Fundamental Statistics
- Data types
- Measures of central tendency and dispersion
- Sampling
- Confidence intervals and hypothesis testing
- Univariate, bivariate and multivariate relationships
- Correlation and regression
- ANOVA
Intermediate / advanced
Data Science
Statistics, Python and machine learning in practical workflows.
01DA
LevelIntermediate / advanced
Duration16 weeks
DeliveryLive online / in person
PracticeAssignments + capstone
Build practical capability, step by step.
Develop practical data science capability through statistics, Python, SQL and business intelligence. Learners progress from core concepts into predictive modelling and machine learning workflows.
Who should join
- Students and fresh graduates
- Working professionals building data capability
- Career switchers moving into analytics roles
What you will gain
- Use statistics to explore relationships in data
- Work with Python packages for analysis and visualisation
- Query and combine data with SQL
- Understand predictive modelling and machine learning
- Present findings through Power BI reports
What the course covers
Data Science
- What is Data Science
- Predictive modelling and machine learning
- Natural language processing
- Corner cases of machine learning
Excel
- Data structure
- Data handling, sorting and formatting
- Conditional formatting
- Analytical functions
- Table operations
- Pivot tables and aggregation
- Variance analysis
- What-if analysis
- Excel charts and add-on tools
Python
- Python basics
- Python packages
- Data types
- Data manipulation
- Operators, loops and functions
- Data visualisation packages
- Statistical packages
SQL
- Introduction to SQL
- Foundation of tables
- Queries and manipulation
- Aggregation
- Joins and functions
- Subqueries with complex conditions
Power BI
- Data upload and manipulation
- Visualisation
- Reports and dashboards
- Publish workbooks
- Table relationships