Data Analytics Fundamentals

The Data Analytics Fundamentals course provides learners with a comprehensive introduction to the principles, methods, and applications of data analytics. Designed as a self-paced course with a recommended duration of 20 hours, the course equips participants with the essential tools to collect, prepare, analyse, and interpret data for effective decision-making. Whether you are new to analytics or looking to formalise your skills, this course will give you the foundation to apply data-driven approaches across industries and sectors.

Course Structure and Content

The course is structured into ten carefully curated chapters, progressing from foundational concepts to more advanced analytical techniques:

1. Introduction to Data Analytics and the Analytics Lifecycle Explore the role of data analytics in modern organisations, the stages of the analytics lifecycle, and the importance of data-driven strategies.
2. Data Collection, Cleaning, and Preparation Gain practical insights into acquiring datasets, handling missing values, and preparing data for analysis through cleaning and transformation techniques.
3. Exploratory Data Analysis (EDA) Learn how to investigate datasets, identify patterns, detect anomalies, and generate hypotheses using descriptive statistics and visual exploration.
4. Statistical Methods for Data Analysis Understand the fundamentals of probability, hypothesis testing, regression analysis, and other statistical tools that form the backbone of analytics.
5. Data Visualisation Techniques Discover methods to present data effectively through charts, dashboards, and storytelling to improve clarity and impact.
6. Machine Learning Algorithms Introduce key concepts in supervised and unsupervised learning, including classification, clustering, and predictive modelling.
7. Time Series Analysis and Forecasting Analyse data over time to identify trends, seasonality, and cycles, and apply forecasting techniques to predict future outcomes.
8. Big Data Analytics and Cloud Computing Explore the opportunities and challenges of handling large datasets, and learn how cloud platforms support scalable analytics.
9. Data Analytics in Business Decision-Making Apply analytical techniques to real-world business cases, demonstrating how insights inform strategy, operations, and performance improvement.
10. Ethical Considerations and Data Privacy in Analytics Examine the ethical implications of data use, focusing on compliance, privacy laws, and responsible analytics practices.

Learning Outcomes

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

• Apply data preparation and exploration techniques to ensure data integrity and usability.
• Use statistical models and predictive analytics to generate insights and support evidence-based decisions.
• Design effective data visualisations and dashboards that communicate findings clearly and persuasively.
• Implement ethical and compliant data practices aligned with privacy regulations and organisational standards.
• Interpret and communicate analytical findings to influence strategic business outcomes.

Assessment

Assessment is conducted through an online quiz at the end of the course. The quiz consists of 20–30 multiple-choice questions (MCQs), designed to test knowledge and application of key concepts. Learners have unlimited attempts, allowing them to reinforce understanding and achieve mastery.

Mode of Study

The course is delivered entirely online through a self-paced format, allowing learners to engage with the content at their convenience. Materials include recorded video lectures, visual slides, case examples, and practical exercises that reflect real-world business challenges.  

Sample Lecture: