Fundamentals of Data Analytics and Data Foundations

• Introduction to data analytics and its importance in modern organizations.
• The data analytics lifecycle: Ask → Prepare → Analyse → Share → Act.
• Types of data: Structured, semi-structured, and unstructured.
• Data sources, data quality, and data governance basics.
• Workshop: Setting up your first analytics project.

Data Preparation and Wrangling Techniques

• Techniques for collecting, cleaning, transforming, and validating data.
• Dealing with missing data, outliers, and anomalies.
• Introduction to SQL basics for data extraction.
• Data preparation best practices in Excel and Power Query.

Exploratory Data Analysis (EDA) and Statistical Thinking

• Introduction to descriptive statistics: Mean, median, standard deviation, etc.
• Visualizing data patterns with charts, graphs, and pivot tables.
• Correlations, distributions, and identifying trends.
• Introduction to hypothesis testing and basic inferential statistics.

Predictive Analytics and Introduction to Machine Learning Concepts

• Understanding supervised vs. unsupervised learning.
• Building predictive models: Linear regression, decision trees, clustering basics.
• How predictive analytics drives business value.
• Introduction to Power BI for data modelling and visualization.
• Case Study: Build a simple predictive model to forecast business outcomes.

Data Storytelling, Decision-Making, and Portfolio Project

• Principles of effective data storytelling and visualization.
• Crafting narratives that drive decisions.
• Communicating findings to non-technical audiences: Executive reporting essentials.
• Final Project: Analyse a real-world dataset from start to finish.