1-Minute Lecturer Introduction Script

Hello everyone,
My name is Hilda Yeboah Addie, and I will be guiding you through this course on Data Analytics this semester.

I work with the Centre for Online Learning and Teaching (COLT) at Ghana Communication Technology University, where I support online course delivery and the use of the Learning Management System.

This course is designed to introduce you to the fundamentals of data analytics, focusing on how data is collected, analyzed, and used to support decision-making in real-world situations. No prior knowledge of data analytics is required — all you need is a willingness to learn and participate.

Throughout the semester, you will engage with practical examples and guided activities to help you build strong analytical thinking skills that will be useful in your studies and future careers.

I look forward to working with you, and I wish you a successful and rewarding semester.

DATA ANALYTICS

Course Overview and Outline
Centre for Online Learning and Teaching (COLT)
Ghana Communication Technology University (GCTU)

Course Lecturer

Hilda Yeboah Addie
Centre for Online Learning and Teaching (COLT)
Ghana Communication Technology University

Whats App : 0246852474

Email : hyeboah@gctu.edu.gh

Semester : First 

Course Overview

Data Analytics is an introductory course designed to equip students with fundamental knowledge and practical skills in analyzing, interpreting, and presenting data for informed decision-making. The course introduces students to data types, data sources, data preparation, basic statistical analysis, and data visualization techniques.

In today’s data-driven world, organizations across all sectors rely on data to improve efficiency, predict trends, and support strategic decisions. This course prepares students to understand how data can be transformed into meaningful insights and applied in real-world contexts such as business, technology, education, healthcare, and public administration.

The course is delivered through the Learning Management System (LMS) using online lectures, practical exercises, and case studies to enhance student engagement and learning.  This course equips students with essential analytical skills required in today’s job market. It enhances employability, supports academic research, and prepares students for advanced studies in data analytics and data science.

Course Aim and Objectives

Aim

The aim of this course is to provide students with a solid foundation in data analytics concepts and practical analytical skills.

Objectives

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

• Understand key data analytics concepts and terminology

• Collect, clean, and organize datasets

• Apply basic statistical techniques to analyze data

• Interpret analytical results to support decision-making

• Present data findings clearly using visual tools

Learning Outcomes (Student Outcomes)

Upon successful completion of this course, students will be able to:

1. Analyze and interpret datasets using basic analytical methods

2. Apply statistical reasoning to real-world problems

3. Perform data cleaning and preparation tasks

4. Create simple data visualizations to communicate insights

5. Demonstrate critical thinking and problem-solving using data

6. Use data to support academic, business, and operational decisions

Course Outline

The course is structured into the following modules:

1. Introduction to Data Analytics

   • Definition, scope, and applications of data analytics

2. Data Types and Data Sources

   • Structured vs unstructured data

   • Primary and secondary data sources

3. Data Collection and Data Preparation

   • Data cleaning, validation, and organization

4. Exploratory Data Analysis (EDA)

   • Summarizing and exploring datasets

5. Basic Statistical Analysis

   • Descriptive statistics and interpretation

6. Data Visualization

   • Charts, graphs, and dashboards

7. Introduction to Predictive Analytics

   • Trends, patterns, and basic forecasting concepts

8. Ethical Issues in Data Analytics

   • Data privacy, security, and responsible data use

9. Practical Applications and Case Studies

   • Real-world examples and guided practice

Learning Resources

Recommended Textbooks

• Provost, F. & Fawcett, T. (2013). Data Science for Business

• Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques

Online Resources

• Coursera – Data Analytics Courses

• edX – Data Analysis and Statistics

• Kaggle (practice datasets)

• IBM Data Analytics Learning Resources

Tools (Introductory Level Exposure)

Students will be introduced to basic analytical tools, including:

• Microsoft Excel – Data entry, sorting, filtering, and basic analysis

• Google Sheets – Collaborative data analysis

• Power BI / Tableau – Basic data visualization concepts

Formative Assessment (Fill-In & Short Questions)

Fill-In-The-Blank Questions

1. Data analytics involves collecting, processing, and __________ data to support decision-making.

2. __________ data is data that is organized in rows and columns.

3. Data visualization helps communicate __________ and patterns in data.

Short Answer Questions

1. Define data analytics in your own words.

2. Mention two areas where data analytics is applied.

3. Why is data cleaning important in data analysis?

Essay / Discussion Questions (LMS Assignment or Forum)

1. Discuss the importance of data analytics in modern organizations.

2. Explain how data analytics can support decision-making in education or business.

3. Describe the ethical issues associated with data collection and data use.