Applied Data Science & Analytics

AIAL

Applied Data Science & Analytics

Course Code: ADS-201
Duration: 18 Months (4 Semesters)
Mode of Delivery: In-Class / Online (Blended)
Training Approach: Highly practical, software-based, project and internship focussed

Course Goal

To equip learners with hands-on skills in data collection, processing, analysis, visualization, predictive modelling and interpretation—so they can meaningfully support data-driven decision-making in organisations, businesses and community settings in Somalia and East Africa.

Course Objectives

By the end of this program, learners will be able to:

  1. Understand data lifecycle (acquisition, cleaning, processing) and apply core statistical methods in a digital environment.
  2. Use key software/tools (Python/R, SQL, Excel, Power BI/Tableau) to perform data wrangling, analysis and visualization.
  3. Build and interpret predictive and classification models (machine learning) for real-world organisational problems.
  4. Communicate data insights effectively to non-technical stakeholders through visualizations and dashboards.
  5. Integrate ethical, governance and domain-specific considerations (e.g., business, health, NGO) into analytics practice.
  6. Design and complete a capstone project that demonstrates full end-to-end data science workflow in a live or simulated business/organisation context.

Delivery Methods

  • Interactive lectures (core concepts)
  • Practical software labs (hands-on)
  • Case studies & real-world simulations (local & global)
  • Group work & project-based assignments
  • Internship/field placement and capstone project

Required Tools & Resources

  • Computer/Laptop with internet access
  • Programming software: Python (e.g., Jupyter notebooks) or R
  • Database tools: SQL (MySQL, PostgreSQL or similar)
  • Data visualization/dashboard tools: Power BI, Tableau or similar
  • Microsoft Excel / Advanced Excel
  • Sample datasets and case-studies aligned with Somali and East African context
  • Access to cloud/Git (optional) for project work

Evaluation & Grading

Component

Weight (%)

Continuous Assessments (Assignments, Lab Work, Class Participation)

20%

Mid-Semester Exams (Theory + Practical)

20%

Practical Projects & Presentations

20%

Final Exam (Comprehensive)

30%

Capstone Project (End-to-End Analytics Workflow)

10%

Total

100%

Program Summary

Semester

Credit Hours

Contact Hours

Semester 1

10

150

Semester 2

11

165

Semester 3

11

165

Semester 4

11

165

Grand Total

43 Credit Hours

645 Contact Hours

Certification

Upon successful completion of this program, graduates will receive the diploma:
Diploma in Applied Data Science & Analytics — awarded by the African Institute of Advanced Learning (AIL). Graduates will be prepared for roles such as Data Analyst, Junior

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