Introduction

Welcome to the Data Analytics Course!

Embark on an exciting journey into the world of data with our comprehensive Data Analytics course, starting from model development. This course is designed to equip you with the essential skills and knowledge to develop and refine predictive models, using real-world data.
Throughout this course, you will:

Dive deep into the concepts of simple and multiple linear regression.
Explore advanced techniques in data preprocessing, including handling missing values, detecting outliers, and feature engineering.
Learn to interpret and validate models to ensure they perform well on unseen data.
Gain practical experience through hands-on labs and projects, applying what you’ve learned to real datasets.

By the end of this course, you’ll have a solid foundation in data analytics and model development, empowering you to make data-driven decisions and insights. Whether you’re a beginner or looking to enhance your skills, this course has something for everyone.

Course Criteria

`Criteria`	`Percentage`
Attendance	10%
Participation & quiz	30%
Midterm Exam or/and Project	15%+15%
Final Project & Presentation	30%

Programming:

You are free you use your favorite programming language Python or …

Course progress

Note: The following table of contents will be progressively updated according to the course advancement.

Topic	Slide	Lab	Solution	Remark
Model Development	Slide	Lab1	Lab1-Solution	✅ Complete
Model Evaluation & Refinement	Slide	Lab2	Lab2-Solution	✅ Complete
Data Visualization	Slide	Lab3	Lab3-Solution	✅ Complete
Deep Learning	Slide	Lab4	Lab4-Solution	...Loading

Midterms, Exams and Projects

In this section, you will find all the information related to the midterms, exams and projects including instructions, starting dates and the deadlines.

Midterm & Exam

A possible midterm date: November 1st, 2024.

Project:

Deadline for the report: 11:59PM of November 24th, 2024.
Where to submit: Canvas
Your report should be in (your favorite) PDF format and include the following criteria:
- Members’ names & contributions.
  - Clearly state each member’s contribution to the report.
  - For example:
    - Luffy: Introduction, Data Preprocessing
    - Gojo: Exploratory Data Analysis, Model Development
    - Naruto: Results and Evaluation, Conclusion
- Introduction:
  - Clearly state the objectives and purpose of the analysis.
  - Provide a brief overview of the dataset used.
- Data Preprocessing:
  - Detail steps taken to clean and preprocess the data.
  - Explain handling of missing values and outliers.
  - Describe any feature engineering techniques applied.
- Exploratory Data Analysis (EDA):
  - Include visualizations (charts, graphs) and descriptive statistics.
  - Identify key patterns, trends, and anomalies in the data.
- Model Development:
  - Explain the choice of model(s) and rationale behind it.
  - Provide details on model training, validation, and testing processes.
  - Include any hyperparameter tuning and model optimization steps.
- Results and Evaluation:
  - Present model performance metrics (e.g., accuracy, precision, recall, F1-score, RMSE).
  - Compare performance across different models if multiple were used.
  - Highlight any insights or conclusions drawn from the model’s predictions.
- Conclusion:
  - Summarize key findings and their implications.
  - Discuss any limitations of the analysis.
  - Suggest potential areas for future research or improvement.
- References:
  - Cite all sources of data and any external libraries or tools used.
  - Include academic references for any theoretical concepts or methods applied.
- Appendix (Optional):
  - Provide additional figures, tables, or code snippets that support the analysis.
Presentation (15mn):
- A possible dates: November 25th, 29th and December 2nd, 2024.
- Presentation + Q & A: 10mn + 5mn.

Resources and Further Reading

Here, you will find additional resources, including books, research papers, and online courses, to further your understanding of Data Analytics.

📚 You will find these books/links helpful…