FULL STACK DATA SCIENCE AND AI

PYTHON FOR DATA SCIENCE


Overview

Python is one of the most popular and versatile programming languages in the world of data science. Its simplicity and extensive libraries make it the go-to language for data analysis, machine learning, and AI. In this module, you will learn how Python can be applied in various stages of the data science workflow, from data cleaning to model building.

Why Python for Data Science?

Python offers an intuitive syntax and an active community, making it easier for beginners and professionals alike. With vast libraries like Pandas, NumPy, and SciPy, Python simplifies complex data operations, helping you process, analyze, and visualize data faster.

Key Benefits:

  • User-Friendly: Easy to learn and implement for all skill levels.
  • Robust Libraries: Access extensive resources for data analysis and machine learning.
  • Powerful Tools: Strong support for data manipulation and visualization.
  • Versatility: Scalable and adaptable across various applications.

What You Will Learn

This module encompasses both foundational and advanced Python techniques as applied to data science:

  1. Introduction to Python - Master the basics: syntax, variables, data types, and control structures.
  2. Data Structures and Functions - Explore built-in data structures: lists, tuples, dictionaries, and sets.
  3. Working with Libraries -
    • Pandas: Data manipulation and analysis.
    • NumPy: Numerical computing and large dataset handling.
    • Matplotlib and Seaborn: Data visualization techniques.
  4. Data Wrangling and Cleaning - Techniques for managing missing data, duplicates, and inconsistencies.
  5. Exploratory Data Analysis (EDA) - Apply statistical techniques and visualizations to summarize datasets.
  6. Introduction to Machine Learning - Understand machine learning fundamentals using scikit-learn.
  7. Data Visualization - Visualize complex datasets using Matplotlib, Seaborn, and Plotly.
  8. Advanced Python for Data Science - Delve into advanced topics like object-oriented programming (OOP) and API integration.

Practical Projects

Throughout this module, students will engage with real-world datasets, including:

  • Analyzing sales data to identify trends and customer behavior.
  • Building predictive models for forecasting.
  • Visualizing data from publicly available sources such as social media and government datasets.

Tools and Technologies Covered

  • Python
  • Pandas
  • NumPy
  • Matplotlib
  • Seaborn
  • scikit-learn

Who Should Enroll?

This module is ideal for both beginners and intermediate learners eager to explore data science. Whether you're a student, a working professional, or someone contemplating a career switch, this course will equip you with essential Python skills for data science.

Course Duration and Structure

  • Duration: 4-6 weeks (self-paced)
  • Format: Online with hands-on projects and quizzes
  • Certification: Earn a "Python for Data Science" certification upon completion.

By the end of this module, you’ll have the skills to effectively use Python in data science applications, allowing you to analyze and build models from real-world data confidently.