Why this course?

Description

Python for Data Science is a dynamic introduction to leveraging the power of Python for extracting insights from data. This course covers fundamental concepts such as data manipulation with Pandas, visualization with Matplotlib and Seaborn, and the basics of machine learning with Scikit-Learn. Dive into Jupiter Notebooks for interactive coding, explore key libraries, and gain the skills needed to handle, analyze, and visualize data effectively.

Whether you're a beginner or an experienced coder, this course provides a practical foundation for harnessing Python's capabilities in the exciting field of data science.

Python for Data Science:

Python for Data Science course, where we embark on a journey to unlock the vast potential of Python in the realm of data analysis, visualization, and machine learning. In the ever-evolving landscape of technology, Python has emerged as a preeminent language, and its versatility makes it a perfect companion for data scientists. Python for Data Science course, your gateway to the dynamic intersection of programming and data analysis.

CONCEPTS

Variables and Data Types:

Variables: Containers for storing data values. In Python, you can create variables and assign values to them easily.

Data Types: Python has various data types, including integers, floats, strings, lists, and more.

Lists and Arrays: Lists are ordered and mutable collections used to store multiple items in a single variable.

Arrays (NumPy): Numerical arrays for efficient mathematical operations.

Functions: Defining Functions: Modular blocks of code that perform a specific task.

Data Visualization: Matplotlib and Seaborn: Libraries for creating a variety of static, animated, and interactive visualizations.

Key Components of Python for Data Science:

Pandas: Master the art of data manipulation with Pandas, your go-to library for handling structured data efficiently.

Matplotlib and Seaborn: Unleash the visual storytelling potential of Matplotlib and Seaborn, turning data into meaningful insights through compelling visualizations.

NumPy: Dive into NumPy for efficient numerical operations, essential for tasks like array manipulation and mathematical computations.

Sickie-Learn: Venture into machine learning with Sickie-Learn, offering a comprehensive toolkit for building predictive models.

Modules of Python for Data Science:

Module 1: Python Basics

Module 2: Python Data Structures

Module 3: Python Fundamentals

Module 4: Class and Object

Module 5: Working with Data in Python using Pandas

Module 6: Working with Numpy Arrays

Module 7: Working with Matplotlib

Module 8: Working with Seaborn

Module 9: Working with Scikit-Learn

Module10: Working with NLTK

Module11: Working with Keras

Module12: Project