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Jooi Centre - Kikuyu Town

Data Analysis and Visualization

Fast-track your Data Analysis career with hands-on projects, real-world case studies, and industry-aligned skills — all packed into a practical, career-focused program. Collect, clean, analyze, and visualize data to tell powerful stories and drive business decisions.

Program Highlights

Key details at a glance — here’s what you can expect from our Data Analysis course.

Learning Format:

Hybrid: Online & Physical

Next Intake:

Intake Ongoing

Duration:

6 Months

About the Course

This course is your entry point into the world of professional Data Analysis and Visualization. You’ll learn how to collect data from various sources, clean and prepare it for analysis, and use powerful tools to create visualizations that reveal insights. You'll master SQL for database queries, Excel for spreadsheet analysis, and Power BI or Tableau for creating dynamic dashboards. By the end, you’ll have a portfolio of projects that demonstrate your ability to find and communicate meaningful patterns in data.

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Sneak Peek of the Course: Analysing data to get valuable insights for good decision making.

Course Details

This course is a practical, career-focused path into data analytics. You’ll work through structured modules that combine theory with applied labs and projects, producing a portfolio of reports and dashboards that demonstrate your ability to find insights in real-world data.

What is Data Analysis and Visualization?

Data Analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, inform conclusions, and support decision-making.
Data Visualization is the graphical representation of data and information. It's the process of putting analyzed data into a visual format that helps people see and understand patterns, trends, and outliers more easily.

Who is this course for?
  1. Students: Build a strong foundation in data analysis and storytelling.
  2. Aspiring Analysts: If you have a passion for numbers and solving problems.
  3. Professionals: Enhance your career by adding data skills to your toolkit.
  4. Business Owners: Learn to track key metrics and make data-driven decisions.
What are the course requirements?
  1. Basic Computer Literacy: Comfortable using a computer and navigating the web.
  2. Access to a Computer & Internet: A modern browser and stable internet connection for labs.

We welcome beginners — the course starts with fundamentals and scales up to professional practice.

Why learn this course?
  1. Practical Data Skills: Learn the tools and languages used by data teams.
  2. Build Real Reports: Create projects you can showcase to employers.
  3. Solve Problems: Use data to answer critical business questions.
  4. Career-Ready: Prepare for roles such as data analyst, business analyst, or data specialist.
Why study at Lio College?
  1. Hands-On Projects: A majority of the course is practical labs and collaborative builds.
  2. Industry-Experienced Instructors: Learn from data professionals and analysts.
  3. Career Support: Guidance on building a portfolio, preparing CVs, and interview practice.
What is the mode of study?

Choose between in-person on campus or online attendance. Classes are live and interactive, supported by an online learning platform with data labs, resources, and mentor support to reinforce learning.

Curriculum Roadmap

Our comprehensive curriculum is strategically divided into four progressive phases, designed to transform you from a beginner into a job-ready Data Analyst. You'll master essential tools, apply theory through real-world projects, and build a powerful professional portfolio.

Phase 1 – Data Acquisition and Business Foundations

This phase establishes your foundation in data management and analytical thinking, focusing on tools essential for every business analyst role.

  1. Analytical Thinking and Problem Definition: Learn to transform a business problem into a measurable data question.
  2. Advanced Excel Proficiency: Master Power Query, pivot tables, VLOOKUP, HLOOKUP, and essential financial/statistical formulas.
  3. SQL for Data Retrieval (Structured Query Language): Write complex queries using JOINs, GROUP BY, and aggregate functions to efficiently extract data from relational databases.
  4. Data Cleaning and Preprocessing: Techniques for identifying, handling, and transforming raw, messy data into a clean, structured format ready for analysis.
Phase 2 – Statistical Programming with Python / R

Dive into statistical computing. You will learn an industry-standard programming language to handle big data and perform sophisticated analysis.

  1. Introduction to Python/R: Fundamentals of syntax, data types, and control flow for statistical analysis.
  2. Data Manipulation Libraries: Master Pandas (Python) or dplyr (R) for efficient data structure handling, filtering, grouping, and aggregation.
  3. Core Statistics: Apply concepts like hypothesis testing, A/B testing, and regression analysis using code to draw significant conclusions.
  4. Automated Reporting: Develop scripts to automate repetitive data extraction and cleaning tasks, saving significant time.
Phase 3 – Advanced Visualization and Data Storytelling

Learn to transform complex numbers into clear, compelling visuals that drive decision-making across an organization.

  1. Power BI and Tableau Mastery: Deep dive into advanced functions, calculated fields (DAX/LOD), and connecting to live data sources.
  2. Actionable Dashboard Design: Principles of visual perception, cognitive load, and creating interactive, user-centric dashboards to monitor Key Performance Indicators (KPIs).
  3. Data Storytelling Frameworks: Learn to structure your findings with a narrative arc, context, and a clear call-to-action for executive audiences.
  4. Visualization Best Practices: Choosing the right chart (e.g., Waterfall, Heatmaps, Treemaps) to avoid misrepresentation and maximize impact.
Phase 4 – Capstone Project and Career Launchpad

The final phase is dedicated to applying all acquired knowledge to a comprehensive project and preparing you for the job market.

  • End-to-End Capstone Project: Independently scope, acquire, clean, analyze, and present a solution for a real-world business challenge.
  • Portfolio Development Workshop: Curate your projects into a professional, interview-ready online portfolio.
  • Interview Preparation and Mock Interviews: Focus on technical questions in SQL, Power BI/Tableau, and behavioral questions for data analyst roles.
  • Ethics in Data: Understanding data governance, privacy (GDPR, HIPAA), and bias in data collection and modeling.

Career Pathways


Completing this course opens doors to high-demand roles in data analysis and business. Select a pathway below to explore how you can apply your skills.

Data Analyst

Collect, clean, and analyze data to identify trends, provide insights, and support business decision-making.

Operations Analyst

Use data to improve business processes, optimize resources, and enhance operational efficiency across industries.

Financial Analyst

Leverage data analysis and visualization techniques to evaluate investments, forecast trends, and guide financial planning.

Marketing Analyst

Analyze customer data, campaign performance, and market trends to support targeted marketing strategies and growth.

Research Analyst

Conduct data-driven research and present findings through reports and visualizations in sectors such as healthcare, education, or policy.

Data Consultant

Provide expert advice to organizations on how to collect, analyze, and visualize data to drive strategic outcomes.

Ready to Build Your Career in Data Analysis?

Join the next intake and gain practical, in-demand skills that will launch your career as a data professional.