Introduction

Python is the leading programming language for data science due to its simplicity, readability, and the extensive ecosystem of libraries available for data analysis, machine learning, and data visualization. This course will guide participants through the essentials of Python programming, with a focus on using Python for data science tasks. Learners will become proficient in Python syntax, data structures, and libraries such as NumPy, Pandas, Matplotlib, and scikit-learn. By the end of this course, participants will be able to use Python to clean, manipulate, analyze, and visualize data, as well as implement basic machine learning models.

Objectives

By the end of this course, participants will:

• Understand the basics of Python programming and how to apply it in data science.
• Be able to work with Python libraries such as NumPy, Pandas, Matplotlib, and scikit-learn.
• Develop skills in data manipulation, cleaning, and preprocessing with Python.
• Gain hands-on experience in creating data visualizations to communicate insights effectively.
• Learn to implement machine learning algorithms in Python.
• Understand best practices for writing clean, efficient, and reusable Python code for data science.

Who Should Attend?

This course is ideal for:

• Beginners to programming who are interested in data science.
• Data analysts, engineers, or professionals transitioning to data science roles.
• Anyone looking to improve their Python skills for data manipulation, analysis, and machine learning.
• Individuals interested in using Python as a tool for extracting insights from data and making data-driven decisions.

Day 1: Introduction to Python and Python for Data Science

Morning Session: Getting Started with Python

• Introduction to Python programming: Why Python for data science?
• Setting up the Python environment: Installing Python and necessary packages (Anaconda, Jupyter Notebook)
• Python syntax: Variables, data types, and operators
• Control structures: if-else statements, loops, and functions
• Working with Python scripts and Jupyter Notebooks for interactive coding
• Hands-on: Writing basic Python scripts and running them in Jupyter Notebooks

Afternoon Session: Data Structures and Manipulation

• Data structures in Python: Lists, tuples, dictionaries, and sets
• Working with strings and string manipulation
• Introduction to data manipulation: Understanding arrays and matrices using NumPy
• Operations on arrays: Indexing, slicing, reshaping, and basic arithmetic operations
• Hands-on: Practice working with lists, tuples, dictionaries, and performing array operations in NumPy

Day 2: Working with Data in Python

Morning Session: Introduction to Pandas

• Understanding the Pandas library: Series, DataFrames, and their differences
• Creating, indexing, and slicing DataFrames and Series
• Importing and exporting data: CSV, Excel, JSON, and SQL databases
• Basic data operations in Pandas: Sorting, filtering, and grouping data
• Hands-on: Importing, cleaning, and manipulating datasets using Pandas

Afternoon Session: Data Cleaning and Preprocessing

• Handling missing data: Techniques for identifying and filling missing values
• Data transformations: Applying functions to DataFrame columns
• Working with categorical data: Encoding and decoding categorical variables
• Combining datasets: Merging, joining, and concatenating DataFrames
• Hands-on: Cleaning and transforming real-world datasets with Pandas

Day 3: Data Visualization with Python

Morning Session: Introduction to Data Visualization

• Importance of data visualization in data science
• Overview of popular Python libraries for visualization: Matplotlib, Seaborn, and Plotly
• Basic plotting with Matplotlib: Line plots, bar charts, histograms, and scatter plots
• Customizing plots: Titles, labels, and colors
• Hands-on: Creating basic visualizations with Matplotlib

Afternoon Session: Advanced Visualization with Seaborn and Plotly

• Creating advanced visualizations with Seaborn: Heatmaps, box plots, pair plots
• Customizing Seaborn plots and handling categorical data
• Introduction to interactive visualizations with Plotly: Line charts, bubble charts, and 3D plots
• Hands-on: Building advanced visualizations using Seaborn and Plotly

Day 4: Introduction to Machine Learning with Python

Morning Session: Machine Learning Concepts and Algorithms

• Overview of machine learning: Supervised vs. unsupervised learning
• Introduction to scikit-learn: A Python library for machine learning
• Common machine learning algorithms: Linear regression, classification, and clustering
• Building a machine learning pipeline: Preprocessing, splitting data, training models, and evaluating performance
• Hands-on: Implementing a simple linear regression model using scikit-learn

Afternoon Session: Supervised Learning Techniques

• Supervised learning models: Logistic regression, decision trees, and random forests
• Model evaluation: Cross-validation, accuracy, precision, recall, and F1-score
• Hyperparameter tuning and model selection
• Hands-on: Implementing a classification model (e.g., logistic regression) using scikit-learn and evaluating its performance

Day 5: Advanced Python for Data Science and Final Project

Morning Session: Advanced Python Techniques for Data Science

• Optimizing Python code for performance: List comprehensions, lambda functions, and map/filter
• Working with large datasets: Techniques for efficient data handling
• Introduction to parallel computing: Using Python libraries like Dask for distributed computing
• Best practices for writing clean, modular, and reusable code
• Hands-on: Writing optimized Python code for a data science task

Afternoon Session: Final Project and Course Wrap-Up

• Final project: Participants will work on a comprehensive data science project that involves data exploration, cleaning, visualization, and machine learning using Python.
• Presenting the results: How to communicate findings through clear visualizations and reports
• Course wrap-up: Review of key concepts, future learning paths, and additional resources for further study
• Certification of completion awarded to participants who successfully complete the course and final project

Materials and Tools:

• Required tools: Python, Jupyter Notebooks, Pandas, NumPy, Matplotlib, Seaborn, Plotly, and scikit-learn
• Real-world datasets (e.g., Kaggle datasets, UCI Machine Learning Repository)
• Access to cloud-based platforms for additional practice (optional)

Conclusion and Final Assessment

• Recap of key concepts: Python syntax, data manipulation, visualization, and machine learning
• Final project presentations and peer feedback
• Certification of completion for those who successfully complete the course and demonstrate practical application of Python for data science