Python for Data Science

Course Overview

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Mentor Based

Program Syllabus

Module 1

About This Course

This program introduces the fundamentals of Python, emphasizing its applications in data science. Participants will learn to use Python for data manipulation, performing basic data analysis, creating visualizations, and understanding basic machine learning concepts.

Module 2

Aim

To provide learners with foundational skills in Python programming, focusing on data analysis, visualization, and introductory machine learning concepts. This course aims to equip participants with essential tools to manipulate, analyze, and visualize data efficiently using Python.

Module 3

Program Objectives

Gain a strong foundation in Python programming.
Learn to manipulate and analyze data using Python libraries.
Create effective data visualizations.
Understand basic machine learning concepts.
Apply Python skills to real-world data science problems.

Module 4

Program Structure

Lead Instructor

Dr. Sarah Chen

PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.

Module 1: Python Basics
1. Introduction to Python
  - Understanding Python’s role in data science
  - Setting up Python environment (Anaconda, Jupyter Notebook)
  - Basic syntax, variables, and data types
2. Control Structures
  - If-else statements
  - For and while loops
  - List comprehensions
3. Functions and Modules
  - Defining functions
  - Importing and using libraries (NumPy, Pandas, Matplotlib)
  - Writing and organizing Python scripts
4. Basic Data Structures
  - Lists, tuples, dictionaries, and sets
  - Manipulating and accessing data structures
Module 2: Data Manipulation with Pandas
1. Introduction to Pandas
  - Understanding DataFrames and Series
  - Loading data from CSV, Excel, and other file formats
2. Data Cleaning and Preprocessing
  - Handling missing data
  - Filtering, sorting, and grouping data
  - Applying functions across data
3. Data Transformation
  - Merging, joining, and concatenating DataFrames
  - Pivot tables and cross-tabulation
  - Data aggregation and summarization
Module 3: Data Visualization with Matplotlib
1. Introduction to Data Visualization
  - Importance of visualization in data science
  - Overview of Matplotlib and Seaborn
2. Creating Basic Plots
  - Line plots, bar charts, and histograms
  - Customizing plots: labels, titles, and legends
3. Advanced Visualization Techniques
  - Scatter plots, box plots, and heatmaps
  - Multiple plots and subplots
  - Saving and exporting visualizations