Course Program: Data Science, Machine Learning, and AI for Non-Technical People

Course Overview

This course is designed to take non-technical individuals from zero to proficiency in data science, machine learning, and artificial intelligence. The program will focus on hands-on projects, using required programming languages such as Python and SQL, to ensure practical understanding and readiness for a new role in the field.

Objectives

This course program aims to empower non-technical individuals with the necessary skills and confidence to transition into the data science field effectively.

Duration

• Total Length: 12 Weeks

• Structure: 3 sessions per week (2 hours each session)

Week 1-2: Introduction to Data Science and Python Basics

• Session 1: What is Data Science?

  • Overview of data science, machine learning, and AI

  • Applications in various industries

• Session 2: Getting Started with Python

  • Setting up Python environment (Anaconda, Jupyter Notebook)

  • Basic Python programming concepts (variables, data types, loops, functions)

• Session 3: Data Structures in Python

  • Lists, tuples, dictionaries, and sets

  • Working with libraries like NumPy

Project: Simple Python Calculator

Week 3-4: Data Manipulation and Visualization

• Session 4: Introduction to Data Handling with Pandas

  • DataFrames and Series

  • Importing and exporting data (CSV, Excel)

• Session 5: Data Cleaning and Preparation

  • Handling missing values, duplicates, and data types

• Session 6: Data Visualization with Matplotlib and Seaborn

  • Creating plots and charts to visualize data

Project: Analyze a public dataset and visualize key insights

Week 5-6: Introduction to SQL for Data Management

• Session 7: SQL Basics

  • Understanding databases, tables, and queries

• Session 8: Data Retrieval with SQL

  • SELECT statements, filtering data, sorting

• Session 9: Data Aggregation and Joins

  • GROUP BY, JOINs, and subqueries

Project: Create a simple database and perform queries to extract insights

Week 7-8: Introduction to Machine Learning

• Session 10: Understanding Machine Learning Concepts

  • Types of machine learning (supervised, unsupervised, reinforcement)

  • Key terminology (features, labels, training, testing)

• Session 11: Linear Regression and Model Evaluation

  • Building a linear regression model using Scikit-learn

  • Understanding metrics (MSE, R^2)

• Session 12: Classification Algorithms

  • Introduction to classification (logistic regression, decision trees)

  • Evaluating classification models (confusion matrix, accuracy)

Project: Build a simple predictive model using a dataset (e.g., house prices, customer churn)

Week 9-10: Advanced Machine Learning Techniques

• Session 13: Ensemble Methods

  • Understanding random forests and boosting techniques

• Session 14: Introduction to Natural Language Processing (NLP)

  • Text data handling, sentiment analysis

• Session 15: Introduction to Neural Networks

  • Basics of neural networks and their applications

Project: Create a sentiment analysis model on social media data

Week 11-12: Capstone Project and Career Preparation

• Session 16: Capstone Project Preparation

  • Selecting a project topic based on interests (e.g., sales prediction, customer segmentation)

• Session 17: Working on the Capstone Project

  • Apply all the skills learned throughout the course

• Session 18: Career Readiness and Next Steps

  • Building a portfolio

  • Resume writing and interview preparation for data science roles

Final Project Presentation: Present your capstone project to the class and receive feedback.

Additional Resources

• Recommended books, online courses, and tutorials for further learning

• Access to a community forum for peer support and networking

Evaluation

• Continuous assessment through projects

• Final capstone project presentation to evaluate understanding and application of concepts

Certification

• A certificate of completion will be awarded upon successful completion of the course.