Date Days Time
November 28 Sat & Sun
Weekend batch
Filling Fast
8:00 PM to 11:00 PM (IST)
December 5 Sat & Sun
Weekend batch

8:00 PM to 11:00 PM (IST)
December 13 Sat & Sun
Weekend batch

8:00 PM to 11:00 PM (IST)
December 19 Sat & Sun
Weekend batch

8:00 PM to 11:00 PM (IST)

Program Price

$129/- $258/-

50% off limited time offer

Program Syllabus

  • • SAS Vs R Vs Python
  • • Business objectives
  • • Key driving factors in the analytics world


  • • How Data Analysis is helpful in the Sales Industry
  • • R studio
  • • Graphical User Interfaces (GUI)
  • • Installing R studio
  • • Install essential packages along with different GUIs


  • • Installing R studio and its essential packages along with different GUIs
  • • Data Structure
  • • Data Types
  • • Vectors
  • • Matrices
  • • Factors
  • • Data frames
  • • Lists
  • • Importing Data
  • • Connecting to database
  • • Exporting Data
  • • Viewing partial data and full data
  • • Variable & Value Labels: Date Values


  • • We will go through a Case Study on HR Analytics
  • • Variables to perform calculations & binning
  • • Operators and using multiple operators
  • • Built-in Functions & User-Defined Functions
  • • Control Structures
  • • Conditional statements
  • • Loops
  • • Functions
  • • Sorting, Merging and Appending Data
  • • Aggregating/summarizing Data
  • • Reshaping & Subsetting Data
  • • Data Type Conversions
  • • Sampling
  • • File preparation
  • • Aggregation
  • • Merging
  • • Appending
  • • Type conversion
  • • Renaming and formatting data
  • • Handling Duplicates/Missing values


  • • Creating new variables to perform calculations & binning
  • • Working with operators
  • • Working with conditional statements
  • • Using loops
  • • Using functions
  • • Sorting, merging, and appending data
  • • Use case to learn about aggregating/summarizing Data
  • • Use case to learn about reshaping & Subsetting Data
  • • Use case to learn about data Type Conversions
  • • Use case to learn about sampling
  • • Learning to implement file preparation, Aggregation, Merging, and Appending
  • • Working on type conversion, Renaming, and formatting data
  • • Handling Duplicates/Missing values
  • • Case study on Descriptive Analytics based-on Industrial based problem statements
  • • Creating Interactive Graphs on R using packages like
  • • GGPLOT, GGPLOT2, and PLOTLY
  • • Working with Histograms & Density Plot
  • • Dot Plots
  • • Bar Plots
  • • Line Charts
  • • Pie Charts
  • • Boxplots
  • • Scatterplots


  • • Creating visualization on top of a large dataset using packages like GGPLOT, GGPLOT 2 and PLOTLY and visualizing various attributes from the relevant rows.
  • • Working with Histograms & Density Plot, Dot Plots, Bar Plots, Line Charts, Pie Charts, Boxplots, Scatterplots
  • • Exploratory Data Analysis (EDA)
  • • Understanding the spread and data points
  • • Understanding the sourced data for better analysis


  • • Working with a Case Study on Exploratory Data Analysis in R using an Industrial-based
  • • Understanding more about Analytics World
  • • Data Science Vs Data Analytics Vs ML Vs AI Vs Business Analysis
  • • Analytics keywords and their definitions
  • • Business Objectives
  • • Key driving factors in the Analytics world


  • • Case Study on how Predictive Analysis is helpful for Sales Industry
  • • Types of Business problems
  • • Mapping of Techniques
  • • Different Phases of Predictive Modeling
  • • EDA - Exploratory Data Analysis and Need of Data preparation
  • • Data Preparation
  • • Performing Data Preparation steps
  • • Consolidation/aggregation
  • • Outlier treatment
  • • Flatliners
  • • Missing values
  • • Dummy Creation
  • • Variable Reduction
  • • Data Alignment and fine-tuning
  • • Cluster and Segmentation in R
  • • Working with various Behavioral Segmentation Techniques
  • • K-Means Cluster Analysis in R
  • • Heuristic Segmentation Techniques
  • • Value-Based, RFM Segmentation
  • • Life Stage Segmentation


  • • Performing Data Preparation
  • • Case Study on Segmentation Modeling
  • • Implementing Decision Tree model in R
  • • Understanding steps to perform the Classification based on inferences on Decision Tree
  • • Extensive standard R Packages and Functions


  • • Hands-on & Case-Study on Decision Tree Modeling Problem Statements
  • • Assumptions of Linear Regression & Logistic Regression
  • • Linear & Logistic Regression
  • • Use of Linear & Logistic Regression Model


  • • Linear & Logistic Regression Analysis
  • • Building Linear & Logistic Regression Model
  • • What is Time Series Data?
  • • Different components of Time Series data
  • • Visualize the data to identify Time Series Components


  • • Working on a Case Study of Time Series and Arima Model
  • • Implement ARIMA model for forecasting
  • • Understanding differences in Statistical learning vs. Machine learning
  • • Understanding essential classes of Machine Learning Algorithms: Supervised vs Unsupervised Learning
  • • Text Mining and Sentiment Analysis in R


  • • Performing Machine learning on Sentiment Analysis
  • A major government bank has approached your company to analyze from their customer loan data set. Since the last 4 months, a lot of customers who are not able to repay their loan amount has increased. You have been assigned a task of analyzing from the data set and giving insights about which customer should be given the loan approval and which shouldn’t be.


  • Develop an ML algorithm to identify the most optimal ratio/ aspects to allocate funds/ spending proportionately by organizations in different areas of expenses like R&D, Marketing, Employee Cost, HR & Admin Cost, Infrastructure Cost, etc. Identifying the optimal ratio of the amount of allocation of funds to various segments is of utmost importance. This would also help the Management team with the below aspects, to increase the revenue and profitability, to better design marketing strategies, to allocate the internal resources better.



Overview

Data Science Certification Training

An industry-oriented course designed by experts. Become a Data Scientist by mastering concepts like data collection, manipulation, analysis, statistical methods, machine learning, and more.

Key Features

  • 5+ Projects and case studies

  • 42+ Hours of interactive learning

  • 30+ Hours of exercise and project work

  • Lifetime access to LMS

  • Attend as many batches for lifetime

  • 24/7 Technical Support

  • Resume Building

  • Placement Assistance

  • Dedicated Learner Delight Team

About Course

R programming, data collection, manipulation, data visualization, algorithms such as regression, decision tree, time-series data, supervised and unsupervised machine learning, and more.


Freshers, anyone willing to build a career as a Data Scientist.


No prerequisite. We teach everything from scratch.


Hiring Companies

Source : Indeed

Our Learners Work For

Course Completion Certificate

What People Think

Nimisha Gera

Nimisha Gera

Product Manager, Bluetooth SIG USA

After lot of analysis and googling I ended up taking Data Science with R course from Eduranz. I am glad I made that decision. The course is well packed with all essentials to understand the Data Science Concepts.

Chandrasekar G

Chandrasekar G

Managing Director - MEA UAE

I really had overwhelming experience with eduranz. Course Material, Course Instructor and Support team helped me a lot to become a Data Scientist and to show my all the learning knowledge in company project.

Dr. Essam Zaghloul

Dr. Essam Zaghloul

Board Member at Algeology Petroleum

"I am currently undergoing Data Science Master's program with Eduranz the course content is top notch, Instructors are good, they are knowledgeable and all of them are professionals from their specific field. "

Ljiljana Spasovic Botha

Ljiljana Spasovic Botha

Business Development Officer at SASLO

Had a great learning session where the concepts are clear to understand and can solve the given assignments easily.

Train Your Employees

We offer flexible and cost-effective group membership for your business, goverment organization.

Connect Now

FAQ

Eduranz offers a unique online Data Science Certification course for professionals who are willing to build a career in this rousing domain. There are many reasons to choose Eduranz: Interactive online instructor-led live classes conducted by SMEs
Personal mentors who will keep a staging track of your progress in the course
A Substantial LMS which allows the users to view their recorded sessions from their live classes along with the self-recorded courses
Real-time exercises, assignments, industry-based use cases and real-world projects
24/7 learning support by the Eduranz’s dedicated tech support team
Large community of learners from across the globe
Industrially as well as globally recognized certificate by Eduranz
Personalized job support, resume and interview preparation

You never miss any lecture at Eduranz, because you will be provided with the recorded sessions of the live class on your LMS within 24 hours and despite that, you can also attend any different live session to cover up the missed topic and ask your doubts from the trainer or you can simply reschedule your batch and get yourself a new batch assigned.

Live Virtual Classes or Online Classes. With online class training, you can access courses via video conferencing from your desktop to increase productivity and reduce work time and personal time.

Eduranz offers a 24/7 request solution and you can pick up your tickets at any time from our dedicated support team. You can use email support for all your questions. If your request is not answered via email, we can also arrange one-on-one discussions with the faculty. You will be glad to know that you can switch to Eduranz support after completing the training. We also don’t limit the number of tickets you can collect when solving questions and doubts.

Yes, Eduranz has a dedicated placement assistance team. Our job assistance program will help you reach the job you have been seeking. Under this program, we help you by building your professional resume and then sharing it across our network companies that we have tie ups with.

Eduranz offers the most up-to-date, relevant and valuable projects in the real world as part of the training program. In this way, you can integrate what you have learned in the real industry. Each training is delivered with various projects where you can thoroughly test your skills, learning and practical knowledge so that you are well prepared for the industry. They work on very interesting projects in the fields of high technology, e-commerce, marketing, sales, networking, banking, insurance and more. After successfully completing your project, your skills will be counted as a result of six months of intensive industry experience.

After completing the Eduranz Training Program along with all real projects, tests and assignments and achieving at least 60% points in the qualification exam; you will receive an industrial recognized certificate by Eduranz. This certification is recognized by companies all across the industry, which includes a lot of top MNCs worldwide.

Our job assistance program will help you reach the job you have been seeking. Under this program, we help you by building your professional resume and then sharing it across our network companies that we have tie ups with. You will also be prepared for interviews through mock sessions. However, Eduranz is not a recruitment agency. We do not guarantee you a job. After we share your profiles with the companies, the further process depends upon your performance and their decision.

All of our highly qualified instructors are industry experts with minimum 10-12 yrs. of relevant IT experience. Each of them underwent a rigorous selection process that included screening profiles, teaching assessments, and training demonstrations.



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