Introduction

Welcome to the Data Analysis Course!

Embark on an exciting journey into the world of data with our comprehensive Data Analysis course. This course covers everything you need to know to transform raw data into meaningful insights.

📋 Course Highlights:

Introduction to Data Analysis: Dive into the world of data analysis, its significance, and real-world applications.
Data Types and Descriptive Statistics: Learn to identify different data types and understand measures of central tendency, dispersion, and summary statistics.
Data Collection and Cleaning: Master the art of gathering data from various sources, handling missing data, and cleaning datasets for accurate analysis.
Data Visualization Basics: Explore essential tools and techniques to present data effectively and make impactful visualizations.
Exploratory Data Analysis (EDA): Develop skills to identify trends, patterns, and anomalies in datasets, guiding deeper analysis.
Practical Labs for Data Analysis: Gain hands-on experience with Python and/or R to perform data analysis and create comprehensive visualizations.

By the end of this course, you’ll have a solid foundation in data analysis, 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	30%
Final Project & Presentation / Practical labs	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	Lab	Solution	Remark
Introduction to Data Analysis	Lab1	Solution1	✅ Completed.
Univariate Analysis	Lab2	Solution2	✅ Completed.
Data Quality & Preprocessing	Lab3	Solution3	✅ Completed.
Data Visualization	Lab4	Solution4	✅ Completed.
EDA: Correlation Analysis	Lab5	Solution5	…Loading
Hypothesis Testing	Lab6	Solution6	...Loading
Linear & Logistic Regression	Lab7	Solution7	…Loading
Categorical Analysis	Lab8	Solution8	...Loading
Introduction to Timeseries Analysis	Lab9	Solution9	…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

Midterm date: 14 March 2025, from 10:20AM to 11:40AM.

Project:

Deadline for the report: 24/04/2025.
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 how to handle 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:
- A possible dates: ...Loading.

Resources and Further Reading

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

📚 You will find these books/links helpful…