## [100% OFF] Data Science with Python Certification Training with Project

### Description:

**Data Scienc**e with

**Python**Programming -

**Course**Syllabus

**1. Introduction to Data Science**

Introduction to Data Science

Python in Data Science

Why is Data Science so Important?

Application of Data Science

What will you learn in this course?

**2. Introduction to Python Programming**

What is Python Programming?

History of Python Programming

Features of Python Programming

Application of Python Programming

Setup of Python Programming

Getting started with the first Python program

**3. Variables and Data Types**

What is a variable?

Declaration of variable

Variable assignment

Data types in Python

Checking Data type

Data types Conversion

Python programs for Variables and Data types

**4. Python Identifiers, Keywords, Reading Input, Output Formatting**

What is an Identifier?

Keywords

Reading Input

Taking multiple inputs from user

Output Formatting

Python end parameter

**5. Operators in Python**

Operators and types of operators

- Arithmetic Operators

- Relational Operators

- Assignment Operators

- Logical Operators

- Membership Operators

- Identity Operators

- Bitwise Operators

Python programs for all types of operators

**6. Decision Making**

Introduction to Decision making

Types of decision making statements

Introduction, syntax, flowchart and programs for

- if statement

- if…else statement

- nested if

elif statement

**7. Loops**

Introduction to Loops

Types of loops

- for loop

- while loop

- nested loop

Loop Control Statements

Break, continue and pass statement

Python programs for all types of loops

**8. Lists**

Python Lists

Accessing Values in Lists

Updating Lists

Deleting List Elements

Basic List Operations

Built-in List Functions and Methods for list

**9. Tuples and Dictionary**

Python Tuple

Accessing, Deleting Tuple Elements

Basic Tuples Operations

Built-in Tuple Functions & methods

Difference between List and Tuple

Python Dictionary

Accessing, Updating, Deleting Dictionary Elements

Built-in Functions and Methods for Dictionary

**10. Functions and Modules**

What is a Function?

Defining a Function and Calling a Function

Ways to write a function

Types of functions

Anonymous Functions

Recursive function

What is a module?

Creating a module

import Statement

Locating modules

**11. Working with Files**

Opening and Closing Files

The open Function

The file Object Attributes

The close() Method

Reading and Writing Files

More Operations on Files

**12. Regular Expression**

What is a Regular Expression?

Metacharacters

match() function

search() function

re.match() vs re.search()

findall() function

split() function

sub() function

**13. Introduction to Python Data Science Libraries**

Data Science Libraries

Libraries for Data Processing and Modeling

- Pandas

- Numpy

- SciPy

- Scikit-learn

Libraries for Data Visualization

- Matplotlib

- Seaborn

- Plotly

**14. Components of Python Ecosystem**

Components of Python Ecosystem

Using Pre-packaged Python Distribution: Anaconda

Jupyter Notebook

**15. Analysing Data using Numpy and Pandas**

Analysing Data using Numpy & Pandas

What is numpy? Why use numpy?

Installation of numpy

Examples of numpy

What is ‘pandas’?

Key features of pandas

Python Pandas - Environment Setup

Pandas – Data Structure with example

Data Analysis using Pandas

**16. Data Visualisation with Matplotlib**

Data Visualisation with Matplotlib

- What is Data Visualisation?

- Introduction to Matplotlib

- Installation of Matplotlib

Types of data visualization charts/plots

- Line chart, Scatter plot

- Bar chart, Histogram

- Area Plot, Pie chart

- Boxplot, Contour plot

**17. Three-Dimensional Plotting with Matplotlib**

Three-Dimensional Plotting with Matplotlib

- 3D Line Plot

- 3D Scatter Plot

- 3D Contour Plot

- 3D Surface Plot

**18. Data Visualisation with Seaborn**

Introduction to seaborn

Seaborn Functionalities

Installing seaborn

Different categories of plot in Seaborn

Exploring Seaborn Plots

**19. Introduction to Statistical Analysis**

What is Statistical Analysis?

Introduction to Math and Statistics for Data Science

Terminologies in Statistics – Statistics for Data Science

Categories in Statistics

Correlation

Mean, Median, and Mode

Quartile

**20. Data Science Methodology (Part-1)**

Module 1: From Problem to Approach

Business Understanding

Analytic Approach

Module 2: From Requirements to Collection

Data Requirements

Data Collection

Module 3: From Understanding to Preparation

Data Understanding

Data Preparation

**21. Data Science Methodology (Part-2)**

Module 4: From Modeling to Evaluation

Modeling

Evaluation

Module 5: From Deployment to Feedback

Deployment

Feedback

Summary

**22. Introduction to Machine Learning and its Types**

What is a Machine Learning?

Need for Machine Learning

Application of Machine Learning

Types of Machine Learning

- Supervised learning

- Unsupervised learning

- Reinforcement learning

**23. Regression Analysis**

Regression Analysis

Linear Regression

Implementing Linear Regression

Multiple Linear Regression

Implementing Multiple Linear Regression

Polynomial Regression

Implementing Polynomial Regression

**24. Classification**

What is Classification?

Classification algorithms

Logistic Regression

Implementing Logistic Regression

Decision Tree

Implementing Decision Tree

Support Vector Machine (SVM)

Implementing SVM

**25. Clustering**

What is Clustering?

Clustering Algorithms

K-Means Clustering

How does K-Means Clustering work?

Implementing K-Means Clustering

Hierarchical Clustering

Agglomerative Hierarchical clustering

How does Agglomerative Hierarchical clustering Work?

Divisive Hierarchical Clustering

Implementation of Agglomerative Hierarchical Clustering

**26. Association Rule Learning**

Association Rule Learning

Apriori algorithm

Working of Apriori algorithm

Implementation of Apriori algorithm

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