Data Science Program Curriculum

Foundation of the Data Science Course

Introduction to Analytics

1. Python/R for Data Science

2. Introduction to Python/R

3. Dealing with Data using Python/R

4. Visualization using Python / R

5. Python-Markdown

6. Missing Value Treatment

7. Exploratory Data Analysis

8. using Python/R

Marketing & CRM

1. Core concepts of marketing

2. Customer Lifetime Value

3. Marketing metrics for CRM Statistics

Methods for Decision Making

1. Descriptive Statistics

2. Introduction to Probability

3. Probability Distributions

4. Hypothesis testing and estimation

5. Goodness of Fit

Business Finance

1. Fundamentals of Finance

2. Working Capital Management

3. Capital Budgeting

4. Capital Structure

SQL Programming

1. Introduction to DBMS

2. ER diagram

3. Schema design

4. Key constraints & basics of normalization

5. Joins

6. Subqueries involving joins & aggregations

7. Sorting

8. Independent subqueries

9. Correlated subqueries

10. Analytic functions

11. Set operations

12.Grouping and filtering

Analytics Techniques

Optimization Techniques

1. Linear programming

2. Goal Programming

3. Integer Programming

4. Non-Linear Programming

Advanced Statics

1. Analysis of Variance

2. Regression Analysis 3. Dimension Reduction Techniques

Predictive Modelling

1. Multiple Linear Regression (MLR) for Predictive Analytics

2. Logistic Regression

3. Linear Discriminant Analysis

Data Mining

1. Introduction to Supervised and Unsupervised learning

2. Clustering

3. Decision Trees

4. Random Forest

5. Neural Networks

Time Series Forecasting

1. Introduction to Time Series

2. Correlation

3. Forecasting

4. Autoregressive Moving Average (ARMA) models

5. Autoregressive Integrated Moving Average (ARIMA) models

6. Case Studies

Machine learning

1. Handling Unstructured data

2. Machine learning Algorithms

3. Bias Variance trade-off

4. Handling unbalanced data

5. Boosting

6. Model Validation

Domain Exposure

Marketing & Retail Analytics

1. Marketing and Retail Terminologies: Review

2. Customer Analytics

3. KNIME

4. Retail Dashboards 5. Customer Churn

6. Association Rules Mining

Web & Social Media Analytics

1. Web Analytics: Understanding the metrics

2. Basic & Advanced Web Metrics

3. Google Analytics: Demo & Hands on

4. Campaign Analytics

5. Text Mining

Finance & Risk Analytics

1. Why Credit Risk-Using a market case study Comparison of Credit Risk Models

2. Overview of Probability of Default (PD) Modeling PD Models, types of models,

steps to make a good model

3. Market Risk

4. Value at Risk- using stock case study

5. Fraud Detection

Supply Chain & Logistics Analytics

1. Introduction to Supply Chain

2. Dealing with Demand Uncertainty

3. Inventory Control & Management

4. Inventory classification Methods (EOQ)

5. Inventory Modeling (Reorder Point, Safety Stock)

6. Advanced Forecasting Methods

7. Procurement Analytics

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