Udemy Free Coupons – Data Science with Python Certification Training with Project

Data Science 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