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Data Science with Python Certification Training and Project

via Udemy

Overview

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

What you'll learn:
• End-to-end knowledge of Data Science
• Prepare for a career path as Data Scientist / Consultant
• Overview of Python programming and its application in Data Science
• Detailed level programming in Python - Loops, Tuples, Dictionary, List, Functions & Modules, etc.
• Decision-making and Regular Expressions
• Introduction to Data Science Libraries
• Components of Python Ecosystem
• Analysing Data using Numpy and Pandas
• Data Visualisation with Matplotlib
• Three-Dimensional Plotting with Matplotlib
• Data Visualisation with Seaborn
• Introduction to Statistical Analysis - Math and Statistics
• Terminologies & Categories of Statistics, Correlation, Mean, Median, Mode, Quartile
• Data Science Methodology - From Problem to Approach, From Requirements to Collection, From Understanding to Preparation
• Data Science Methodology - From Modeling to Evaluation, From Deployment to Feedback
• Introduction to Machine Learning
• Types of Machine Learning - Supervised, Unsupervised, Reinforcement
• Regression Analysis - Linear Regression, Multiple Linear Regression, Polynomial Regression
• Implementing Linear Regression, Multiple Linear Regression, Polynomial Regression
• Classification, Classification algorithms, Logistic Regression
• Decision Tree, Implementing Decision Tree, Support Vector Machine (SVM), Implementing SVM
• Clustering, Clustering Algorithms, K-Means Clustering, Hierarchical Clustering
• Agglomerative & Divisive Hierarchical clustering
• Implementation of Agglomerative Hierarchical Clustering
• Association Rule Learning
• Apriori algorithm - working and implementation

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

• 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

• 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

• 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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