## Available courses

**Introductory Topics to Analytics**

- Understanding the need for Analytics in Specific Domain; CRISP Modeling
- Introduction to Data Management
- Properties & Types of Data, Measurement Scale, Basic Statistics on Data
- Types of Analytics - Business, Marketing, Finance, etc

**Basics of R Programming**

- Need for R, Features of R, Download, Setup, Installation ; R & R Studio; Configuration. eg. Learning to setup R and share Code
- Data Structures in R
- Creating and Understanding Basic Data Structures in R - Vector, List, Matrix, Array, Data Frame & Factors which help in creating data in R programming
- Data Manipulation & Summarisation in R
- Understanding how data can be summarised in different ways to do Descriptive Analysis which describes features of data
- Import Export Data - CSV, Table, Excel, Google Sheet, Other Formats

**Module 2 : ****Analytical Modeling**

- Understand what is Modeling and how it can be used in Various Domain
- Statistical Tests -P Value, Z Value, Hypothesis, Null Hypothesis and Alternative Hypothesis, F Test, ANOVA Introduction)
- Linear Regression (Using R) Start of Machine Learning, Develop a Prediction Model for predicting a financial values based on one or more than 1 Independent Variable; Understand the assumptions and measures of goodness of Model, Understand its prediction ability
- Visualisation using Graphs. Creating Graph in R and understanding which graph to be used when
- Missing Value and Outlier Analysis
- Understanding how missing values & outliers are handled in data summarisation & modeling
- Logistic Regression . Predicting Binary Outcome (Buy or not, Churn or not, Loan Default or not) based on Independent Variables eg. Predicting Cases for Fraud, Default on Payment etc

**Module 3**

- Clustering. Grouping Customers based on characteristics so that they can be target for sale increase
- Decision Trees. When to use CART & CHAID to create decision tree based on categorical variables.
- Ensemble (Bagging & Boosting)
- Random Forest, XGBoost: Problems of Decision Tree covered in Random Forest, How group thinking impacts the decisions (from business point of view)

**Module 4**

- Association Rule Analysis. Understand how Association Rules can be created using Market Basket Analysis. Finding Interesting association between items purchased by Customers and building strately to sell more
- Twitter Analysis. Configure Twitter Account & Application; Setup for downloading tweets and analyse them for positive and negative sentiments related to Financial News/ Articles

**Code and Data Sharing through Git Hub, ****Git Configuration, ****Monthly Brushup Sessions**

- Teacher: Kanika Tiwari
- Teacher: Dhiraj Upadhyaya

** Analytics using Python Programming - Game Based Learning**

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data modeling, and more!

**Topics covered:**

- Python Environment in Anaconda
- Using Git for Project Management
- Data Structures incl - Lists, Tuple, Numpy, Pandas
- Data Import and Export
- Data Cleaning and Transformation
- Data frame manipulation
- Summarizing the Data
- Visualising Data
- Building machine learning Models - Linear Regression, Logistic Regression, Classification & Decision Trees, Clustering, Association Rule, Time Series, Text Mining

Data Analysis with Python will be delivered through lecture, online Video, LMS, lab, and assignments. It includes following parts:

- E Books, Cheat sheets
- Video Lectures
- Power Points , Notes
- Assignment
- Practise Tests, Final Assessment
- Feedback
- Learning Through Gamification and Game Based Learning Activities
- Git Repositories

Different types of machine learning - Supervised learning , Unsupervised learning, Reinforcement learning , Decision trees ,Linear regression, Logistic regression ,The naive Bayes classifier, The k-means clustering , Hierarchical clustering etc.

- Teacher: Kanika Tiwari
- Teacher: Dhiraj Upadhyaya

Skip course categories