Data Science Course Online Training and Certification

Data Science course lets you master skills like data analytics, R programming , statistical computing, Machine Learning algorithms, time-series analysis, K-Means Clustering, and more. This course is primarily designed to make you a successful Data Scientist with multiple hands-on exercise and project work in different domains like banking, finance, entertainment, e-commerce, etc. Intellipaat Online Data Science Courses and certification is well recognized across 500+ employers.

Free MS Excel self-paced course

Key Features

42 Hrs Instructor Led Training
28 Hrs Self-paced Videos
56 Hrs Project work & Exercises
Certification and Job Assistance
Flexible Schedule
Lifetime Free Upgrade
24 x 7 Lifetime Support & Access

About Data Science Course

This data scientist course online provides detailed learning through self-paced videos as well as live online instructor led sessions that help you gain skills in shortest possible time with interactive learning. Data Scientists are one of the highest paid and most in demand skill. This is an in-depth data scientist course that covers what is data science, Statistical Methods, Data Acquisition, Data Analytics, Machine Learning algorithms, predictive analytics, data structure, etc. At the end of course you will work on building recommendation engine for an e-commerce site as well as you will work on real time industry capstone project.

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

  1. Introduction to Data Science and its importance
  2. Data Science life cycle, Data acquisition
  3. Experimentation, evaluation and project deployment tools
  4. Various Machine Learning algorithms
  5. Predictive analytics and segmentation using clustering
  6. Fundamentals of Big Data Hadoop integration with R
  7. Roles and responsibilities of a Data Scientist
  8. Using real-world data sets to deploy recommender systems
  9. Working on data mining, data structures, and data manipulation.
  • Information Architects, Statisticians
  • Developers looking to master Machine Learning and Predictive Analytics
  • Big Data, Business Analyst and Business Intelligence
  • Aspirants looking to work as Machine Learning Experts, Data Scientist, etc.

There are no prerequisites for taking this training course. If you like mathematics, you can accelerate your learning through the data scientist course.

  • Data Scientists are the best job in  the 21st century – Harvard Business Review
  • The number of jobs for all data professionals in the United States will increase to 2.7 million per year – IBM
  • Global Big Data market achieves  $122 billion in sales in six years – Frost & Sullivan

The demand for Data Scientists far exceeds its supply. This is a serious problem in a data-driven world that we are living in today. As a result, most organizations are willing to pay the highest salary for professionals with appropriate Data Science skills.

Data science training online will help you become proficient in Data Science, R programming, Data Analysis, Big Data and more. Thus, you can easily accelerate your career in this evolving domain and take it to the next level.

In the United States, the average salary of a data scientist is US$112,957. The average salary of data scientists in India is ₹853,191.

A few of the many top companies that hire data scientists include Amazon, Google, IBM, Facebook, Microsoft, Wal-Mart, Target, Visa, Bank of America, Mu-Sigma, Accenture, Fractal Analytics and more.

There are several ways to become a data scientist. Evidently, data scientists use a large number of tools/technologies, such as the R and Python programming languages, and analysis tools, such as SAS, etc.

As a budding data scientist, you should be familiar with data analysis and statistical software packages, large data sets transformation and storage using like Hadoop and Spark. At the end the most important skill is data visualization where facts needs to be presented to business team so that they can understand the insights behind the data.

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? Full-life cycle analysis, including business needs, activities, and design Implement technology solutions. Develop, analyze and report business capabilities Statistical analysis and develop Machine Learning systems
Average Salaries US$68,465 US$ 75,218 US$ 112,957

This data scientist training online includes real-world 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

Intellipaat follows a rigorous certification process. To become a certified Data Scientist, you must meet 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

1. Understand the Problem

A Data Scientist should be aware of the business pain points and ask the right questions.

2. Collect Data

Data Scientists need to collect enough data to understand the problem at hand and to better solve it in terms of time, money, and resources.

3. Process the Raw Data

Data is rarely used in its original form. It must be processed, and there are several ways to convert it into a usable format.

4. Explore the Data

Once the data has been processed and converted into a form that can be used later, the data scientist must further examine the data to determine the characteristics of the data and find out more about obvious trends, correlations, and more.

5. Analyze the Data

Data scientists use a variety of tool libraries in their repositories, such as machine learning, statistics and probability, linear and logistic regression, time series analysis, and more to understand the data.

6. Communicate the Results

At last, the results must be communicated to the right stakeholders, laying the groundwork for all identified issues.

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Course Fees

Self Paced Training

  • 28 Hrs e-learning videos
  • Lifetime Free Upgrade
  • 24 x 7 Lifetime Support & Access

Online Classroom preferred

  • Everything in self-paced, plus
  • 42 Hrs of instructor-led training
  • 1:1 doubt resolution sessions
  • Attend as many batches for Lifetime
  • Flexible Schedule
  • 07 Jun
  • SAT - SUN
  • 08:00 PM TO 11:00 PM IST (GMT +5:30)
  • 09 Jun
  • TUE - FRI
  • 07:00 AM TO 09:00 AM IST (GMT +5:30)
  • 13 Jun
  • SAT - SUN
  • 08:00 PM TO 11:00 PM IST (GMT +5:30)
  • 21 Jun
  • SAT - SUN
  • 08:00 PM TO 11:00 PM IST (GMT +5:30)
$ 499 $399 10% OFF Expires in

Corporate Training

  • Customized Learning
  • Enterprise grade learning management system (LMS)
  • 24x7 support
  • Strong Reporting

Data Science Course Content

Module 01 - Introduction to Data Science with R preview videos

1.1 What is Data Science
1.2 Significance of Data Science in today’s digitally-driven world, applications of Data Science, lifecycle of Data Science, components of the Data Science lifecycle
1.3 Introduction to big data and Hadoop, introduction to Machine Learning and Deep Learning,
1.4 Introduction to R programming and R Studio

Hands-on Exercise

1. Installation of R Studio
2. Implementing simple mathematical operations and logic using R operators, loops, if statements and switch cases.

2.1 Introduction to data exploration
2.2 Importing and exporting data to/from external sources
2.3 What is data exploratory analysis, data importing, dataframes
2.4 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

1. Accessing individual elements of customer churn data
2. Modifying and extracting the results from the dataset using user-defined functions in R.

3.1 Need for Data Manipulation
3.2 Introduction to dplyr package
3.3 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
3.4 Combining different functions with the pipe operator, Implementing sql like operations with sqldf.

Hands-on Exercise

1. Implementing dplyr
2. perform various operations for abstracting over how data is manipulated and stored.

4.1 Introduction to visualization
4.2 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
4.3 Multivariate analysis with geom_boxplot
4.4 Univariate Analysis with Bar-plot, histogram and Density Plot, multivariate distribution
4.5 Bar-plots for categorical variables using geom_bar(), adding themes with the theme() layer
4.6 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
4.7 Working with co-ordinates and themes to make the graphs more presentable, Intro to plotly & various plots, visualization with ggvis package
4.8 Geographic visualization with ggmap(), building web applications with shinyR.

Hands-on Exercise

1. Creating data visualization to understand the customer churn ratio using charts using ggplot2
2. Plotly for importing and analyzing data into grids
3. Visualize tenure, monthly charges, total charges and other individual columns by using the scatter plot.

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

Hands-on Exercise

1. Building a statistical analysis model that uses quantifications, representations, experimental data for gathering
2. Reviewing, analyzing and drawing conclusions from data.

6.1 Introduction to Machine Learning
6.2 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
6.3 Predicting results and finding p-value, introduction to logistic regression
6.4 Comparing linear regression and logistics regression, bivariate & multi-variate logistic regression
6.5 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
6.6 understanding the summary results with Null Hypothesis, p-value & F-statistic,
building linear models with multiple independent variables.

Hands-on Exercise

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

7.1 Introduction to Logistic Regression
7.2 Logistic Regression Concepts, Linear vs Logistic regression, math behind Logistic Regression
7.3 Detailed formulas, logit function and odds, Bi-variate logistic Regression, Poisson Regression
7.4 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
7.5 Finding the right threshold by building the ROC plot, cross validation & multivariate logistic regression, building logistic models with multiple independent variables
7.6 Real-life applications of Logistic Regression.

Hands-on Exercise

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

8.1 What is classification and different classification techniques
8.2 Introduction to Decision Tree
8.3 Algorithm for decision tree induction, building a decision tree in R
8.4 Creating a perfect Decision Tree, Confusion Matrix, Regression trees vs Classification trees
8.5 Introduction to ensemble of trees and bagging
8.6 Random Forest concept, implementing Random Forest in R
8.7 what is Naive Bayes, Computing Probabilities, Impurity Function – Entropy, understand the concept of information gain for right split of node
8.8 Impurity Function – Information gain, understand the concept of Gini index for right split of node
8.9 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

1. Implementing Random Forest for both regression and classification problems.
2. 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,
3. using ROCR for performance metrics.

9.1 What is Clustering & it’s Use Cases, what is K-means Clustering
9.2 What is Canopy Clustering
9.3 What is Hierarchical Clustering
9.4 Introduction to Unsupervised Learning
9.5 Feature extraction & clustering algorithms, k-means clustering algorithm
9.6 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
9.7 Hierarchical clustering & Dendogram, understand Hierarchical clustering, implement it in R and have a look at Dendograms
9.8 Principal Component Analysis, explanation of Principal Component Analysis in detail, PCA in R, implementing PCA in R.

Hands-on Exercise

1. Deploying unsupervised learning with R to achieve clustering and dimensionality reduction
2. K-means clustering for visualizing and interpreting results for the customer churn data.

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

Hands-on Exercise

1. Deploying association analysis as a rule-based machine learning method,
2. Identifying strong rules discovered in databases with measures based on interesting discoveries.

Self Paced

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

12.1 What is Time Series
12.2 Techniques and applications, components of Time Series, moving average, smoothing techniques, exponential smoothing
12.3 Univariate time series models, multivariate time series analysis
12.4 Arima model
12.5 Time Series in R, sentiment analysis in R (Twitter sentiment analysis), text analysis.

Hands-on Exercise

1. Analyzing time series data
2. Sequence of measurements that follow a non-random order to identify the nature of phenomenon and to forecast the future values in the series.

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

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

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

01 – The Market Basket Analysis (MBA) case study

1.1 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.
1.2 It includes finding out what are the items that go hand in hand and hence can be clubbed together.
1.3 This is used for various real world scenarios like a supermarket shopping cart and so on.

02 – Logistic Regression Case Study

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

03 – Multiple Regression Case Study

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

04 – Receiver Operating Characteristic (ROC) case study

4.1 You will work with various data sets in R,
4.2 Deploy data exploration methodologies,
4.3 Build scalable models
4.4 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 01 – Market Basket Analysis

Domain – Inventory Management

Problem Statement – As a new manager in the company, you are assigned the task of increasing cross selling

Topics  – Association Rule Mining, Data Extraction, Data Manipulation


  • Performing association rule mining
  • Understanding where to implement Apriori Algorithm
  • Setting association rules with respect to confidence

Project 02  – Credit Card Fraud Detection

Domain – Banking

Problem Statement – Analysis of probability of being involved in a fraudulent operation

Topics – Algorithms, V17 Predictor, Data Visualization, R Language


  • Understanding working with the credit card dataset
  • Performing data analysis on various labels in the data
  • Making use of V17 as predictor and using V14 for analysis
  • Plotting score performance with respect to variables

Project 03 – Data Cleaning using Census Dataset

Domain – Government

Problem Statement – Performing Data Cleansing operation on a raw dataset

Topics – Data Analysis, Data preprocessing, Cleaning Ops, Data Visualization, R Language


  • Understanding working with the census dataset
  • Changing around various with respect to a label to perform analysis
  • Creation of functions to eliminate values which are not required
  • Verifying the completion of data cleansing operation

Project 04 – Loan Approval Prediction

Domain -Banking

Problem Statement – Prediction of approval rate of a loan by using multiple labels

Topics – Data Analysis, Data preprocessing, Cleaning Ops, Data Visualization, R Language


  • Performing Data Preprocessing
  • Building a model and applying PCA
  • Building a Naïve Bayes model on the training dataset
  • Prediction of values after performing analysis

Project 05 – Book Recommendation System

Domain – E-Commerce

Problem Statement – Creating a model, which can recommend books, based on user interest

Topics – Data Cleaning, Data Visualization, User Based Collaborative Filtering


  • Finding the most popular books using various techniques
  • Creating a Book Recommender model using User Based Collaborative Filtering

Project 06 – Netflix Recommendation System

Domain – E-Commerce

Problem Statement Simulating the Netflix Recommendation System

Topics – Data Cleaning, Data Visualization, Distribution, Recommender Lab


  • Working with raw data
  • Using the Recommender Lab library in R
  • Making use of real data from Netflix

Project 07 – Creating a Pokemon Game using Machine Learning

Domain – Gaming

Problem Statement – Creating a game engine for Pokemon using Machine Learning

Topics – Decision Tress, Regression, Data Cleaning, Data Visualization


  • Predicting which Pokemon will win based Attack vs Defense
  • Finding whether a Pokemon is legendary using Decision Trees
  • Understanding the dynamics of decision making in Machine Learning

Case Study 01 – Introduction to R Programming

Problem Statement – Working with various operators in R

Topics – Arithmetic Operators, Relational Operators, Logical Operators


  • Working with Arithmetic Operators
  • Working with Relational Operators
  • Working with Logical Operators

Case Study 02 – Solving Customer Churn using Data Exploration

Problem Statement – Understanding what to do to reduce customer churn using Data Exploration

Topics – Data Exploration


  • Extracting Individual columns
  • Creating and applying filters to manipulate data
  • Using loops for redundant operations

Case Study 03 – Creating Data Structures in R

Problem Statement – Implementing various Data Structures in R for various scenarios

Topics – Vectors, list, Matrix, Array


  • Creating and Implementing Vectors
  • Understanding Lists
  • Using Arrays to store Matrices
  • Creating and implementing Matrices

Case Study 04 – Implementing SVD in R

Problem Statement – Understanding the use Single Value Decomposition in R by making use of the MovieLense Dataset

Topics – 5-fold cross validation, Real Rating Matrix


  • Creating a custom  recommended movie set for each user
  • Creating User Based Collaborative Filtering Model
  • Creating RealRatingMatrix for Movie recommendation

Case Study 05 – Time Series Analysis

Problem Statement – Performing TSA and understanding concepts of ARIMA for a given scenario

Topics – Time Series Analysis, R Language, Data Visualization, ARIMA model


  • Understand how to fit an ARIMA model
  • Plotting PACF charts and finding optimal parameters
  • Building the ARIMA model
  • Prediction of values after performing analysis
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Data Science Certification

The entire Data Scientist course content is designed by industry professionals to get the best jobs in top MNCs.  As part of Data Science online courses, 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 Data Science online 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 the top 80+ MNCs and our alumni work in organizations like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.

Our Alumni works at top 3000+ companies

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Data Science Training Review


Mr Yoga


John Chioles




Dileep & Ajay

Swetha Pandit

Big Data Developer at Accenture

Their Data scientist courses online 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.

Giri Karnal


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 Data Science training courses, 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.

Nitesh Kumar Dash


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.

Vikrant Singh

Big Data Analytics

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

Bhanukumar Muppalla

Software Engineer at DXC Technology

Data Science training online includes a lot of constituent components, and the Intellipaat courses provide 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.

Bharat Rathore

Expert in Data Analysis & Data Science

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


PS Consultant at Genesys

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

Varsha Tyagi

Cloud Architect at Huawei Technologies

I was searching for Data science courses online, 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 data scientist course. Trainers command of the particular technology is great. The support team is also good. Really appreciate.

Sharath Reddy Yellapati

The Data Scientist 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 Data Science are for beginners in the IT stream. I suggest this as the best Data Science course available online.

Shreyash Limbhetwala

Technical Delivery Lead

I want to talk about the rich LMS that Intellipaat data science program offered. The extensive set of PPTs, PDFs, and other related Data Science online courses 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.

Kevin K Wada

Oracle Developer at Free Agent

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!

Ramyasri Mandepudi

Recruiter at Goodwill Technologies

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.

Prasil das

SEO Specialist at Jain

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.

Sulekha Roy

Sr. Data scientist at Hewlett Packard Enterprise

I think Data Science online courses are a very good way of starting to learn Data Science and make a career in it. The instructors are reasonably good. The projects were also very interesting and relevant to current industry trends.

Kavita Mehra

Hadoop Developer at TCS

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.

Frequently Asked Questions on Data Science

Why should I learn Data Science from Intellipaat?

Intellipaat offers exclusive Data Science Online Courses for professionals who want to expand their knowledge base and start a career in this exciting field. Many reasons for choosing Intellipaat include:

  • Personal mentor to track your progress at each stage
  • Immersive online instructor-led sessions conducted by SMEs
  • Extensive LMS, allowing you to view recorded sessions within 3 hours
  • Real-time exercises and assignments; and real-world projects
  • 24/7 learning support
  • Large community of like-minded learners
  • Industry recognized Intellipaat badge
  • Personalized job support
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.
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.
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.
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.
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.
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.
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.
Apparently, No. Our Job Assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and assists you in finding a well-paid job, matching your profile. The final decision on your hiring will always be based on your performance in the interview and the requirements of the recruiter.
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