1. Python Basics:
- Students will begin by mastering the fundamentals of Python programming language, covering syntax, data types, control structures, and functions. Through interactive lessons and hands-on exercises, they will gain a solid understanding of Python's core concepts.
2. Understanding Jupyter notebook and VSC:
- This section focuses on familiarizing students with two essential development environments: Jupyter Notebook for interactive data exploration and visualization, and Visual Studio Code (VSC) for writing and debugging Python code efficiently. Students will learn how to leverage these tools effectively throughout the course.
3. Learning Python Libraries (NumPy, Pandas, Matplotlib, Seaborn) in a Project:
- Students will dive into practical application by working on a project that utilizes essential Python libraries for data manipulation, analysis, and visualization. They will learn to leverage NumPy for numerical computing, Pandas for data manipulation, and Matplotlib and Seaborn for data visualization.
4. Data Wrangling and Preprocessing:
- In this module, students will learn essential data wrangling and preprocessing techniques, including handling missing data, outliers, and feature engineering. Through real-world examples and hands-on exercises, they will acquire the skills needed to clean and prepare datasets for analysis.
5. Exploratory Data Analysis (EDA):
- This module covers exploratory data analysis techniques, including various data visualization methods such as histograms, scatter plots, and box plots. Students will also learn to summarize data statistically and uncover relationships and patterns within the data.
6. Statistical Methods for Data Science:
- Students will delve into foundational statistical methods essential for data science, including probability theory, distributions, statistical inference, and correlation/regression analysis. They will gain insights into how to apply these methods to analyze and interpret data effectively.
7. Supervised vs Unsupervised Learning:
- This module introduces the concepts of supervised and unsupervised learning, exploring the differences between them and their respective applications in machine learning. Students will understand the importance of labeled data in supervised learning and the challenges and benefits of unsupervised learning.
8. Regression:
- Students will learn about regression analysis, a fundamental supervised learning technique for predicting continuous outcomes. They will explore various regression algorithms and learn to evaluate and interpret regression models.
9. Classification:
- This module focuses on classification algorithms, which are used to predict categorical outcomes. Students will learn about popular classification algorithms such as logistic regression, decision trees, and support vector machines, and how to evaluate their performance.
10. Decision Trees:
- Students will dive deeper into decision tree algorithms, understanding their structure, decision-making process, and how to interpret and visualize decision trees. They will also learn about ensemble techniques such as bagging and boosting for improving decision tree performance.
11. Ensemble Techniques:
- This module explores ensemble learning techniques, including random forests, gradient boosting, and ensemble methods. Students will learn how to combine multiple models to create stronger predictive models and improve overall performance.
12. Deep Learning:
- Finally, students will be introduced to deep learning, a subset of machine learning that utilizes neural networks with multiple layers to model complex patterns in data. They will learn about deep learning architectures, training strategies, and practical applications.
Course Details:
a. Each topic is presented through engaging PowerPoint slides and accompanied by clear mathematical explanations to ensure a thorough understanding of the concepts. Following conceptual learning, students will apply their knowledge by implementing the topics in code.
b. To reinforce learning, each topic includes a quiz to assess understanding and an assignment to provide practical application of the concepts covered.
c. Additionally, each modeling topic (e.g., regression, classification) includes a project component, where students will apply their skills to solve real-world problems and showcase their work on GitHub for portfolio building and sharing with potential employers.
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