• Chennai : 93450 45466Coimbatore : 95978 88270Madurai : 97900 94102Online : 93450 45466

  • Data Science Course in Chennai

    5716 Ratings | Read Reviews

    • Real-Time Experts as Trainers
    • LIVE Project
    • Certification
    • Affordable Fees
    • Flexibility
    • Placement Support

    Learn Data Science Course in Chennai at FITA – Rated as No 1 Data Science Training institute in Chennai by leading Data Scientists from the industry. The Data Science Course at FITA is the right track for any aspirant to become an expert in the field of Data Science. Designed by our faculty data scientists with immense experience in the industry, the course combines sound theory coupled with real-time projects in the subject.

    Course Highlights & Why Data Science Course in Chennai at FITA?

    Course taught by industry experts with ample experience in the field.
    You can join the course at any of our branches in Chennai or our branch at Coimbatore & Madurai.
    Latest equipment and software versions.
    Small batches (5-6-students only per batch) that ensure individual attention.
    Real-life projects and case studies.
    Unlimited Lab-time and Usage.
    Training expertise vouched by numbers: we have trained over 20,000+ aspirants to become IT professionals.
    Course timings designed to suit working professionals and students
    Regular contact-sessions with visiting industry experts.
    With established contacts with over 600+ corporations.

    Upcoming Batches

    5th Dec Weekend Saturday (Saturday - Sunday)
    7th Dec Weekdays Monday (Monday - Friday)
    9th Dec Weekdays Wednesday (Monday - Friday)
    13th Dec Weekend Sunday (Saturday - Sunday)

    Classroom Training

    • Get trained by Industry Experts via Classroom Training at any of the FITA 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!

    Curriculum

    • 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

      •  FITA trainers are the experts who have 8+ years of experience in the Data Science field.
      •  Trainers are experienced on various real time projects.
      •  They are working professionals in the MNC companies.
      •  We have certified professionals with strong practical and theoretical knowledge.
      •  Trainers provide hands-on training and make the students work on real-time projects to get industry exposure.
      •  Trainers train the students with the recent algorithms and tools that are used in data science.
      •  Trainers provide necessary individual attention and helps the students according to their academic needs.
      •  In FITA, trainers guide the students with necessary interview tips & supports in resume building
      •  Tutors guide the students to enhance their technical skills in Data Science.
    Quick Enquiry

    Features

    Real-Time Experts as Trainers

    At FITA, 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. Also, get Equipped to Clear Global Certifications. 72% FITA Students appear for Global Certifications and 100% of them Clear it.

    Affordable Fees

    At FITA, 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, 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 1000+ Small & Medium Companies to Support you with Opportunities to Kick-Start & Step-up your Career.

    Data Science Certification Training

    About Data Science Certification Training in Chennai
    at FITA

    Data Science Certification Training

    Data Science course certification is the professional qualification that shows the ability of the candidate to attain complete subject knowledge and learn all the basic tools and algorithms used in Data Science. This certification will make the student get the leading job posts in the MNC. This certification is offered to you with the necessary skills required to start your career in the Data Science industry. With the help of this certification, you can make a positive impact on yourself during the interview and you can grab the job opportunity with ease. You will gain the core knowledge of the major services in this field. The aspirants who are looking to kick start their career in the Data Science can take up this Data Science Course in Chennai at FITA which leads to a successful path to their career.

    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

    Fuelled by requirements for applications that incorporate big data and artificial intelligence, demand for data science is consistently growing. P&G uses data science generated time series models to understand the future demands of their products and Netflix uses data science to understand movie viewing patterns of their audience to decide which series they should produce next.

    However, the supply is not growing at the pace of demand. Therefore, it is the best time to become a data scientist. More employers are looking to employ data scientists. Major corporations require data scientists to turn the large amounts of data streaming in through social media and e-commerce sites into action. Most companies also view data scientists as the right path to embracing AI technologies.

    The best news is that in addition to all the major companies and the digital native companies, smaller companies are also ready to invest in data mining operations. With all these comes a prediction of an increase of about 30% in the number of data science jobs over the last year. It is indeed the best time to improve your expertise in data science.

    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 platform offers 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, 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 the best Data Science Training institute in Chennai.

    If data science is in demand, why is it so hard to get a data scientist job?

    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, helps you to acquire the desired skill sets to land in your dream career as a Data Scientist. Data Science Training in Chennai at FITA 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 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 language 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 if highly data-driven (e.g, Google, Facebook, Uber, etc.). Candidate 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. candidate 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. 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.

    Anyone willing to become a data scientist can acquire and develop their skills by joining the Data Science course in Chennai, provided by FITA. 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. Aspirants residing nearby Tambaram can enroll yourself in Data Science Training in Tambaram at FITA.

    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 comprise 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 convert 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 helps 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 enrol in the Data Science course in Chennai, provided by FITA. 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. Business Intelligence (BI) Developer Business Analyst Data Architect Applications Architect Machine Learning Scientist Machine Learning Engineer Statistician

    Submit the quick enquiry form for more details to learn the Data Science Training in Chennai at FITA.

    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. FITA helps aspiring candidates to land in their dream job as a data scientist and excel in it by strengthening the fundamentals during the course. Candidates residing in and around Velachery can join Data science training in Velachery at FITA.

    Student Testimonials

    P
    Preethi krishnan

    It was a good experience to learn Data science. Here a practical oriented approach teaching was provided. The trainer was very friendly and taught me all the topics in detail.All the doubts were cleared immediately. The training infrastructure was very good. Many practical example were given.

    T
    Thenmozhi raj

    I have done data science course here. Very friendly staff and wonderful atmosphere. Every session was perfect with the best explanation. Perfect place to learn this course.

    Have Queries? Talk to our Career Counselor
    for more Guidance on picking the right Career for you! .

    FAQ

    • Data Science Course at FITA is designed & conducted by Data Science experts with 12+ years of experience in the BI & Data Science domain
    • Only institution in Chennai with the right blend of theory & practical sessions
    • In-depth Course coverage for 60+ Hours
    • More than 20,000+ students trust FITA
    • 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
    We are happy and proud to say that we have strong relationship with over 600+ small, mid-sized and MNCs. Many of these companies have openings for data scientists. Moreover, we have a very active placement cell that provides 100% placement assistance to our students. The cell also contributes by training students in mock interviews and discussions even after the course completion.
    You can contact our support number at 93450 45466 or directly walk-in to one of the FITA branches in Chennai or Coimbatore
    The syllabus and teaching methodology is standardized across all our branches in Chennai. We also have a FITA branch in Coimbatore. However, the batch timings may differ according to the type of students who present themselves.
    We are proud to state that in the last 7+ years of our operations we have trained over 20,000+ aspirants to well-employed IT professionals in various IT companies.
    We have been in the training field for close to a decade now. We set up our operations in the year 2012 by a group of IT veterans to offer world class IT training.
    We at FITA believe in giving individual attention to students so that they will be in a position to clarify all the doubts that arise in complex and difficult topics. Therefore, we restrict the size of each data science batch to 5 or 6 members.
    Our Data Science faculty members are industry experts who have extensive experience in the field handing 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. The students can rest assured that they are being taught by the best of the best from the Data science industry.
    Our courseware is designed to give a hands-on approach to the students in Data Science. The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students’ time and commitment.
    We accept Cash, Card, Bank transfer and G Pay.

    In India, big companies like IBM, Accenture, Flipkart, Amazon, Myntra, CTS, Capgemini, and other MNCs, offer job opportunity for Data Scientists with handsome salary package in the range of 8-16 lacs per annum. Data Scientists are  hired to solve the problems in the company utilizing the whole dataset of the organization. The insight provided by a data scientist regarding the problem is the value you add to the organization, and for those valuable insights, Data scientist is one among the highest paid jobs of the twenty-first century.

    Mostly these companies recruit experienced data scientists and for anyone to begin a career in data science requires a lot of skills to get placed in the job. According to recent estimates, there is a huge demand for data scientists in various fields. As a data scientist, one must possess exceptional skills in Python, R-Programming, Probability, Statistics, Statistical models, visualization tools like Tableau, communication,  data assimilation and many more. Being a data scientist does not limit oneself to the technological aspects of the business alone because as a problem solver & strategist, your insights using the data are vital in steering the company on the path of development.

    Skilling yourself with the desired skills and preparing for big companies as a Data scientist is not a far reach, as FITA provides best-in-class training for the Data Science course in Chennai with branches located at Velachery, Anna Nagar, T Nagar, and Thoraipakkam (OMR). Call 93450 45466 for more details to learn Data Science Training in Chennai and Coimbatore as well.

    As a Data scientist, you are expected to perform various tasks on technological tools and high-level programming language like Python or R for identifying the best performing model for deriving business strategies. Arriving at the best solution requires hands-on experience in working with these tools. The right balance of practical and theoretical knowledge provided in FITA enables learners to hone their skills. We train students with advanced concepts in R like Exploratory Data Analysis & Machine Learning with R, as these are considered an un-written prerequisite by huge organizations.  FITA has been instrumental in transforming students and IT professionals into the industry-ready workforce with 100% placement support and continued guidance even after course completion. Being certified through Data Science training in FITA will add value to your profile, and with the skills acquired in the training process, your dream job as a Data Scientist is close at hand. Enrolling yourself into our Data Science Course in Chennai at FITA will act as a launchpad for your rocketing growth in career.

    Resume acts as the virtual face of a candidate, which should highlight the key skills and capabilities of the candidate even without one's physical presence. Anyone who possesses skills like Python & R-Programming, Database design & management, SQL, Tableau, Data Mining, etc., mentioned in the resume is more likely to be interviewed for the role of a Data Scientist. Based on WEF’s “Future of Jobs 2018 report”, Data Scientists and Data Analysts are among the few roles which will see a huge rise in demand in the period up to 2022.

    Hard skills (math, logical analysis, troubleshooting, Tableau, data warehousing, pattern & trend identification, etc.) and soft skills (Communication, report writing, critical thinking, creativity, teamwork, etc.) are a few of the skills sought in a person aspiring to be a Data Scientist. If you have done any projects in Data Science during your graduation, mention the objective of the project, tools & techniques utilized, your contribution to the project briefly in the resume. Any certification course in Data Science will add a feather in your cap, cause of the hands-on experience on the tools(Python, R, SQL ) trained during the course. Data Science certification course in Chennai, provided by FITA helps aspiring candidates to land in their dream job and excel in it due to strengthened fundamentals during the course. FITA provides best-in–class training for the Data Science course in Chennai with branches located at Velachery, Anna Nagar, T Nagar, and Thoraipakkam (OMR).

    Here is how a typical day of a data scientist looks. I am writing about a data scientist who worked for a project at Amazon.

    The employee was tasked with a wide and open-ended project. He was given huge volumes of equipment failure data in AWS Data Centres. Amazon operates and owns plenty of data centers around the world, which stores information when any equipment fails. 

    He had access to a huge amount of data and working on infinite possibilities using engineering to arrive at a solution. In a few months, he produced a report assimilating the humongous data and provided insights on the important facets of data. When the report was provided with much clarity and confidence in the findings in the report, he was satisfied with his role as a data scientist.

    The professional life of a data scientist is like a roller-coaster ride which gives you high when you arrive at vital insights to the organization with the skills you possess as a data scientist.

    Data Science certification course in Chennai, provided by FITA helps aspiring candidates to land in their dream job as data scientist and excel in it due to strengthened fundamentals during the course.

    Airbnb has optimized its hiring process for data scientists in recent years. They have reduced lots of one-to-one interviews and now following a systemic approach to test your skills as a data scientist.

    Airbnb prefers candidates with experience in the field, and entry-level candidates with excellent problem-solving approaches and skills in technical tools like python, R-programming, and Tableau are more likely to be hired. Keeping the resume updated with the skills you possess as a data scientist, leads you to the next level of the hiring process where the company provides you with a data set and asks a few basic questions. 

    In the next stage, the candidate sits along with the team and gets access to in-house data and asked to solve a broader question. The team also supports the candidate and encourages them to ask more questions to provide deep learning. At the end of the day, a  candidate has to present their methodologies and findings in front of a small team, where their data processing skills, modelling ability from the processed data, and communication skills are scrutinized.

    On clearing these rounds, a candidate is interviewed by two business partners to assess the candidate ability to work collaboratively. Also, the candidate is interviewed for assessing their orientation towards the core values and mission of Airbnb.

    Data Science course in Chennai, provided by FITA helps aspiring candidates to land in their dream job as a data scientist and excel in it by strengthening the fundamentals during the course. FITA provides best-in-class training for the Data Science Training in Chennai with 100% placement assistance and continued guidance even after course completion.

    Machine Learning (ML) is an important part of Data Science but knowledge in ML algorithms alone does not make a candidate eligible to become a data scientist in techno-giants like Google, Microsoft, Facebook.

    Here are some common aspects expected from candidates by huge companies. 

    A candidate is expected to know programming languages like Python or R.

    Statistics is one of the important things to be known by a data scientist to determine important aspects of the huge datasets. 

    Regarding ML, one must possess a clear knowledge of K-nearest neighbours, ensemble methods, random forests, and unsupervised learning. 

    Candidates are also expected to possess skills in data processing, data wrangling, data visualisation, and data engineering tools and techniques.

    Apart from knowledge in technical aspects, a data scientist is expected to have problem-solving skills and provide vital insights for the companies development from the dataset they work on.

    A data scientist is also expected to have business acumen, cause they are expected to be an important member in developing business strategies.

    Candidates who are willing to make a career in data science can enroll yourself in the Data Science course in T Nagar, provided by FITA. FITA helps aspiring candidates to land in their dream job as a data scientist and excel in it by strengthening the fundamentals during the course. FITA has various branches in Chennai located at Velachery, OMR, Tambaram and Porur. Candidates nearby these locations can enroll in our Data Science Training in Chennai at FITA.

    Data has become an important asset in businesses these days. The organizational data and consumer data helps the organization in providing valuable insights on the preferences of the customer and helps in improving the processes involved in the business. Data science has enabled many companies to tap into their data to realize, analyze and predict the outcomes of the business. Machine Learning has played a major role in creating predictive mathematical models (supervised/unsupervised) using the existing input data to predict outcomes and also enables one to build a model for desired outcomes. ML utilizes various methods like Artificial neural networks, Decision trees, Bayesian networks and various other models to predict the outcomes.

    ML has its application in finance, education, automobile, aviation, robotics,  healthcare, banking, biotechnology, pharmaceuticals, DNA sequencing, etc.

    Learning ML techniques and equipping with skills in ML tools, will help aspiring Data Scientists to get placed in their dream job. Due to its wide applicability, many companies are hiring people who possess ML skills and provide them with a handsome package. ML has started making footprints across sectors, and with ML skills, one gets an added advantage to be hired by the majority of the companies. 

    Aspiring students can enroll in the Data Science course in Chennai, provided by FITA. 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 data scientists. 

    Enquire for more details about the Data Science course in Chennai at FITA.

    Addition Information

    Implementing Data Science

    As we all know 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 Integration and Cleansing:

    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.

    Data Acquisition and Cleaning: 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.

    Data Warehousing: It is the method of managing and collecting the data from different resources to provide valuable business insights for the users. Generally, Data Warehousing helps in the process of analyzing and connecting business data from diverse sources. And it is the blend of different components and technologies which helps in using the data strategically. Data Warehousing is the method of storing the Data electronically by a business to transfer the data and information to make it readily available for the users at any time.

    Data Warehousing Tools: Some of the tools used here are Google Big Query, Amazon Redshift, and SnowFlake.

    Google Big Query

    It is a serverless and rapidly scalable data warehouse tool. The tool is designed on the basis of a productive analysis of the data. Also, the Data are analyzed on creating a sequential and logical data warehouse above the columnar storage and data from spreadsheets and object storage. This tool consists of a report with in-memory BI and blazing-fast dashboards report. Google Big Query permits its users to share the data securely within an organization and further as queries, spreadsheets, reports, and datasets.

    Significant Features

    • The significant feature of the Data Warehouse is that we can set it in a few seconds and start searching the data within a few seconds.
    • It can leverage Google’s Serverless Infrastructure and this is used for high-performance streaming and automatic scaling.
    • This tool has the capacity to support BI tools like MicroStrategy, Looker, Tableau, and Data Studio.
    • BigQuery reduces data operation with immediate data replication during disaster recovery and the data is highly available for processing with no additional charges.
    • Also, in this tool users are requested to pay the costs for what they use.

    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 a Redshift and Clients are 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 compute.
    • 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. The tools such as JavaScript, Node.JS, Spark, R, and Python have the potential to unlock the power of the 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
    • RapidMiner 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 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 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 the 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 for the best in class Data Science Course in Chennai to have a blissful future

    Data Science Interview Questions

    What do you infer from logistic regression?

    Logistic regression is a statistical model which utilises logistic function for modelling dependent variables which are binary in nature. Logistic regression is deployed for the prediction of binary outcome from any linear combination for the predictor variables.

    Define Selection Bias.

    Selection bias describes a situation where the sample analyzed differs from the whole set of data in important aspects for which they are analyzed, resulting in biased conclusions. This is a type of error, which occurs during the decision process. It is also known as the Selection effect.

    The various kinds of selection bias consists of:

    Sampling bias- This occurs when the samples present in a population are non-random.

    Time interval- The termination of the trial occurs at an early stage for extreme value.

    Data- When particular data subsets are selected for the supporting of conclusion over arbitrary grounds.

    Attrition- It is also a type of selection bias that is caused due to attrition.

    How is data cleaning important in analysis?

    For each data analysis process, data cleansing is very important though it may consume more time. Also, data is accumulated from various sources to convert the whole dataset into a specific format, which can be processed by the data scientists. There might be data that are duplicate, redundant and unworthy to the analysis being carried out.

    What do you infer from Normal distribution?

    Normal Distribution, also known as Gaussian distribution is a distribution in probability where the data is spread symmetrically about the mean, which implies that Data closer to the mean occurs more frequently than the data farther from the mean. Data is distributed about the central value irrespective of its reach that may occur in the form of a bell curve.

    Characteristics of normal distribution are:

    • Unimodal
    • Bell
    • Symmetrical
    • Asymptotic
    • Mean, median and mode

    Differentiate between Systematic and Cluster sampling.

    The major difference between Systematic and Cluster sampling is the manner in which they pick sample from the population.

    Systematic Sampling

    • Here, Sample is picked from the population from any random starting point and at regular intervals from the starting point depending on the size of the population.
    • Provide accurate results
    • Probability sampling method
    • This is used when the population contains important units throughout and while making important decisions based on the sampling.

    Cluster Sampling

    • In Cluster Sampling, the population is divided into clusters and a random sample is taken from each cluster.
    • Results are less accurate compared to systematic sampling
    • Random sampling method
    • This can be used when the population is large and it reduces the time and money spent.

    Point out the difference between underfitting and overfitting.

    Both of these terms denote the state of the statistical models or machine learning algorithms which hampers reliable predictions from the generated data.

    When we consider overfitting, the statistical model characterizes noise or inaccurate data rather than the underlying relationship. Here, the model is fed with tons of data exceeding the capacity of the model. Its usage is visualized when there is a presence of a complex model that comprises of many parameters related to the counting of observations.

    The Underfitting process occurs when any statistical model is unable to grasp the fundamental trend of data. This happens when there is insufficient data for building an accurate model.

    How is Supervised learning different from unsupervised learning?

    Supervised learning is a process in which an algorithm is learned from the training dataset. Here we use an algorithm to map the input and output using the available input/output variables in the training dataset. This mapping enables to predict outcome variables when new input data is fed.

    In Unsupervised learning, there is no training dataset and we only have input data that contains hidden patterns and distributions of data. It aims at modeling the hidden patterns in the data to understand more about the data.

    Data scientists predominantly use both types of learning where unsupervised learning is used to process the data during exploratory analysis and to train supervised learning algorithms using the generated data set from unsupervised learning. Supervised learning is used for financial analysis, training neural networks,  forecasting, facial recognition, and various other processes.

    Explain univariate, bivariate and multivariate analysis.

    Univariate analysis is considered to be the simplest form of statistical analysis, which utilizes only one variable data set. It is used to describe, summarise and find patterns in the Data.

    Bivariate analysis tries to build a relationship between the available two-variable data sets.

    Multivariate Analysis deals with acquiring knowledge of multiple variables to understand the aftermath of the presence of variables over the responses.

    What do you mean by box cox transformation in the regression model?

    The response variable involved in regression analysis may not satisfy numerous assumptions of any ordinary square regression. A Box-Cox transformation helps to convert non-normal dependent variables into a normal shape. If the data is abnormal, applying Box-Cox transformation can help to run a wide range of tests as normality is an important criterion for many statistical techniques.

    What do you infer from Eigen  vectors and Eigen  values?

    The Eigenvectors are widely used for the understanding of linear transformation. The user calculates Eigenvectors for the covariance matrix during the data analysis. They are considered to be the directions for a specific linear transformation by the act of flipping, stretching or compressing.

    The eigenvalue is known as strength for the change in direction of Eigenvector.

    Data Science Tutorial

    List few assumptions related with linear regression. The presumptions regarding linear regression are mentioned below:

    • The relationship that persists amidst dependent variables and regressors is considered to fit the data that has been actually created by the user.
    • The distribution of error happens to be normal as well as being independent.
    • The multiple correlation is very minimum amidst the variables.
    • Variance all around regression line is identical for every predictor variable.

    What do you infer from exploding gradients?

    They are a sort of error gradients that gets accumulated while training a neural network algorithm resulting in huge updates for the neural network. This accumulation causes the neural network weights to increase abnormally during the process of training thus providing results in NaN values. Gradients are used to train and update the network weights, which is beneficial when the gradients are small and controlled. If the magnitude of the error gradient accumulates it causes instability in the neural network algorithms spoiling the purpose of training.

    What do you infer from SVM machine learning algorithm?

    Support Vector Machine(SVM) can be deployed for Classification and Regression. SVM attempts to find a hyperplane (in N-dimension space) that can classify every single feature with specific coordinates. This also makes use of hyperplanes to separate various classes based on the kernel function. A hyperplane is just a boundary between two or more classes of data.

    How will be statistics used by Data scientists?

    Statistics is the real soul of Data Science. Statistics is the most important subject that provides the tools and techniques to structurize data and find deeper insights from data. Its main usage will be seen in identifying the patterns and conversion of raw data to business insights. This also aids the data scientists to develop excellent ideas that are expected by the customers. It is easier for them to analyze the interest, consumer behavior, engagement, retention along with the perspective statistics. This also helps in building up of data models for the validation of certain interferences. Every aspect is possible to be converted to any business proposition.

    What do you mean by Random Forest? And explain its working?

    It is a flexible method deployed in machine learning to perform classification and regression tasks. It is used in dimensionality reduction, outlier values, and treats missing values. This is considered to be a kind of ensemble in which weak models are combined to create a powerful model. It is possible to grow several trees against any single tree for the classification of new objects that are based on attributes.

    What do you mean by Extrapolation and Interpolation?

    Both of these terms are considered very important in statistical analysis. Extrapolation is done to estimate value by extending a known sequence of values beyond the known area to infer implicit information from the provided information. Interpolation is something used for the determination of specific value that lies amidst fixed values that is useful when there are two extremities of a specific region.

    Differentiate between Data modelling and Database design.

    Data modeling is the initial step in the process of Database design. This helps in the creation of a conceptual model that is based on connection amidst different data models. It consists of the transition of the conceptual stage to a logical model. Database design is deployed for the plotting of a database that aids in the creation of a precise data model. This consists of physical design choices along with storage parameters.

    List some of the disadvantages of linear model.

    The most important drawbacks of this model are:

    • The expectation of linearity errors.
    • Outcomes count cannot be calculated.
    • Overfitting problems that are impossible to resolve.

    When partial data is given by the user in any search engine, how is it possible to predict the search?

    It can be predicted by the previous frequencies of various word sequence, conditional probabilities can be constructed for the next sequences, which can be shown up. The order that consists of the highest conditional probabilities will be shown on top of the suggestions list. For the additional improvisation of the mentioned algorithm, the user can add weight over the past series that appeared in the recent time.

    What do you infer from Boosting ?

    This is an ensemble methodology for attempting in the creation of a strong classifier with the help of numerous weak classifiers primarily to reduce bias and variance. The performance of every tree is used for weighing how much attention is to be given to every next tree that is built-up. Every model is created in a follow-up manner, further updating weights.

    What are all the pre-processing steps that are highly recommended?

    List the pre-processing stages which are recommended.

    • Missing value treatments
    • Outlier Analysis
    • Feature engineering
    • Structural Analysis

    Location

    FITA Academy offers the best Data Science Training in Chennai from MNC specialists. Do visit once and get placed in your dream company. We are located at T-Nagar, OMR, Anna Nagar, Tambaram and Velachery in Chennai nearby you.

    Related Blog

    Best Data Science ToolsData Science vs Big DataTechnical and Non Technical skills required to become a Data ScientistTop Programming Languages that every Data Scientist should KnowWhat Future Scope of Data Science and Data Scientist