Data Science

Module 1 - Introduction to Python

What is Python and brief history

Why Python and who use Python

Discussion on Python 2 and 3

Unique features of Python

Discussion on various IDE’s

Demonstration of practical use cases

Python use cases using data analysis

Module 2 - Setting up and Installations

Installing python

Setting up Python Development Environment

Installation of Jupyter Notebook

How to access our course material using Jupyter

Write your first program in Python

Deployment on local and cloud platforms using Google Colab

Module 3 - Python Object And Data Structures Operations

Introduction to Python objects

Python built-in functions

Number objects and operations

Variable assignment and keywords, String objects and operations

Print formatting with strings

List objects and operations

Tuple objects and operations

Dictionary objects and operations

Sets and Boolean

Object and data structures assessment test

Module 4 - Python Statements

Introduction to Python statements

If, elif and else statements

Comparison operators

Chained comparison operators

What are loops

For loops

While loops

Useful operator

List comprehensions

Statement assessment test

Game challenge

Module 5 - UDF Functions and Methods

Methods

What are various types of functions

Creating and calling user defined functions

Function practice exercises

Lambda Expressions

Map and filter

Nested statements and scope

Args and kwargs in Python

Functions and methods assignment

Milestone Project using Python

Module 6 - File and Exception Handling

Process files using python

Read/write and append file object

File functions

File pointer and operations

Introduction to error handling

Try, except and finally

Python standard exceptions

User defined exceptions

Unit testing

File and exceptions assignment

Module 7 - Python Modules, Packages & Inbuilt Modules

Python inbuilt modules

Creating UDM-User defined modules

Passing command line arguments

Writing packages

Define PYTHONPATH

__name__ and __main__

Module 8 - OOPs Concepts in Python

Object oriented features

Implement object oriented programming with Python

Creating classes and objects

Creating class attributes

Creating methods in a class

Inheritance

Polymorphism

Special methods for class

Assignment - Creating a python script to replicate deposits and withdrawals in a bank with appropriate classes and UDFs

Module 9 - Advanced Python Modules

Collections module

Datetime

Python debugger

Timing your code

Regular expressions

StringIO

Python decorators

Python generators

Module 10 - Package Installation and Parallel Processing

Install packages on python

Introduction to pip, easy install

Multithreading

Multiprocessing

Module 11 - Introduction to Machine Learning with Python

Understanding Machine Learning

Scope of ML

Supervised and Unsupervised learning

Milestone Project - 2

Module 12 - Data Analysis with Python

Introduction to data analysis

Why Data analysis?

Data analysis and Artificial Intelligence Bridge

Introduction to Data Analysis libraries

Data analysis introduction assignment challenge

Module 13 - Data Analysis Using Numpy

Introduction to Numpy arrays

Creating and applying functions

Numpy Indexing and selection

Numpy Operations

Exercise and assignment challenge

Module 14 - Pandas and Advanced Analysis

Introduction to Series

Introduction to DataFrames

Data manipulation with pandas

Missing data

Groupby

Merging, joining and Concatenating

Operations

Data Input and Output

Pandas in depth coding exercises

Text data mining and processing

Data mining applications in Data engineering

POC - Analysis of e-commerce dataset using pandas

POC - Getting insights on employee salaries data using data analysis in python

Module 15 - Data Visualization with Python

Matplotlib

Plotting using Matplotlib

Plotting Numpy arrays

Plotting using object-oriented approach

Subplots using matplotlib

Matplotlib attributes and functions

Matplotlib exercises

Seaborn Visualization

Categorical Plot using Seaborn

Distributional plots using Seaborn

Matrix plots

Grids

Seaborn exercises

Project- Getting insights using python analysis and visualizations on finance credit score data.

Assignment- Pandas built-in data visualization Data visualization

Module 15 - Data Visualization with Python

Comparison Between Tableau & Programming Based Data Visualization Need Of Tableau Types Of Data Sources Supported By Tableau For Report Development How To Build Report & Dashboard in Tableau How To Build Charts In Tableau Data Visualization Using Tableau Features

Module 16 - Mathematics and Statistics for Data Science

Need of Mathematics for Data Science

Exploratory data analysis (EDA)

Numeric Variables

Qualitative and Quantitative Analysis

Types of Data Formats

Measuring the Central Tendency - The Model

Measuring Spread - Variance and Standard Deviation

Euclidean Distance

Confidence Coefficient

Understanding Parametric Tests

Module 17 - Machine Learning Algorithms

Introduction to Data Science

Introduction to Artificial Intelligence

Introduction to Machine Learning

Need of Machine learning in forecasting

Demand of forecasting analytics in current industrial trends

Introduction to Machine Learning Algorithms Categories

Introduction to Natural Language Processing (NLP)

Introduction to Deep Learning

Linear Regression with Python

Introduction to Regression

Exercise on Linear Regression using Scikit Learn Library

Project on Linear regression using USA_HOUSING data

Evaluation of Linear regression using python visualizations

Practice project for Linear regression using advertisement data set to predict appropriate advertisements for users.

K- Nearest neighbours using Python

Exercise on K-Nearest neighbors using Sci-kit Learn Library

Project on Logistic regression using Dogs and horses’ dataset

Getting the correct number of clusters

Evaluation of model using confusion matrix and classification report

Standard scaling problem

Practice project on KNN algorithm.

Decision tree and Random forest with python

Intuition behind Decision trees

Implementation of decision tree using a real time dataset

Ensemble learning

Decision tree and random forest for regression

Decision tree and random forest for classification

Evaluation of the decision tree and random forest using different methods

Practice project on decision tree and random forest using social network

Data to predict if someone will purchase an item or not

Support Vector Machines

Linearly separable data

Non-linearly separable data

SVM project with telecom dataset to predict the users portability

Principal Component Analysis

Introduction to PCA

Need for PCA

Implementation to select a model on breast-cancer dataset

Model evaluation

Bias variance trade-off

Accuracy paradox

CAP curve and analysis

Clustering in unsupervised learning

K-means clustering intuition

Implementation of K-means with Python using mall customers data to implement clusters on the basis of spending and income

Hierarchical clustering intuition

Implementation of Hierarchical clustering with python

Association Algorithms

A priori theory and explanation

Market basket analysis

Implementation of Apriori

Evaluation of association learning

POC - To make a model to predict the relationship between frequently bought products together on the given dataset from a supermarket.

Module 18 - Natural Language Processing with NLTK

Introduction to Natural Language processing

NLTK Python library

Data stemming technique

Data Vectorization

Exercise on NLTK

POC- Apply NLP techniques to understand reviews given by customers in a dataset and predict if a review is good/bad without human intervention.

Module 19 - Deep Learning with TensorFlow and Keras

Neural Network and Deep Learning

What is TensorFlow?

TensorFlow Installation

TensorFlow basics

TensorFlow with Contrib Learn

TensorFlow Exercise

What is Keras?

Keras Basics

Pipeline implementation using Keras

MNIST implementation with Keras

Module 20 - Rest API with Flask and Python

REST principles

Creating application endpoints

Implementing endpoints

Using Postman for API testing

Module 21 - Rest API Integration with Databases for Web App Development

CRUD operations on database

REST principles and connectivity to databases

Creating a web development API for login registers and connecting it to the database

Deploying the API on a local server

Module 22 - Major Project

Project use cases Introduction

Project Scenarios

Project life cycle

What is version controlling in project management

What is GitHub

Significance of GitHub in project management

Code submission for testing and deployment

Predictive analytics tools and techniques

Project best practices