Start your career as Data Scientist from scratch. Learn Data Science with Python. Predict trends with advanced analytics

Published en 10 Dec 2020

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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

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