Data Science & Analytics
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.
- Duration: 12 weeks
- Skill Level : Beginner to Intermediate
- Course Price: £99
Curriculum
- 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
- Data Sources: Surveys, Databases, API’s, Files (CSV, Excel)
- Data Collection Ethics and Privacy
- Handling Missing Values, Duplicates, and Outliers
- Data Transformation and Formatting
- Summary Statistics: Mean, Median, Mode, Standard Deviation
- Data Distributions and Skewness
- Identifying Patterns, Anomalies, and Correlations
- Using Pandas, Excel, and Visualization for Insights
- Functions and Formulas (IF, VLOOKUP, INDEX-MATCH)
- Pivottables, Conditional Formatting, Charts
- Dashboard Design and Interactivity
- Real-World Case Study: Analyzing Program Outcomes
- Introduction to Python & Jupyter Notebook
- Core Libraries: NumPy, Pandas, Seaborn, Matplotlib
- Data Cleaning, Filtering, Grouping, Aggregating
- Use Case: Analyzing Health, Education, or Business Datasets
- 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
- Descriptive & Inferential Statistics
- Measures of Central Tendency and Variability
- Confidence Intervals, Z-Test, T-Test, Chi-Square, ANOVA
- Real-World Interpretation for Decision-Making
- Relational & Non-Relational Databases
- SQL Syntax: SELECT, WHERE, JOIN, GROUP BY
- Writing Queries to Extract Insights
- Using Platforms like MySQL, PostgreSQL, Google BigQuery
- Writing Executive Summaries and Data Reports
- Creating Visual Stories and Interactive Presentations
- Tools: Powerpoint, Canva, Power BI
- Data Storytelling Best Practices
- 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
- Introduction To BI Tools: Dashboards, KPI’s, Interactive Visualizations
- Connecting Data Sources and Designing Reports
- Creating Filters, Slicers, and Storyboards
- Real-World Dashboard Project
- Data Privacy, Security, and Ethical Considerations
- Governance Frameworks and Compliance Basics
- Responsible AI and Bias Awareness
- Policies for Businesses and Organizations
Benefits of Data Science & Analytics
What you'll gain from this course
- Informed Decision-Making
- Improved Efficiency
- Competitive Advantage
- Career Opportunities