Data Science with ML Training Syllabus

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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

Conclusion

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Data Science with Machine Learning Course Syllabus