Master in Data and Cloud Technologies

Python Programming Section - A

Module 01 – 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 02 – Setting up and Installations

Installing Python

Setting up Python environment for development Installation of Jupyter Notebook

How to access our course material using

Jupyter Write your first program in python

Python code deployment and execution on cloud using Google Colab

Module 03 – Python Object And Data Structures Operations

Introduction to Python objects Python built-in functions

Number objects and operations

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 04 – Python Statements

Introduction to Python statements

If, elif and else statements

Comparison operators

Chained comparison operators

What are loops

For loops

While loops

Useful operators

List comprehensions

Statement assessment test

Game challenge
Module 05 - 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

Functions and methods assignment

Module 06 - 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
Module 07 – Python Modules And Packages Python Inbuilt Modules

Python inbuilt modules

Creating UDM-User-defined modules Passing command-line arguments

Writing packages

Define PYTHONPATH

__name__ and __main__ in python
Module 08 – Object-Oriented Programming (OOP) in Python

Object oriented features

Implement object oriented 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

Install packages on python

Introduction to pip, easy install

SQL Section – B

MODULE 01 – Introduction to SQL Language

Introduction to SQL

Need of SQL

Introduction to RDBMS

Need of SQL for RDBMS

Real life examples where SQL is used

Module 02 – Data Definition Language (DDL)

Introduction to DDL

DDL Create clause

DDL Drop clause

DDL alter clause

Data types

How to create a table

How to alter table

How to drop table

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Module 03 – Data Manipulation Language (DML)

Introduction To DML

Insert Clause

Update Clause

Delete Clause

How To Work On Bulk Insert, Update, Delete

Module 04 – Data Retrieval Language (DRL)

Select Clause

Select Clause Multiple Variants With Keywords And Clauses

Real-life Queries Example

Module 05 – Transaction Control Language (TCL)

Need of TCL

Commit

Rollback

Best Practices

Module 06 – CRUD operations

Scenarios based approach to perform CRUD operations

Need of CRUD operations in projects

Module 07 – Python And SQL Integration

Introduction to Flask

Decorators

SQL and Python Integration

REST API

Postman

Module 08 – SQL And Python Based Project Use Cases

Use cases explanation

Problem understanding

Tools and frameworks require to solve problem statement

Development and unit testing

Q & A

Data Analytics Section – C

Module 01 – Data Analysis With Python

Introduction to data analysis

Why Data analysis?

Data analysis and Artificial bridge

Introduction to Data Analysis libraries

Data analysis introduction assignment challenge

Module 02 – Data Analysis Using Numpy

Introduction to Numpy arrays

Creating and applying functions

Numpy Indexing and selection

Numpy Operations

Exercise and assignment challenge

Module 03 – Pandas And Advanced Analysis

Pandas series

Introduction to DataFrames Missing data

Groupby

Merging, joining and Concatenating Operations

Data Input and Output

Pandas in-depth coding exercises

Data Visualisation Section – D

Module 01 – Data Visualization With Python

Matplotlib Library

Plotting using Matplotlib

Plotting Numpy arrays

Plotting using object-oriented approach

Subplots using matplotlib

Matplotlib attributes and functions

Matplotlib exercises

Seaborn Visualization Library

Categorical Plot using Seaborn

Distributional plots using Seaborn

Matrix plots

Grids

Seaborn exercises

Data Visualization Using Tableau

Need of Tableau

Comparison between tableau and Programming based data visualization

Types of data sources supported by Tableau for report development

How to build charts in Tableau

How to build report and Dashboard in Tableau

Data visualization using Tableau features

Data Science (Machine Learning, NLP, Deep Learning) Section – E

Module 01- Machine Learning Algorithms

Linear Regression with Python

Introduction to Regression

Exercise on Linear Regression using Sci-kit 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

Logistic Regression

Introduction to Logistic Regression

Data set preprocessing using python libraries

Data Prediction using Logistic Regression

Module 03- Machine Learning Algorithm KNN

K- Nearest neighbors 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

Module 04 - 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

Module 05 - Support Vector Machine(SVM)

Linearly separable data

Non-linearly separable data

SVM project with telecom dataset to predict the users portability

Module 06 - K-means clustering

Clustering in unsupervised learning

K-means clustering intuition

Implementation of K-means with Python using Mall customer’s data to implement clusters on the

basis of spending and income

Hierarchical clustering intuition

Implementation of Hierarchical clustering with python

Module 07 - Apriori algorithm

Apriori theory and explanation Market basket analysis

Implementation of Apriori

Evaluation of association learning

Module 08 - Natural Language Processing (NLP)

Introduction to Natural Language processing

NLTK Python library

Exercise on NLTK

Natural data mining

Data processing using stemming

Data processing using stop words

Significance of pattern matching

MODULE 09- Deep Learning

Neural Network and Deep Learning

What is TensorFlow

TensorFlow examples

TensorFlow Exercise

What is Keras

Keras exercise

Pipeline implementation using Keras

MODULE 10- Project Implementation

Project Implementation using Python and Data Science libraries

Bigdata Hadoop Section – F

Module 01 - Introduction To Hadoop

Hadoop- Demo

What is Bigdata

When data becomes Bigdata

3V’s of Bigdata

Introduction to Hadoop Ecosystem

Why Hadoop? If Existing Tools and Technologies are there in the market for decades?

How Hadoop is getting two categories Projects- New projects on Hadoop

Clients want POC and migration of Existing tools and Technologies on Hadoop

Clients want POC and migration of Existing tools and Technologies on Hadoop Technology

How Open Source tool (HADOOP) is capable to run jobs in lesser time which take longer time in

other tools in the market.

Hadoop Processing Framework (Map Reduce) / YARN

Alternates of Map Reduce

Why NoSQL is in more demand nowadays

Distributed warehouse for DFS

Most demanding tools which can run on the top of Hadoop Ecosystem for specific requirements in

specific scenarios

Data import/Export tools

Module 2 - Hadoop Setup Installation And HDFS Basics

Hadoop installation

Introduction to Hadoop FS and Processing Environment’s UIs

How to read and write files

Basic Unix commands for Hadoop

Hadoop’s FS shell

Hadoop’s releases

Hadoop’s daemons

Module 03 - Hive Basic, Hive Advanced

Hive Introduction

Hive Advanced

Partitioning

Bucketing

External Tables

Complex Use cases in Hive

Hive Advanced Assignment

Real-time scenarios of Hive

Module 04 - Data Ingestion Using Sqoop

Need of Sqoop

Data ingestion from RDBMS in HDFS using Sqoop

Data ingestion from RDBM in Hive table using Sqoop

Different types of ingestion techniques

Module 05 - Spark And Python

Introduction to Spark

Introduction to Python

Pyspark concepts

Advantages of Spark over Hadoop

Is Spark a replacement for Hadoop?

How Spark is Faster than Hadoop

Spark RDD

Spark Transformation and Actions

Spark SQL

Datasets and Data Frames

Real-time scenarios examples of Spark where we prefer Spark over Hadoop

How Spark is capable to process complex data sets in lesser time

In-Memory Processing Framework for Analytics

Data Science on the top of Hadoop

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Module 06 - NoSQL using HBase

Introduction to NOSQL

Need of NOSQL

SQL vs NOSQL

CAP Theorem vs ACID properties

HBase commands hands on

Hadoop and HBase integration

Module - 07 Project on BigData Hadoop

Cloud Computing using AWS Section – G

Module 01 - Introduction To Cloud Computing

Introduction to Cloud Computing

Advantages of Cloud Computing

Cloud Services & deployment models Cloud service providers

What is AWS?

AWS Account

AWS services

AWS Regions and AZ's

AWS suite Starting off with AWS

Billing Dashboard & Cost Explorer Setting up Billing Alarm $ Budget

Module 02 - Simple Storage Service - S3

Basics of Storage System

Storage Services provided by AWS

Difference Between Object storage and Block Storage

Introduction to Simple Storage Service - S3

Components of S3

Important Properties of S3 bucket

Module 03- Elastic Compute Cloud - EC2

Basics of Virtual Servers

Components of a Virtual Server

Introduction to Elastic Cloud Compute - EC2 Use cases and important features of EC2

Introduction to AMI - Its Uses

Introduction to Instance and its types

Security Groups - Creation & Management Key Pair - Why & How

Launching & Connecting to Window Instance Launching & Connecting to Linux Instance

Setting up a web server on linux Instance - Hosting a website Elastic IP Address Placement Group

Instance Pricing Model Tenancy Models

Module 04 - Virtual Private Cloud (VPC)

Basics of Networking

IP Address and CIDR Block

Concept of Virtual Cloud

Introduction to Virtual Private Cloud -VPC Subnet and Route Tables

Internet Gateway and NAT

Creating and managing a NAT Instance

Access Control List - ACL

VPC Peering

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MODULE 05 - Relational Database Service (RDS)

Introduction to Database - Its Components Database Services provided by AWS

Introduction to RDS Components of RDS

DB engines provided by RDS

Snapshots and Back-up in RDS Read Replicas in RDS

Creating and connecting to a RDS database RDS Security

Pricing in RDS

Module 06 - Simple Notification Service (SNS)

Introduction to Simple Notification Service - SNS How SNS Works?

Important Components of SNS

Creating and Managing Topics in SNS Adding Subscriber in SNS

Managing SNS Policy

Module 07 - Cloudwatch

Important Components of CloudWatch

Creating and Managing metrics in CloudWatch

Creating and Managing Events in CloudWatch

Creating and Managing Dashboards in Cloudwatch

Creating and Managing Alarms in CloudWatch

Creating and Managing Logs in Cloudwatch

Module 08 - Cloudtrail

Introduction to Cloudtrail

Creating and Managing trails

Setting up trail for Root login Notification

Milestone Project Section – H

Milestone project based on hybrid technologies

POC to evaluate individual performance

Code development

Code submission in github repository

Interview Preparation Section – I

GD – Group Discussion

Resume Building

Mock PI – Mock Personal Interview

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