Data Science with Python Certification Training with Project
- Description
- Curriculum
- FAQ
- Reviews
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
-
25Analysing Data using Numpy and Pandas - part 1Video lesson
-
26Analysing Data using Numpy and Pandas - part 2Video lesson
-
27Analysing Data using Numpy and Pandas - part 3Video lesson
-
28Analysing Data using Numpy and Pandas - part 4Video lesson
-
29Analysing Data using Numpy and Pandas - part 5Video lesson
External Links May Contain Affiliate Links read more