Introduction to Data Science with Python

Introduction to Data Science with Python involves learning the fundamentals of the Python programming language and its application in data science. Below is an outline for a beginner-level training program:

Module 1: Introduction to Data Science

1. What is Data Science?
   • Definition and scope of data science.
   • Applications and impact on various industries.
2. Data Science Lifecycle:
   • Overview of the data science process.
   • Steps involved in a typical data science project.

Module 2: Introduction to Python

1. Getting Started with Python:
   • Installation and setup of Python.
   • Introduction to Python’s syntax and basic operations.
2. Python Basics:
   • Variables, data types, and basic operations.
   • Control structures (if statements, loops).

Module 3: Data Wrangling with Python

1. Data Import and Export:
   • Reading and writing data with Python.
   • Handling different data formats (CSV, Excel, etc.).
2. Data Cleaning:
   • Identifying and handling missing data.
   • Removing duplicates and outliers.

Module 4: Exploratory Data Analysis (EDA)

1. Descriptive Statistics:
   • Calculating measures of central tendency and dispersion.
   • Creating histograms and box plots.
2. Data Visualization with Matplotlib and Seaborn:
   • Introduction to data visualization libraries.
   • Creating bar charts, scatter plots, and line graphs.

Module 5: Statistical Analysis with Python

1. Hypothesis Testing:
   • Introduction to statistical hypothesis testing.
   • Conducting t-tests and chi-square tests.
2. Correlation and Regression:
   • Analyzing relationships between variables.
   • Building and interpreting regression models.

Module 6: Machine Learning Basics with scikit-learn

1. Introduction to Machine Learning:
   • Overview of machine learning concepts.
   • Supervised vs. unsupervised learning.
2. Machine Learning Algorithms in Python:
   • Introduction to common machine learning algorithms.
   • Implementing algorithms using scikit-learn.

Module 7: Introduction to Jupyter Notebooks

1. Creating Reports with Jupyter Notebooks:
   • Overview of Jupyter Notebooks.
   • Creating dynamic and reproducible reports.
2. Presenting Data Findings:
   • Using Jupyter Notebooks to present analysis results.
   • Creating interactive documents.

Module 8: Real-world Applications and Case Studies

1. Industry Applications:
   • Data science applications in various industries.
   • Real-world case studies.
2. Capstone Project:
   • Undertaking a small data science project using Python.
   • Integrating various skills learned throughout the training.

Additional Considerations:

• Hands-On Exercises and Labs:
  • Incorporate practical exercises for hands-on learning.
  • Provide datasets for participants to work on.
• Interactive Sessions:
  • Q&A sessions and discussions.
  • Peer-to-peer learning activities.
• Resources and Further Learning:
  • Share additional resources for ongoing learning.
  • Provide links to Python documentation and tutorials.
• Certification or Assessment:
  • Consider offering a certification or assessment for participants who complete the training.