Course Overview
This one-hour program provides an introduction to the fundamentals of Python, with a focus on its applications in data science. Participants will learn how to use Python for data manipulation, basic data analysis, creating visualizations, and understanding the basics of machine learning. This course is designed to give learners the essential tools needed to start working with data using Python.
Course Goals
The aim of this course is to provide learners with a solid foundation in Python programming, with a specific focus on data analysis, visualization, and introductory machine learning concepts. By the end of the course, participants will be equipped with the skills necessary to efficiently manipulate, analyze, and visualize data using Python.
Program Objectives
- Build a strong foundation in Python programming.
- Learn to manipulate and analyze data using Python libraries.
- Create effective and insightful data visualizations.
- Gain an understanding of basic machine learning concepts.
- Apply Python skills to real-world data science problems.
Program Structure
Module 1: Python Basics
- Introduction to Python: Understanding Python’s role in data science and setting up your environment (Anaconda, Jupyter Notebook).
- Basic Syntax, Variables, and Data Types: The building blocks of Python programming.
- Control Structures: Mastering if-else statements, loops, and list comprehensions.
- Functions and Modules: Defining functions, importing libraries (NumPy, Pandas, Matplotlib), and writing organized Python scripts.
- Basic Data Structures: Working with lists, tuples, dictionaries, and sets, and learning how to manipulate and access these data structures.
Module 2: Data Manipulation with Pandas
- Introduction to Pandas: Working with DataFrames and Series, and loading data from various file formats.
- Data Cleaning and Preprocessing: Handling missing data, filtering, sorting, and grouping data efficiently.
- Data Transformation: Merging, joining, and concatenating DataFrames, using pivot tables, and performing data aggregation.
Module 3: Data Visualization with Matplotlib
- Introduction to Data Visualization: The importance of visualization in data science and an overview of Matplotlib and Seaborn.
- Creating Basic Plots: Line plots, bar charts, histograms, and how to customize them.
- Advanced Visualization Techniques: Creating scatter plots, box plots, heatmaps, and working with multiple plots. Learning how to save and export visualizations.
Eligibility
- This course is ideal for beginners in programming, data analysts, researchers, and students interested in data science.
Learning Outcomes
- Achieve proficiency in Python for data analysis and visualization.
- Gain the ability to manipulate and analyze datasets using Python.
- Understand basic machine learning concepts.
- Develop the capability to create and interpret data visualizations.
- Acquire hands-on experience with Python tools in data science.
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