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Data Analytics

Overview

In this Data Analytics course, you will learn essential skills in data collection, cleaning, and analysis using industry-standard tools, gain hands-on experience through real-world projects, and enhance your ability to extract valuable insights to solve business problems effectively.

Program Tuition

For enrollment in the data analytics cohort, you have two payment options available. You can either make a one-time payment of NGN 180,000 or opt for installments, with an initial payment of NGN 100,000 followed by the balance after 30 days.

Program Duration

Accelerated Learning: Complete the course in just 12 weeks, allowing you to acquire essential skills efficiently.

Flexible Schedule: Dedicate 6 hours per week to online learning, enabling you to balance your studies with other commitments.

Accessible Anywhere: Access course materials and resources fully online, providing you with the convenience of learning from anywhere with an internet connection.

What you'll learn

In this Data Analytics course, you will learn to collect, clean, analyze, and visualize data using industry-standard tools like Python, R, and SQL, gaining the skills needed to transform raw data into actionable insights, create compelling data visualizations, and effectively communicate findings to drive informed business decisions.

Q1. Week 1: Introduction to Data Analytics and Excel Basics

Topics Covered:

  • What Data Analytics is and the key steps in the Data Analytics process.
  • Introduction to Excel and its interface.
  • Basic Excel functionalities: cells, rows, columns, and worksheets.

Learning Outcomes:

  • Understand the fundamental concepts of Data Analytics.
  • Navigate and utilize basic Excel functionalities effectively.
Q2. Week 2: Data Structures, File Formats, and Sources of Data

Topics Covered:

  • Different types of data structures and file formats.
  • Sources of data: internal and external.
  • Introduction to data tables in Excel.

Learning Outcomes:

  • Describe various data structures and file formats.
  • Identify and source data from various origins.
  • Create and modify data in tables using Excel.
Q3. Week 3: Working with Business-Oriented Data Sets

Topics Covered:

  • Identifying and working with business-oriented data sets.
  • Managing multiple worksheets and workbooks in Excel.

Learning Outcomes:

  • Handle business-oriented data sets with confidence.
  • Manage multiple worksheets and workbooks efficiently in Excel.
Q4. Week 4: Importing and Preparing Data for Tableau/Power BI

Topics Covered:

  • Importing data into Excel and preparing it for analysis.
  • Introduction to Tableau/Power BI.
  • Data preparation and cleaning techniques.

Learning Outcomes:

  • Import and prepare data for analysis in Excel.
  • Get familiar with the Tableau/Power BI interface.
  • Clean and prepare data for loading into Tableau/Power BI.
Q5. Week 5: Creating Data Models and Aggregations

Topics Covered:

  • Creating flexible data aggregations using pivot tables.
  • Introduction to data modeling in Tableau/Power BI.

Learning Outcomes:

  • Create and manipulate pivot tables in Excel for data aggregation.
  • Develop basic data models in Tableau/Power BI.
Q6. Week 6: Visualizing Data with Pivot Charts and Tableau/Power BI

Topics Covered:

  • Representing data visually using pivot charts.
  • Creating visualizations in Tableau/Power BI.

Learning Outcomes:

  • Create and customize pivot charts in Excel.
  • Build and customize basic visualizations in Tableau/Power BI.
Q7. Week 7: Advanced Data Modeling in Tableau/Power BI

Topics Covered:

  • Creating complex data models
  • Using DAX functions
  • Managing relationships in data

Learning Outcomes:

  • Develop advanced data models
  • Apply DAX functions effectively
  • Manage data relationships in Tableau/Power BI
Q8. Week 8: Data Analysis Techniques with Excel

Topics Covered:

  • Advanced data analysis with Excel
  • Using advanced formulas and functions
  • Analyzing data sets with pivot tables

Learning Outcomes:

  • Perform advanced data analysis in Excel
  • Utilize complex formulas and functions
  • Analyze large data sets effectively
Q9. Week 9: Creating Interactive Dashboards in Tableau/Power BI

Topics Covered:

  • Designing interactive dashboards
  • Using filters and slicers
  • Best practices for dashboard design

Learning Outcomes:

  • Create interactive dashboards
  • Implement filters and slicers
  • Apply best practices in dashboard design
Q10. Week 10: Advanced Visualization Techniques

Topics Covered:

  • Advanced chart types
  • Custom visualizations
  • Combining multiple data sources

Learning Outcomes:

  • Utilize advanced chart types
  • Create custom visualizations
  • Combine data from multiple sources
Q11. Week 11: Sharing and Collaborating with Tableau/Power BI

Topics Covered:

  • Sharing reports and dashboards
  • Collaboration tools
  • Managing access and permissions

Learning Outcomes:

  • Share Tableau/Power BI reports
  • Collaborate using built-in tools
  • Manage user access and permissions
Q12. Week 12: Capstone Project and Review

Topics Covered:

  • Capstone project presentation
  • Course review and Q&A
  • Next steps in your data analytics journey

Learning Outcomes:

  • Apply all skills in a final project
  • Review key course concepts
  • Plan your next steps in data analytics

Why Enroll in This Course?

Lucrative Career Prospects

Cultivate in-demand skills in penetration testing, unlocking diverse job prospects within the cybersecurity realm.

Flexibility

Experience the flexibility to work across different sectors, including government, finance, healthcare, and technology, broadening your professional horizons

Competitive Pay

Enjoy attractive salaries and benefits commensurate with your expertise in cybersecurity, reflecting the industry's demand for skilled penetration testers