higher than the year before. In fact, it is the perfect time for you to advance your knowledge of data science and improve your abilities.
Why is becoming a data scientist so difficult?
Becoming a data scientist is not so difficult as questioned by many students. Candidates who have skills in working on the tools and techniques of data science are vital to become a data scientist. Equipping yourself with the technical skills along with statistics and applied mathematics helps you to prosper in career as a data scientist.
A person should have hands-on experience in the tools and programming languages like R or Python which are widely used by data scientists. An aspiring data scientist would have thorough practical knowledge about the functionality of the tools and methods used. In recent days, numerous online platforms offer data science courses, but could not convert learners to data scientists due to lack of continued guidance and personal training.
Data Science course in Chennai, provided by FITA Academy, covers a wide syllabus which helps to land in your dream career as a Data Scientist. Training is provided by professionals with more than a decade of experience in this field and with exceptional placement support making FITA Academy the best Data Science Training institute in Chennai.
What are the challenges about getting a data scientist job if data science is in demand?
Competency is a keyword to be kept in mind if you wish to be hired as a data scientist. With the increasing demand for data scientists, companies are in search of candidates with exceptional skills in data science.
A data scientist should have sound analytical skills, technical skills to perform tasks using various tools and techniques, programming ability, knowledge in statistics and understanding of the business.Many aspiring data scientists, fail to understand the requirements of the industry due to the numerous guidance they receive from various sources, which provides superficial knowledge about Data Science.
In short, Data Scientist is a person who finds the important aspects of data using math and statistics skills, correlates and finds the linkage between different sets of data, develop models with the data using programming languages like Python or R and provide valuable business insights or strategies for the company. Possessing exceptional knowledge in statistics without sufficient programming skills or a clear understanding of the business leads nowhere close to becoming a data scientist. One must possess hands-on experience in the tools used in the field of Data Science. Arriving at vital findings from data for developing business strategies using the data science tools and technique makes an authentic data scientist.
Though most of the companies hire freshers from IITs, aspiring candidates from any university with expertise in skill sets can become a data scientist. Data Science course in Chennai, provided by FITA Academy, helps you to acquire the desired skill sets to land in your dream career as a Data Scientist.
What are the skills required to be a Data Scientist?
Data Science, as a field, has grown rapidly in recent years and the demand for quality Data Scientists are high. Below are some common skills, which will be expected of an aspiring Data Scientist by various companies.
- Programming language – A candidate should be well versed in coding using programming languages like Python, R and querying languages like SQL. Python & R are used by a vast majority of organizations and they would like to hire a candidate with an excellent skill set in these programming languages.
- Data Visualisation – Data scientists should visualize the data using the visualization tools like Matplotlib, Tableau and various other methods, to convert the results into an understandable format. These tools display the results in the form of graphs, bar-charts, pie-charts, etc. Having hands-on experience in these tools, helps the organization to derive business insights quickly from the data processed. Thus a data scientist is expected to possess these skills.
- Machine Learning – A person is expected to know Machine Learning methods, if the company’s product itself is highly data-driven (e.g, Google, Facebook, Uber, etc.). Candidates should have a clear understanding of the applicability of the following ML methods like K-Nearest Neighbour, ensemble methods, random forests, support vector machines, etc. to deduce the most vital insights from the processed data.
- Statistics – Statistics is vital for a data scientist to understand various techniques which have a valid approach. candidates should be well-known with statistical tests, distributions, etc. A deep understanding of statistics helps the data scientist to provide valuable insights to make strategic business decisions.
- Communication skills – Organisations that hire Data Scientists, expect the candidate to have sound communication skills, so that the technical findings of a data scientist will be known within the organization across non-technical departments (sales, marketing, etc.). The clarity in communication saves a lot of time and resources, thereby increasing business productivity.
Anyone willing to become a data scientist can acquire and develop their skills by joining the Data Science course in Chennai, provided by FITA Academy. Training is provided by professionals with more than a decade of experience in this field which will enable candidates to increase their competency to excel in their career as a data scientist.
What are the differences between Data scientist vs Data Analyst vs data engineer?
Data science has become the most prominent word in recruitment sites due to its demand in various organizations around the world. You could have noticed various designations like Data Scientists, Data Analyst, Data Engineer, and various other terms also. Some people tend to think that these terms are synonymous and use them interchangeably. Although, all the three roles involve the usage of data, let us discuss the differences among Data Scientist, Data Analyst and Data Engineer.
The key difference lies in the various tasks they perform using the data.
Data Analyst: Data Analysts add value to the organization by utilizing the data to answer questions and arrive at better solutions for business problems. This is the role predominantly given to entry-level-professionals in the Data Science field. The common tasks of a Data Analyst consist of data cleaning, creating visualizations of the findings thereby helping the company to make better data-driven decisions.
Data Scientist: Data Scientists use their expertise in statistics and develop Machine Learning models to make predictive analysis and answer vital business problems. Data scientists unfold business insights from the data using supervised or unsupervised learning methods in their ML models. Data scientists train their mathematical models for better identification of patterns to predict the trends of business accurately. The key difference between a Data Analyst and Data Scientist is that Data scientist provides a whole new approach of understanding data and builds models for new questions whereas a Data Analyst analyses recent trends using the data and converts the results for key business decisions.
Data Engineer: Data Engineers help in optimization of the systems, allowing data scientists and analysts to perform their task. The task of a data engineer is to make sure data is properly collected, stored and made available to its users. Data engineers should possess strong technical knowledge for the creation and integration of API (Application Program Interface) and help in the maintenance of the data infrastructure.
In the following table, you can find the skill set required for these three roles in Data Science.
Data Engineer | Data Analyst | Data Scientist |
SQL | Analytics | R, Python coding |
Data warehousing | Data warehousing | SQL |
Hadoop | SQL | ML algorithms |
Data Architecture | Statistical skills | Data Mining |
Data Visualisation & reporting | Data Visualisation & reporting | Data optimisation and decision making skills |
Data Science has grown rapidly in recent years due to its wide applicability in various sectors and helps in strategic decision making for organizations.
Anyone can achieve great heights in Data Science with the appropriate skillset, and if you wish to acquire skills in Data Science, you can enroll in the Data Science course in Chennai, provided by FITA Academy. Training is provided by professionals with more than a decade of experience in this field which will enable candidates to increase their competency to excel in their careers as data scientists.
What are the job opportunities on course completion?
There are ample job opportunities for our students on course completion. Students are trained in higher-level languages like R, Python, and SQL, by professional trainers with hands-on experience in the field. With the skills acquired here, you can land in your dream job in Data Science. Below we have listed a few of the roles which are in huge demand.
- Data Scientist
- Data Engineer
- Data Analyst
- Machine Learning Engineer
- Business Analyst
- Product Analyst
- Business Intelligence Analyst
Submit the quick enquiry form for more details to learn the Data Science Training in Chennai at FITA Academy.
What is the hiring process of a data scientist?
The hiring process for the role of data scientist differs based on companies.
Most of the startups will have an aptitude test comprising probability, statistics, logical reasoning, etc. Programming tests will be conducted to check your skills in Python, R or SQL. On clearing the test, there will be a final interview by the HR or Technical team.
In MNCs, there will be an aptitude test as the first round, followed by an interview with a senior data scientist or person in any designation equivalent to it. Here the technical knowledge of the candidate is gauged and if the candidate is technically eligible, there might be a technical test to check the ability and expertise of the candidate in advanced tools utilized by a data scientist. In some companies, the candidate’s way of thinking and problem-solving approaches are also evaluated before hiring.
To improve yourself with advanced tools like Python and R, join the Data Science course in Chennai, provided by FITA Academy. And also helps aspiring candidates to land in their dream job as a data scientist and excel in it by strengthening the fundamentals during the course.
Here are Some of the job roles and responsibilities after Completing the Data Science Course in Chennai at FITA Academy are:
Data scientist
Data scientists are in high demand. Businesses are hiring them to find patterns in data. They are also looking for people who can build models that can predict future trends or outcomes. In order to do that, they need to collect data and analyze it. They use statistical methods to analyze the data and create models that predict future trends or outcomes. Data scientists are responsible for creating models that can predict customer behavior and help businesses make better decisions.They also work closely with business analysts to understand how their data will be used and what questions it will answer.
Roles and responsibilities of Data Scientist
- The role of data scientist is to analyze data from various sources and come up with insights that help businesses make better decisions. This requires an understanding of statistics, machine learning, programming, and business knowledge. A good data scientist should be able to work on different types of projects ranging from simple ones to complex ones.
- Data scientists are often referred to as “data miners” because they spend a lot of time analyzing large amounts of data. They can also be called “analysts” or “statisticians” depending on the type of project they are working on.
- Data scientists use statistical methods such as regression analysis, clustering, classification, and association rules to extract useful information from data. They also use tools like R, Python, Hadoop, Hive, Pig, etc. to perform these tasks.
- Data scientists need to understand how their findings will affect the bottom line for businesses. They must also have the ability to communicate their findings clearly so that other people within the organization can understand them.
- Data scientists may work alone or in teams. Some companies prefer to hire multiple data scientists who work together to solve problems.
- Data scientists usually start out by doing exploratory data analysis (EDA). EDA involves looking at data without any preconceived ideas about what it might reveal. It helps data scientists see patterns and trends in the data that would otherwise go unnoticed.
- Once the data has been analyzed, data scientists write code to automate processes or build models using predictive analytics. These models can then be used to predict future outcomes based on past events.
- Data scientists also create dashboards and visualizations to display important results. Dashboards and visualizations are helpful when communicating results to others.
- Many organizations also require data scientists to take part in research projects. In this case, they collect new data and apply statistical techniques to find answers to questions that were not previously known.
- Data scientists can specialize in specific areas of data science. For example, some focus on building predictive models while others focus on extracting meaningful insights from unstructured data.
- Data scientists often work closely with analysts and statisticians. Analysts tend to look at the big picture and provide context for data scientists. Statisticians typically do the heavy lifting of data collection and cleaning.
Data Engineer
Data engineers are responsible for creating and maintaining the infrastructure required for storing and analyzing data. They also perform the technical tasks involved in collecting and cleaning data. Data engineers may also work with other types of engineers to design and build data systems. The job of a data engineer is to make sure that data is stored in the best way possible and that it is easy to retrieve. Data engineers may work with database administrators (DBAs), software developers, and information architects (IA). They also work with business users to understand what kinds of data are needed and how it will be used. Data engineers must be very knowledgeable about databases and databases management systems (DBMSs). This is because they are responsible for designing and building the database.
Roles and responsibilities of Data Engineer
- The role of data engineer is to design and develop systems that store, process, and distribute data across the enterprise. He/she works with database administrators, software developers, and operations staff to ensure efficient and reliable access to data.
- Data engineers are responsible for designing and implementing solutions that integrate databases with applications and other IT components. They also manage the infrastructure required to support these systems.
- Data engineers are involved in every stage of the development cycle. They begin by defining requirements and documenting functional specifications. Next, they implement the solution using appropriate technologies. Finally, they test the system and monitor its performance.
- Data engineers use a variety of tools and languages to accomplish their tasks. Commonly used programming languages include Java, C#, Python, Ruby, PHP, Perl, JavaScript, SQL, and XML.
- A typical day for a data engineer includes working with clients to define business needs; analyzing data to determine which information is most useful; developing algorithms to extract relevant information; writing computer programs to perform those functions; testing the program’s accuracy; and deploying the program into production.
- Data engineering requires an understanding of different types of data structures, including relational databases, object-oriented databases, NoSQL databases, key-value stores, document stores, and graph databases.
- Data engineers must have strong problem solving skills because they need to identify issues early in the project lifecycle. This helps them avoid problems later in the project.
Data Analyst
A data analyst is responsible for collecting and organizing large amounts of data, and analyzing them to identify patterns and trends. The data analyst may also analyze the data to predict future trends. Data analysts are usually found in large organizations. They usually work closely with data scientists and software engineers. The job involves a lot of crunching numbers, and the data analyst must have a strong knowledge of databases and programming languages. In addition, the data analyst must be familiar with statistics and mathematical equations. They need to be very proficient in data manipulation. They also need to understand the meaning of the data, and its importance. Data analysts are expected to analyze the data, and provide reports and recommendations based on the analysis.
Roles and responsibilities of Data Analyst
- Data Analyst is a person who collects, analyzes and interprets data to provide information for decision making. The main purpose of the job is to analyze and interpret data collected from different sources like web, mobile apps, social media etc.
- A data analyst has to work with large amounts of data that are stored in databases or spreadsheets. They need to be able to quickly identify patterns and trends within this data.
- A data analyst will also have to use statistical methods to make sense of the data they collect. This includes using tools such as Excel, SPSS, R, SAS, Python, Tableau etc.
- A data analyst can be involved in any stage of an organization’s business process. For example, they may be responsible for analyzing sales data to determine which products are selling well, or they could be working on developing new marketing strategies based on customer behavior.
- A data analyst needs to be familiar with both structured and unstructured data. Structured data refers to data that is organized into columns and rows. Examples include data sets that come from surveys, questionnaires, and other kinds of research. Unstructured data comes from things like emails, documents, images, audio files, video clips, and even tweets.
- A data analyst must be able to communicate effectively. This means being able to explain their findings clearly so that others understand what they mean. It also involves being able to write reports and presentations that are clear and concise.
- A data analyst should be flexible when it comes to changing tasks. They should be able to adapt to new situations and learn new skills quickly.
- A data analyst should always strive to improve themselves by learning new techniques and technologies.
- A data analyst should know how to find relevant data and how to extract meaningful insights from them.
- A data analyst should possess good problem-solving skills and be able to think critically.
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Data Science Course in Chennai Frequently Asked Questions (FAQ)
- This FITA Academy Data Science Course is designed and Trained by Data Science experts with 12+ years of BI and Data Science experience.
- We are the only institution in Chennai with a blend of hand-on practical sessions with real world examples.
- More than 50,000+ students trust FITA Academy.
- Affordable fees, keeping students and IT working professionals in mind.
- Course timings designed to suit working professionals and students.
- Interview tips and training.
- Resume building support.
- Real-time projects and case studies.
Our Data Science faculty members are industry experts who have extensive experience in the field handling real-life data and completing mega real-time projects in related areas like Big Data, AI and Data Analytics in different sectors of the industry. We assure you that you will be taught by expert data science instructors.
- Placement team will start the recruitment process immediately after completing your data science training in chennai at FITA Academy.
- Detailed analysis of the candidate profile will be done by the placement team.
- Candidates will receive detailed feedback about their strengths and weaknesses.
- Placement team will help candidates prepare for interviews and if required will also provide resume building services.
Data science can be classified into a number of types. These include:
- Descriptive data science: This is all about understanding and describing data. It involves summarising data, finding patterns and trends, and identifying outliers.
- Inferential data science: This is about using data to make predictions. It involves using statistical techniques to find relationships between variables, and then using these relationships to make predictions about future data.
- Predictive data science: This is about using data to make predictions. It involves using statistical techniques to find relationships between variables, and then using these relationships to make predictions about future data.
- Causal data science: This is about using data to understand cause and effect. It involves finding relationships between variables, and then using these relationships to understand how changes in one variable can cause changes in another variable.
Data science is a field of study that combines statistics, computer science, and modeling to gain insights from data. Data science is used to analyze data sets to find trends and patterns, make predictions, and build decision-making models.
There are many different applications of data science. Some examples include:
- Analyzing customer data to find trends in customer behavior
- Building predictive models to forecast future demand
- Optimizing business processes through data-driven decision making
- Detecting fraud or anomalies in data sets
- Analyzing social media data to understand public opinion
- Building recommender systems to suggest products or content to users
Additional Information
The age of data is upon us. With each passing day, we notice an increase in the amount of data created online. For example, let’s say you want to know what is trending across social media platforms like Twitter, Instagram, YouTube etc. To do this, you need to look at the data. So, how does one go about analyzing such large amounts of information? Well, it starts with Data Science.
In today’s world, Data Science is becoming increasingly important. Every industry needs data analysis to make better decisions and improve processes. Marketing is where data science is most often used. It helps companies understand their customers better and target them accordingly.
One way to learn these skills is through a data science course in chennai at FITA Academy. Our courses provide students with the knowledge and tools they need to work with data effectively.
The course will provide you with in-depth coverage of topics such as data analysis, machine learning, and big data management. After completing this program, you’ll be able to identify patterns in large datasets, perform predictive analytics, and build effective dashboards.
Learning Outcomes from Data Science Training in Chennai at FITA Academy
If you are looking for a well-rounded training in data science, FITA Academy is the right place for you. We provide top-notch data science and machine learning training, which will equip you with the skills required to succeed in the industry. Here is a list of learning outcomes by enrolling in our comprehensive Data Science course in Chennai to help you understand what you will learn:
- Understand the concepts of Big Data and its importance in today’s world.
- Learn about different types of data sets and how they can be used to solve real-world problems.
- Learn about various algorithms and their applications.
- Learn about different machine learning models and how they work.
- Learn about different statistical methods and how they can be applied to solve real-world challenges.
- Learn how to use R, Python, SAS, and other programming languages to create predictive models.
- Locate and understand the difference between supervised and unsupervised learning.
- Learn about different visualization techniques and how they can be effectively applied to solve real-life problems.
- Learn about different databases and how they can be leveraged to store, analyze and visualize data.
- Learn about different software tools and how they can be integrated to create powerful solutions.
- Learn about the role of big data analytics in business and how it can be implemented in organizations.
- Learn about the current trends in big data analytics and how they can be adopted by businesses.
- Learn about the important components of big data infrastructure.
- Learn about the different ways of deploying big data systems.
- Learn about the use cases of big data analytics in different industries.
- Learn about the different roles of data scientists in the modern workplace.
- Learn about the different certifications available for data scientists.
- Learn about the Data Scientist Salary For Freshers and what factors impact this.
- Learn about the different job profiles for data scientists and how they differ from each other.
- Learn about the different skill sets needed to succeed in the field.
- Learn about the different educational paths to pursue if you want to become a data scientist.
- Learn about the different certification programs available to data scientists.
FITA Academy will help you learn the skills and techniques needed to work with data, including analysis, modeling, and visualization. This is an excellent option for those looking to gain a strong foundation in data science, and it can be tailored to your specific needs. We provide students with the skills and knowledge they need to succeed in the data science field. The academy offers this Data Science Training in Chennai that is perfect for beginners, intermediates, and experts alike. Students can choose between self-paced online modules or in-person classes that are led by experienced professionals.
Basic Concepts of Data Science
FITA Academy has a well-designed curriculum that helps students learn the basics of data science. We also provide ample opportunity for hands-on learning. In addition, the trainers at FITA Academy are experienced professionals who can help you understand the concepts better.
The goal of this Data Science course in Chennai is to introduce you to the basics of data science by teaching you how to use Python for data analysis and R for statistical modeling. You will learn about different types of data (e.g., text, images, audio), how they are collected, stored, and analyzed. We will also discuss some basic concepts in Module 1 of this data science program, such as classification, clustering, regression, time series forecasting, feature selection, dimensionality reduction, and visualization.
This course is intended for students who have no prior experience in any of these areas but would like to gain an overview of what data science is all about. Here are some basic concepts for you to know what, why and How data science works with its process.
What is Data Science?
Combining statistics, mathematics, computer science, and other related subjects is known as Data Science. In simple terms, it is the process of extracting insights from data using statistical methods. This can be done by creating models or algorithms based on the available data. These models can then be used to predict future trends and outcomes.
Why Do We Need Data Science?
Every business needs to know about data science. Today, businesses gather a lot of information. But they don’t always have the tools to look at all this information. So, they need assistance. This is where the work of data scientists comes in. Through data analytics, they turn raw data into useful information.You will learn everything from scratch from the best Data science course in Chennai to help you get started in this field.Data science typically uses statistics, mathematics, and computer programming to understand data.
Data scientists are needed to then interpret the results of these calculations and applications into a form that is easier for businesses to use. They study information by collecting it, extracting patterns from it and using computational techniques such as machine learning or data mining to make predictions or build models.
How Does Data Science Work?
To understand how data science works, let’s first understand some basic concepts.
- Data: A collection of facts or numbers stored in a database.
- Modeling: Using mathematical formulas to describe real-world phenomena.
- Algorithms: An automated way of solving problems.
- Predictions: Making predictions based on existing data.
- Interpretation: Understanding why something happened.
- Visualization: Presenting data visually.
- Machine Learning: Automating tasks using machine learning techniques.
- Statistics: Describing patterns in data.
- Big Data: Large volumes of data collected over time.
- Data Mining: Finding hidden patterns in data.
- Data Analytics: Combining various tools to extract meaningful insights from data.
- Data Science: Combining data mining, predictive modeling, and visualization to create actionable insights.
- Data Engineering: Creating databases and managing big data.
- Data Science Tools: Software packages designed specifically for data science.
- R Programming Language: Used for data analysis.
- Python: Popular programming language used for data science.
- SAS: Statistical software package used for data analysis.
- Hadoop: Distributed computing framework used for data processing.
- Spark: Scalable distributed computing platform used for data processing.
- Tableau: Data analysis is done with this business intelligence tool.
The above list shows just a few examples of data science. There are many more tools and technologies involved in data science which you will learn in your data science training in Chennai at FITA Academy. Let’s now see how these tools work together to solve complex problems with the process given below.
Data Science Process
Now that we have seen some basic concepts, let’s discuss the process.You will learn this process easily with our Data Science course in Chennai to enable you to get into the field of data science.A number of data visualization tools are now available to help you explore your data and present it. Here’s what happens when you start working with data science:
- Collect data: The first step in data science is collecting data. You must ensure that your data is clean and accurate before starting any analysis.
- Clean up data: Once you have collected the required data, you will need to clean it up. For example, if you are analyzing customer behavior, you might want to remove duplicate records.
- Analyze data: Now that the data is cleaned up, you can start analyzing it. Using descriptive statistics, you may determine which clients are most likely to purchase your products. You may also examine whether there is a correlation between product pricing and sales volume using regression analysis.
- Create Models: After analyzing the data, you can create a model. For example, you can build a decision tree to classify customers into groups.
- Test models: Finally, you can test the accuracy of your model by comparing its predictions against actual outcomes. This helps you identify areas where your model needs improvement.
- Evaluate results: Once you have tested your model, you can evaluate its performance. If the model performs well, you can deploy it in production. Otherwise, you can modify it until it meets your requirements.
- Deploy Model: When you have created an effective model, you can deploy it on a server or cloud service.
- Monitor Results: To monitor the performance of your model, you can collect metrics such as response times and error rates. These metrics help you understand how your model is performing.
- Improve Model: If your model isn’t performing well, you can improve it using machine learning techniques.
- Repeat Steps 1-9: In this way, you can continuously refine your model until it achieves optimal performance.
A complete overview of the various aspects of Data Science will be provided in this Data science Tutorial. You can learn about the Data Science process, using tools like R and Python.
Data Science Components
- Statistics- Statistics is one of the core concepts of data science. It involves finding patterns in large amounts of data.
There are two types of statistics:
- Descriptive
- Inferential
Descriptive statistics describe the characteristics of a population while inferential statistics infer relationships between variables.
Descriptive statistics include mean, median, mode, standard deviation, skewness, kurtosis, etc.
Inferential statistics includes correlation, regression, classification, clustering, principal component analysis (PCA), factor analysis, etc.
- Visualization- Data visualization is another important concept of data science. It allows us to see our data in new ways.
We can visualize data through charts, graphs, maps, tables, etc. There is a plethora of tools available online that gives us the ability to visualize data. Some of them are listed below:
- Tableau Software – A business intelligence tool used to analyze data.
- Microsoft Power BI- An easy-to-use business analytics platform.
- Google Fusion Tables- Allows users to upload their own datasets and visualize them.
- Machine Learning- Machine learning is all about training computers to learn from experience.
It is based on three main components:
- Algorithms- The algorithms we use to train machines.
- Datasets- The data sets we feed into the algorithm.
- Metrics- How we measure the success of the algorithm.
The process of developing a machine learning algorithm starts with defining what problem we want to solve. Then, we define the features of the dataset. We need to select appropriate algorithms and choose suitable metrics.
Once we have defined these things, we can write code to implement the solution.
Machine Learning experienced professionals will guide you through the best data science training in Chennai to help you learn the process and the skills that are required to be a data scientist.
- Deep Learning- Deep learning is a subset of machine learning.
In deep learning, we try to build computer programs that mimic human brain functions.
These programs are called artificial neural networks. They consist of multiple layers of neurons. Every neuron is connected to other neurons and acts as a relay station for passing information between them. Neurons also receive feedback from other neurons. This forms connections between neurons. Artificial neural networks are trained using backpropagation. Backpropagation helps us identify which parts of the network should be changed so that they perform better. The ultimate goal of this Data science course in Chennai is to provide you with the ability to create, interpret, and communicate your own ideas.
Difference Between Data Science with BI (Business Intelligence)
Data science and business intelligence are two fields that are often confused. However, there are some key differences between these two fields. Data science is focused on the statistical analysis of data. Business intelligence is focused on the use of data to make business decisions. Find the below Difference Between Data Science with BI.
Data Science |
BI (Business Intelligence) |
Data Science focuses on analyzing data |
Business Intelligence focuses on reporting data |
Data Science uses statistical methods to find patterns in data |
Business Intelligence uses graphical techniques to report data. |
Data Science uses mathematical models to predict future outcomes |
Business Intelligence uses predictive modeling to understand past trends. |
Data Science uses descriptive statistics to describe data |
Business Intelligence uses inferential statistics to draw conclusions. |
Data Science uses visualizations to present data |
Business Intelligence uses reports to communicate results. |
Data Science uses big data to store large amounts of information |
Business Intelligence uses small data to provide quick answers. |
Data Science uses data mining to discover new insights |
Business Intelligence uses data visualization to explain existing knowledge. |
Data Science uses data cleansing to cleanse data |
Business Intelligence uses data quality control to ensure accuracy. |
Data Science uses data integration to combine different types of data |
Business Intelligence uses database management to create databases |
Data Science without Business Intelligence
Data scientists work independently. They do not report to anyone else. Their job is to find insights in data.
They may or may not know how to present those findings to others.
Data Science with Business Intelligence
BI teams usually have a dedicated team of data scientists who help companies make sense of their data.
Their role is to understand the business problems and then provide solutions.
They often work closely with IT departments. Get immediate job opportunity after completing our data science course in Chennai as FITA Academy will provide you with 100% placement assistance to find you a perfect job right after completing the course.
Implementing Data Science
As we all know now Data Science is a vast term, and it uses different tools for different processes. Data Science has primarily four main processes and they are, Data Integration and Cleansing, Data Warehousing, Data Analytics, and Data Visualization. Now, let us see the major tools that are used to implement Data Science for these different processes.
Data Acquisition and Cleaning
Data Acquisition is the initial stage of the Data Science lifecycle. There are numerous ways to gather data. But, the real challenge over here is that the collected data should be useful and reliable for the business. Also, the collected data may not always be a structured one. It can be semi-structured or unstructured as well. Further, the collected data will be of voluminous quantity. To ease the workload of the Data Scientists there are some popular ETL tools. Below are the popular ETL Tools and its features.
The Tools used here are Talend, IBM Data Camp, and OnBase
Talend
It was developed in the year 2005, and it is an open-source tool. This tool is designed for deriving at the software solutions for application integration, data integration, and preparation. The major advantages of this tool are that it can be easily managed, scaled, cleaned, designed and collaborated quickly.
Significant Features
- This is an affordable Open-Source tool.
- With Talend, it is easy to develop, deploy, maintain, and automate the tasks.
- This tool has a huge community and a unified platform.
- Talend can not be outdated as soon as it is designed based on present and future requirements.
IBM Data Camp
The prime purpose of this tool is to gather or collect the documents, extract the details or facts, and update the documents into the businesses for further processes. This tool can efficiently perform tasks with more flexibility, accuracy, and rapid automation. This tool is capable of supporting multi-channel capture through processing the documents on different devices like mobile, scanners, fax, and peripherals. Also, this tool makes use of natural language processing and delivers useful information for making a faster decision.
Significant Features
- IBM Data Camp has enriched mobility. It provides improved mobility for iOS and Android apps and also supports SDK features.
- It has the best Data Protection feature. It permits users to access and control the confidential data and also lays restrictions on the content for the users thus, providing the necessary content.
- This tool has the ability to classify the structured and unstructured data quickly even from highly variable and complex documents.
OnBase
It was developed by Hyland. Also, this is a single enterprise of information platform which is primarily designed for processing and managing the user’s content. OnBase focuses on prioritizing the user’s business content to a secured location. Also, this provides relevant information for the users when they require it. This tool permits the organization to be more efficient, capable, and agile by increasing the delivering service quality and productivity and also minimize the risk of the enterprise.
Significant Features
- This is a single platform that supports building content-based applications and supports the various other business systems.
- OnBase could be deployed on the cloud and can be extended in the mobile device and other existing applications that are integrated.
- OnBase is the low-code application platform for development. Besides, it reduces the cost and the time for development as it supports in creating content-enabled solutions quickly.
Amazon Redshift
The Amazon Redshift is the petabyte-scale that is completely managed by the AWS cloud. This warehouse allows the organizations to scale up from a few hundred gigabytes and more. Also, this tool permits users to make use of the data and gather insights for the customers and businesses. The Redshift consists of nodes also known as Amazon Redshift clusters. This provision of clusters permits the users to upload the datasets to a data warehouse. Also, customers can perform the queries and analyses of the data here.
Significant Features
- RedShift could be launched within a VPC and also through the Virtual Networking Environment, where the users have access to the control of the cluster.
- The Data which is stored could be encrypted and installed during creating tables.
- The connection between Redshift and Clients is encrypted using the SSL.
- Also, the number of nodes shall be easily scaled in a few clicks on the Redshift of the Data Warehouse.
- Besides, Amazon Redshift is cost-effective and it does not charge any up-front costs.
SnowFlake
It is the complete relational ANSI SQL warehouse data where the users could leverage the skills and tools of the organization that is already in use. The administration demand for big data platforms and traditional data warehouses is eliminated with the help of snowflakes. The SnowFlake could immediately handle the availability, data protection, optimization, and infrastructure so that the users can give more focus on using the data rather than managing it.
Significant features
- Snowflakes are capable of supporting every form of business data whether it is from machine-generated or traditional sources without any complex procedures in it.
- We can easily scale up and scale down the downtime without any interruption during the storage and computation.
- SnowFlakes has the ability to replicate the data across the cloud providers and also across the cloud regions. It keeps the apps and the data operation without any failures and ensures business continuity.
- We can quickly integrate the snowflake with the package and the custom application tools. Tools such as JavaScript, Node.JS, Spark, R, and Python have the potential to unlock the power of cloud data warehousing for tools and developers to use different frameworks and languages.
- This tool also follows the principle of pay for what we use.
Data Analysis
It is the method of processing, modeling, cleaning, and transforming the data to explore useful insights or patterns for the business in decision-making. The primary operations that are involved in the data analyzing process are extraction, data cleansing, data profiling, and data debug. There are various techniques and methods for data analysis and they are Statistical Analysis, Text Analysis, Inferential Analysis, Descriptive Analysis, Predictive Analysis, Prescriptive Analysis, and Diagnostic Analysis.
Data Analysis Tools: Rapid Miner, Informatica Power Center, and KNIME
Rapid Miner
This tool is primarily created for the researchers and non-programmers who work in the Data Science platform for analyzing the data quickly. This tool efficiently supports importing ML models, and other web applications such as Android, Node JS, iOS, and much more by unifying the complete wheel of Big Data Analytics.
Significant Features
- It provides the platform that provides support for Data processing, building ML models and deployment
- This tool can load data from different frameworks such as Cloud, RDBMS, Hadoop, NoSQL and much more
- Rapid Miner is capable of generating predictive modeling using automated models
- This tool can also support Artificial Intelligence models and Deep Learning models like Gradient Boost, XGBoots, and Random Forests
Informatica Power Center
This is the most widely and commonly used Data Integration tool. Also, according to the recent survey report, it is confirmed that the average revenue of this company is around US Dollar 1.05 billion. It is because this tool provides versatile features and data integration capabilities for its users.
Significant Features
- It helps in extracting the data from different sources and transforming it into the accordance of the business requirements and deploy efficiently into the warehouse.
- This tool proficiently supports grid computing, distributed processing, dynamic partitioning, pushdown optimization, and adaptive load balancing.
- It supports rapid prototyping, validation, and profiling.
KNIME
It makes the Data workflow and its components accessible to all by being open, intuitive, and constantly integrating the new developments.
Significant Features
- It can combine simple text formats like PDF, XLS, JSON, CSV, and XML from the time series data and unstructured data types.
- This tool can connect data warehouses and database for integrating data from Microsoft SQL, Apache Hive, Oracle, and much more.
- KNIME can retrieve and access data from different sources like AWS S3, Azure, Google Sheets, and Twitter.
- This tool can perform all the statistical functions efficiently such as mean, standard deviation, quantiles, and hypothesis testing. Also, this tool can perform dimension reduction, correlation analysis, and workflows.
- KNIME can proficiently filter, sort, aggregate, and join data on the local machines and in the distributed big data environments.
Data Visualization tools
These tools are used for representing the data in a graphical or pictorial format. These tools are created for checking the data analytics visually and to make others understand the complex concepts easily. Usually, the Data Visualization extracts Data from different disciplines like information graphics, scientific visualization, and statistical graphics. These tools help in displaying the information in delightful ways such as pie charts, dials and gauges, geographic maps, infographics, bar diagrams, and ferver charts. The visualization tools are primarily needed in analytics for making data-driven insights and demonstrating the data to other employees easily and quickly in an organization. In short, you can easily give the overview of the data to everyone with this tool.
Data Visualization Tools: Google Fusion Tables, Microsoft Power BI, SAS, and Qlik
Google Fusion Tables
It is the web service that is provided by Google for handling the data. The services are used for visualizing, collecting, and sharing data tables. Also, the Data that is stored in multiple tables can be viewed and downloaded by users. The Google Fusion Tables provides numerous means for visualizing the data with timelines, scatterplots, pie charts, bar charts, and geographical maps to its users.
Significant Features
- Firstly, the Fusion Tables are in the Online Format, and the table always distributes the appropriate version of data.
- It is capable of importing the data by itself and provides visualization instantly.
- It can easily merge with new data upon feeding, and it is always up-to-date.
- Also, this tool always provides what the users need, and it can easily build on the public data set.
Microsoft Power BI
This is one of the analytics services that provide valuable insights to make fast, informed, and accurate decisions. Also, this tool can transfer the data to visuals and enables you to share with others irrespective of any device. Also, this tool is capable of exploring and analyzing data on the Cloud as well. The Power BI shares interactive reports and customized dashboards and supports the organization with built-in security and governance.
Significant Features
- This tool is capable of providing both the self-service needs and the enterprise data analytics needs on a common platform.
- Power BI can share and create interactive data visually over public clouds in the global data center, and therefore complies with the users and regulation needs.
- It simplifies the methods of sharing the massive volume of the data to the users and also analyzes the relevant data.
- Power BI gets support from AI Technology and aids the non-data scientist’s professionals to build ML models easily, prepare data, and find the information rapidly from both the structured and the unstructured data along with images and texts.
- For professionals who are familiar with Office 365 can just connect the data models, reports, and excel queries to the Power BI Dashboards at ease. Also, it helps the professionals to analyze, share, and publish the Excel business data in numerous ways.
SAS
SAS is the most popular statistical software tool that was developed for data management, business intelligence, predictive analysis, and data visualization.
Significant Features
- This tool can reveal the stories that are hidden behind your data. This tool immediately shows the identities and suggestion related methods.
- SAS provides advanced data visualization techniques to guide analysis via auto charting.
- SAS can combine the traditional data sources within the given location for analyzing the geographical context.
- It can join tables and import data for applying essential data quality functions with drag-drop capabilities.
Qlik
It provides a centralized hub that permits every user to share and find the relevant data analyses. Also, this tool is capable of unifying the data from different databases such as Oracle, Cloudera Impala, IBM DB2, Sybase, Teradata, and Microsoft SQL Server. Businesses of different sizes can explore any types of Data such as Simple and Complex on their datasets with the help of data discovery tools.
Significant Features
- It has robust security with centralized sharing features
- It has Hybrid multi-cloud architecture
- The users can create interactive data visualizations for presenting the reports in a storytelling format with just the drop and drag interface.
Data Science Training in Chennai at FITA Academy provides in-depth training of the four major components of Data Science – Data Acquisition, Data Warehousing, Data Cleansing, and Data Visualization clearly under the mentorship of real-time Data Science professionals. Our Trainer provides the complete guidance to have a successful career path in the Data Science domain.
Future Of Data Science
Accurate analysis of data can provide vital insights essential to take major decisions in the businesses. Data Analysis can be integrated with machine learning to render best results with minimum cost to the organization. Data science has made a positive impact in almost every sector, resulting in the phenomenal growth of Data Science in the modern era. Let us see the impact of data science in the arena of automation, IoT, social media and machine learning. Enroll yourself at FITA Academy for the best in class Data Science Course in Chennai to have a blissful future
Data Science Interview Questions and Answers
Finding a data science job can be challenging, whether you’re a recent graduate or a seasoned professional. We’ve compiled a list of common data science interview questions and answers to help you prepare for your next data science interview.
From questions about statistical methods to machine learning, our list covers a range of topics that are essential for any data scientist. If you’re just starting out or want to take your career to the next level with our data science course in Chennai, this list is a great resource for preparing for your next data science interview.
1. Can you explain what Data Science means?
- Data science is the study of gathering, managing, storing, retrieving, processing, analysing, interpreting, presenting, and spreading large amounts of information.
- The study of data is an area that draws from many disciplines that uses knowledge from statistics, computer science, mathematics, engineering, economics, and other fields to make sense of raw data.
- A process where we use different statistical methods like regression, classification, clustering, principal component analysis, etc. to analyze the collected data.
- A set of skills used to solve problems involving extracting meaningful insights from big data sets.
- A mix of many fields, such as statistics, computer science, math, engineering, and others.
- A method of analyzing data using computers and software to discover hidden trends and patterns.
Data science is a rapidly growing field. Companies are increasingly collecting data, and they need skilled workers to help them make sense of it all. In case you are interested in a career in data science, now is the time to get started with our data science course in Chennai in order to get you started on the path to success.
2. Why is data analytics different from data science?
- Data Analytics: It’s a subset of data science which involves applying advanced mathematical techniques on structured or unstructured data in order to gain insight into it.
- Data Science: This is an umbrella term for all the activities involved in collecting, managing, storing, retrieving, processing, analyzing, interpreting, presenting, and disseminating large amounts of data.
3. Can you describe sampling and some of its techniques used?
- In data science, sampling is one of the most important tools. The main reason why we sample our data is because we want to get a representative view of the whole population. We can do this by randomly selecting samples from the entire population.
- There are two types of sampling techniques – simple random sampling and stratified sampling.
- Simple random sampling is when we select a number of elements at random from the total population.
- Stratified sampling is when we divide the population into groups based on certain criteria (like gender, age, income level, etc.) and then select a number of elements from each group.
4. Describe the conditions for overfitting and underfitting?
- Overfitting occurs when we fit too much noise into our model. In other words, we try to predict something that doesn’t exist. For example, if we have a model that predicts whether someone will buy a product or not, but the training data only contains people who bought the product before, then the model will overfit and make predictions about people who haven’t even purchased anything.
- Underfitting happens when we don’t include enough features in our model. If we have a model that tries to predict the price of a house, but the training data has no prices, then the model won’t be able to learn any useful information.
5. Difference between the long and wide format data?
There are many ways to format data, but the two most common are the long and wide formats. Each has its own advantages and disadvantages which you will learn in your data science course in Chennai, so it’s important to choose the right one for your needs. Here’s a brief overview of the two formats:
Long Format Data |
Wide Format Data |
Long format data is organized in rows |
Wide format data is organized in columns |
Long format data has more variables than observations |
Wide format data has fewer variables than observations. |
Long format data is organized in a matrix |
Wide format data is arranged in a table |
Long format data is stored in a file |
Wide format data is stored in database |
6. What are Eigenvectors & Eigenvalues and define Eigen decomposition?
- Eigenvectors are the basis vectors of a linear transformation. They are also known as eigenvectors.
- An eigenvalue is the value of the characteristic equation of a square matrix. An eigenvector is a vector with non-zero entries whose corresponding eigenvalue is equal to 1.
The eigen decomposition of a square matrix M is defined as follows:
M V D V-1 where V is a matrix containing the eigenvectors and D is a diagonal matrix containing the eigenvalues.
7. Explain how PCA works?
PCA is a dimensionality reduction technique which reduces the dimensions of the dataset without losing any information. It does so by finding the directions along which the variance is maximum. These directions are called principal components.
8. Can you give an example of a case in which p-values are high and low?
A p-value is the probability that you would obtain the same or better results if the null hypothesis was true. If the value is less than 5%, we reject the null hypothesis and conclude that there is evidence against the null hypothesis. On the contrary, if the p-value is greater than 95%, we accept the null hypothesis and conclude there is no evidence against the null hypothesis and say that the data supports the null hypothesis.
The lower the p-value, the stronger our evidence against the null hypothesis, while the higher the p-value, our evidence for the null hypothesis increases.
For example, suppose we want to test whether the average temperature in January is statistically different from the average temperature in July. We calculate a t-test statistic and find that the difference is 2 degrees Celsius. Our p-value is 0.042; therefore, we cannot reject the null hypothesis, and we conclude that the average temperatures in January and July are likely to be similar.
On the contrary, if we wanted to test whether the average height of men is statistically different than the women, we calculated a t-test statistic of 4 inches and found a p-value of 0.01. Therefore, we reject the null hypothesis, concluding that men are taller than women.
When it comes to data science, there is a lot to learn in order to be successful. However, one of the most important things to understand is how to interpret p-values. P-values can be high or low, and it is important to know how to read them in order to make the best decisions for your data. FITA Academy Experts can help you through the data science course in Chennai to learn which p-values are high and low, and what that means for your data.
9. What are the types of Resampling and When it is Done?
Resampling is a method used to improve the quality of predictions and estimate the uncertainty of population parameters such as mean, variance, standard deviation, etc. This process is done to ensure the prediction model is robust and accurate by sampling the data set multiple times and observing how it changes. Resampling helps us understand whether our model is biased towards certain values, and gives us confidence about the accuracy of the model.
There are three types of resampling methods:
- Bootstrap – A bootstrapping technique involves generating samples from a distribution of interest and calculating statistics based on those samples. For example, we could generate 1000 samples from a normal distribution and calculate the mean and standard deviation of each sample. We repeat this process many times to obtain an average value and standard deviation.
- Cross validation – In cross validation, one splits the original data into several parts/folds and trains the model on some of the folds and validates it on the remaining ones. Once the model is trained, it is tested on the entire data set.
- Randomization – This is a simple way to make sure that the model is unbiased. One simply shuffles the data randomly and retrains the model. If the model performs well, then we know there is no bias present in the data.
10. How do you define Imbalanced Data?
Data is said to be highly skewed if it is distributed unevenly across different categories. For example, if there are 10 times more images of cats than dogs, then the data set is considered to be imbalanced. This imbalance creates problems for machine learning algorithms because they require balanced data sets to perform well. If the dataset contains too many examples of one class, then the algorithm will learn to predict that class. However, if the dataset contains too few examples of another class, then the algorithm won’t know how to classify those examples correctly.
The problem becomes even worse when we look at image classification tasks like facial recognition and object detection. In such cases, the number of positive samples (images containing faces or objects) is much smaller compared to the negative samples (images without faces or objects). As a result, most models tend to overfit the training data and fail to generalize to unseen test data.
To help you learn about this, FITA Academy’s Data science training in Chennai will guide you with a few ways to deal with imbalanced data, but the most common is to oversample the minority class or undersample the majority class.
11. How does the expected value differ from the mean value?
The difference between the expected value and the mean value is subtle. You might think that the expected value is always greater than the mean value because you know that the average number of people who attend a party is less than the total number of guests. However, this isn’t true. For example, the mean value for the number of people attending a party is 5 and the expected value is 4.5. This is because some parties have fewer than five people while others have more than 10. In fact, most parties have an expected value of around four. If we add up the numbers of people who attended each party, we find that the sum is equal to the total number of people invited. Therefore, the mean value is actually closer to the actual number of attendees.
In statistics, the expected value is often written as E(X), where X represents a random variable. The mean value is usually written as μ, although sometimes it is written as M.
The expert will guide you with each and every step with conceptual examples in your data science course in Chennai which is perfect for beginners in data science to understand the concepts easily.
12. How would you define Survivorship Bias?
Survivorship bias is a common cognitive bias where people tend to focus on facts that support their beliefs and ignore evidence to the contrary. In statistics, it refers to the tendency to look for patterns among events that happened in the past rather than looking at what might happen in the future. For example, if someone believes that humans are responsible for global warming, he or she might overlook evidence that suggests otherwise.
The term was coined by psychologist Daniel Kahneman in his book Thinking Fast and Slow. He used the term to describe the way people think about the world around them. They see things happening now and assume that they will continue to happen in the future. However, they don’t consider the possibility that something could change and make the current situation obsolete.
13. What is KPI?
KPI stands for Key Performance Indicator. It is a metric that can help organizations measure performance. KPIs are commonly used in business management and finance. A good KPI should meet three criteria:
- Be easy to understand
- Have clear goals
- Provide actionable information
14. What is Lift?
Lift is a measurement of how well a model performs on new data. When we say “lift”, we are referring to the increase in accuracy of our predictions. For example, When we use a logistic regression model, with two features, then the lift is 2.0. This means that adding one feature increases the prediction accuracy by 2%.
15. What is model fitting?
Model fitting is the process of finding the best set of parameters (or weights) for a given model. Model fitting involves using statistical methods such as linear regression, logistic regression, decision trees, neural networks, etc. With hands-on practical exercises, you will have a good understanding of how to use these statistical methods in an R program which is covered in your data science training in Chennai.
16. What is Robustness?
Robustness is the ability of a model to perform well under different conditions. For example, a model that predicts whether a person will buy a product based on their gender and age will be robust against changes in these variables. On the other hand, a model that uses only one variable like income may not work when there are multiple factors influencing purchase decisions.
17. What is DOE?
DOE stands for Design of Experiments. It is a method for evaluating models. We can use DOE to test various combinations of input values and observe which combination gives us the highest accuracy.
18. What are confounding variables?
Confounding variables are variables that affect both dependent and independent variables. Confounding variables can lead to biased results. For example, if an experiment shows that increasing the price of a product leads to higher sales, this does not necessarily mean that increasing prices always leads to increased sales. If the price of the product decreases, then sales will decrease too. So, the effect of price on sales depends on many other factors besides just the price itself.
19. What is Selection Bias?
Selection bias occurs when researchers choose participants who have characteristics similar to those of the population from which they were drawn. The result is that the sample is not representative of the population.
For example, if you want to study the effects of smoking on health, you would probably exclude smokers from your sample. The reason is that smokers have more health problems than non-smokers. As a result, the findings of your research cannot be generalized to all smokers.
20. Explain the types of Selection Bias?
There are many different types of selection bias, and FITA Academy trainers can help you navigate through them to ensure you’re making the best choices in your data science course in Chennai to gain the best career opportunities in the data science industry. Here are some types of Selection Bias listed below:
- Sampling Bias – where the researcher chooses participants who have characteristics similar or identical to those of the population.
- Self Selection Bias – where the researcher selects participants who share his/her own interests.
- Volunteer Bias – where the researcher recruits people who are interested in participating in the study.
- Response Bias – where the researcher asks questions that are likely to elicit certain responses.
21. Explain bias-variance trade-off?
Bias-variance trade-offs occur when we try to find the optimal balance between bias and variance. Variance refers to the amount of error associated with each observation. In general, the smaller the variance, the better the estimate. However, the larger the variance, the less precise the estimate. To minimize the risk of overfitting, we need to reduce our variance by reducing the number of observations used to fit the model. This means that we must increase the size of our training data.
22. How do we deal with Bias-Variance Trade-Offs?
We can use cross validation techniques to address this problem. Cross validation helps us determine how much data should be used to train the model.
23. Explain why we need to use Cross Validation Techniques?
Cross validation techniques help us avoid overfitting. Overfitting happens when we build a model using too few samples. This causes the model to fit the noise instead of the signal. When we apply the model to new data, it will give us inaccurate predictions.
24. Why do we need to use Cross Validations?
Cross validations allow us to evaluate the performance of a model without making any assumptions about the underlying distribution of the data.
25. What is K-fold Cross Validation?
K-fold cross validation is a technique that divides the dataset into k equal sized subsets. Then, we randomly assign k partitions to the learning algorithm. Next, we run the learning algorithm on each partition. Finally, we average the results across all the partitions. We repeat this process until every subset has been assigned to the learning algorithm.
26. What is Leave One Out Cross Validation?
Leave one out cross validation (LOOCV) is a special case of k-fold cross validation. It involves leaving out one observation at a time and running the learning algorithm on the remaining observations.
27. What is Bootstrap Cross Validation?
Bootstrap cross validation is another name for leave one out cross validation. It uses random resampling to generate multiple datasets. Each dataset contains n – 1 observations. We then run the learning algorithm on these datasets. Finally, we average all the results together.
28. What is a confusion matrix?
The confusion matrix is a table indicating the performance of a binary classifier on a given dataset. A confusion matrix has 2 rows and 2 columns. Each row represents one class, while each column represents another class. In other words, for a binary classification problem, there are four possible outcomes: positive, negative, true positives, and false negatives.
As a result, if this isn’t the case learn more about confusion matrix through the FITA Academy’s data science course in Chennai and figure out what you need to do to create one with your own data and model, in addition to the best way to go about it.
29. What is logistic regression and explain how it works?
Logistic Regression is also called the logit model. It is a technique to make predictions about the probability of an event occurring. In our case, it predicts whether a candidate will win or lose an election. We use the term “logistic” because we are predicting a binary outcome—either the candidate wins or loses.
For example, suppose we wanted to know what the probability is that Donald Trump will win the presidency. This is a very simple problem. If we take the total number of votes cast for him, we divide it by the number of votes he needs to win. If he gets 50% plus one vote, then he will win; otherwise, he won’t.
This is exactly what logistic regression does. Let’s say we had some data points describing how many people voted for each candidate in the 2016 presidential election. Then, we could fit a logistic regression model to predict the probability that Trump would win based on those numbers.
Let’s look at an example. Suppose there were 10,000 voters in the United States. Of those, 3,500 voted for Hillary Clinton and 7,500 voted for Trump. Now, we want to calculate the probability that Trump will win. To do this, we must solve 2 problems. First, To begin with, we must determine how many votes Trump needs to win. Second, we need to determine the probability of winning given that many votes.
To answer the first question, we simply add up the number of votes for Trump and subtract the number of votes for Clinton. In this case, that gives us 4,900 votes. But, since we don’t know how many votes Trump actually received, we need to estimate his true vote count.
We should assume that Trump’s votes are spread out like a normal distribution. An example of a normal distribution is the following, which is symmetric around the mean:
As you can see, it peaks at zero, meaning that most of the votes for Trump fall within a range centered around 0.Therefore, we can assume that half of the votes for Trump were cast fell within the range -2,700 to +3,300, and half fell outside that range. Therefore, we can set our predicted value to be halfway between the maximum and minimum values.
30. Describe the concept and working of a random forest?
The Random Forest Classifier is one of the most widely used supervised machine learning techniques. There are many decision trees in a random forest. A bootstrap sample is a randomly selected subset of samples used to train each tree. We use the term “random” here because each tree is built independently of others. This independence leads to different decisions being taken by different trees. In fact, each tree makes a prediction based on its training data and then this prediction is compared against the actual label assigned to the test instance. If the predicted value is closer to the true label, then it is considered correct. Otherwise, it is wrong.
This process continues till every single node in the decision tree becomes a leaf node. At that point, the tree stops growing further. Finally, we take the average of predictions made by all the trees in the forest.
Random forests are extremely useful for dealing with high dimensional datasets where there are too many features to consider individually. For example, It’s impossible to evaluate all combinations of feature values if you have a dataset with hundreds of thousands of features. However, a random forest allows us to do this efficiently.
31. How does deep learning differ from machine learning and what are the differences between them?
Among the most popular topics in Artificial Intelligence (AI) is deep learning. This topic deals with the application of AI techniques to solve real world problems.Deep learning algorithms include Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), etc. These models are used to classify images, detect objects, play video games, translate languages, etc.
Machine Learning is another important branch of AI. Machine learning uses statistical methods to learn patterns from training examples without being explicitly programmed. For example, we can use a computer program to teach itself how to recognize cats just by feeding it enough pictures of cats. Machine learning is often applied to natural language processing, speech recognition, robotics, recommendation systems, game playing programs, image classification, spam filtering, etc.
In contrast to traditional machine learning, deep learning aims to mimic the biological processes of the human brain. And deep learning algorithms mimic the way neurons communicate with each other inside our brains. We call this process unsupervised learning since there are no instructions telling what to do.
32. How do gradients and gradient descents work?
A gradient is a mathematical representation of the steepness of a function. A gradient represents the direction in which the function changes most rapidly. This helps us understand whether the function increases or decreases as we move along the direction of the gradient.
Gradient descent is a method used to find the global minimum of a function. Starting with some initial guesses about the solution, we then iteratively update our guesses based on the difference between current and previous values.
The process continues till the difference becomes zero.
33. What are the feature vectors?
A feature vector is an n dimensional vector of numerical features that represents an object. For example, you could use a feature vector to describe an animal like a cat, such as “has four legs”, “is black and white”, “lives in the wild”, etc. Feature vectors can be used to classify objects into groups based on similar properties.
In machine learning, feature vectors can be used to represent numeric or symbolical characteristics (called features) that make up an object. For example: a feature vector could be used to describe a person as having blue eyes, brown hair, a large nose, etc. Feature vectors are most commonly used to categorize data into different classes. One common application is to label images as being either cats or dogs. Another example might be assigning each customer in a database to one of three categories depending on their purchase history.
34. Describe the steps involved in making a decision tree?
Decision trees are used to classify data based on some attribute. They work like this: You take the whole data set as input. Then you look for a split that maximises the separation of the classes (i.e., the difference between the two groups). This split is called a node. At each node, you apply the same process again to the divided data. If you stop at a particular node, you know that there is no further splitting possible. Each node represents a class. For instance, let’s say we want to categorize customers based on gender. We could use a decision tree algorithm to find out whether male or female customers spend more money on our products.
The first thing we do is take the entire data set as an input. We divide it into two parts: males and females. Next, we look for a split that separates the two groups. In this case, the best split is gender Male versus gender Female. Because the split is good, we continue applying the same process to both halves.
For example, if we had a customer database of 1000 records, we might start with a split by age group. The next step would be to split by income level. And so on. Once we have found all the splits, we end up with two sub-groups.
Here are the steps involved in making a decision tree:
- Selecting the right variables for splitting nodes – The first step involves deciding which attributes should be used to split a node. Each attribute has a cost associated with it. Attributes with higher costs are more useful for splitting than those with lower costs.
- Choosing the best split criterion – After selecting the attributes, the next step is choosing the best criterion to determine if a given node should be split or not. There are many criteria available including Gini index, information gain, entropy, etc.
- Finding out if the node should be split – Once the best criterion is chosen, the final step is finding out if the node needs to be divided or not. If the node does need to be split then the next step is determining where the cut should occur.
- Calculating the cost of every possible split – Once all these steps are done, you’ll be able to figure out how much each split will cost. These costs are added together to get the overall cost of the entire tree. These costs are added together to get the overall cost of the entire tree.
- Growing the tree – Finally, once the optimal tree has been found, it is grown by adding new nodes to the tree until the desired number of trees is reached.
- Pruning the tree – Once the tree is complete, pruning is performed to remove any unnecessary branches from the tree.
Making a decision tree can be a daunting task, but with the right steps, it can be a breeze. FITA Academy’s data science course in Chennai will train you with the steps involved in making a decision tree, so you can be confident in your decisions.
35. What does root cause analysis mean?
Root cause analysis was originally developed in the 1960s to help prevent industrial accidents. This approach involves identifying the factors that led to the accident and analyzing how each contributed to it. If you find out what caused the problem, you can avoid recurrence of the same issue.
The term “root cause analysis” is often confused with “cause and effect analysis.” Cause and effect analysis looks at the sequence of events leading up to a problem; whereas, root cause analysis looks at all contributing factors that lead to the problem.
For instance, if you don’t know why your car won’t start, you might check how much petrol is in the tank, whether the battery is dead, whether the ignition switch is faulty, etc. You could even take apart the vehicle to see whether there is something wrong with the wiring or fuel pump. However, if you discover that the spark plugs are dirty or the battery cable is loose, those are both fixable issues that would prevent the car from starting again. Those things aren’t considered part of the chain of events that leads to the problem because they don’t contribute directly to the problem itself. They’re just symptoms of the real problem.
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