What else?

It’s a complete online/offline analytical course not only making you trained on data science using R  but making you equipped with job ready skills on day of your new analytics job.

We are offering:

• 6 Assignments
• 3 real time industry projects on healthcare, banking and retail
• Resume that will help you merge your previous experience with data analytics
• Placement preparation- Question bank, PDF’s, E-books, Sharing your CV with our Industry links
• Certification at end of the course

Starting packages with R+ Stats + Machine Learning Skills–

As much as you would love to get – Still 6 lacs to 8 lacs is the industry average

Duration- 3 month’s weekend course (3 hours a week) – Fees- 25K ($500- International students)

Don’t want to do Machine learning? Try (R programming + SQL) – Fees-20K

 

Session-1 Introduction to Data Science 

• What is Data Analytics & Data Science
• Different types of Data Analytics (Descriptive, Predictive, Prescriptive)
• What is Artificial Intelligence
• Machine Learning (Supervised & Unsupervised Learning)
• Deep Learning (Artificial Neural Networks, CNN)
• Overview of Banking, Healthcare, Telecom domain
• Real world Applications of Machine Learning & Deep Learning
• What to expect from this course (Salary, Market trends, job roles, Domain)

 

Session-2 SQL in R

• What is SQL
• SQL in R
• SQL Architecture
• SQL Syntax
• Data Types
• Operators
• Fundamentals of SQL
• Creating Database
• Creating Tables
• Insertion of rows
• Deletion of rows
• Null Values
• Clauses
• Removing Duplicate data
• Sorting data
• Alteration of Data
• Joins
• Relationships in SQL
• Understanding Joins
• Types of Joins
• Functions
• Brief about Functions
• Aggregation Function

 

Session-3 Complete Practical Session- Assignemnt Discussion

 

Session-4-5  R Programming

• Introduction to R Programming
• Data Types in R
• Functions in R
• Summarizing data by using various functions
• Indulge into a class activity to summarize the data
• Various sub setting methods

Data Importing

• Data import technique in R
• Import data from spread sheets and text files into R
• Install packages used for data import

Objects in R

• Vector
• Matrix
• Dataframe
• List  Array

Function in R

• Numeric functions
• Character functions
• Date functions

 

Session-6-7  Data Manipulation in R

• Know the various steps involved in data cleaning
• Tacking the problem faced during data cleaning
• Data manipulation using Dplyr
• How to coerce the data
• Apply, Lapply,Mapply,Sapply functions.
• Subset data, Rename columns, Operations on data.

Data Exploration and Visulaization in R

• What is data exploration
• Data exploring using Summary(), mean(), var(), sd(), unique()
• Use summarize, aggregate function
• Learning correlation and cor() function
• Visualizing data using plot and its different flavours
• Boxplots
• Gain understanding on data visualization  Learn the various graphical functions present in R
• Plot various graph like tableplot, histogram, boxplot etc.

Session-8-9 Introduction to Business Analytics

• Relevance in industry and need of the hour
• Analytics use cases in –Healthcare, Insurance, Finance, Medical etc.
• Future of analytics and critical requirement

Fundamentals of Statistics

• Basic statistics; descriptive and summary
• Inferential statistics
• Statistical tests

Data Prep and Reduction techniques

• Need for data preparation
• Check Skewness of data
• Outlier treatment
• Missing values treatment
• Factor Analysis

Basic Analytics

• Statistics Basics Introduction to Data Analytics and Statistical Techniques
• Variable Distributions and Probability Distributions
• Normal Distribution and Properties
• Hypothesis Testing Null/Alternative Hypothesis formulation
• P Value Interpretation
• Correlation

Session-10-11 Linear Regression Model

• Basics of regression analysis
• Correlation, VIF, missing value imputations and outliers
• Create Linear regression model
• Interpretation of results
• Performance metrics for model.

Hands on project for implementing Linear Regression

Session-12-13 Logistic Regression Model

• Use cases of Logistic regression model.
• Create a logistic regression model in R
• Churn prediction models and management
• Sensitivity, specificity, Confusion matrix.
• ROC curve.
• Performance metrics of logistic regression

Hands on project for implementing Logistic Regression

Session-13-14  Clustering

• What is K-means clustering model  Create a clustering model in R.
• Interpreting results to select numbers of clusters for model.
• Checking accuracy of the model.

Hands on project for implementing Clustering

Session 15  Other Machine Learning techniques

• Time Series
• Survival Analysis
• Random Forest
• Association Rules

Real Time Industry Projects

Session-16-17 

Project1: Predicting who will default and who will not for a bank loan or Credit Card

Session-18

Project2: Predicting the optimal price of a car to be launched

Session 19

Project 3: Segmentation of customer base for a retail chain

Session-20

Project 4: (Case study in any one domain)

                  Specialization in Banking 

                 Specialization in Healthcare

                Specialization in Telecom