1 Data Science Foundation

·      Introduction to Data Science

·      Data Science vs Business Analytics vs Big Data

·      Classification of Business Analytics

·      Data Science Project Workflow

·      Various Roles in Data Science

·      Application of Data Science in various industries

2 Python for Data Science

·      Introduction to Data Science with Python

·      Python Basics: Basic Syntax, Data Structures

·      Data objects, Math, Comparison Operators, Condition Statements, loops, lists, tuples, dicts, functions

·      Numpy Package

·      Pandas Package

·      Python Advanced: Data Munging with Pandas

·      Python Advanced: Visualization with Matplotlib

·      Exploratory Data Analysis: Data Cleaning, Data Wrangling

·      Exploratory Data Analysis: Case Study

3 Statistics for Data Science

·      Introduction to Statistics

·      Harnessing Data

·      Exploratory Analysis

·      Distributions

·      Hypothesis & Computational Techniques

·      Correlation & Regression

4 Visual Analytics Foundation

·      Visual Analytics Basics

·      Basic Charts, Plots

5 SQL for Data Science

·      Install SQL packages and Connecting to DB

·      RDBMS (Relational Database Management) Basics

·      Basics of SQL DB, Primary key, Foreign Key

·      SELECT SQL command, WHERE Condition

·      Retrieving Data with SELECT SQL command and WHERE Condition to Pandas DataFrame.

·      SQL JOINs

·      Left Join, Right Joins, Multiple Joins

6 Machine Learning Basics

·      Machine Learning Introduction

·      What is ML? ML vs AI. ML Workflow, Statistical Modelling of ML. Application of ML

·      Machine Learning Algorithms

·      Popular ML algorithms, Clustering, Classification and Regression, Supervised vs Unsupervised.

·      Choice of ML

·      Supervised Learning

·      Simple and Multiple Linear Regression, KNN, and more

·      Linear Regression and Logistic Regression

·      Theory of Linear regression, hands on with use cases

·      K-Nearest Neighbour (KNN)

·      Decision Tree

·      Naïve Bayes Classifier

·      Unsupervised Learning: K-Means Clustering

7 Machine Learning Expert

·      Advanced Machine Learning Concepts

·      Tuning with Hyper parameters

·      Random Forest – Ensemble

·      Ensemble Theory, Random Forest Tuning

·      Support Vector Machine (SVM)

·      Simple and Multiple Linear Regression, KNN

·      Natural Language Processing (NLP)

·      Text Processing with Vectorization, Sentiment Analysis with Text Blob, Twitter Sentiment Analysis.

·      Naïve Bayes Classifier

·      Naïve Bayes for Text Classification, New Articles Tagging

·      Artificial Neural Network (ANN)

·      Basic ANN network for Regression and Classification

·      TensorFlow Overview

·      Deep Learning Intro

8 Time Series Foundation

·      What is a Time-Series?

·      Trend, Seasonality, Cyclical and Random

·      White Noise

·      Auto Regressive Model (AR)

·      Moving Average Model (MA)

·      ARMA Model

·      Stationarity of Time Series

·      ARIMA Model – Prediction Concepts

·      ARIMA Model Hands on with Python

·      Case Study Assignment on ARIMA

9 Deep Learning Foundation

·      Introduction to Deep learning

·      What is Deep Learning?

·      Various Deep Learning models in practice and applications.

·      Convolutional Neural Network CNN Intro

·      Case Study: Keras–TensorFlow Image Classification

·      CNN hands on application for classification of images of Cats and Dogs