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Data Science Course

5 591 Ratings 10,847 Learners

This Data Scientist course in collaboration with IBM offers you multiple courses on Data Science to let you master skills such as data analytics, R programming, statistical computing, Machine Learning algorithms, k-means clustering, and more. It includes multiple hands-on exercises and project work in the domains of banking, finance, entertainment, etc. Intellipaat’s online Data Science courses are well recognized across 500+ employers helping you to land in your dream job.

Ranked #1 Data Science program by India TV

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Free MS Excel Self-paced Course

Key Features

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

Career Transitions

Mahesh Chowdary
Mahesh Chowdary
Non Tech to Tech
Aircraft Maintenance Engineer intellipaat-image
Data Scientist intellipaat-image
Jeanette Masso
Jeanette Masso
60% Hike
Computer Technical Specialist intellipaat-image
Big Data Developer intellipaat-image
Prosenjeet Saha
Prosenjeet Saha
Manager to Data Scientist
Manager Analytics & Data Science intellipaat-image
Data Scientist intellipaat-image
Nishchay Agrawal
Nishchay Agrawal
Fresher to Data Scientist
Fresher
Data Engineer intellipaat-image
Sahas Barangale
Sahas Barangale
Consultant to Program Manager
Microsoft Dynamics Consultant intellipaat-image
Program Manager intellipaat-image
Bhawna Kaliraman
Bhawna Kaliraman
Lecturer to Data Scientist
Lecturer intellipaat-image
Data Science Intern intellipaat-image

Course Benefits

5/5 Student Satisfaction Rating
Students Transitioned for Higher Positions
Started a New Career After Completing Our Courses
Got Better Salary Hike and Promotion
Average Salary Per Year $ 16983
Associate Data Scientist
Data Scientist
Chief Data Scientist
$ 12053 Starting
$ 16983 Median
$ 50651 Experienced
Companies Hiring Data Scientist Professionals
intellipaat-image intellipaat-image
intellipaat-image intellipaat-image
And 1,000+ Global Companies

Data Science Course Overview

This Data Scientist course online provides detailed learning through self-paced videos and live instructor-led sessions that help you gain skills in the shortest possible time. Data Scientists are among the highest-paid and most in demand professionals. Our in-depth Data Science programs cover ‘What is Data Science?,’ statistical methods, data acquisition and analysis, Machine Learning algorithms, predictive analytics, data modeling, etc. At the end of the program, you will work on building a recommendation engine for an ecommerce site and will work on a real-time capstone project.

Why should you take up Data Science courses?

The average annual salary of Data Scientists as per Indeed is approximately US$122,801 in the United States.

  • Data Scientist is 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 – IBM
  • Global Big Data market achieves US$122 billion in sales in 6 years – Frost & Sullivan

The demand for Data Scientists far exceeds the 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 high salaries for professionals with appropriate Data Science skills.

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

 

Advantages of Data Science

In this program, you will learn about:

  1. Introduction to Data Science and its importance
  2. Data Science life cycle and 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
  7. Roles and responsibilities of a Data Scientist
  8. Using real-world datasets to deploy recommender systems
  9. Working on data mining and data manipulation

This course can be signed up by:

  • Information Architects and Statisticians
  • Developers looking to master Machine Learning and Predictive Analytics
  • Big Data, Business Analyst, Business Intelligence and Software Engineering professionals
  • Aspirants looking to work as Machine Learning experts, Data Scientists, etc.

There are no prerequisites for taking up this course. If you like mathematics, you can accelerate your learning through these Data Scientist online courses.

Yes, Intellipaat offers a master’s course on Data Science. In which you will learn real-time analytics, statistical computation, SQL, parsing machine-generated data, and finally the domain of Deep Learning. You will also get an insight into how to use Big Data Analytics with Spark for Data Science as well. Additionally, you will have exclusive access to IBM Watson Cloud Lab for Chatbots. This course curriculum is designed by industry experts, and it includes 10 courses and 53 industry-based projects with 1 CAPSTONE project. The following courses will be covered in this course:

Online Instructor-led Courses:

  • Course 1: Data Science with R
  • Course 2: Python for Data Science
  • Course 3: Machine Learning
  • Course 4: Artificial Intelligence and Deep Learning with TensorFlow
  • Course 5: Big Data Hadoop & Spark
  • Course 6: Tableau Desktop 10
  • Course 7: Data Science with SAS

Self-paced Courses:

  • Course 8: Advanced Excel
  • Course 9: MongoDB
  • Course 10: MS-SQL

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.

Many top companies hire Data Scientists. A few of them are Amazon, Google, IBM, Facebook, Microsoft, Walmart, Target, Visa, Bank of America, Accenture, Fractal Analytics, etc.

There are several ways to become a Data Scientist. Evidently, Data Scientists use a large number of Data Science tools/technologies, such as R and Python programming language, and analysis tools, like SAS.
As a budding Data Scientist, you should be familiar with data analysis, statistical software packages, data visualization and handling large data sets. Data Scientist major time spent in data exploration and data wrangling.

Criteria Data Analyst Business Analyst Data Scientist
Skill set Analyzing business needs Analyzing historical data Making 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 designing Implementing technology solutions and analyzing and reporting business capabilities Statistical analysis and the development of Machine Learning systems
Average salaries US$68,465 US$75,218 US$112,957

This Data Scientist training online includes 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
Designing a Movie Recommendation Engine Entertainment Building a movie recommendation engine based on user interests
Making Sense of Customer Buying Patterns Ecommerce Deploying target selling to customers
Fraud Detection in the Banking System BFSI Deploying Data Science to detect fraudulent activities and taking remedial actions
  1. Understand the Problem

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

  1. Collect Data

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

  1. 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.

  1. Explore the Data

Once the data has been processed and converted into a usable form, Data Scientists must examine it to determine the characteristics and find out obvious trends, correlations, and more.

  1. Analyze the Data

To understand the data, they use a variety of tool libraries, such as Machine Learning, statistics and probability, linear and logistic regression, time series analysis, and more.

  1. Communicate Results

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

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With data collection, ’the sooner the better’ is the best answer. - CEO of Yahoo
Everything is going to be connected with data and mediated by softwares. - CEO of Microsoft
The world is now awash in data and we can see consumers in a lot cleaner way. - Co-founder PayPal

Skills Covered

  • R Programming
  • Exploratory Data Analysis
  • Data Manipulation
  • Data Visualization
  • Statistics 
  • Machine Learning Algorithms
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Fees

Self Paced Training

  • 28 Hrs e-learning videos
  • Lifetime Free Upgrade
  • 24 x 7 Lifetime Support & Access
$264

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
  • 22 Jun
  • TUE - FRI
  • 07:00 AM TO 09:00 AM IST (GMT +5:30)
  • 26 Jun
  • SAT - SUN
  • 08:00 PM TO 11:00 PM IST (GMT +5:30)
  • 29 Jun
  • TUE - FRI
  • 07:00 AM TO 09:00 AM IST (GMT +5:30)
  • 04 Jul
  • 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

1.1 What is Data Science?
1.2 Significance of Data Science in today’s data-driven world, its applications of, , lifecycle, and its components
1.3 Introduction to R programming and RStudio

Hands-on Exercise:

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

Module 02 - Data Exploration

2.1 Introduction to data exploration
2.2 Importing and exporting data to/from external sources
2.3 What are data exploratory analysis and data importing?
2.4 DataFrames, working with them, accessing individual elements, vectors, factors, operators, in-built functions, conditional and looping statements, user-defined functions, and data types

Hands-on Exercise:

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

3.1 Need for data manipulation
3.2 Introduction to the dplyr package
3.3 Selecting one or more columns with select(), filtering records on the basis of a condition with filter(), adding new columns with mutate(), sampling, and counting
3.4 Combining different functions with the pipe operator and implementing SQL-like operations with sqldf

Hands-on Exercise:

1. Implementing dplyr
2. Performing various operations for manipulating data and storing it

4.1 Introduction to visualization
4.2 Different types of graphs, the grammar of graphics, the ggplot2 package, categorical distribution with geom_bar(), numerical distribution with geom_hist(), building frequency polygons with geom_freqpoly(), and making a scatterplot with geom_pont()
4.3 Multivariate analysis with geom_boxplot
4.4 Univariate analysis with a barplot, a histogram and a density plot, and multivariate distribution
4.5 Creating barplots for categorical variables using geom_bar(), and adding themes with the theme() layer
4.6 Visualization with plotly, frequency plots with geom_freqpoly(), multivariate distribution with scatter plots and smooth lines, continuous distribution vs categorical distribution with box-plots, and sub grouping plots
4.7 Working with co-ordinates and themes to make graphs more presentable, understanding plotly and various plots, and visualization with ggvis
4.8 Geographic visualization with ggmap() and building web applications with shinyR

Hands-on Exercise:

1. Creating data visualization to understand the customer churn ratio using ggplot2 charts
2. Using plotly for importing and analyzing data
3. Visualizing tenure, monthly charges, total charges, and other individual columns using a scatter plot

5.1 Why do we need statistics?
5.2 Categories of statistics, statistical terminology, types of data, measures of central tendency, and measures of spread
5.3 Correlation and covariance, standardization and normalization, probability and the types, hypothesis testing, chi-square testing, ANOVA, normal distribution, and binary distribution

Hands-on Exercise:

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

6.1 Introduction to Machine Learning
6.2 Introduction to linear regression, predictive modeling, simple linear regression vs multiple linear regression, concepts, formulas, assumptions, and residuals in Linear Regression, and building a simple linear model
6.3 Predicting results and finding the p-value and an introduction to logistic regression
6.4 Comparing linear regression with logistics regression and bivariate logistic regression with multivariate logistic regression
6.5 Confusion matrix the accuracy of a model, understanding the fit of the model, threshold evaluation with ROCR, and using qqnorm() and qqline()
6.6 Understanding the summary results with null hypothesis, F-statistic, and
building linear models with multiple independent variables

Hands-on Exercise:

1. Modeling the relationship within data using linear predictor functions
2. Implementing linear and logistics regression in R by building a model with ‘tenure’ as the dependent variable

7.1 Introduction to logistic regression
7.2 Logistic regression concepts, linear vs logistic regression, and math behind logistic regression
7.3 Detailed formulas, logit function and odds, bivariate logistic regression, and Poisson regression
7.4 Building a simple binomial model and predicting the result, making a confusion matrix for evaluating the accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR
7.5 Finding out the right threshold by building the ROC plot, cross validation, multivariate logistic regression, and building logistic models with multiple independent variables
7.6 Real-life applications of logistic regression

Hands-on Exercise:

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

8.1 What is classification? Different classification techniques
8.2 Introduction to decision trees
8.3 Algorithm for decision tree induction and building a decision tree in R
8.4 Confusion matrix and regression trees vs classification trees
8.5 Introduction to bagging
8.6 Random forest and implementing it in R
8.7 What is Naive Bayes? Computing probabilities
8.8 Understanding the concepts of Impurity function, Entropy, Gini index, and Information gain for the right split of node
8.9 Overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning, pruning a decision tree and predicting values, finding out the right number of trees, and evaluating performance metrics

Hands-on Exercise:

1. Implementing random forest for both regression and classification problems
2. Building a tree, pruning it using ‘churn’ as the dependent variable, and building a random forest with the right number of trees
3. Using ROCR for performance metrics

9.1 What is Clustering? Its use cases
9.2 what is k-means clustering? What is canopy clustering?
9.3 What is hierarchical clustering?
9.4 Introduction to unsupervised learning
9.5 Feature extraction, clustering algorithms, and the k-means clustering algorithm
9.6 Theoretical aspects of k-means, k-means process flow, k-means in R, implementing k-means, and finding out the right number of clusters using a scree plot
9.7 Dendograms, understanding hierarchical clustering, and implementing it in R
9.8 Explanation of Principal Component Analysis (PCA) in detail and 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 and MBA
10.2 Measures of association rule mining: Support, confidence, lift, and apriori algorithm, and implementing them in R
10.3 Introduction to recommendation engines
10.4 User-based collaborative filtering and item-based collaborative filtering, and implementing a recommendation engine in R
10.5 Recommendation engine 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 Course Content

11.1 Introducing Artificial Intelligence and Deep Learning
11.2 What is an artificial neural network? TensorFlow: The computational framework for building AI models
11.3 Fundamentals of building ANN using TensorFlow and working with TensorFlow in R

12.1 What is a time series? The techniques, applications, and components of time series
12.2 Moving average, smoothing techniques, and exponential smoothing
12.3 Univariate time series models and multivariate time series analysis
12.4 ARIMA model
12.5 Time series in R, sentiment analysis in R (Twitter sentiment analysis), and text analysis

Hands-on Exercise:

1. Analyzing time series data
2. Analyzing the sequence of measurements that follow a non-random order to identify the nature of phenomenon and 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 the Bayes theorem?
14.2 What is Naïve Bayes Classifier?
14.3 Classification Workflow
14.4 How Naive Bayes classifier works and classifier building in Scikit-Learn
14.5 Building a probabilistic classification model using Naïve Bayes and the zero probability problem

15.1 Introduction to the concepts of text mining
15.2 Text mining use cases and understanding and manipulating the text with ‘tm’ and ‘stringR’
15.3 Text mining algorithms and the quantification of the text
15.4 TF-IDF and after TF-IDF

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Data Science Projects Covered

Market Basket Analysis

This is an inventory management project where you will find the trends in the data that will help the company to increase sales. In thisRead More..

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Credit Card Fraud Detection

The project consists of data analysis for various parameters of banking dataset. You will be using a V7 predictor, V4 predictor for analysis, and dataRead More..

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Loan Approval Prediction

In this project, you will use the banking dataset for data analysis, data cleaning, data preprocessing, and data visualization. You will implement algorithms such asRead More..

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Netflix Recommendation System

Implement exploratory data analysis, data manipulation, and visualization to understand and find the trends in the Netflix dataset. You will use various Machine Learning algorithmsRead More..

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Case Study 1: Introduction to R Programming

In this project, you need to work with several operators involved in R programming including relational operators, arithmetic operators, and logical operators for various organizationalRead More..

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Case Study 2: Solving Customer Churn Using Data Exploration

Use data exploration in order to understand what needs to be done to make reductions in customer churn. In this project, you will be requiredRead More..

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Case Study 3: Creating Data Structures in R

Implement numerous data structures for numerous possible scenarios. This project requires you to create and use vectors. Further, you need to build and use metrics,Read More..

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Case Study 4: Implementing SVD in R

Utilize the dataset of MovieLens to analyze and understand single value decomposition and its use in R programming. Further, in this project, you must buildRead More..

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Case Study 5: Time Series Analysis

This project required you to perform TSA and understand ARIMA and its concepts with respect to a given scenario. Here, you will use the RRead More..

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Data Science Certification

What should I do to unlock my Intellipaat certificate?

You can unlock your Intellipaat certificate in three simple steps:

  1. Complete the Data Science online course along with the given assignments
  2. Work on several industry-based projects and execute the same successfully
  3. Pass the certification exam

The Data Science certification you receive from Intellipaat is valid for your entire lifetime and is recognized by top organizations across the world.

On successfully completing the Data Science online course and passing the exam, you will receive Intellipaat’s Data Science certificate via our Learning Management System. You can download or share your certificate from this through either email or LinkedIn.

Yes, the certification given by Intellipaat is industry-recognized. Besides, due to our affiliation with IBM, you will also receive a Data Science course completion certificate from IBM.

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Data Science Training Review

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Mr Yoga

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John Chioles

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Ritesh

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Dileep & Ajay

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Sagar

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Ashok

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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!

Vikrant Singh

Big Data Analytics

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

Kevin K Wada

Oracle Developer at Free Agent

Thank you very much for your top-class service. A special mention should be made for your patience in listening to my queries and giving me a solution, which was exactly what I was looking for. I am giving you a 10 on 10!

Bhanukumar Muppalla

Software Engineer at DXC Technology

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

Nitesh Kumar Dash

Professional

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

Giri Karnal

Professional

I had taken the Data Science master’s program, which is a combo of SAS, R programming language, and Apache Mahout. Since there are so many technologies involved in courses, getting our 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.

Shreyash Limbhetwala

Technical Delivery Lead

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

Varsha Tyagi

Cloud Architect at Huawei Technologies

I was searching for various programs of Data Science, and I landed on Intellipaat’s course page. It was really good in terms of content. The sample video provided was also awesome, which impressed me a lot while deciding to take up the one among various courses. Moreover, the trainer’s command over the technology was great. The support team was really good. Really appreciable!

Sudipto

PS Consultant at Genesys

Intellipaat’s Data Scientist training is outstanding. The trainer is an experienced Data Scientist who has a good hold on the subject. Now, I’m an expert in Data Science, and I am already placed in a reputed firm.

Thejaswar Reddy

Programmer Analyst

I signed up for Intellipaat's Data science course online certification when i learned that it is a great place for learning new technologies. The course material offered by them is extremely useful. I chose to opt for the weekend classes as per my convenience. The trainer of this course was really good and helped me learn the subject well. Besides, the online support team helpedRead More..

Adarsh Vijay

Student at RTU

Excellent course, I found Intellipaat's coaching team to be talented in their respective domain. My learning was very good as it helped me in upgrading my skills, which proved to be a turning point in my career. Intellipaat's mentor was well-experienced and his teaching method was really great. This, one of the best Data Science programs helped me to get deep understanding of the technology.Read More..

Afsana Zaman

Data Scientist

Great experience. The support team has always been supportive and efficient to help us. The collaboration of practical with theoretical knowledge makes intellipaat highly suitable for those who wants to upgrade their career.

Sairam Patrudu Makena

Student partner at Bolt IoT

Intellipaat’s instructors and the learning support team for the Data Science courses were so accommodating that I could freely ask any questions and issues I had. I was happy with what I was taught, and Intellipaat’s practical teaching methods helped me understand the applications better. I am looking forward to another training session with Intellipaat soon.

Vishal Rathour

Artificial Intelligence Specialist at India Today Group

I have enrolled to the Artificial Intelligence Master's Course in Association with IBM. The trainers are awesome and they have really good knowledge in the subjects related to AI such as ML and DL. The support team is also always available to help (24/7) and resolves any query in a very short time.

Naveen Kumar

Data Science/Machine Learning Enthusiastic

It was an amazing experience learning Data Science with R from Intellipaat. The trainers were friendly, experienced, and very interactive with the learners. I would recommend this course for people who are interested in Data Science. The support center is very active 24 hours a day. Overall, the design of the course structure is good and can help the learner master the concepts easily.

Shadab Alam

Data Scientist Enthusiast

The trainers are very professional and accomodating to our queries. With lots of hands-on experience on the assignments and the live classes, it helped me gain expertise in the field of Data Science. Investing money in Intellipaat means investing in building your career.

Srijan Mukherjee

Intern at Ashok Leyland

The assignments and the project work of the course provided a good hands-on experience and additionally, the Intellipaat support team responses are always prompt and up to the mark.

Vidhathri Sarraf

Student at Kamala Institute of Technology

I am absolutely satisfied with the training. The course is well-structured and the learning management system is an added advantage.

Frequently Asked Questions on Data Science

Why should I learn Data Science from Intellipaat?

Intellipaat offers the best Data Science courses online for professionals who want to expand their knowledge base and start a career in this field. There are many reasons for choosing Intellipaat:

  • A personal mentor to track your progress
  • Immersive online instructor-led sessions conducted by SMEs
  • Extensive LMS, allowing you to view recorded sessions within 3 hours
  • Real-time exercises, assignments, and projects
  • 24/7 learning support
  • Large community of like-minded learners
  • Industry-recognized Intellipaat badge
  • Personalized job support

At Intellipaat, you can enroll in either 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, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.

Intellipaat is offering the 24/7 query resolution, and you can raise a ticket with the dedicated support team at anytime. You can avail of the email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our trainers.

You would be glad to know that you can contact Intellipaat support even after the completion of the training. We also do not put a limit on the number of tickets you can raise for query resolution and doubt clearance.

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 real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, 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. After completing the projects successfully, your skills will be equal to 6 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 such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.

You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.

Once you complete Intellipaat’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500companies.

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 find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.

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