Topics for this course

Total learning: 74 Topics     Chapter:  9

Statistics and Exploratory Data Analytics

·         Overview of Statistics

·         Types of Data

·         Range

·         Central Tendency

·         Normal Distribution

·         Introduction to Probability

·         Probability Distributions

·         Sampling Distributions

·         Introduction to Statistical Inference

·         Hypothesis Testing

·         Introduction to EDA

·         Data Visualization

Python Programming

·         Introduction to Python

·         Python/Installations & Configuration

·         Programming Basics-Data types ,variable

·         Control Statement

·         Data Structure -list,tuple,set,dictionary

·         Introduction to Functional Programming

·         Introduction to Web Scraping and NLP (Working with Text in Python)

·         Introduction to Data Manipulation using Numpy and Pandas

·         Scikit-learn, Seaborn libraries

·         Data Visualization by Matplotlib

 Database - SQL

·         Introduction to database and SQL

·         DDL and DML

·         SQL Constraints

·         Hands on Experience in DDL , DML

·         Hands on experience in index, variables

·         Accessing data

·         Filtering data

·         Operators

·         Subquery

·         Working with Strings (Emphasize on how to decide which string function to use )

·         Date Time Functions

·         Window Aggregate Functions

·         Case Statements

·         Procedures

·         Functions

·         Views in SQL

 Data visualization with Power BI

·         What is Power BI

·         Power BI Reports & Auto Filters

·         Report Visualization & Properties

·         Chart & Map Report Properties

·         Hierarchies & Drill Drown Report

·         Power Query & M Language

·         Power BI Development & Cloud

·         Data Modelling

·         Data Cleaning

·         Data Transformation

·         Insights & Subscriptions

 Machine Learning

·         Linear Regression

·         Linear Regression Assignment

·         Logistic Regression

·         Naive Bayes

·         Model Selection

·         Advanced Regression

·         Support Vector Machine (Optional)

·         Tree Models

·         Model Selection - Practical Considerations

·         Boosting

·         Unsupervised learning: Clustering

·         Unsupervised Learning: Principal Component Analysis

·         Telecom Churn Case Study

 Deep Learning

·         Introduction to Neural Networks

·         Convolutional Neural Networks - Industry Applications and Assignments

·         Recurrent Neural Networks

 Natural Language Processing

·         Lexical Processing

·         Syntactical Processing and Assignment

·         Case Study: Classifying Customer Complaint Tickets

  AI strategy

·         AI Strategy Framework, Structured Problem Solving/ Data Storytelling

·         Mapping ML with Data architecture strategy

·         Executing AI Strategy

·         AI strategy: Assignment

 Capstone Project

·         Data Science Project 01

·         Data Science Project 02