Facebook-square Twitter Youtube
Contact us

Data Science

DCM 002: Introduction to Data Science

Course Overview

Data science plays a vital role in all aspects of the modern world. Our program will ensure you have a strong foundation in this rapidly expanding, highly in-demand field to achieve your career aspirations.

Our program delivers a broad yet rigorous grounding in computer science and statistics, adopting both theoretical and practical learning approaches. You will gain cutting-edge knowledge and skills through state-of-the-art equipment and excellent teaching offered by both the School of Computing and Communications and the Department of Mathematics and Statistics.

This engaging programme, and our reputation for excellence in research, means that you will receive high quality teaching delivered by academics who are experts in their field. Throughout the program, you will develop a range of discipline specific skills and gain specialist knowledge that will prepare you for your chosen career.

Course aims

The course will include a mix of lectures, and hands-on exercises which  will allow students to gain experience using the theory and techniques delivered in the lectures in the field of Data Science at large.

Course Objectives:

  • Learning about the Nature of Data & Data Science enabling technologies.
  • Introduction to the Applied Data Science Pipeline Process.
  • Exploring Data Visualisation & its application.

Teaching methods

The course will be mostly theory based, with a few hands-on introductory practical sessions in Data Wrangling & Data visualization tools.

Learning outcomes

  • To provide an introduction to various topics such as Big Data, data visualisation, advanced databases and cloud computing, along with a toolkit to use with data.

Assessment methods

The students will need to complete a written assessment on the last session of the course.

Students must submit a completed Declaration of Authorship form at the end of term when submitting your final piece of work.

MODULE 1: EXPERIENCE ANALYTICS

1.1       Introduction

  1. Video- Course Overview
  2. What will I learn in this Module?
  3. Video- Welcome to Data Crunchers

1.2       What is Data?

  1. Video- What is Data and Where Does It Come from?
  2. Practice Item- Where Does It Come from
  3. Video- Data usage in Daily Life
  4. Practice Item- Data Usage in Daily Life
  5. The importance of visualization
  6. Ways to Visualize Data
  7. Practice Item- ways to Visualize Data

1.3       Data is All Around Us

  1. Discrete vs Continuous Data
  2. Practice item- Discrete vs continuous Data
  3. Data Types
  4. Practice Item- Data Types
  5. Variety of Data
  6. Structured vs Unstructured Data
  7. Practice Item- Structured Data Vs Unstructured Data
  8. Selecting relevant Data
  9. Practice Item- Selecting Relevant Data

 

1.4       Business Understanding

  1. Video – Data Analytics Business Insights
  2. Practice Item- Data Analytics for Business Insights
  3. Video- Humanitarian Insights from Data Analytics
  4. Social Examples
  5. Practice Item- Social examples
  6. Environmental Example- Climate Change
  7. Practice Item- Environment Examples- Climate Change
  8. Lab- Pivot Charts

1.5       Experience Analytics Summary

  1. What Did I Learn in This Module?
  2. Reflection
  3. Quiz- Experience Analytics

2.1       Introduction

  1. What Will I Learn in This Module?

2.2       Understanding Big Data

  1. Define Big Data
  2. Big Data Characteristics
  3. Practice Item- Big Data Characteristics
  4. The Potential Benefits of Data Growth

2.3       Understanding Big Data Management

  1. Data Pipelines
  2. Lab- Interactive Widget Lab2

2.4       Data Collection and Gathering Summary

  1. What Did I Learn in This Module?
  2. Reflection
  3. Quiz- Data Collection and Gathering

3.1       Introduction

  1. Video- What is ML and AI?
  2. What Will I Learn in This Module?

3.2       AI Fact and Fiction

  1. Video- ‘Data Crunchers’ Use of Artificial Intelligence in Agriculture
  2. AI All Around US
  3. Practice Item- AI All Around Us
  4. Video- AI in Action
  5. Lab- Explore CopyAI

3.3       Big Data and Machine Learning

  1. Video- What Is Machine Learning?
  2. Practice Item- What Is Machine Learning?
  3. Types of Machine Learning Analysis
  4. Practice Item- Types of Machine Learning Analysis
  5. The Machine Learning Process
  6. Practice Item- The Machine Learning Process
  7. Training Machines to Recognize Patterns
  8. Practice Item- Training Machines to Recognize Data

3.4       AI AND ML summery

  1. What Did I Learn in This Module?
  2. Reflection
  3. Quiz- Big Data, AI and ML

 4.1 Introduction

  1. Video- Introduction to Job Roles in Data Science
  2. What Will I Learn in This Module?

 4.2 Preparing for A Career in Data Analytic

  1. Roles in the Data Analytics Professions
  2. Practice Item- Roles in the Data Analytics Professions
  3. The Job Market
  4. Tools and Skills
  5. Practice Item- Job Market

 4.3 Taking the next steps

  1. video Item- The Importance of Project Portfolios
  2. Practice Item- The Importance of Project Portfolios
  3. Starting Your Project Portfolio
  4. Your Pathway to a New Career

MODULE 5: FINAL PROJECT

  1. Apply the concepts learned throughout the course to a real-world problem
  2. Use appropriate tools and techniques
  3. Present solutions in an organized and effective manner

Other Rwanda TVET Board Courses

Connect With Us

Facebook-square Twitter Instagram Youtube-play

Certification's Partners

Accreditation Partners

Newsletter

Join our newsletter for latest Updates

All Right reserved by © Wibena Business Institute 2024