Why this course?

Description

"Python for Data Science" is a program that focuses on teaching you how to leverage the powerful Python programming language to extract insights and knowledge from complex data sets. Through a combination of lectures, hands-on exercises, and projects, you'll learn how to use various Python libraries and tools for data analysis and visualization. Specifically, you'll explore libraries like NumPy, Pandas, Matplotlib, and Seaborn, which are essential for manipulating, analysing, and visualizing data. In addition, you'll delve into the world of machine learning and learn how to use Python to build predictive models, from simple linear regression to more complex neural networks.

The course also covers best practices in data science, including data cleaning, exploratory data analysis, and feature engineering. You'll learn how to preprocess and transform data to prepare it for analysis, as well as how to create compelling visualizations that communicate your findings effectively. By the end of the course, you'll have a solid foundation in Python for data science and be equipped with the skills and tools needed to tackle real-world data problems.

Total Duration: 10 Hrs | Modules: 7 | Assignments: 5 | Projects – 2

Related Tags : 

Module 1:  an introduction to python language. 

•             Learning objective
•             What is python language?
•             History of python language
•             Importance or features of python language.
•             Applications of python language .
•             Data types
•             Expression and variable
•             String operations
•  

Module 2: basic fundamental concepts of python  

• Learning objective
• Condition and branching
• Loops
• Functions

Module 3:  Python Data structure

Key Learning Objectives:  This Module will introduce the participants the Overview of  data structures in python language.

Assignment 1: what are the differences between lists , tuple and dictionary .

 Lesson 1: list 

We need to learn about manipulation of list of elements and apply various operations .

Assignment 2:  take two list of data and print the complete list and do slicing operation to the list of data.

Lesson 2: tuple  

Implement tuple elements to access with the help of index values .

Assignment 3: take two tuple of data and change the tuple of elements

 Lesson 3: dictionary  

Learn how to implement various operations on dictionary elements ,

Assignment 4 -    consider two dictionaries and manipulate the dictionary elements .

 

Module 4: Class and objects

Key Learning Objectives:

List the concept of oops in python language .

Lesson 4:  concept of class and object 

Importance of concept of class and object in python language .

 

Module 5: working with data using pandas , numpy , matplotlib,seaborn, scikit library files 

Key Learning Objectives:

The key objective is to learn about various library files in python langugae along with its usage and describe various examples.

Lesson 5:  pandas  

• How to Load csv file
• Data analysis using different functions
• Data preprocessing
• Imputation (fillna)
• Dropping (dropna)
• Duplicates removal

Lesson 6:  numpy 

• Numpy one-dimensional array
• Working with one-dimensional array
• Numpy two-dimensional array
• Working with two dimensional array

Lesson 7:  matplotlib and seaborn library files .

Various plots using matplotlibrary and seaborn library files.

Lesson 8:  scikit library file

Access various packages and modules from scikit library files .

Lesson 9: nltk tool

Implement nltk tool for    sentiment analysis of data.

Lesson 10: keras

Understand keras framework along with its various layers 

Module 6: applications  

Project 1- Sentiment analysis- twitter

Project 2 – Crop yield prediction using NN

 

Software / Tools:  

Anaconda Navigator Software Tool :

https://www.anaconda.com/products/distribution

Python Programming Installation link :-

https://www.python.org/

Commands to install library files in python  :-

pip install pandas

pip install numpy

pip install matplotlib

pip install seaborn

pip install sklearn

pip install nltk