Data Science Course Online Training and Certification

Intellipaat Data Science course training lets you master data analysis, R statistical computing, connecting R with Hadoop framework, Machine Learning algorithms, time-series analysis, K-Means Clustering, Naïve Bayes, business analytics and more. In this Data science online course and certification, you will gain hands-on experience in Data Science by engaging in several real-life projects in domains of banking, finance, entertainment, e-commerce, etc. So, get the best online Data Science courses training from top data scientists!

Get MS Excel self-paced course free with this course. Enroll Now!

Key Features

  • Instructor Led Training : 42 Hrs
  • Self-paced Videos : 28 Hrs
  • Exercises & Project Work : 56 Hrs
  • Certification and Job Assistance
  • Flexible Schedule
  • Lifetime free upgrade
  • 24 x 7 Lifetime Support & Access

About Data Science Online Course

This is a complete Data Science boot camp specialization training course from Intellipaat that provides you with detailed learning in Data Science, Data Analytics, project life cycle, data acquisition, analysis, statistical methods and Machine Learning. You will gain expertise to deploy Recommenders using R programming, and you will also learn data analysis, data transformation, experimentation and evaluation.

What will you learn in this Data Science course online training?

  1. Data Science introduction and importance
  2. Data acquisition and Data Science life cycle
  3. Experimentation, evaluation and project deployment tools
  4. Different algorithms used in Machine Learning
  5. Predictive analytics and segmentation using clustering
  6. Big Data fundamentals and Hadoop integration with R
  7. Data Scientist roles and responsibilities
  8. Deploying recommender systems on real-world data sets
  9. Working on data mining, data structures and data manipulation

Who should take up this Data Science online course?

Big Data, Business Intelligence and Business Analyst Professionals, Information Architects, Statisticians, Developers looking to master Machine Learning and Predictive Analytics and those looking to take up the roles of Data Scientist and Machine Learning Experts

What are the prerequisites for learning Data Science?

There are no particular prerequisites for this training course. If you love mathematics, it is helpful to learn Data Science. You will also get MS Excel self-paced course free with this course.

Why should you take up the Data Scientist certification course online?

  • Data Scientist is the best job of the 21st century Harvard Business Review
  • Global Big Data market to reach $122 billion in revenue in six years Frost & Sullivan
  • The number of jobs for all the US Data Professionals will increase to 2.7 million per year IBM

The demand for Data Scientists far outstrips the supply of them. This is a serious problem in a data-driven world that we are living in today. Most of the organizations are ready to pay top-dollar salaries for professionals with the right Data Science skills. This Data Science course online will provide you with all skills needed to master Data Science along with Big Data, Data Analytics and R programming. All this means that you can fast-track your career to take on more lucrative and promising job roles and take your career to the next level.

What is the average salary for a Data Scientist in India and in the US?

The average salary of a Data Scientist in the United States is $118,000. The average salary of a Data Scientist in India is ₹620,000.

Which are the top companies hiring Data Scientist professionals?

Today, every company is hiring Data Scientists. Here are some of the top companies hiring Data Scientists: Google, Amazon, Microsoft, IBM, Facebook, Walmart, Visa, Target, Bank of America and others.

What are the different paths to enter Data Science?

There are multiple paths to becoming a Data Scientist. There are a set of tools that are being extensively used by a Data Scientist like the programming languages of R and Python, along with the analytical tools like SAS and others. The person should be well aware of data analytics and statistical packages. He should also be aware of Big Data Hadoop and Spark which can be very useful for a Data Scientist. When the data is converted into business insights, the Data Scientist is supposed to have a good knowledge of various visualization and reporting tools. He should be firmly grounded in various aspects such as coming up with compelling visualizations, charts, maps and reports that can help anybody to understand the data.

How Data Scientists are different from Business Analysts or Data Analysts?

Criteria Data Analyst Business Analyst Data Scientist
Skill Set Analyze business needs Analyze historical data Make data-driven decisions
Who is eligible? Anybody can learn Anybody can learn Anybody can learn
What do they do? Developing technical solutions to business problems Develop, analyze and report business capabilities Do statistical analysis and develop Machine Learning systems
Average Salaries  $60,000 $70,000 $110,000

What Data Science projects you will you be working in this Data Science training?

This course includes real-life industry-based projects, which will help you in gaining hands-on experience and prepare you for challenging Data Science roles

Project Name Industry Objective
Cold Start Problem in Data Science Entertainment Building a recommender system without historical data
Movie Recommendation Engine Entertainment Building a movie recommendation engine, based on user interests
Making Sense of Customer Buying Pattern E-commerce Deploying target selling to customers
Fraud Detection in Banking System BFSI Deploying Data Science to detect fraudulent activities and take remedial actions

How is Intellipaat Data Science Certification awarded?

Intellipaat follows a rigorous certification process. To become a certified Data Scientist, you must fulfil the following criteria:
Online Instructor-led Course

    1. Successful completion of all projects, which will be evaluated by trainers
    2. Scoring minimum 60% in the Data Science quiz conducted by Intellipaat

Self-paced Course

    1. Completing all course videos in our LMS
    2. Scoring minimum 60% in the Data Science quiz conducted by Intellipaat

What does a Data Scientist do?

  • Understand the Problem

A Data Scientist should learn about the issue at ground and ask the right questions.

  • Collect Enough Data

As the name implies, a Data Scientist has to collect enough data in order to make sense of the problem at hand and get a better grip of the issue with respect to the time, money and resources needed.

  • Process the Raw Data

Data can rarely be used in its original form. It needs to be processed, and there exist various methods to convert it into a usable format.

  • Explore the Data

After the data has been processed and converted into a form that can then be used in the later stages, the Data Scientist need to explore it further so as to get the characteristics of the data and find out more about the obvious trends, correlation and more.

  • Analyze the Data

This is where the magic happens. The Data Scientist deploys various arsenals in his repository like Machine Learning, statistics and probability, linear and logistic regression, time-series analysis and more in order to make sense of the data.

  • Communicate the Results

At the end of the entire process, there is a need to communicate the findings to the right stakeholders in order to get the groundwork done for all recognized issues.

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Data Science Course Content

Introduction to Data Science with R

What is Data Science, significance of Data Science in today’s digitally-driven world, applications of Data Science, lifecycle of Data Science, components of the Data Science lifecycle, introduction to big data and Hadoop, introduction to Machine Learning and Deep Learning, introduction to R programming and R Studio.

Hands-on Exercise – Installation of R Studio, implementing simple mathematical operations and logic using R operators, loops, if statements and switch cases.

Data Exploration

Introduction to data exploration, importing and exporting data to/from external sources, what is data exploratory analysis, data importing, dataframes, working with dataframes, accessing individual elements, vectors and factors, operators, in-built functions, conditional, looping statements and user-defined functions, matrix, list and array.

Hands-on Exercise – Accessing individual elements of customer churn data, modifying and extracting the results from the dataset using user-defined functions in R.

Data Manipulation

Need for Data Manipulation, Introduction to dplyr package, Selecting one or more columns with select() function, Filtering out records on the basis of a condition with filter() function, Adding new columns with the mutate() function, Sampling & Counting with sample_n(), sample_frac() & count() functions, Getting summarized results with the summarise() function, Combining different functions with the pipe operator, Implementing sql like operations with sqldf.

Hands-on Exercise – Implementing dplyr to perform various operations for abstracting over how data is manipulated and stored.

Data Visualization

Introduction to visualization, Different types of graphs, Introduction to grammar of graphics & ggplot2 package, Understanding categorical distribution with geom_bar() function, understanding numerical distribution with geom_hist() function, building frequency polygons with geom_freqpoly(), making a scatter-plot with geom_pont() function, multivariate analysis with geom_boxplot, univariate Analysis with Bar-plot, histogram and Density Plot, multivariate distribution, Bar-plots for categorical variables using geom_bar(), adding themes with the theme() layer, visualization with plotly package & building web applications with shinyR, frequency-plots with geom_freqpoly(), multivariate distribution with scatter-plots and smooth lines, continuous vs categorical with box-plots, subgrouping the plots, working with co-ordinates and themes to make the graphs more presentable, Intro to plotly & various plots, visualization with ggvis package, geographic visualization with ggmap(), building web applications with shinyR.

Hands-on Exercise – Creating data visualization to understand the customer churn ratio using charts using ggplot2, Plotly for importing and analyzing data into grids. You will visualize tenure, monthly charges, total charges and other individual columns by using the scatter plot.

Introduction to Statistics

Why do we need Statistics?, Categories of Statistics, Statistical Terminologies,Types of Data, Measures of Central Tendency, Measures of Spread, Correlation & Covariance,Standardization & Normalization,Probability & Types of Probability, Hypothesis Testing, Chi-Square testing, ANOVA, normal distribution, binary distribution.

Hands-on Exercise – Building a statistical analysis model that uses quantifications, representations, experimental data for gathering, reviewing, analyzing and drawing conclusions from data.

Machine Learning

Introduction to Machine Learning, introduction to Linear Regression, predictive modeling with Linear Regression, simple Linear and multiple Linear Regression, concepts and formulas, assumptions and residual diagnostics in Linear Regression, building simple linear model, predicting results and finding p-value, introduction to logistic regression, comparing linear regression and logistics regression, bivariate & multi-variate logistic regression, confusion matrix & accuracy of model, threshold evaluation with ROCR, Linear Regression concepts and detailed formulas, various assumptions of Linear Regression,residuals, qqnorm(), qqline(), understanding the fit of the model, building simple linear model, predicting results and finding p-value, understanding the summary results with Null Hypothesis, p-value & F-statistic, building linear models with multiple independent variables.

Hands-on Exercise – Modeling the relationship within the data using linear predictor functions. Implementing Linear & Logistics Regression in R by building model with ‘tenure’ as dependent variable and multiple independent variables.

Logistic Regression

Introduction to Logistic Regression, Logistic Regression Concepts, Linear vs Logistic regression, math behind Logistic Regression, detailed formulas, logit function and odds, Bi-variate logistic Regression, Poisson Regression, building simple “binomial” model and predicting result, confusion matrix and Accuracy, true positive rate, false positive rate, and confusion matrix for evaluating built model, threshold evaluation with ROCR, finding the right threshold by building the ROC plot, cross validation & multivariate logistic regression, building logistic models with multiple independent variables, real-life applications of Logistic Regression.

Hands-on Exercise – Implementing predictive analytics by describing the data and explaining the relationship between one dependent binary variable and one or more binary variables. You will use glm() to build a model and use ‘Churn’ as the dependent variable.

Decision Trees & Random Forest

What is classification and different classification techniques, introduction to Decision Tree, algorithm for decision tree induction, building a decision tree in R, creating a perfect Decision Tree, Confusion Matrix, Regression trees vs Classification trees, introduction to ensemble of trees and bagging, Random Forest concept, implementing Random Forest in R, what is Naive Bayes, Computing Probabilities, Impurity Function – Entropy, understand the concept of information gain for right split of node, Impurity Function – Information gain, understand the concept of Gini index for right split of node, Impurity Function – Gini index, understand the concept of Entropy for right split of node, overfitting & pruning, pre-pruning, post-pruning, cost-complexity pruning, pruning decision tree and predicting values, find the right no of trees and evaluate performance metrics.

Hands-on Exercise – Implementing Random Forest for both regression and classification problems. You will build a tree, prune it by using ‘churn’ as the dependent variable and build a Random Forest with the right number of trees, using ROCR for performance metrics.

Unsupervised learning

What is Clustering & it’s Use Cases, what is K-means Clustering, what is Canopy Clustering, what is Hierarchical Clustering, introduction to Unsupervised Learning, feature extraction & clustering algorithms, k-means clustering algorithm, Theoretical aspects of k-means, and k-means process flow, K-means in R, implementing K-means on the data-set and finding the right no. of clusters using Scree-plot, hierarchical clustering & Dendogram, understand Hierarchical clustering, implement it in R and have a look at Dendograms, Principal Component Analysis, explanation of Principal Component Analysis in detail, PCA in R, implementing PCA in R.

Hands-on Exercise – Deploying unsupervised learning with R to achieve clustering and dimensionality reduction, K-means clustering for visualizing and interpreting results for the customer churn data.

Association Rule Mining & Recommendation Engine

Introduction to association rule Mining & Market Basket Analysis, measures of Association Rule Mining: Support, Confidence, Lift, Apriori algorithm & implementing it in R, Introduction to Recommendation Engine, user-based collaborative filtering & Item-Based Collaborative Filtering, implementing Recommendation Engine in R, user-Based and item-Based, Recommendation Use-cases.

Hands-on Exercise – Deploying association analysis as a rule-based machine learning method, identifying strong rules discovered in databases with measures based on interesting discoveries.

Self Paced

Introduction to Artificial Intelligence 

Introducing Artificial Intelligence and Deep Learning, what is an Artificial Neural Network, TensorFlow – computational framework for building AI models, fundamentals of building ANN using TensorFlow, working with TensorFlow in R.

Time Series Analysis

What is Time Series, techniques and applications, components of Time Series, moving average, smoothing techniques, exponential smoothing, univariate time series models, multivariate time series analysis, Arima model, Time Series in R, sentiment analysis in R (Twitter sentiment analysis), text analysis.

Hands-on Exercise – Analyzing time series data, sequence of measurements that follow a non-random order to identify the nature of phenomenon and to forecast the future values in the series.

Support Vector Machine - (SVM)

Introduction to Support Vector Machine (SVM), Data classification using SVM, SVM Algorithms using Separable and Inseparable cases, Linear SVM for identifying margin hyperplane.

Naïve Bayes

What is Bayes theorem, What is Naïve Bayes Classifier, Classification Workflow, How Naive Bayes classifier works, Classifier building in Scikit-learn, building a probabilistic classification model using Naïve Bayes, Zero Probability Problem.

Text Mining

Introduction to concepts of Text Mining, Text Mining use cases, understanding and manipulating text with ‘tm’ & ‘stringR’, Text Mining Algorithms, Quantification of Text, Term Frequency-Inverse Document Frequency (TF-IDF), After TF-IDF.

Case Study

The Market Basket Analysis (MBA) case study

This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.

Logistic Regression Case Study

In this case study you will get a detailed understanding of the advertisement spends of a company that will help to drive more sales. You will deploy logistic regression to forecast the future trends, detect patterns, uncover insights and more all through the power of R programming. Due to this the future advertisement spends can be decided and optimized for higher revenues.

Multiple Regression Case Study

You will understand how to compare the miles per gallon (MPG) of a car based on the various parameters. You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc. It includes the model building, model diagnostic, checking the ROC curve, among other things.

Receiver Operating Characteristic (ROC) case study

You will work with various data sets in R, deploy data exploration methodologies, build scalable models, predict the outcome with highest precision, diagnose the model that you have created with various real world data, check the ROC curve and more.

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Data Science Projects

What projects I will be working in this Data Science certification course?

Project 1 : Augmenting retail sales with Data Science

Industry :  Retail

Problem Statement : How to deploy the various rules and algorithms of Data Science for analyzing stationary store purchase data.

Topics : In this project you will deploy the various tools of Data Science like association rule, Apriori algorithm in R, support, lift and confidence of association rule. You will analyze the purchase data of the stationary outlet for three days and understand the customer buying patterns across products.


  • Association rules for transaction data
  • Association mining with Apriori algorithm
  • Generating rules and identifying patterns.

Project 2 : Analyzing pre-paid model of stock broking

Industry : Finance

Problem Statement : Finding out the deciding factor for people to opt for the pre-paid model of stock broking.

Topics : In this Data Science project you will learn about the various variables that are highly correlated in pre-paid brokerage model, analysis of various market opportunities, developing targeted promotion plans for various products sold under various categories. You will also do competitor analysis, the advantages and disadvantages of pre-paid model.

Highlights :

  • Deploying the rules of statistical analysis
  • Implementing data visualization
  • Linear regression for predictive modeling.

Project 3 : Cold Start Problem in Data Science

Industry : Ecommerce

Problem Statement :  how to build a recommender system without the historical data available

Topics : This project involves understanding of the cold start problem associated with the recommender systems. You will gain hands-on experience in information filtering, working on systems with zero historical data to refer to, as in the case of launching a new product. You will gain proficiency in working with personalized applications like movies, books, songs, news and such other recommendations. This project includes the various ways of working with algorithms and deploying other data science techniques.

Highlight :

  • Algorithms for Recommender
  • Ways of Recommendation
  • Types of Recommendation -Collaborative Filtering Based Recommendation, Content-Based Recommendation
  • Complete mastery in working with the Cold Start Problem.

Project 4 : Recommendation for Movie, Summary

Topics : This is real world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting ‘rating’, learning about user ‘preference’ and so on. You will exclusively work on data related to user details, movie details and others. The main components of the project include the following:

  • Recommendation for movie
  • Two Types of Predictions – Rating Prediction, Item Prediction
  • Important Approaches: Memory Based and Model-Based
  • Knowing User Based Methods in K-Nearest Neighbor
  • Understanding Item Based Method
  • Matrix Factorization
  • Decomposition of Singular Value
  • Data Science Project discussion
  • Collaboration Filtering
  • Business Variables Overview

Project 5 : Prediction on Pokemon dataset

Industry :Gaming

Problem Statement :For the purpose of this case study, you are a Pokemon trainer who is on his way to catch all the 800 Pokemons

Topics :This real-world project will give you a hands-on experience on the data science life cycle. You’ll understand the structure of the ‘Pokemon’ dataset & use machine learning algorithms to make some predictions. You will use the dplyr package to filter out specific Pokemons and use decision trees to find if the Pokemon is legendary or not.

Highlight :

  • dplyr package to filter Pokemons
  • Decision Tree algorithm
  • Linear regression algorithm.

Project 6 : Book Recommender System

Industry :E-commerce

Problem Statement :Building a book recommender system for readers with similar interests

Topics :This real-world project will give you a hands-on experience in working with a book recommender system. Depending on what books are read by a particular user, you will be in a position to provide data-driven recommendations. You will understand the structure of the data and visualize it to find interesting patterns.


  • Data analysis & visualization
  • Recommender Lab
  • User Based Collaborative Filtering Model.

Project 7: Census Income

Domain: Social

Problem Statement: In this project, you will process the data and then develop an understanding of different features of the data by performing explanatory analysis and creating the visualizations. After having enough knowledge about the attributes, you will perform a predictive task of classification to predict whether an individual makes over 50K a year or less by using different Machine Learning Algorithms.

Topics: An end-to-end exhaustive project comprising topics in:

  • Data Processing
  • Data Manipulation
  • Data Visualization
  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • Random Forest

Project 8: Loan Prediction

Domain: Banking

Problem Statement:  You are the Senior Data Scientist at a major private bank. Since the last 6 months, the number of customers who are not able to repay their loan has increased. Keeping this in mind, you have to look at your customer data and analyse which customers should be given the loan approval and which customers should be denied.

Topics: An exhaustive project on Customer_loan Dataset comprising topics in:

  • Data Processing
  • Model Building

Project 9: Capstone

Industry: Analytics

Problem Statement: Predicting if the customer will churn or not.

Topics: An end-to-end capstone project comprising:

  • Manipulating and envisioning the data for insights.
  • Implementing the linear regression model to predict continuous values.
  • Implementing classification models – decision tree, logistic regression, and random forest on “customer churn”.


An end-to-end capstone project covering all the modules. You’ll start off by manipulating and visualizing the data to get interesting insights. Then you’d have to implement the linear regression model to predict continuous values. Following which you’ll implement these classification models – logistic regression, decision tree & random forest on the “customer churn” data frame to find if the customer will churn or not.

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Sample Data Science Video Tutorials

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Data Science Certification

This course is designed for clearing Intellipaat Data Science Certification Exam. The entire course content is designed by industry professionals to get the best jobs in top MNCs.  As part of this training, you will be working on real-time projects and assignments that have immense implications in the real-world industry scenarios, thus helping you fast-track your career effortlessly.

At the end of this training program, there will be quizzes that perfectly reflect the type of questions asked in the respective certification exams and help you score better.

Intellipaat Course Completion Certification will be awarded upon the completion of the project work (after expert review) and upon scoring at least 60% marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.

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Data Science Training Review

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  1. Profile photo of swetha pandit Swetha Pandit 

    Valuable material for learning. Worth spending!

    Their Data Science courses are well structured and taught by recognized professionals which helps one to learn Data Science fast. I have found the videos to be of excellent quality. Thanks.

  2. Profile photo of Giri Karnal Giri Karnal 

    Excellent training

    I had taken the Data Science masters’ program which is a combo of SAS, R and Apache Mahout. Since there are so many technologies involved in the Data Science course, getting your query resolved at the right time becomes the most important aspect. But with Intellipaat, there was no such problem as all my queries were resolved in less than 24 hours.

  3. Profile photo of Nitesh Kumar Dash Nitesh Kumar Dash 

    Learner-friendly training

    The Intellipaat Data Science certification training videos really made me excited about studying Data Science. They were so elaborate and so professionally created that I could learn Data Science from the comfort of my home, thanks to those learner-friendly videos. I am grateful to Intellipaat.

  4. Profile photo of DATTATREYA R Vikrant Singh 

    Good work

    It was a wonderful experience and learning from Intellipaat trainers. The trainers were hands on and provided real-time scenarios. According me, for learning cutting-edge and latest technologies Intellipaat is the right place.

  5. Profile photo of muppallabhanubigdatahadoopadm Bhanukumar Muppalla 

    Comprehensive learning experience

    Data Science training includes a lot of constituent components, and the Intellipaat Data Science training provided the most comprehensive and in-depth learning experience. I really liked the projects in Data Science which were real-world projects that helped me take on a Data Science role in the real world much easier.

  6. Profile photo of Bharat Rathore Bharat Rathore 

    Quality Content

    I really appreciate the quality of the material and the content of this Data Science certification course!! Thanks to all Intellipaat team!

  7. Profile photo of ghosh.sudipto Sudipto 

    Best platform to learn data science.

    Intellipaat data science is outstanding. Trainer is experienced data scientist who has good hold on the subject.Now i'm an expert in data science and can confidently make career in it.

  8. Varsha Tyagi 

    Good Learning Experience

    I was searching for a Data science course on the internet, then I landed upon intellipaat. That's really good in terms of content. Their sample video is also awesome which impressed me a lot to take the course. Trainers command of the particular technology is great. The support team is also good. Really appreciate.

  9. Profile photo of ksharat.1234 Sharath Reddy Yellapati 

    Well-organized classes, and highly intellectual instructors.

    The course material was very well organized. The trainer explained the basics of each module to me. All my queries were addressed very clearly. The trainer also made me realize how important this course is for beginners in IT stream. I suggest this is the best training course.

  10. Profile photo of shrey.9129 Shreyash Limbhetwala 

    Highest Quality Training

    I want to talk about the rich LMS that Intellipaat data science training offered. The extensive set of PPTs, PDFs, and other related Data Science course material were of the highest quality and due to this my learning with Intellipaat was excellent and I could clear the Cloudera Data Scientist certification in the first attempt.

  11. Profile photo of KevinKWada Kevin K Wada 


    Thank you very much for your top class service provided. A special mention should be made regarding your patience in listening to my query and giving me a solution which was exactly what I was looking for in the first place. I am giving a 10 out of 10!

  12. Profile photo of mandepudisri5 Ramyasri Mandepudi 

    You make a difference

    My issue was resolved thanks to the deep domain expertise of the trainer. I am greatly indebted to you for assigning such knowledgeable and experienced trainers for Data Science certification course. It really makes a difference to the learner.

  13. Profile photo of Prasil das Prasil das 

    Fully Satisfied

    "Awesome response time to query resolution. Thanking you for resolving all my issues and helping me realize the tough concepts through the highly insightful videos "

  14. Profile photo of Sulekha Roy Sulekha Roy 


    I think this Data Science certification training is a very good way of starting to learn Data Science and make a career in it. The instructors are reasonably good. The projects was also very interesting and relevant to current industry trends.

  15. Profile photo of Kavita Mehra Kavita Mehra 

    Perfect Training!

    The classes were highly interactive and also practical oriented. The office staff was cordial and co-operative. Every teaching session was recorded each day and was put on-line by the institute which was really helpful. The trainer was very patient and able to solve or give some hints to solve all the questions posed to him.

Data Science Online Course Advisor

Suresh Paritala

A Senior Software Architect at NextGen Healthcare who has previously worked with IBM Corporation, Suresh Paritala has worked on Big Data, Data Science, Advanced Analytics, Internet of Things and Azure, along with AI domains like Machine Learning and Deep Learning. He has successfully implemented high-impact projects in major corporations around the world.

Samanth Reddy

A renowned Data Scientist who has worked with Google and is currently working at ASCAP, Samanth Reddy has a proven ability to develop Data Science strategies that have a high impact on the revenues of various organizations. He comes with strong Data Science expertise and has created decisive Data Science strategies for Fortune 500 corporations.

David Callaghan

An experienced Blockchain Professional who has been bringing integrated Blockchain, particularly Hyperledger and Ethereum, and Big Data solutions to the cloud, David Callaghan has previously worked on Hadoop, AWS Cloud, Big Data and Pentaho projects that have had major impact on revenues of marquee brands around the world.

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Frequently Asked Questions on Data Science

Why should I learn Data Science from Intellipaat?

Intellipaat provides the best Data Science training for professionals looking to master this exciting and challenging field. In this training course, you will learn about Data Science, methods of data acquisition, project life cycle, deploying Machine Learning and statistical methods, along with studying Apache Mahout, data transformation and working with recommenders.

You will be working on real-time projects and step-by-step assignments that have high relevance in the corporate world, and the curriculum is designed by industry experts. Upon the completion of the training course, you can apply for some of the best jobs in top MNCs around the world at top salaries. Intellipaat offers lifetime access to videos, course materials, 24/7 support and course material upgrading to the latest version at no extra fee. Hence, it is clearly a one-time investment.

What are the different modes of training that Intellipaat provides?
At Intellipaat you can enroll either for the instructor-led online training or self-paced training. Apart from this Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience and they have been actively working as consultants in the same domain making them subject matter experts. Go through the sample videos to check the quality of the trainers.
Can I request for a support session if I need to better understand the topics?
Intellipaat is offering the 24/7 query resolution and you can raise a ticket with the dedicated support team anytime. You can avail the email support for all your queries. In the event of your query not getting resolved through email we can also arrange one-to-one sessions with the trainers. You would be glad to know that you can contact Intellipaat support even after completion of the training. We also do not put a limit on the number of tickets you can raise when it comes to query resolution and doubt clearance.
Can you explain the benefits of the Intellipaat self-paced training?
Intellipaat offers the self-paced training to those who want to learn at their own pace. This training also affords you the benefit of query resolution through email, one-on-one sessions with trainers, round the clock support and access to the learning modules or LMS for lifetime. Also you get the latest version of the course material at no added cost. The Intellipaat self-paced training is 75% lesser priced compared to the online instructor-led training. If you face any problems while learning we can always arrange a virtual live class with the trainers as well.
What kind of projects are included as part of the training?
Intellipaat is offering you the most updated, relevant and high value real-world projects as part of the training program. This way you can implement the learning that you have acquired in a real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning and practical knowledge thus making you completely industry-ready. You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. Upon successful completion of the projects your skills will be considered equal to six months of rigorous industry experience.
Does Intellipaat offer job assistance?
Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this we are exclusively tied-up with over 80 top MNCs from around the world. This way you can be placed in outstanding organizations like Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation part as well.
Is it possible to switch from self-paced training to instructor-led training?
You can definitely make the switch from self-paced to online instructor-led training by simply paying the extra amount and joining the next batch of the training which shall be notified to you specifically.
How are Intellipaat verified certificates awarded?
Once you complete the Intellipaat training program along with all the real-world projects, quizzes and assignments and upon scoring at least 60% marks in the qualifying exam; you will be awarded the Intellipaat verified certification. This certificate is very well recognized in Intellipaat affiliate organizations which include over 80 top MNCs from around the world which are also part of the Fortune 500 list of companies.
Will The Job Assistance Program Guarantee Me A Job?
In our Job Assistance program we will be helping you land in your dream job by sharing your resume to potential recruiters and assisting you with resume building, preparing you for interview questions. Intellipaat training should not be regarded either as a job placement service or as a guarantee for employment as the entire employment process will take part between the learner and the recruiter companies directly and the final selection is always dependent on the recruiter.
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