Data Analytics

Module 1: Fundamentals of Statistics & Data Science

Fundamentals of Data Science and Mathematical Statistics

Introduction to Data Science

Need of Data Science

BigData and Data Science

Data Science and machine learning

Data Science Life Cycle

Data Science Platform

Data Science Use Cases

Skill Required for Data Science

Mathematics For Data Science

Linear Algebra

Vectors

Optimization

Theory Of optimization

Gradients Descent

Introduction to Statistics

Descriptive vs Inferential Statistics

Types of data

Measures of central tendency and dispersion

Hypothesis & inferences

Hypothesis Testing

Confidence Interval

Central Limit Theorem

Probability and Probability Distributions

Probability Theory

Conditional Probability

Data Distribution

Distribution Functions

Normal Distribution

Binomial Distribution

Module 2: RDBMS: Basics of SQL

An Introduction to RDBMS & SQL

Data Retrieval with SQL

Pattern matching with wildcards

Basics of sorting

Order by clause

Aggregate functions

Group by clause

Having clause

Nested queries

Inner join

Multi join

Outer join

Adding and Deleting  columns

Changing column name and Data Type

Creating Table from existing Table

Changing Constraints Foreign key

Module 3: Python for Data Science

An Introduction to Python

Why Python , its Unique Feature and where to use it?

Python environment Setup/shell

Installing Anaconda

Understanding the Jupyter notebook

Python Identifiers, Keywords

Discussion about installed module s and packages

Conditional Statement ,Loops and File Handling

Python Data Types and Variable

Condition and Loops in Python

Decorators

Python Modules & Packages

Python Files and Directories manipulations

Use various files and directory functions for OS operations

Python Core Objects and Functions

Built in modules (Library Functions)

Numeric and Math’s Module

String/List/Dictionaries/Tuple

Complex Data structures in Python

Python built in function

Python user defined functions

4. Introduction to NumPy

Array Operations

Arrays Functions

Array Mathematics

Array Manipulation

Array I/O

Importing Files with Numpy

5. Data Manipulation with Pandas

Data Frames

I/O

Selection in DFs

Retrieving in DFs

Applying Functions

Reshaping the DFs - Pivot

Combining DFs

Merge, Join

Data Alignment 

6. SciPy

Matrices Operations

Create matrices

Inverse, Transpose, Trace,   Norms , Rank etc

Matrices Decomposition

Eigen Values & vectors

SVDs

7. Visualization with Seaborn

Seaborn Installation

Introduction to Seaborn

Basics of Plotting

Plot Generation

Visualizing the Distribution of a Dataset

Selection color palettes

8.  Visualization with Matplotlib

Matplotlib Installation

Matplotlib Basic Plots & it's Containers

Matplotlib components and properties

Pylab & Pyplot

Scatter plots

2D Plots

Histograms

Bar Graphs

Pie Charts

Box Plots

Customization

Store Plots

9. SciKit Learn

Basics

Data Loading

Train/Test Data generation

Preprocessing

Generate Model

Evaluate Models

10. Descriptive Statistics

Observations, variables, and data matrices

Types of variables

Measures of Central Tendency

Arithmetic Mean / Average

Merits & Demerits of Arithmetic Mean and Mode

Merits & Demerits of Mode and Median

Merits & Demerits of Median Variance

11. Probability Basics

Notation and Terminology

Unions and Intersections

Conditional Probability and Independence

12. Probability Distributions

Random Variable

Probability Distributions

Probability Mass Function

Parameters vs. Statistics

Binomial Distribution

Poisson Distribution

Normal Distribution

Standard Normal Distribution

Central Limit Theorem

Cumulative Distribution function

13.  Tests of Hypothesis

Large Sample Test

Small Sample Test

One Sample: Testing Population Mean

Hypothesis in One Sample z-test

Two Sample: Testing Population Mean

One Sample t-test - Two Sample t-test

Paired t-test

Hypothesis in Paired Samples t-test

Chi-Square test

14. Data Analysis

Case study- Netflix

Deep analysis on Netflix data

Module 4: Machine Learning

Exploratory Data Analysis

Data Exploration

Missing Value handling

Outliers Handling

Feature Engineering

Feature Selection

Importance of Feature Selection in Machine Learning

Filter Methods

Wrapper Methods

Embedded Methods

Machine Learning: Supervised Algorithms Classification

Introduction to Machine Learning

Logistic Regression

Naïve Bays Algorithm

K-Nearest Neighbor Algorithm

Decision Tress

SingleTree

Random Forest

Support Vector Machines

Model Ensemble

Model Evaluation and performance

K-Fold Cross Validation

ROC, AUC etc...

Hyper parameter tuning

Regression

classification

Machine Learning: Regression

Simple Linear Regression

Multiple Linear Regression

Decision Tree and Random Forest Regression

Machine Learning: Unsupervised Learning Algorithms

Similarity Measures

Cluster Analysis and Similarity Measures

Ensemble algorithms

Bagging

Boosting

Voting

Stacking

K-means Clustering

Hierarchical Clustering

Principal Components Analysis

Association Rules Mining & Market Basket Analysis

7. Recommendation Systems

Collaborative filtering model

Content-based filtering model.

Hybrid collaborative system

Module 5: Artificial Intelligence & Deep Learning

Artificial Intelligence

An Introduction to Artificial Intelligence

History of Artificial Intelligence

Future and Market Trends in AI

Intelligent Agents – Perceive-Reason- Act Loop

Search and Symbolic Search

Constraint-based Reasoning

Simple Adversarial Search (Game- Playing)

Neural Networks and Perceptions

Understanding Feedforward Networks

Boltzmann Machines and Autocoders

Exploring Backpropagation

Deep Networks & Structured Knowledge

Understanding Sensor Processing

Natural Language Processing

Studying Neural Elements

Convolutional Networks

Recurrent Networks

Long Short-Term Memory (LSTM) Networks

Natural Language Processing

Natural Language Processing

NLP in Python

Studying Deep Learning

Artificial Neural Networks

ANN Intuition

Plan of Attack

Studying the Neuron

The Activation Function

Working of Neural Networks

Exploring Gradient Descent

Stochastic Gradient Descent

Exploring Back propagation 

Artificial and Conventional Neural Network

Understanding Artificial Neural Network

Building an ANN

Building Problem Description

Evaluation the ANN

Improving the ANN

Tuning the ANN

Image Processing / Machine Vision

Image basics

Loading and saving images

Thresholding

Bluring

Masking

Image Augmentation 

Conventional Neural Networks

CNN Intuition

Convolution Operation

ReLU Layer

Pooling and Flattening

Full Connection

Softmax and Cross-Entropy

Building a CNN

Evaluating the CNN

Improving the CNN

Tuning the CNN

Recurrent Neural Network

Recurrent Neural Network

RNN Intuition

The Vanishing Gradient Problem

LSTMs and LSTM Variations

Practical Intuition

Building an RNN

Evaluating the RNN

Improving the RNN

Tuning the RNN

Time Series Data

Introduction to Time series data

Data cleaning in time series

Pre-Processing Time-series

Data Predictions in Time Series using ARIMA, Facebook Prophet models.

GIT: Complete Overview

Introduction to Git & Distributed Version Control

Life Cycle

Create clone & commit Operations

Push & Update Operations

Stash, Move, Rename & Delete Operations

Module 6: Machine Learning in Cloud

Machine Learning Features & Services

Using python in Cloud

How to access Machine Learning Services

Lab on accessing Machine learning services

Uploading Data

Preparation of Data

Applying Machine Learning Model

Deployment by Publishing Models using AWS or other cloud computing

Module 7: Data Visualization with Tableau

Introduction to Data Visualization & Power of Tableau

Architecture of Tableau

Product Components

Working with Metadata and Data Blending

Data Connectors

Data Model

File Types

Dimensions & Measures

Data Source Filters

Creation of Sets

2. Scatter Plot

Gantt Chart

Funnel Chart

Waterfall Chart

Working with Filters

Organizing Data and Visual Analytics

Working with Mapping

Working with Calculations and Expressions

Working with Parameters

Charts and Graphs

Dashboards and Stories

Module 8: Project Work and Case Studies

Machine Learning end to end Project blueprint

Case study on real data after each model.

Regression predictive modeling - E-commerce

Classification predictive modeling - Binary Classification

Case study on Binary Classification – Bank Marketing

Case study on Sales Forecasting and market analysis

Widespread coverage for each Topic

Various Approaches to Solve Data Science Problem

Pros and Cons of Various Algorithms and approaches