Golad Academy · Course syllabus
Data Science & Analytics
- Duration
- 12 weeks
- Skill level
- Beginner to Intermediate
- Next cohort
- 1 March 2027
- Course price
- £99
Overview
The Data Science & Analytics course equips learners with essential skills to interpret data, uncover insights, and make informed decisions. Covering statistics, programming, data visualization, and machine learning, the course blends theory with practical applications. Ideal for beginners and professionals, it builds a strong foundation for thriving in today’s data-driven world.
Format: Live online classes + GADLE
Learning outcomes
- Clean, explore and analyse data with Excel, SQL and Python (pandas)
- Apply statistics and hypothesis testing to real business questions
- Build interactive dashboards in Power BI or Tableau
- Communicate insights clearly through data storytelling
- Build and evaluate introductory machine-learning models
- Publish a portfolio project on GitHub
Curriculum
Module 1: Introduction to Data Analysis
- What is Data Analysis? Types and Roles (Descriptive, Diagnostic, Predictive, Prescriptive)
- Data Analytics Lifecycle (from data collection to storytelling)
- Key Tools & Technologies: Excel, SQL, Python, BI tools
- Real-world Applications in Business, Health, Governance
Module 2: Data Collection & Cleaning
- Data Sources: Surveys, Databases, APIs, Files (CSV, Excel)
- Data Collection Ethics and Privacy
- Handling Missing Values, Duplicates, and Outliers
- Data Transformation and Formatting
Module 3: Exploratory Data Analysis (EDA)
- Summary Statistics: Mean, Median, Mode, Standard Deviation
- Data Distributions and Skewness
- Identifying Patterns, Anomalies, and Correlations
- Using Pandas, Excel, and Visualization for Insights
Module 4: Data Analysis with Excel
- Functions and Formulas (IF, VLOOKUP, INDEX-MATCH)
- PivotTables, Conditional Formatting, Charts
- Dashboard Design and Interactivity
- Real-World Case Study: Analyzing Program Outcomes
Module 5: Data Analysis with Python
- Introduction to Python & Jupyter Notebook
- Core Libraries: NumPy, Pandas, Seaborn, Matplotlib
- Data Cleaning, Filtering, Grouping, Aggregating
- Use Case: Analyzing Health, Education, or Business Datasets
Module 6: Data Visualization
- Principles of Effective Data Storytelling
- Chart Types: Bar, Line, Scatter, Histogram, Heatmaps, Maps
- Tools: Excel, Python (Seaborn/Plotly), Power BI/Tableau Basics
- Creating Dashboards and Infographics
Module 7: Statistical Analysis & Hypothesis Testing
- Descriptive & Inferential Statistics
- Measures of Central Tendency and Variability
- Confidence Intervals, Z-Test, T-Test, Chi-Square, ANOVA
- Real-World Interpretation for Decision-Making
Module 8: Introduction to Databases & SQL
- Relational & Non-Relational Databases
- SQL Syntax: SELECT, WHERE, JOIN, GROUP BY
- Writing Queries to Extract Insights
- Using Platforms like MySQL, PostgreSQL, Google BigQuery
Module 9: Communicating Data Insights
- Writing Executive Summaries and Data Reports
- Creating Visual Stories and Interactive Presentations
- Tools: PowerPoint, Canva, Power BI
- Data Storytelling Best Practices
Module 10: Capstone Project & Portfolio Building
- Choose a Dataset (Social Program, Business KPI, Survey)
- Apply Full Pipeline: Cleaning → Analysis → Visualization → Insights
- Present Findings in a Dashboard + Written Report
- GitHub/Portfolio Setup for Career Purpose
Module 11: Power BI or Tableau for Beginners
- Introduction to BI Tools: Dashboards, KPIs, Interactive Visualizations
- Connecting Data Sources and Designing Reports
- Creating Filters, Slicers, and Storyboards
- Real-World Dashboard Project
Module 12: Introduction to Data Ethics and Governance
- Data Privacy, Security, and Ethical Considerations
- Governance Frameworks and Compliance Basics
- Responsible AI and Bias Awareness
- Policies for Businesses and Organizations
Industry-extension modules (optional, on GADLE)
- Machine Learning Fundamentals — Supervised vs unsupervised learning; Regression and classification with scikit-learn; Model evaluation: train/test split, cross-validation, precision and recall; Clustering and feature engineering basics
- AI & Generative AI for Analysts — How large language models work, and where they fail; Using AI assistants for SQL, Python and analysis — with verification; Text analytics and sentiment analysis basics; Responsible AI: privacy, bias and explainability
Tools
Excel, SQL (PostgreSQL, MySQL, BigQuery), Python, Jupyter, pandas, NumPy, Matplotlib & Seaborn, Plotly, scikit-learn, Power BI, Tableau, GitHub
Prepares you for
- Microsoft PL-300: Power BI Data Analyst
- Google Data Analytics Professional Certificate
- Tableau Certified Data Analyst
Career paths
Data Analyst, Junior Data Scientist, BI Analyst, Reporting Analyst, Insights Analyst, Junior Analytics Engineer
What you’ll gain
Informed Decision-Making · Improved Efficiency · Competitive Advantage · Career Opportunities