Data Science with Python Certification Training with Project



Data Science with Python Certification Training with Project — Udemy — Last updated 12/2020 — Free download


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


  • Enthusiasm and determination to make your mark on the world!


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
  • 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


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

Who this course is for:

  • Data Scientists
  • Data Analysts / Data Consultants
  • Senior Data Scientists / Data Analytics Consultants
  • Newbies and beginners aspiring for a career in Data Science
  • Data Engineers
  • Machine Learning Engineers
  • Software Engineers and Programmers
  • Python Developers
  • Data Science Managers
  • Machine Learning / Data Science SMEs
  • Digital Data Analysts
  • Anyone interested in Data Science, Data Analytics, Data Engineering


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