Course Highlights & Why Data Science Course in Bangalore at FITA?
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- Overview of Python
- Beginning with Python
- Introduction to the installation of Python
- Introduction to Python IDE's and Editors like Canopy, Pycharm, Jupyter, Rodeo, and Ipython.
- Understanding the Jupyter notebook and Customize Settings
- Concept of Packages and Libraries
- Important packages like NumPy, SciPy, Sci-kit-learn, Pandas, and Matplotlib
- Installation of loading Packages and the Name Spaces
- Data Types
- Data structures/objects (strings, Tuples, Lists, and Dictionaries)
- Dictionary and List Comprehensions
- Value Labels and Variable including Time Values and Date
- Basic Operations - string- Mathematical - date
- Reading data
- Writing data
- Simple plotting
- Conditional statements and Control flow
- Code profiling and Debugging
- Importing Data from different sources which include (CSV, Excel, txt, and access, etc)
- Database Input (Connecting to the database)
- Viewing Data objects - methods and subsetting
- Exporting Data to different formats
- Important Python modules: Pandas
- Cleansing the Data with Python
- Data Manipulation steps(Sorting, duplicates, subsetting, filtering, merging, appending, sampling, derived variables, Data type conversions, renaming, and formatting)
- Data manipulation tools (Operators, Loops Packages, Functions, Control structures, and arrays)
- Python inbuilt functions (Text, Date, numeric, date, and utility functions)
- Python User for Defined Functions
- Stripping out the extraneous information
- Normalizing data
- Formatting data
- Important Python modules for Data manipulation (Pandas, Numpy, re, math, string, and DateTime)
- Introduction to exploratory data analysis
- Descriptive statistics, Summarization, and Frequency Tables
- Univariate Analysis (Graphical Analysis and Distribution of data )
- Bivariate Analysis(Cross Tabs, Distributions, Relationships, and Graphical Analysis)
- Creating Graphs- Bar/Line chart/Histogram/ Scatter/ Boxplot/Pie/Density )
- Significant Packages for Exploratory Analysis (NumPy, Arrays, Pandas, Matplotlib, and Scipy.stats)
- Fundamentals of Statistics - Measures of Variance and Central Tendencies
- Building blocks
- Probability Distributions
- Normal distribution
- Central Limit Theorem
- Inferential Statistics -Sampling
- Concept of the Hypothesis Testing
- Statistical Methods - Z/t-tests
- Test based on One sample, independent, paired),
- ANOVA, Chi-square, and Correlation
- Modules that are important for statistical methods: Numpy, Scipy, and Pandas
- Introduction to Predictive Modeling and Machine Learning
- Kinds of Business problems include Mapping Techniques -classification versus segmentation - Regression versus classification - Segmentation versus Forecasting
- Important Classes of the Learning Algorithms
- Supervised versus Unsupervised Learning
- Numerous Phases of Predictive Modeling
- Data Pre-processing
- Model Building
- Linear Regression
- Cluster Analysis (K-Means)
- Support Vector Machines(SVM)
- Decision Trees (CART/CD 5.0)
- Other Techniques (KNN, and Naïve Bayes)
- Important Python modules for Machine Learning (SciKit Learn and scipy)
- Artificial Neural Networks(ANN)
- History of R
- Features of R
- Introduction to the R studio
- Advantages of using the R studio
- Installation of R in the system
- Setting up of workspace
- Windows in the R studio
- Introduction to packages
- Configuring and loading packages
- Managing packages
- Command prompt
- R script file
- Data Frames
- Kinds of vectors
- Creation of vectors
- Creation of multiple elements vectors
- Accessing vector elements
- Manipulating vectors
- Creation of matrices
- Accessing the elements of a matrix
- Computation and Matrix processing
- Creation of arrays
- Naming rows and columns
- Accessing the array elements
- Manipulation of array elements
- Creation of list
- Naming list elements
- Accessing list elements
- Manipulation of list elements
- Creation of factors
- Factors in the data frame
- Changing the level of order
- Generation of factor levels
- Conversion of characters to factors
- Creation of data frames
- The distinction between the matrix and data frame
- Subsetting data from the data frame
- Extract data from the data frame
- Joining columns and rows in the data frame
- Merging of data frames
- As.numeric ,as.character,as.matrix, and as.data.frame
- Is.character, is.numeric,is.matrix, and is.data.frame
- Arithmetic operators
- Relational operators
- Logical operators
- Assignment operators
- Miscellaneous operators
- If statements
- If-else statements
- Switch statements
- Repeat loop
- While loop
- For loop
- Break statements
- Next statements
- Definition of function
- Function components
- Inbuilt function
- User-defined function
- Character function
- String functions
- Numeric function
- Statistical function
- Time and Date function
- Creation of function
- Calling of function
- Lazy evaluation of the function
- Descriptive statistics
- A measure of central tendency
- Inferential statistics
- Hypothesis testing
- Exploratory data analysis
- Pie charts
- Bar charts
- Box plots
- Line graphs
- Scatter plots
- History and Origin of Machine Learning
- The distinction between Machine Learning and AI
- Differences between Data Science, Data mining, Statistics, and Machine learning
- Applications of Machine Learning
- Limitations of Machine Learning
- Future of Machine Learning
- Implementing Machine Learning in R - programming
- Collecting data
- Preparing data and Pre-processing
- Choosing a model
- Exploring data
- Training the model
- Evaluating the model
- Improving the performance of a model
- Machine Learning theories
- Machine Learning theories to algorithms
- Definition of algorithm
- Significance of Algorithms in the Machine learning
- Components of the Machine Learning algorithm
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Semi-supervised learning
- Nearest neighbor (instance-based /non-parametric)
- Naive Bayes theorem (probabilistic/parametric )
- Decision trees ( symbolic/non-metric)
- Linear regression
- Artificial Neural networks ( Deep Learning and Neural networks)
- Support vector machines that are non-probabilistic
- Association rules (rule-based learning)
- K-means clustering
- Random forest
- Neural net
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- Trainers at FITA are fervent believers in the blended method of learning.
- Data Science Tutors at FITA trains the students with step by step real Data Analytics examples. Thus, helping the students to have practical exposure to Data Science and its application.
- Data Science Trainers in Bangalore at FITA are Industry Experts in the Big Data field and have 12+ years of experience.
- Trainers are working professionals who possess hands-on experience in Data Science.
- Upskills the knowledge of the students with recent updates in Data Science and market-relevant skills.
- Data Science Trainers in Bangalore at FITA provide students the necessary individual attention and assess them regularly.
- Trainers support the students in Resume building and guide them with useful Interview tips as well.
- Trainers help the students with necessary corporate training and enrich their knowledge about the professional environment.
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.
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!
Get Certified by FITA. Also, get Equipped to Clear Global Certifications. 72% FITA Students appear for Global Certifications and 100% of them Clear it.
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.
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.
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 in Bangalore
About Data Science Certification Training in Bangalore at FITA
Data Science Certification Training in Bangalore
Data Science Course Certification is the professional accreditation that states the ability of a person to perform efficiently in the complex Data Science project. It also states that the person has sufficient knowledge in Data Science life cycle which includes, collecting, maintaining, processing, analyzing, and communicating the Data. Also, having a Data Science course certificate on the resume or curriculum vitae creates a positive impact while the interview and the chances of being prioritized are comparatively high.
Data Science Course Certification in Bangalore at FITA provides training with certification for the beginners and software professionals as well to enhance their knowledge in Data Analytics. Training is offered by experienced trainers who have a decade of experience in the Big Data field and they will help you to improve your knowledge in Data Science skills with in-depth knowledge.
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Job Opportunities After Completing Data Science Training in Bangalore
Various Job roles in Data Science
The field of Data Science is multidisciplinary and in the process of diversification of various job roles based on the needs of the industry. Top firms are in the process of establishing separate teams to work on Data and extract useful insights for Business Development Strategies of the organizations. Various Job roles involved in Data Science are listed below.
SAS Analyst, Data Analyst, Research Analyst, Business Analyst, Statistical Analyst, Hadoop Developer, Analytics Manager, Analytics Consultant
On average, a fresher in Data Science can earn between Rs. 6-8 Lakhs per annum. With 4-5 years of experience and an excellent skill set, a Professional Data scientist earns above Rs. 15 Lakhs per annum.
Benefits of Learning Data Science
- There is a huge demand for skilled Data Scientists in India. Based on various reports for 2019, there are more than 1,00,000 vacancies in India for Skilled Data Scientists with 97% full-time job roles.
- More than 20 % of Job vacancies in India are available for freshers with strong fundamentals in Statistics, Programming language, Data Visualisation and enthusiastic to learn and develop themselves regularly.
- Data Analysts with knowledge in Python are most sought by various top MNC. Candidates with excellent skills in Python can join Data Science Training in Bangalore at FITA to update themselves with industry-relevant skills in Data Analytics and make a successful career in Data Science.
- IT Professionals willing to make a shift in their career path can opt for Data Science since it promises a better future with its phenomenal growth in the Indian IT Sector. Candidates willing to work in a dynamic and challenging environment can opt for the Data Science field to escape job roles that are monotonous with the minimal scope of the advancement of knowledge. Bangalore, the Silicon Valley of India, accounts for 24% of total Data Science related job vacancies in India.
- Candidates interested in learning programming languages such as Java or Python can join Java Training in Bangalore or Python Training in Bangalore.
Data Science is one of the highly paid jobs in recent times and will set to make a huge footprint in the Indian IT Sector.
Top Organisations with most Job Opening in Analytics
Banking, Financial Services, and Insurance (BFSI) sector accounts for a majority of job vacancies for Data Science related jobs in India. Data Science has become a necessity for various organizations to keep pace with rapidly evolving cutting edge technologies across the globe. Top employers of Data Science related jobs in India are listed below.
- Wells Fargo
- Ernst & Young
- eClerx Services
- Dell International
- Hexaware Technologies
Roles and Responsibility in Data Science Career
- Data Analysts determine the goals of the organization by collaborating with Tech teams, Management and Data Scientists.
- Data Analysts are tasked with mining of data from internal and external sources of the organization and recognize patterns in Data using various mathematical and computer algorithms.
- Data Analyst helps to figure out the long term and short term trends of the organization for directing the organization in the development path.
- Generating key insights from Data that profits the organization and presentation of the key findings using various Data Visualisation tools and techniques.
- Predictive modeling with the aid of various analytics programs, Machine Learning and Statistical Methods.
- Suggest Cost-effective business processes and strategies.
- Maintenance of Database and Data Systems.
Data Science as a profession requires a wide range of skills with expertise in the tools and techniques involved in Data Science. Some of the key skills expected from aspiring Data Scientists are provided below.
Statistical Skills – Statistics is vital for Data Science to process and analyze huge volumes of data and derive useful business insights from the processed Data. Segmentation, Clustering, and Classification are a few Statistical techniques of high demand in the Data Science field. The convergence of computer science with statistics opens up a popular field of study, Machine Learning; critical in detecting patterns from processed Data.
Programming Skills – Based on various reports it is conclusive that Candidates with excellent programming skills in Python or Java are in great demand. Certain companies also recruit candidates with hands-on experience in R programming.
Cloud Computing Skills – Data Science aspirants are expected to be well versed in AWS or Azure since a majority of top MNC are incorporating cloud-based workflow for better optimization. Candidates interested in learning AWS or Azure can join AWS Training in Bangalore or Azure Training in Bangalore at FITA.
Data Visualisation Skills – Visual representation of key insights from the analyzed data is essential for Data Scientists. Data Scientists utilize a variety of tools such as Matplotlib, Tableau, Microsoft Power BI, and Qlikview. These Business Intelligence tools help to visualize the key insights in various understandable formats in the form of bar-charts, graphs, pie-charts, etc.
Database Skills – Candidates should possess a sound knowledge of various Database Querying languages such as SQL, NoSQL, Oracle, and MongoDB. Candidates interested to learn SQL or Oracle can join SQL Training in Bangalore or Oracle Training in Bangalore at FITA.
BIG DATA Skills – Data Science Aspirants are expected to possess strong fundamentals in various Big Data tools such as Hadoop and Spark. These tools are essential for Processing, Mining, and Extraction of key insights from huge volumes of Data. Candidates interested in learning advanced Big Data tools such as Hadoop or Spark can join Hadoop Training in Bengaluru or Spark Training in Bangalore.
Machine Learning Skills – Candidates willing to be a part of Data-driven companies such as Facebook, Google, Uber, Amazon, Flipkart, etc. should possess a deep understanding of various Machine Learning methods and their applicability. Knowledge of key ML concepts such as K-Nearest Neighbour, random forests, ensemble methods, support vector machines, etc. is essential to extract vital insights from processed Data. Students enthusiastic to learn advanced concepts such as Machine Learning can join Machine Learning Training in Bangalore at FITA.
Communication Skills – Good communication enhances collaboration and cooperation in Business environments by establishing robust networks across various teams in the organization. Data Scientist gathers every piece of organizational Data from various departments in the organization. Effective communication is critical for Data Scientists to perform their tasks by communicating with various teams and essential to present their key insights to the organization.
Data Science Interview Questions
1. List the types of Bias?
- Selection bias
- Under coverage bias
- Survivorship bias
2. What are the kinds of the kernel in SVM?
- Linear Kernel
- Polynomial kernel
- Radial basis kernel
- Sigmoid kernel
3. List a few kinds of Deep Learning Frameworks?
- Tensor Flow
- Microsoft Cognitive Toolkit
4. What are the libraries used in Machine learning in Data Science and explain their purpose?
|Statsmodels||Time -Series Analysis|
|Scikit Learn||Preprocessing and Data modeling|
|NLTK and Regular Expression||Text processing Regular Expression|
|Pytorch and TensorFlow||Deep Learning|
5. What are the important skills that are required to have in Python concerning data analysis?
- Masters in NumPy Arrays and N-dimensions.
- Proficient knowledge in Pandas data frames
- Performing element-wise matrix operations and vector on the NumPy arrays.
- Familiar with Sci-kit learn
- Understanding of built-in data types.
For more Data Science Interview Questions with Answers for freshers and experienced candidates click here.
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Data Science Frequently Asked Questions (FAQ)
- The Data Science Course in Bangalore at FITA is designed by industry leaders with more than 12+ years of experience in the Big Data and Data Science field.
- Blended learning to make the students understand and apply the concepts easily.
- Extensive coverage of Data Science Course in Bangalore at FITA with 60+ hours of training.
- More than 20,000+ students trust FITA.
- The nominal fee structure for students and working professionals.
- Convenient Batch timing for the students and the working professionals as well.
- Professional Data Science Training in Bangalore at FITA with certification.
- We are proud to state that we have tie-ups with more than 600+ small, medium and large scale companies. Most of the companies have a job opening for the Data Scientist role.
- FITA has an active placement portal to support the students.
- FITA helps the students with mock interviews and group discussions and trains them professionally to take an interview.
Data Science is an interdisciplinary field that involves Statistical methods, Programming skills in Python or R Programme and sound business acumen. A Qualified Data Scientist is expected to possess excellent statistical and Programming skills with an eye for detail. Data science is utilized in a wide range of sectors in the economy and expected to shape the future of the business world.
Netflix, an online streaming entertainment portal, utilizes Data Science to aid its Hybrid recommendation systems for enriched user experience. They capture the viewing pattern data of consumers and helps to optimize search results based on the user’s preferences. Various E-commerce companies also utilize Data Science to provide better delivery of services by bringing out the best offers on products that are searched or in wishlist on their platforms.
Data Science is making a huge impact on various businesses in the manufacturing sector by predicting the error-prone zones in the manufacturing processes as well as in marketing strategies. Data Scientist primarily works for various organizations to analyze the companies data and extract useful business insights through various techniques and algorithms. In short, Data Scientists combine their statistical skills and Programming skills to create models using valid datasets and derive business strategies.
The world is moving towards the Fourth Industrial Revolution driven by the fusion of various technologies to blur the boundaries of physical, digital and biological entities. Data Science aids various technologies such as Artificial Intelligence, Big Data, Robotics, Machine Learning, Internet of Things(IoT), Quantum Computing, Biotechnology, Genetic Engineering, autonomous vehicles, etc.
A career in Data Science related jobs will bring in a good income to your coffers since the value added by Data Scientists to the organizations has a great impact on the businesses.
Data Science Life Cycle
Data Science’s prime purpose is to resolve business challenges by providing potential insights from the analyzed data. Life Cycle of Data Science begins with the identification of various business challenges. In this stage, the Data Scientist is expected to have the sound domain knowledge to identify the hurdles faced by the organization. Relevant Data Acquisition happens in the next level, where Data from various sources such as web servers, logs, APIs, Databases and online repositories are accumulated and provided as input to the next stage, Data Preparation. Data Preparation involves Data Cleaning to remove inconsistent datatypes & duplicate values; utilize various tools like Talend and Informatica for better data integration and management. Exploratory Data Analysis that follows Data Preparation; helps to define & refine data for the selection of feature variables that will be utilized in model development. Data Modelling ropes in various ML algorithms to identify a suitable model based on business requisites and select the best performing model using R or Python. Data Visualisation utilizes various Business Intelligence tools such as Tableau, Power BI and Qlikview to represent the key insights from the analyzed data and generate reports. The approved model is tested, deployed and maintained by Data Scientists who generate periodic performance reports.
Why Data Science?
- The potential of Data has been realized in recent years and Data will be an important driver of future businesses.
- Data Science helps in Predictive analysis from the huge volumes of the organization’s data; aids to formulate business strategies for the benefit of the organizations.
- Data Science is utilized in a vast majority of sectors such as Banking, e-commerce, Finance, Gaming, Healthcare, Education, Telecommunications, Travel, and Tourism.
- Globally, businesses are in favor of incorporating automation in their workflows and Data Science will fuel the automation processes using Big Data and Machine Learning techniques.
- Data Science aids marketing to a greater level since it processes bulk Data and derive consumer spending patterns and products most preferred by consumers in the businesses. Companies can promote products most likely to be preferred by consumers based on a user’s search history and purchasing pattern using Data Science.
- With increased Internet Penetration and Data Proliferation, the dire need of skilled Data Scientists for Business Organisations is visible in the near future to manage and derive potential insights from the voluminous data for setting a clear roadmap for the organizations.
Data Science Course Tracks offered in Bangalore at FITA
- Data Science Course using Python
- Data Science Course using R
- Data Science Course using SAS
- Data Visualization using Tableau
At FITA we provide training for the above-mentioned courses. In case if the students want to learn both Python and R they can go with the integrated Data Science Master program. In this course, they will learn about Machine Learning, Python, Tableau and SAS. Data Science Training in Bangalore at FITA provides professional training with certification under the guidance of working professionals.
Tools Covered in the Data Science Course
What you will learn at the Data Science Training in Bangalore at FITA?
- Cleaning, Analysis, and Processing the Data
- Creating Basic Tableau Visualization
- Capable of Reading Confusion Matrix
- Configuring and Navigating SQL Server
- Creating Scripts in the SQL
- Creation of Dummy variables
- Building the CAP curve on the Excel
- Creating Logistic and Linear Regression
- A better understanding of the odds ratio
- Able to perform efficiently all steps in the complex Data Science project.
Prerequisites to learn Data Science
There are no such requirements to learn the Data Science Course. If you have a basic knowledge of Mathematics and Statistics it would be of more benefit to you.
Eligibility Criteria to learn Data Science
Any freshers who aspire to begin their career in Data Science can choose this course. Besides, this course can be opted by working professionals to broaden their career opportunities and they are
- IT Professionals
- Marketing Managers
- Business Analysts
- Financial and Banking professionals
Data Science with Python
In this course, the students will be introduced to the fundamentals of Python Programming and its environment. Here you will also know about the basic techniques of Python programming like reading files, manipulating the CSV files, numpy library, and lambda.
Also, you know the cleaning techniques using the Python tools for Data Science. Functioning of groupby, pivot tables, and meger functions are taught effectively. Students can also learn about Applied Plotting, Data and Chart Representation in Python. Also, you will learn about the visualization basics with the help of the matplotlib library. At the end of this session, one can understand the manipulation and cleaning of data and perform fundamental inferential statistical analyses.
In Machine Learning with Python, the students will get to know about what is Machine Learning and the descriptive analysis using the scikit toolkit. Dimensions of Data, Clustering of Data, Evaluation of Clusters are explained briefly to the students. Creating Predictive models using the supervised and unsupervised approaches will be discussed in a detailed manner. Methods of processing data, cross-validation, overfitting, building ensembles, and limitation of the predictive analysis model will be explained. By the end of this session, the students will be able to differentiate between clusters and classification techniques and find the specific dataset and feature that is required for given data. Also, the students will be able to write the Python code for carrying out the analysis.
Data Science with R
Here the students will be taught about the major ideas and tools in the Data Scientist toolbox. Introduction to the conceptual turning of data to reasonable knowledge. Practical introduction of tools that are used for programs like markdown, control, R, Github, and RStudio.
You will learn to program in R and use R for analyzing the data effectively. Installation of the required software for the statistical program environment and concepts of a programming language when implemented on the high-level statistical language. You will also know about the practical issues in the statistical computing that include programming in R, accessing R package, writing R functions, reading data in an R function, Profiling R code, debugging and commenting the R Code.
Also, you will learn about obtaining the Data from the database, web, and APIs in multiple formats. Components like data set which includes raw data, codebooks, processing instructions, and processed data will be taught. We will teach about exploratory techniques that are used for summarizing the data. Techniques that are applied for modeling commences and for the development of a complex statistical model. Plotting the systems in R and the fundamental principle of constructing data graphics are explained briefly.
Data Science with SAS
Here the students are trained with the critical programming skills of SAS. Knowing to access, manipulating, transforming, developing the data quality for analytics and report. The basics of analytics and statistics and how to work with Hadoop, Pig, Hive, and SAS are explained briefly. Visualization and Exploration Data techniques.
Predictive and Machine learning modeling techniques and applying those techniques in distributed and big data sets. Pattern detection and Time-series forecasting are also taught clearly. Data Science Training in BTM at FITA train the students by explaining the concepts briefly and upskills the knowledge of the students with market-relevant skills.
Data Science with Visualization using Tableau
The Basic concepts of Data Visualization and application of Data Visualization are taught here. Exploration of Tableau interface and the application of multiple tools that are offered by Tableau. Preparation of significant data in the tableau and communicating the relationship between Data Visualization and Analytics. Similarities between the exploratory and explanatory analysis in Data Visualization is discussed here. Assessment of Data and selecting appropriate visual representation for the data.
Getting to know more about the Tableau tools that are used in areas like Charting, Dates, mapping, and tableau calculation. Types of chart which includes Gantt charts, scatter plots, bullet charts, and histograms. A brief explanation of continuous and discrete data concept.
Learning to create a quick table for custom calculation and their parameters. Prelude of mapping and identifying how the Tableau uses various types of geographical data.
Primarily used Data Science tools
Below are the general tools that are required for Data Science
Data Analysis Tools: Python, R, Statistics, Jupyter, SAS, R Studio, RapidMiner, Matlab and Excel.
Data Warehousing tools: SQL, Hadoop, ETL, AWS Redshift, Talend/Informatica
Data Visualization Tools: Tableau, Cognos, Jupyter, and R
Machine Learning Tools: Mahout, Azure ML studio, and Spark.
Data Science and various disciplines
Data Science and Business Intelligence
Generally, people presume that Data Science and Business Intelligence are the same. But, it is not so. To know the differences between the Data Science and Business Intelligence one should have a clear idea of their basic functionality. The Table that is given below would clearly explain the differences between them.
|Basis of Criterion||Business Intelligence||Data Science|
|Data Source||It predominantly deals with the structure data. Example – Data Warehouse||It deals with all sets of Data like Structured and Unstructured data. Example – Feedback and Weblogs.|
|Method||Business Intelligence follows the Analytical method.||Data Science Follows the Scientific method and Predictive Analytics.|
|Skills||Here we require the Visualization and Statistics skills for Business Intelligence.||Visualization, Statistics, and Machine Learning Skills are required for Data Science.|
|Focus||They focus on both the present and past Data||It focuses on the past, present and future predictions as well.|
Data Science is used in business intrinsically, where numerous disciplines are applied together for extracting insights from the complex data provided by the business. Whereas, Business Intelligence is used in monitoring the present situation of the business with the help of the historical data and conclude.
In simple Data Science, analyses the Data to arrive at the future predictions of a business, where Business Intelligence just interprets the past Data. Data Science Training in Marathahalli at FITA trains the students by explaining all the concepts that are related to Data Science in an efficient way. Also, the tutors at FITA train the students with industry-relevant skills and enhances the career opportunities of the students.
Data Science and Big Data
Often we would have heard Data Science and Big Data terms together. It is because of both these subjects are dealing with data. But, the operation and usage differ. With the differences, we can also see in what ways they are related.
Generally, Big Data deals with managing and handling an enormous amount of Data. Before Big Data, the industries, and companies did not have the relevant resources and required tools for managing large amounts of Data. Later, with the emergence of Hadoop and MapReduce, it was easy to handle the form of data. Whereas Data Science is known for the scientific analysis of the data. Data Science is quantitative and it uses numerous statistical methods to identify the insights of data. Given below table will clearly explain the key differences between Data Science and Big Data.
|Basis of Criterion||Big Data||Data Science|
|Concept||Big Data deals with a diverse amount of Data types that are generated from various sources.||Data Science is more of Analysing the Data with specific tools and techniques.|
|Functions||Generate insights from an ample amount of data.||Data Science usually understands the pattern within data and make decisions based on that.|
|Industry||Big Data is used in TeleCommunication, E-Commerce services, and Security Services.||Sales, Financial Sectors, Risk Analytics, Image and Speech Recognition, and Advertisements.|
|Tools||Big Data uses tools like Spark, Hadoop, and Flink||Data Science uses tools like Python, R, and SAS|
Organization requires Big Data for knowing new markets, improving efficiencies, and developing the competitiveness. While Data Science helps with mechanisms or methods for utilizing and understanding the potential performance of the Big Data. Big Data makes use of analysis for performing the mining function of useful insights from huge volumes of data sets. While Data Science makes use of Machine Learning Algorithms and Statistical methods for training the computer to understand without much programming for making predictions from big data.
Big Data is represented by its variety, volume, and velocity which is also known as 3Vs, where Data Science provides the techniques and methods for analyzing the data represented by the 3Vs.
In a nutshell, Data Science is a part or concept of Big Data. it works on Big Data for deriving useful information in a predictive analysis method to make smart decisions. Data Science Training in Bangalore at FITA provides a wide academic curriculum to the students and hones the necessary professional skills that are required for a Data Scientist professional efficiently.
Data Science and Machine Learning
Machine Learning is a part of Data Science. The word”Learning” in the term “Machine Learning” indicates that the algorithms rely on a few data which are used as the training set for the tuning model or the algorithm parameters. It includes numerous techniques such as Supervised learning, Navie, Bayes, and Regression. Also, not every technique can fit into this category. For instance, unsupervised learning focus on detecting clusters without any prior training or knowledge to classify the algorithms. But here the Machines require the assistance of a human to label the clusters. Few of the techniques are hybrid while others are semi-supervised.
While Data Science is more than what is Machine Learning when compared. The Data for the Data Science may or may not come from Machine Learning or the Mechanical process. But the primary difference between these two is the fact that Data Science includes the complete cycle of Data processing, not just statistical or the algorithm aspects. Data Science includes Distributed Architecture, Data Integration, Data Visualization, Automated Machine Learning, Big Data engineering, and DashBoard.
|Basis of Criterion||Data Science||Machine Learning|
|Collection of Data||Data Science focuses on extracting the details or data in the tabular or image forms.||Machine Learning focuses on Polynomial Structures, Algorithms, and Word adding.|
|Level of Complexity||Data Science usually handles unstructured data.||Machine Learning uses Mathematical Concepts, Algorithms, Spatial Analysis, and Statistics.|
|Hardware Requirement||It requires scalable systems horizontally with High Disk and Ram storage.||It needs the Tensor Processor and Graphic Processor that is high-level hardware.|
|Skills Required||Data Science requires skills such as Data Profiling, ETL, Reporting, and NoSQL.||Machine Learning requires skills like R, Maths, Python, Stats, and SQL methods.|
To conclude Data Science or the Data Scientists use Machine Learning as a tool to arrive at a decision. Data Science Training in Bangalore at FITA trains the students efficiently from the basics to the advanced topics holistically to the students under the working professional.
Data Science and Artificial Intelligence
As we all know Data Science is a vast subject, it includes numerous disciplines in it. Artificial Intelligence is also one of the disciplines that are used by Data Science to arrive at a decision when data is presented. Mentioned below are the major difference between Data Science and Artificial Intelligence.
|Basis of Criterion||Data Science||Artificial Intelligence|
|Function Extent||Data Science involves numerous underlying operations.||Artificial Intelligence is limited by the implementation of the ML Algorithms.|
|Kinds of Data||Data Science can deal with both structured and unstructured data.||Artificial Intelligence can deal with the standardized form of vectors and embedding.|
|Tools utilized||Python, R, SAS, Tensor Flow, SPSS, Keras and Scikit- learn.||Scikit-learn, Kaffee, Tensor Flow, PyTorch, Mahout and Shogun.|
|Application||Data Science is used in Marketing, Internet Search Engines, and Advertisements.||Artificial Intelligence is used in Automation, Robotics, Transportation, Manufacturing and Health care.|
|Control||It deals with the manipulation of data within the Data Science technique.||It can do Robotic control with the support of machine learning and artificial intelligence techniques.|
In a nutshell, Data Science is the collection of data for the analysis process while Artificial Intelligence is the implementation of those data in the Machine for knowing the data. Data Science makes use of Statistics for analyzing where Artificial Intelligence uses Machine learning. Data Science is all about identifying the hidden pattern in a given data while AI is imparting the autonomy of the data model. Data Science Courses in Bangalore at FITA provides professional Data Science training under the guidance of working professionals with certification and 100% placement support.
Application of Data Science
Data Science is part of Big Data that is intended for providing important data from the complex data provided to them. Data Science usually integrates the tools from inter-disciplines for collecting a data set and processing them. At present Data Science have made a tremendous impact on the various industries. In the field of Medicine, the algorithms provided by Data Science have helped in predicting the side effects of a patient during a diagnosis. In the field of sports, the models and metrics provided by Data Science have defined the athletic potential accurately. Also, it has handled the traffic congestion, with the route-optimization models and diverting the traffic. The attribute of Data Science is enormous, also it is becoming one of the required fields in most of the domains. Data Science is applied in wide arrays, mentioned below are some of the important domains where Data Science is used predominantly.
- Healthcare Sector
- Logistics Delivery
- Image Recognition
- Internet Searching
- Speech Recognition
- Airline Route Planning
- Digital Advertisements
- Fraud and Risk Detection
- Price Comparison Website
Gaming: Data Science is primarily used in the field of sports. With the application of Data Science – Sony, EA Sports, Sony, Nintendo, Zynga, and Activation- Blizzard has improved their gaming experience or skills to the next level. Currently, many games are designed using the Machine Learning Algorithms and that eventually enhances the gaming experience. Besides, in the motion games, the challenger could check your previous moves and then shape the game accordingly with the help of Data Science.
Healthcare Sector: This sector is highly benefited from Data Science. This industry uses Data Science for artery stenosis, identifying tumors, and organ delineation apply frameworks such as MapReduce for finding the ideal parameters for the function like lung texture sorting. Here they apply Machine learning methods, analysis wavelet for the solid texture classification, support vector machine (SVM), and content-based medical photo/image indexing.
Logistics Delivery: Data Science techniques are also applied in companies like FedEx, DHL, and UPS for improving their operational efficiency. By using the application of Data Science techniques these companies found the best way to ship their products at the appropriate time to their customers. Also, this has made their work easier and cost-effective. Data Science Course in Bangalore at FITA provides a holistic understanding of the Data Science concepts and techniques under the guidance of working professionals. Data Science Tutors are industry experts and they provide the necessary industry-relevant skills to the students.
Image Recognition: This is also one of the major fields where Data Science is used. To understand image recognition, let’s take the example, where you upload a picture of you and your friend on Facebook, later you get the idea of tagging your friend. The automatic tag suggestion feature is deployed by the use of a face recognition algorithm on facebook. Similarly, while using the WhatsApp web, we can scan the barcode on your web browser by using our cell phones. Besides, Google provides you the option of searching the images by uploading them. One can use an image recognition algorithm and it gives them the relevant results.
Internet Searching: This is probably the first thing that would strike for anyone when they think about Data Science. Rather than Google, there are a number of search engines like Bing, Yahoo, Ask and AOL. All the above search engines including Google uses the Data Science algorithms for providing the best results in a few seconds for the searched query on their sites. It is to be noted that Google alone processes above 20 petabytes of facts/ information/ data daily. To conclude, there wouldn’t have been Google today, if there was no Data Science. Data Science is a vital reason for the existence of Google.
Speech Recognition: Speech Recognition is similar to an image recognition application. Examples of Speech Recognition products are Siri, Google Voice, and Cortana. When you are not able to text a message, we can possibly send the message through the speech recognition feature. The only thing you have to do is to speak the message out and the feature would automatically convert it into text.
Airline Route Planning: It is a well-known fact that the Airline Industry across the world is known for holding its devastating misfortunes. Previously there had been numerous flight delays, change of time, location and so in the Airline Industry due to the natural circumstances. But, now with the help of Data Science they were able to distinguish the important areas of enhancements. Currently, by using the Data Science techniques the aircraft organizations could do the following things,
- Predicting the delay in the flights.
- Making a decision on which class of planes to be purchased.
- Whether to reach a certain destination directly or could we make a stop in between somewhere. For example, A flight can have a direct route from Chennai to Newyork. But, on the other hand, it could also stop in any other nation and proceed with their fly to avoid natural calamities.
- Alaska Airlines and Southwest Airlines are the Airline companies that have included Data Science in their organization to make changes in the order of their working.
Digital Advertisements: Digital Advertising platforms primarily rely on Data Science for advertisement. Even though internet surfing is an important application of Data Science and Machine Learning, the complete Digital Marketing spectrum is its other application. The Data Science algorithms are used for displaying the banners on multiple websites and digital billboards in airports. This is the reason behind the digital advertisements to gain higher Click-through rates compared to traditional ads. Data Science Training in Bangalore at FITA enhances the knowledge of the students in Data Science with market-relevant skills under the guidance of working professionals.
Fraud and Risk Detection: Data Science initially were used in the finance sector. The companies were constantly fed-up due to an increase in losses and bad debts year after year. Generally, banks collect lots of paperwork in the initial stage while sanctioning a loan. Instances were there where the companies missed storing data of their customers. It is then they decided to bring Data Scientists to overcome the loss. Now with the help of Data Science, the companies collect and divide data through past expenditure, customer profiling, and essential variables for analyzing the probabilities of default and risks. Data Science also helps in promoting their banking products based on the purchasing power of the customer.
Price Comparison Website: Most of the Price Comparison website uses Data Science for collecting the feeds. Here on this website, you can find numerous sellers on a single platform for one single product. For example sites like PriceGrabber, PriceRunner, Shopzilla, Junglee, and Deal Time. Currently, the price comparison websites are used in every sector like durable apparel, automobiles, and accommodation.
Future and Beyond
The future of a Data Science-based career is having a positive graph with increasing demand for talented and skilled Data Science Professionals. Metropolitan cities specifically, Bangalore is set to experience an exponential growth of Data Science careers in a wide range of sectors from Agriculture to Aerospace. Top Organisations are setting up separate departments to incorporate the latest technologies such as Machine Learning and Artificial Intelligence into their organizations as pilot projects. Data Science can be utilized in the latest technological advancements such as Artificial Intelligence, Big Data, Genetic Engineering, Autonomous Vehicles, Internet of Things (IoT), Robotic Process Automation, etc.
An important aspect that hinders top organizations from recruiting Skilled Data Scientists is the lack of a skilled workforce in India. The growth of skilled Data Scientists is falling behind the growth rate of Data Science related career opportunities leaving gaps in employment. Though organizations upskill their employees with relevant industrial training on Data Science, it is insufficient for the growing needs of this field. Data Science Course in Bangalore at FITA bridges the skill gaps in the workforce by providing best-in-class training from experienced Data Science Professionals. Students at FITA undergo hands-on training of the Data Science related tools and techniques under the guidance of industry experts. Professional experience of the Tutors exposes the students to the latest industrial practices in Data Science and enabling them to make a successful career in Data Science.
The time is ripe to become a certified Data Scientist through Data Science Course in Bangalore at FITA since the latest reports highlight the demand for Talented Data Scientists is increasing while there is a lack of employability skills in the workforce.
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