Not Applicable
Applicable
An Overview of Analytics and Data Science
Analytics Methodology and Problem Solving Frameworks
Models & Algorithms - How they work and Scope
R for Data Science - Setup and Introduction
Data Import from Multiple Sources - Flat Files, Databases, Web Sources etc.
Basic Data Manipulation with R - Summarizing, Aggregating, Functions
Advanced Data Manipulation with R including Custom Functions
Building Effective Visualizations with R
IBM: Data Journalism - First Steps, Skills and Tools
Quiz
Case Study - Credit Card Spend Patterns
Case Study - Response Modeling Data
Using Statistics to Summarize Data Effectively
Building Descriptive Analytics Dashboards
Case Study - Direct Marketing
Exploratory Data Analysis
Cleaning Data - Missing Values, Outliers
Preparing Data for Modeling - Transformations, Derived Variables
Visualization Methods and Applications in Excel
Case Study - Campaign Response Data - Exploration and Preparation
Case Study - Second Hand Car Pricing
Case Study - Auto Insurance
Introduction to Inferential Statistics
Understanding Probability and Distributions
Sampling Theory and How to Choose Representative Samples
Hypothesis Testing Concepts and Frameworks
Single Sample Hypothesis Tests - Z and T
Two Sample Tests - Independent and Paired
Multiple Samples Tests - ANOVA, Chi Square
Non-Parametric Tests
Case Study - HR Analytics
Case Study - Sales and Marketing Effectiveness
Linear Regression Models
OLS Algorithm and Implementation in R
Model Building and Iterations with Linear Models
Interpretation of Output and Evaluating Model Results
Generating Business Insights and Outcomes from Linear Models
Logistic Regression Models and the MLE Algorithm
Understanding the Odds Ratio
Building Logistic Models in R
Evaluating Logistic Regression Output - Probabilities, Confusion Matrix, Concordance, Lift
Generating Business Insights and Outcomes from Linear Models
Time Series Concepts
Simple Exponential Smoothing
Holt-Winter's Forecasting
ARIMA
Case Study - Market Mix Modeling to Calculate ROI on Marketing Activities
Case Study - Building a Default Risk Scorecard Model
Case Study - Predicting Debit Card Usage based on Historical Spends
Our programs have been designed for all students regardless of any prior knowledge of analytics, statistics or coding. We have had and continue to have many successful students who are from non-IT or non-mathematics backgrounds. But the subject matter is quantitative, and hence a background in maths, statistics or coding is helpful. If you have specific questions regarding eligibility or prerequisites for any program, please contact us at +91 9019217000.
Full Stack Programs are designed to give you the combination of skills that’s most in-demand in the industry. For example, the Full Stack Data Science will equip you with the knowledge of SAS, R, Python, Excel, VBA and Advanced SAS. This combination will give you a better chance of entering the field of analytics than learning a single course.You can speak to a Jigsaw Academy counselor if you still have questions.
If you are enrolled for any of our in-person classes, you can ask your questions during the live sessions. For online learning, you can reach our faculty through email, call, chat (Google hangouts) or ask via the forum. Once the course is over, you can still get in touch with us via email.
We have a strong network in the field of analytics. We are constantly in touch with various companies for their hiring and training needs. We identify the right opportunities for our students and help them get in touch with the relevant HR teams.
"Kanav, an IT professional, switched to analytics, and gained a HUGE salary hike in six months After working for over two years as a data test engineer with a leading software MNC, Kanav decided ..."
Kanav Nayyar
Founder, Data Analyst, Leading Analytics CompanyX
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