ExpiredData Science with Python Certification Training with Project
Description
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
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
