Data Analysis Using Python - Course Overview

Course Description

This course, offered by Mella Technology Services LLC, provides a comprehensive introduction to data analysis using Python. Designed for beginners to intermediate learners, the curriculum covers essential Python libraries like Pandas, NumPy, Matplotlib, and Seaborn. Students will gain hands-on experience in data manipulation, cleaning, visualization, and exploratory data analysis (EDA) using real-world datasets.

Key Learning Objectives

By the end of the course, students will be able to:

• Use Python fundamentals for data analysis (variables, loops, functions, data structures).
• Manipulate and clean data with Pandas (DataFrames, Series, handling missing values).
• Perform statistical analysis using NumPy and SciPy.
• Create insightful visualizations with Matplotlib and Seaborn.
• Conduct end-to-end EDA on real datasets (CSV, Excel, JSON).
• Complete a capstone project showcasing their skills.

Course Outline

Weeks 1–6: Core Topics

• Week 1: Python basics, Jupyter Notebook, and essential libraries.
• Week 2: Python data structures (lists, tuples, dictionaries, sets) for data analysis.
• Week 3: Pandas for data manipulation (filtering, sorting, aggregation).
• Week 4: Data cleaning (duplicates, outliers, merging datasets).
• Week 5: Exploratory Data Analysis (descriptive statistics, correlation).
• Week 6: Data visualization (Matplotlib, Seaborn, storytelling).

Weeks 7–12: Capstone & Real-World Applications

• Weeks 7–10: End-to-end project using real-world datasets.
• Weeks 11–12: Top project selection, refinement, and launch on Mella’s platform.

Tools & Software

• Python: Version 3.7+.
• Jupyter Notebook or Google Colab.
• Key Libraries: Pandas, NumPy, Matplotlib, Seaborn, Basemap.
• Advanced Tools: Handling netCDF/FITS files, geographic mapping, time-series animations.

Assessment

• Quizzes & Exercises: 25% (weekly coding tasks).
• Assignments: 25% (hands-on data analysis).
• Final Project: 50% (EDA on a provided dataset).

Recommended Resources

• Books:
  • Python for Data Analysis by Wes McKinney.
  • Data Science from Scratch by Joel Grus.
• Online:
  • [Tesfay-Github] (https://github.com/Tesfay-Tesfu)
  • Pandas Documentation
  • Kaggle for datasets and competitions.
  • (🔗 Google Colab )
  • (🔗 jupyter notebook )
  • Mella Tecnology LLC Python Data Analysis Course Syllabus

Instructor

Tesfu:

• Email: tesfayphysics@gmail.com
• Mobile: 301-9961201

Company

Company Contacts:

• Email: contact@mellatechllc.com
• Phone: 305 332 4364

Participation & Flexibility

• Engage with content at your own pace.
• Focus on areas aligned with your goals.
• Diverse learning methods are encouraged.

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© 2025 Mella Technology Services LLC | 962 Wayne Ave #902, Silver Spring, MD 20910
Syllabus Version: June 17, 2025