Data Science with Machine Learning Course Syllabus

Module 1 – Core Java Fundamentals

• Java Programming Language Keywords
• Literals and Ranges of All Primitive
• Data Types
• Array Declaration, Construction, and Initialization

Module 2 – Declarations and Access Control

• Declarations and Modifiers
• Declaration Rules
• Interface Implementation

Module 3 – Object Orientation, Overloading and Overriding, Constructors

• Benefits of Encapsulation
• Overridden and Overloaded Methods
• Constructors and Instantiation
• Legal Return Types

Module 4 – Flow Control, Exceptions, and Assertions

• Writing Code Using if and switch statements
• Writing Code Using Loops
• Handling Exceptions
• Working with the Assertion Mechanism
• Write Java Programs

Module 5 – Testing

• Setting up TestNG
• Testing with TestNG
• Composing test and test suites
• Generating and analyzing HTML test reports
• Troubleshooting

Module 6 – Machine Learning

• Introducing Machine Learning
• To Automate or Not to Automate?
• Test Automation for Web Applications
• Machine Learning Components
• Supported Browsers
• Flexibility and Extensibility

Module 7 – Machine Learning -IDE

• Introduction
• Installing the IDE
• Opening the IDE
• IDE Features
• Building Test Cases
• Running Test Cases
• Debugging
• Writing a Test Suite
• Executing Machine Learning -IDE Tests on Different Browsers

Module 8 – XPATH

• Understanding of Source files and Target
• XPATH and different techniques
• Using attribute
• Text ()
• Following

Module 9 – Machine Learning

• Introduction
• How Machine Learning Works
• Installation
• Configuring Machine Learning With Eclipse
• Machine Learning RC Vs Machine Learning
• Programming your tests in WebDriver
• Debugging WebDriver test cases
• Troubleshooting
• Handling HTTPS and Security Pop-ups
• Running tests in different browsers
• Handle Alerts / Pop-ups and Multiple Windows using Web Driver

Module 10 – Automation Test Design Considerations

• Introducing Test Design
• What to Test
• Verifying Results
• Choosing a Location Strategy
• UI Mapping
• Handling Errors
• Testing Ajax Applications
• How to debug the test scripts

Module 11 – Handling Test Data

• Reading test data from excel file
• Writing data to excel file
• Reading test configuration data from text file
• Test logging
• Machine Learning Grid Overview

Module 12 – Building Automation Frameworks Using Machine Learning

• What is a Framework
• Types of Frameworks
• Modular framework
• Data Driven framework
• Keyword driven framework
• Hybrid framework
• Use of Framework
• Develop a framework using TestNG/ Web Driver

Fundamental Python – Overview

• A brief history of python
• Application and trends in python
• Available python versions

Python – Environment Setup

• Getting and installing python
• Environmental variables and idle
• Executing python from command line

Fundamentals

• I/o
• Naming conventions
• Datatypes:
• Numbers
• String
• List
• Tuple
• Dictionary
• Set

Python Operators

• List, Tuple, Dictionary, Set Methods
• Statements: If, elif, Break, Continue
• Loops: For loop, while loop
• Functions
• Python Operators
• List, Tuple, Dictionary, Set Methods
• Statements: If, elif, Break, Continue
• Loops: For Loop, While Loop
• Functions

Oops Concepts:

• Class and objects
• Getters and setters
• Properties
• Inheritance
• Polymorphism
• Special Functions of Python: Lambda, Map, Reduce, Filter

Accordion Title

Modules in Python :

• Math
• Arrow
• Geopy
• Beautiful soup
• Numpy
• Sys
• Os

Multithreading

• Introducing threads and life cycles
• Priorities
• Dead Locks

Exceptional Handling

• Errors
• Runtime errors
• Exceptional model
• Exceptional hierarchy
• Handling multiple exception
• Raise exceptions

File Handling

• Text files
• Csv files

Regular Expressions

• Simple character matches
• Flags, quantifers, greedy matches
• Grouping and matching objects
• Matching at beginning or end
• Substituting and splitting a string
• Compiling regular expressions
• Generators Iterators
• Decorators
• Closures

Gui Interfacing: Tkinter

• Widgets
• Integrated application
• Mysql/with application
• Converting .exe

Analytical Python Syllabus – Introduction

Datascience Modules:

• pandas
• numpy
• scipy
• matplotlib

Python Data Processing

• Python Data Operations
• Python Data cleansing
• Python Processing CSV Data
• Python Processing JSON Data
• Python Processing XLS Data
• Python Relational databases
• Python NoSQL Databases
• Python Date and Time
• Python Data Wrangling
• Python Data Aggregation
• Python Reading HTML Pages
• Python Processing Unstructured Data
• Python word tokenization
• Python Stemming and Lemmatization

Python Data Visualization

• Python Chart Properties
• Python Chart Styling
• Python Box Plots
• Python Heat Maps
• Python Scatter Plots
• Python Bubble Charts
• Python 3D Charts
• Python Time Series
• Python Geographical Data
• Python Graph Data

Statistical Data Analysis

• Python Measuring Central Tendency
• Python Measuring Variance
• Python Normal Distribution
• Python Binomial Distribution
• Python Poisson Distribution
• Python Bernoulli Distribution
• Python P-Value
• Python Correlation
• Python Chi-square Test
• Python Linear Regression