Why this course?

Description

Short -Description:

This course is designed to provide participants with a comprehensive understanding of Python programming for data science. It covers the basics of Python programming, including data types, expressions, variables, and data structures. Participants will learn how to work with data using Pandas and Numpy, visualize data using Matplotlib and Seaborn, and apply machine learning algorithms using Scikit-Learn, NLTK, and Keras. The course also includes hands-on data science project to apply the concepts and skills learned in the course. By the end of this course, participants will have a solid foundation in Python programming for data science and be able to use it to solve real-world problems.

Total Duration: 10 Hrs | Modules: 12 || Projects – 1

Module 1:  Python basics 

•           Data types
•           Expression and variable
•           String operations

 

Module 2: Python Data Structures

• List and Tuples (including operation)
• Sets
• Dictionaries

 

Module 3: Python Fundamentals

• Condition and branching
• Loops
• Functions

Module 4: Class and Object

• Class and Object
• Methods and attributes
• Types of Variables
• Inheritance
• Operator Overloading

Module 5: Working with pandas

• Loading CSV file
• Data Analysis using pandas
• Data Preprocessing - Handling null values

Module 6: Working with Numpy

• Working with numpy arrays
• Mathematical manipulation using numpy arrays

Module 7: Working with Matplotlib

• Line plot
• Scatter plot
• Bar plot
• Histogram
• Box plot
• Pie chart

Module 8: Working with Seaborn

• Scatter plot
• Count plot
• Box plot
• Bar plot
• Pair plot
• Heat map
• Dist plot
• Violin plot

Module 9: Working with Scikit-Learn

• Scaling
• Encoding
• Train test split
• Algorithms
• How to choose the right machine learning algorithm
• Model evaluation

Module 10: Working with NLTK

• Tokenization
• Stop words
• Regular Expression
• Stemming
• Lemmatization
• Bag of words
• TF-IDF vectorizer

Module 11: Working with Keras

• Deep learning
• Neurons & Neural Network
• Layers in Neural network
• Functions in Neural networks
• Types of Neural networks
• ANN
• CNN
• RNN

Module 12: Project: Twitter sentiment analysis 

 

Module 1: Python Basics

Key Learning Objective: In this module, you will learn the fundamental concepts of Python programming language such as data types, expressions, variables, and string operations. You will learn how to recognize and use different data types in Python, perform operations using expressions and variables, and manipulate strings using Python's built-in string operations.

 

Module 2: Python Data Structures

Key Learning Objective: In this module, you will learn about the different data structures in Python such as lists, tuples, sets, and dictionaries. You will learn how to create and manipulate these data structures, including common operations such as accessing elements, adding or removing elements, and slicing elements.

 

Module 3: Python Fundamentals

Key Learning Objective: In this module, you will learn the fundamental programming constructs in Python such as conditional statements, loops, and functions. You will learn how to use conditional statements and branching to control program flow, use loops to iterate through data and perform operations, and create and use functions.

 

Module 4: Class and Object

Key Learning Objective: In this module, you will learn about object-oriented programming (OOP) concepts in Python such as classes, objects, methods, and attributes. You will learn how to define classes and objects, create methods and attributes for classes, use inheritance to create subclasses, and implement operator overloading.

 

Module 5: Working with Pandas

Key Learning Objective: In this module, you will learn how to use Pandas library to perform data analysis. You will learn how to create and manipulate data using Pandas Series and DataFrames, import and analyze data from CSV and JSON files, clean data to remove errors and duplicates, and calculate correlations and visualize data through scatter plots and histograms.

 

Module 6: Working with Numpy

Key Learning Objective: In this module, you will learn how to use Numpy library for mathematical operations. You will learn how to create and manipulate Numpy arrays, perform mathematical operations using Numpy arrays, and use Numpy for various scientific computing applications.

 

Module 7: Working with Matplotlib

Key Learning Objective: In this module, you will learn how to use Matplotlib library for data visualization. You will learn how to create line plots, scatter plots, bar plots, histograms, box plots, and pie charts. You will also learn how to customize these visualizations to make them more informative and visually appealing.

 

Module 8: Working with Seaborn

Key Learning Objective: In this module, you will learn how to use Seaborn library for advanced data visualization. You will learn how to create scatter plots, count plots, box plots, bar plots, pair plots, heat maps, and dist plots. You will also learn how to use Seaborn's advanced customization features to create highly informative and visually appealing visualizations.

 

Module 9: Working with Scikit-Learn

Key Learning Objective: In this module, you will learn how to use Scikit-Learn library for machine learning. You will learn how to scale data using Scikit-Learn, encode data using Scikit-Learn, split data into training and testing sets, choose the right machine learning algorithm, and evaluate machine learning models.

 

Module 10: Working with NLTK

Key Learning Objective: In this module, you will learn how to use NLTK library for natural language processing (NLP). You will learn how to tokenize text data, remove stop words from text data, use regular expressions to search text data, stem and lemmatize text data, create a bag of words representation of text data, and create a TF-IDF vectorizer for text data.

 

Module 11: Working with Keras

Key Learning Objective: In this module, you will learn how to use Keras library for deep learning. You will learn about the concepts of deep learning and neural networks, create and train neural networks using Keras library.

Module 12: Project

Twitter sentiment analysis

 

Software / Tools:  Google colab: https://colab.research.google.com

 

For More Projects: 

NLP Projects (19) - https://www.pantechelearning.com/students-project/nlp-projects/

Python Projects (132) - https://www.pantechelearning.com/students-project/python-projects/

For More Products:

Raspberry pi - https://www.pantechelearning.com/diy-kits-sensors/raspberry-pi-kits-accessories/ 

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Deep-Learning-using-PyTorch  - https://lms.pantechelearning.com/courses/Deep-Learning-using-PyTorch-1675409854096-63dcb9bee4b037e33c9b2f9b

Pantech eLearning offers flexible eLearning courses online