Home All Courses Big Data and Analytics Data Science Certification Training

Data Science Certification Training

Data Science course helps you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, Naive Bayes using R. You'll learn the concepts of Statistics, Time Series, Text Mining and an introduction to Deep Learning. You'll solve real life case studies on Media, Healthcare, Social Media, Aviation, HR.

Watch the demo class

Why this course ?

  • Businesses Will Need One Million Data Scientists by 2018 - KDnuggets
  • Roles like chief data & chief analytics officers have emerged to ensure that analytical insights drive business strategies - Forbes
  • The average salary for a Data Scientist is $113k (Glassdoor)
  • 24K + satisfied learners. Reviews

Instructor-led live online classes


Sun - Thu ( 15 Days )
09:30 PM - 11:30 PM ( EDT )
15% Off


Fri - Sat ( 5 Weeks )
09:30 PM - 12:30 AM ( EDT )
15% Off


Sat - Sun ( 5 Weeks )
11:00 AM - 02:00 PM ( EDT )
15% Off


Mon - Fri ( 15 Days )
11:00 AM - 01:00 PM ( EDT )
15% Off

Edureka For Business

Train your employees with exclusive batches and offers and track your employee's progress with our weekly progress report.

Instructor-led Sessions

35 Hours of Online Live Instructor-led Classes. Weekend class: 10 sessions of 3.5 hours each. Weekday class: 15 sessions of 2.5 hours each.

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of Data Science.


Each class will be followed by practical assignments which will aggregate to minimum 40 hours.

Lifetime Access

You get lifetime access to the Learning Management System (LMS). Class recordings and presentations can be viewed online from the LMS.

24 x 7 Expert Support

We have 24x7 online support team available to help you with any technical queries you may have during the course.


edureka certifies you in the Data Science course based on the project reviewed by our expert panel. 


We have a community forum for all our customers wherein you can enrich their learning through peer interaction and knowledge sharing.

Data science is a "concept to unify statistics, data analysis and their related methods" to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization. The Data Science Certification Training enables you to gain knowledge of the entire Life Cycle of Data Science, analyzing and visualizing different data sets, different Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes. 

After the completion of the course, you should be able to:

·        Gain insight into the 'Roles' played by a Data Scientist

·        Analyze several types of data using R

·        Describe the Data Science Life Cycle

·        Work with different data formats like XML, CSV etc.

·        Learn tools and techniques for Data Transformation

·        Discuss Data Mining techniques and their implementation

·        Analyze data using Machine Learning algorithms in R

·        Explain Time Series and it’s related concepts

·        Perform Text Mining and Sentimental analyses on text data

·        Gain insight into Data Visualization and Optimization techniques

·        Understand the concepts of Deep Learning

The incorporation of technology in our everyday lives has been made possible by the availability of data in enormous amounts. Data is drawn from different sectors and platforms including cell phones, social media, e-commerce sites, various surveys, internet searches, etc. 

However, the interpretation of vast amounts of unstructured data for effective decision making may prove too complex and time consuming for companies, hence, the emergence of Data Science.

Data science incorporates tools from multi disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes. The disciplinary areas that make up the data science field include mining, statistics, machine learning, analytics, and some programming. Data mining applies algorithms in the complex data set to reveal patterns which are then used to extract useable and relevant data from the set. Statistical measures like predictive analytics utilize this extracted data to gauge events that are likely to happen in the future based on what the data shows happened in the past. Machine learning is an artificial intelligence tool that processes mass quantities of data that a human would be unable to process in a lifetime. Machine learning perfects the decision model presented under predictive analytics by matching the likelihood of an event happening to what actually happened at the predicted time.

The course is designed for all those who want to learn about the life cycle of Data Science, which would include acquisition of data from various sources, data wrangling and data visualization. Applying Machine Learning techniques in R language, and wish to apply these techniques on different types of Data. 

The following professionals can go for this course:

1. Developers aspiring to be a 'Data Scientist'

2. Analytics Managers who are leading a team of analysts 

3. Business Analysts who want to understand Machine Learning (ML) Techniques

4. Information Architects who want to gain expertise in Predictive Analytics

5. 'R' professionals who want to captivate and analyze Big Data

7. Analysts wanting to understand Data Science methodologies

There is no specific pre-requisite for the course, however basic understanding of R can be beneficial. Edureka offers you a complimentary self-paced course, i.e. "R Essentials" when you enroll in Data Science Certification Training.

If you have a Windows system you should have :

Microsoft Windows 7 or newer (32-bit and 64-bit)
Microsoft Server 2008 R2 or newer
Intel Pentium 4 or AMD Opteron processor or newer
2 GB memory
1.5 GB minimum free disk space
1366 x 768 screen resolution or higher

If you have a MAC system you should have :

iMac/MacBook computers 2009 or newer
OSX 10.10 or newer
5 GB minimum free disk space
1366 x 768 screen resolution or higher

For executing the practical, you will set-up R programming IDE on your machine, which you can:
Download for free, RStudio Desktop Open Source License from the Rstudio Official Website. 
Or, Purchase the licensed Full- version of RStudio Desktop Commercial License.

The detailed step by step installation guides will be present in your LMS which will help you to install and set-up the required environment. In case you come across any doubt, the 24*7 support team will promptly assist you.

Towards the end of the Course, you will be working on a live project. We will emphasize the concepts learned in the various Modules through different case studies. The various case studies are listed below:

Project#1: Movie Dataset

Industry: Entertainment Industry

Description: The goal of this Use-Case is to explore the movie dataset, given the parameters like: "duration", "movie title", "gross collection", "budget", "title year", etc.  

  •     Know top ten movies with the highest profits.

  • Know top rated movies in the list and average IMDB score.

  • Plot a graphical representation to show number of movies released each year.

  •     Group the movies into clusters based on the Facebook likes.

  • Group the directors based on movie collection and budget.

Project #2: Real Estate price prediction

Industry: Business Intelligence and Analytics

Description: The goal of this Use-case is to make predictions using Real Estate market data. The dataset contains the of the price of apartments in Boston. This data contains values such as crime rate, age, accessibility, population etc.

  •    Based on this data, the company wants to decide on the price of new apartments.

Project #3: Diabetes Prediction

Industry: Healthcare

Description: The Use-Case focuses on making predictions based upon the patient’s characteristic data set, the data set contains attributes such as glucose level, blood pressure, age, etc. At last the goal is to make a high accuracy machine learning model which can predict, whether a patient is Diabetic or not.

Project #4: Recommendation System for Grocery store

Industry: Food Retail industry

Description: The Use-Case scenario is to create recommendations for customers of a grocery store based upon historic transactional data, the goal is to create a recommendation engine which could recommend preferable articles.

Project #5: Twitter Analytics

Industry: Social Media Analytics

Description: This Use-Case focuses on social media analytics. The problem can be defined as Measuring, Analyzing, and Interpreting interactions and associations between people, topics and ideas. The dataset to be analyzed is captured by Live Twitter Streaming. The task is to perform Sentiment analysis on the tweets obtained and visualize the conclusions. In this Use-Case we will compare two football clubs, based upon the tweets they are receiving from their fans.

Project #6: Air Passengers forecasting

Industry: Commercial Aviation

Description: This Use-Case is about analyzing the data and applying time series model to forecast the number of bookings an Airline firm can expect each month the dataset we will analyze contains monthly totals of international airline passengers between 1949 to 1960.

This information can help management to make informed decisions on staffing, hospitality and pricing for tickets.

Goal - Get an introduction to Data Science in this Module and see how Data Science helps to analyze large and unstructured data with different tools.

Objectives - At the end of this Module, you should be able to:

Define Data Science

Discuss the era of Data Science

Describe the Role of a Data Scientist

Illustrate the Life cycle of Data Science

List the Tools used in Data Science

State what role Big Data and Hadoop, R, Spark and Machine Learning play in Data Science


What is Data Science?

What does Data Science involve?

Era of Data Science

Business Intelligence vs Data Science

Life cycle of Data Science

Tools of Data Science

Introduction to Big Data and Hadoop

Introduction to R

Introduction to Spark

Introduction to Machine Learning

Goal - In this Module, you should learn about different statistical techniques and terminologies used in data analysis.

Objectives - At the end of this Module, you should be able to:

Define Statistical Inference

List the Terminologies of Statistics

Illustrate the measures of Center and Spread

Explain the concept of Probability

State Probability Distributions


What is Statistical Inference?

Terminologies of Statistics

Measures of Centers

Measures of Spread


Normal Distribution

Binary Distribution

Goal - Discuss the different sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format.

Objectives - At the end of this Module, you should be able to:

Discuss Data Acquisition techniques

List the different types of Data

Evaluate Input Data

Explain the Data Wrangling techniques

Discuss Data Exploration


Data Analysis Pipeline

What is Data Extraction

Types of Data

Raw and Processed Data

Data Wrangling

Exploratory Data Analysis

Visualization of Data


•       Loading different types of dataset in R 

•       Arranging the data 

•       Plotting the graphs

Goal - Get an introduction to Machine Learning as part of this Module. You will discuss the various categories of Machine Learning and implement Supervised Learning Algorithms.

Objectives - At the end of this module, you should be able to:

Define Machine Learning

Discuss Machine Learning Use cases

List the categories of Machine Learning

Illustrate Supervised Learning Algorithms


What is Machine Learning?

Machine Learning Use-Cases

Machine Learning Process Flow

Machine Learning Categories

Supervised Learning

    o     Linear Regression

    o     Logistic Regression


Implementing Linear Regression model in R

Implementing Logistic Regression model in R 

Goal - In this module, you should learn the Supervised Learning Techniques and the implementation of various Techniques, for example, Decision Trees, Random Forest Classifier etc. 

Objectives - At the end of this module, you should be able to:

Define Classification

Explain different Types of Classifiers such as,

   o   Decision Tree

   o   Random Forest

   o   Naïve Bayes Classifier

   o   Support Vector Machine


What is Classification and its use cases?

What is Decision Tree?

Algorithm for Decision Tree Induction

Creating a Perfect Decision Tree

Confusion Matrix

What is Random Forest?

What is Navies Bayes?

Support Vector Machine: Classification


Implementing Decision Tree model in R

Implementing Linear Random Forest in R

Implementing Navies Bayes model in R

Implementing Support Vector Machine in R

Goal - Learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.

Objectives - At the end of this module, you should be able to:

Define Unsupervised Learning

Discuss the following Cluster Analysis

    o     K - means Clustering

    o     C - means Clustering

    o     Hierarchical Clustering


What is Clustering & its Use Cases?

What is K-means Clustering?

What is C-means Clustering?

What is Canopy Clustering?

What is Hierarchical Clustering?


Implementing K-means Clustering in R

Implementing C-means Clustering in R

Implementing Hierarchical Clustering in R

Goal - In this module, you should learn about association rules and different types of Recommender Engines.

Objectives - At the end of this module, you should be able to:

Define Association Rules

Define Recommendation Engine

Discuss types of Recommendation Engines

    o     Collaborative Filtering

    o     Content-Based Filtering

Illustrate steps to build a Recommendation Engine


What is Association Rules & its use cases?

What is Recommendation Engine & it’s working?

Types of Recommendation Types

User-Based Recommendation

Item-Based Recommendation

Difference: User-Based and Item-Based Recommendation

Recommendation Use-case


Implementing Association Rules in R

Building a Recommendation Engine in R

Goal - Discuss Unsupervised Machine Learning Techniques and the implementation of different algorithms, for example, TF-IDF and Cosine Similarity in this Module. 

Objectives - At the end of this module, you should be able to:

Define Text Mining

Discuss Text Mining Algorithms

    o     Bag of Words Approach

    o     Sentiment Analysis


The concepts of text-mining

Use cases

Text Mining Algorithms

Quantifying text


Beyond TF-IDF


Implementing Bag of Words approach in R

Implementing Sentiment Analysis on twitter Data using R

Goal - In this module, you should learn about Time Series data, different component of Time Series data, Time Series modelling - Exponential Smoothing models and ARIMA model for Time Series forecasting.

Objectives - At the end of this module, you should be able to:

Describe Time Series data

Format your Time Series data

List the different components of Time Series data

Discuss different kind of Time Series scenarios 

Choose the model according to the Time series scenario

Implement the model for forecasting

Explain working and implementation of ARIMA model

Illustrate the working and implementation of different ETS models

Forecast the data using the respective model


What is Time Series data?

Time Series variables

Different components of Time Series data

Visualize the data to identify Time Series Components

Implement ARIMA model for forecasting

Exponential smoothing models

Identifying different time series scenario based on which different Exponential Smoothing model can be applied

Implement respective ETS model for forecasting


•       Visualizing and formatting Time Series data

•       Plotting decomposed Time Series data plot

•       Applying ARIMA and ETS model for Time Series forecasting

•       Forecasting for given Time period

Goal - Get introduced to the concepts of Reinforcement learning and Deep learning in this Module. These concepts are explained with the help of Use cases. You will get to discuss Artificial Neural Network, the building blocks for artificial neural networks, and few artificial neural network terminologies.

Objectives - At the end of this module, you should be able to:

Define Reinforced Learning

Discuss Reinforced Learning Use cases

Define Deep Learning

Understand Artificial Neural Network

Discuss basic Building Blocks of Artificial Neural Network

List the important Terminologies of ANN’s


Reinforced Learning

Reinforcement learning Process Flow

Reinforced Learning Use cases

Deep Learning

Biological Neural Networks

Understand Artificial Neural Networks

Building an Artificial Neural Network

How ANN works

Important Terminologies of ANN’s

"You will never lose any lecture. You can choose either of the two options:
  • View the recorded session of the class available in your LMS.
  • You can attend the missed session, in any other live batch."
edureka is committed to provide you an awesome learning experience through world-class content and best-in-class instructors. We will create an ecosystem through this training, that will enable you to convert opportunities into job offers by presenting your skills at the time of an interview. We can assist you in resume building and also share important interview questions once you are done with the training. However, please understand that we are not into job placements.
We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrolment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in the class.
We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrolment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in the class.
You can give us a CALL at +91 88808 62004/1800 275 9730 (US Tollfree Number) OR email at sales@edureka.co

  • Once you are successfully through the project (Reviewed by a edureka expert), you will be awarded with edureka’s Data Science certificate.
  • edureka certification has industry recognition and we are the preferred training partner for many MNCs e.g.Cisco, Ford, Mphasis, Nokia, Wipro, Accenture, IBM, Philips, Citi, Ford, Mindtree, BNYMellon etc. Please be assured.