Data Analytics Bootcamp

You can clean raw spreadsheets, write dynamic formulas, and build automated financial models in Excel.

1. The analytics workflow90 min
   • Data ingestion pipelines
   • Defining stakeholder questions
   • Identifying data quality issues
2. Excel for analysis and modelling2 hrs
   • XLOOKUP and INDEX/MATCH
   • Named ranges and dynamic arrays
   • Scenario analysis with Data Tables
3. Cleaning and shaping data2 hrs
   • TRIM, CLEAN, and text splitting
   • Handling date and time serials
   • Conditional formatting rules
4. Pivot tables and lookups2 hrs
   • Calculated fields in pivot tables
   • Grouping date and numerical fields
   • Slicers and timeline filters
5. Charts that communicate90 min
   • Choosing effective visualization types
   • Formatting for executive presentation
   • Dynamic chart ranges

Hands-on project: Build an automated monthly sales forecasting model using Power Query, data validation, and dynamic lookups to process fragmented regional reports.

You can query relational databases using complex joins, window functions, and CTEs to extract actionable business insights.

1. Querying databases with SQL2 hrs
   • SELECT, WHERE, and GROUP BY
   • Aggregate functions and HAVING
   • Sorting and limiting result sets
2. Filtering, joining and aggregating2 hrs
   • INNER, LEFT, and FULL OUTER JOIN
   • Self-joins for hierarchical data
   • Handling NULL values in aggregates
3. Window functions2 hrs
   • ROW_NUMBER, RANK, and DENSE_RANK
   • LAG and LEAD for period comparisons
   • Running totals with SUM() OVER()
4. Subqueries and CTEs2 hrs
   • Scalar and correlated subqueries
   • WITH clause for readable queries
   • Recursive CTEs for tree structures
5. Answering business questions in SQL2 hrs
   • Customer lifetime value queries
   • Funnel conversion rate extraction
   • Query performance optimization basics

Hands-on project: Query a million-row PostgreSQL database to calculate customer retention cohorts, find drop-off points, and resolve transactional log discrepancies.

You can apply descriptive statistics and hypothesis testing to validate conclusions and keep business analysis honest.

1. Descriptive statistics90 min
   • Mean, median, and mode
   • Variance and standard deviation
   • Percentiles and interquartile range
2. Distributions and variability90 min
   • Normal distribution characteristics
   • Skewness and kurtosis
   • Central limit theorem
3. Correlation vs causation90 min
   • Pearson and Spearman correlation
   • Confounding variables
   • Interpreting scatter plot trends
4. Hypothesis testing basics2 hrs
   • Null and alternative hypotheses
   • P-values and significance levels
   • T-tests and Z-tests
5. Avoiding common pitfalls90 min
   • Survivorship bias in datasets
   • Simpson's paradox examples
   • Sample size pitfalls

Hands-on project: Conduct an exploratory statistical review on a marketing dataset to verify whether a campaign lift is statistically significant.

You can write Python scripts using Pandas and NumPy to clean datasets, reshape data frames, and automate repetitive tasks.

1. Python, Pandas and NumPy2 hrs
   • Pandas Series and DataFrames
   • Vectorized operations in NumPy
   • Reading CSV, JSON, and Excel files
2. Exploratory data analysis2 hrs
   • DataFrame info, describe, and head
   • Filtering and Boolean indexing
   • Grouping and aggregation with groupby()
3. Cleaning messy datasets2 hrs
   • Handling missing values with fillna()
   • Removing duplicate rows
   • Type casting and string manipulation
4. Visualisation with Matplotlib/Seaborn2 hrs
   • Line plots, bar charts, and histograms
   • Correlation heatmaps with Seaborn
   • Customizing figure layouts and labels
5. Automating repetitive analysis90 min
   • Writing modular Python functions
   • Batch processing multiple CSV files
   • Exporting cleaned dataframes to disk

Hands-on project: Build an automated Jupyter notebook using Pandas to ingest, clean, and summarize multi-gigabyte server log files.

You can transform chaotic, inconsistent enterprise data into reliable, structured tables ready for downstream reporting.

1. Handling missing and dirty data90 min
   • Imputation strategies vs dropping rows
   • Detecting outlier values with IQR
   • String normalization and regex matching
2. Deduplication and validation90 min
   • Fuzzy matching for duplicate records
   • Schema validation checks
   • Primary key integrity checks
3. Reshaping and merging datasets2 hrs
   • Merging, joining, and concatenating
   • Pivoting and melting data frames
   • Handling multi-index structures
4. Feature creation for analysis90 min
   • Binning continuous variables
   • Extracting date-time components
   • Calculating derived ratios
5. Reproducible preparation90 min
   • Writing clean transformation scripts
   • Logging data pipeline steps
   • Version controlling analytical code

Hands-on project: Merge disparate customer transaction feeds, resolve schema mismatches, and build a reproducible data cleaning pipeline.

You can connect Power BI to live data sources, write DAX measures, and publish interactive executive dashboards.

1. Power BI fundamentals2 hrs
   • Power Query editor in Power BI
   • Connecting to SQL and cloud sources
   • Building star schema models
2. Data modelling and relationships2 hrs
   • Active and inactive relationships
   • Cardinality and filter propagation
   • Managing snowflake schemas
3. DAX basics2 hrs
   • Calculated columns vs measures
   • SUMX, AVERAGEX, and iterator functions
   • CALCULATE and filter context
4. Interactive dashboards2 hrs
   • Drill-throughs and bookmarks
   • Cross-filtering and slicer synchronization
   • KPI cards and matrix visuals
5. Publishing and sharing90 min
   • Publishing to Power BI Service
   • Configuring scheduled data refreshes
   • Sharing workspaces and report access

Hands-on project: Design a multi-page Power BI executive dashboard with DAX measures for year-over-year growth and row-level security.

You can build interactive Tableau workbooks complete with parameters, trend lines, and calculated fields.

1. Tableau fundamentals90 min
   • Connecting to data sources
   • Dimensions vs measures
   • The Tableau workspace layout
2. Building visualisations2 hrs
   • Marks card customizations
   • Dual-axis charts and combined views
   • Geographic mapping with spatial data
3. Dashboards and stories2 hrs
   • Dashboard layout containers
   • Action filters and highlight actions
   • Building sequential story points
4. Calculated fields90 min
   • IF-THEN logic and parameters
   • Table calculations (percent difference)
   • LOD (Level of Detail) expressions
5. Sharing insights90 min
   • Publishing to Tableau Public or Server
   • Exporting images and PDF reports
   • Performance recording optimization

Hands-on project: Build a Tableau storytelling workbook tracking marketing campaign ROI with custom parameters and action filters.

You can design intuitive, accessible dashboards that communicate clear metrics without visual clutter.

1. Principles of effective dashboards90 min
   • The 5-second rule for metrics
   • Visual hierarchy and layout flow
   • Avoiding common dashboard traps
2. Choosing the right chart90 min
   • Bar charts vs pie charts vs tables
   • Showing distribution over time
   • Visualizing proportions and parts of a whole
3. Layout and interactivity90 min
   • Grid systems for dashboard design
   • Grouping related KPI indicators
   • Minimizing cognitive load
4. Colour and accessibility90 min
   • Color theory for data visualization
   • Colorblind-friendly palettes
   • Using contrast to highlight anomalies
5. KPIs and metrics90 min
   • Defining leading vs lagging indicators
   • Designing effective sparklines
   • Contextualizing numbers with targets

Hands-on project: Redesign a poorly structured metrics report into a clean, accessible executive dashboard following UI/UX best practices.

You can frame business problems and present technical insights to non-technical stakeholders with clarity.

1. Framing the question90 min
   • Translating business goals into metrics
   • Defining the analytical scope
   • Setting up hypotheses
2. Building a narrative90 min
   • The data storytelling arc
   • Leading with the conclusion
   • Structuring supporting evidence
3. Presenting to non-technical audiences90 min
   • Translating statistical jargon
   • Using analogies for complex findings
   • Handling slide design
4. Turning insight into action90 min
   • Formulating concrete recommendations
   • Cost-benefit analysis of proposals
   • Tracking implementation outcomes
5. Handling stakeholder questions90 min
   • Defending data methodology under scrutiny
   • Addressing pushback gracefully
   • Managing ambiguous follow-up requests

Hands-on project: Prepare and deliver a 10-minute strategic presentation based on analytics findings for a simulated executive board.

You can execute cohort analyses, funnel reviews, and A/B test evaluations common in product and tech companies.

1. Cohort and funnel analysis2 hrs
   • Retention cohort grids
   • Conversion funnel step-throughs
   • Identifying churn inflection points
2. A/B testing basics2 hrs
   • Control vs treatment groups
   • Sample size and power calculation
   • Evaluating conversion rate lifts
3. Marketing and product metrics90 min
   • CAC, LTV, and payback period
   • DAU/MAU stickiness ratios
   • Net Promoter Score analysis
4. Forecasting fundamentals90 min
   • Moving averages and trend analysis
   • Seasonality adjustments
   • Simple linear regression projections
5. Case studies90 min
   • SaaS subscription growth analysis
   • Marketplace supply and demand balance
   • Retail inventory turnover reports

Hands-on project: Analyze an e-commerce funnel dataset to identify drop-off bottlenecks and run an A/B test significance evaluation.

You can automate complex spreadsheet workflows using Power Query and advanced formulas.

1. Advanced formulas90 min
   • LAMBDA functions and custom formulas
   • LET variables for formula efficiency
   • Advanced error handling with IFERROR
2. Power Query2 hrs
   • M code fundamentals
   • Merging folders of CSVs automatically
   • Conditional custom columns in Power Query
3. Dashboards in Excel90 min
   • Form controls and spinners
   • Dynamic sheet switching
   • Executive-ready sheet formatting
4. Automating reports90 min
   • Refreshing data connections on open
   • Automating repetitive formatting
   • Macro recording and basic VBA review
5. Connecting Excel to databases90 min
   • ODBC and SQL Server connections
   • Direct query refresh setups
   • Parameterizing database queries

Hands-on project: Build an automated multi-source financial reporting template in Excel connected directly to relational database exports.

You can design a star schema so a dashboard answers questions the raw tables cannot.

1. Star schema and grain90 min
   • Declaring the grain
   • Fact vs dimension
   • Conformed dimensions
2. Slowly changing dimensions90 min
   • Type 1 vs Type 2
   • Surrogate keys
   • Effective dating
3. Relationships and filter flow90 min
   • Cardinality and direction
   • Bi-directional pitfalls
   • Role-playing dates
4. Measures that survive slicing90 min
   • CALCULATE and FILTER
   • Time intelligence
   • Measure vs calculated column

Hands-on project: Model the course dataset as a star schema with one fact and four conformed dimensions, implement it with a proper date table, and add three DAX measures including a year-over-year comparison that stays correct under slicing.

You can execute an end-to-end analytics project from raw data to stakeholder presentation and enter interviews with confidence.

1. End-to-end analytics capstone3 hrs
   • Scoping the capstone problem statement
   • Data ingestion, cleaning, and SQL modelling
   • Dashboard assembly and insight extraction
2. Portfolio and dashboard reviews2 hrs
   • GitHub repository organization for analysts
   • Writing clear README documentation
   • Peer review and feedback incorporation
3. Data-analyst mock interviews2 hrs
   • Live SQL coding test simulations
   • Business case study walkthroughs
   • Portfolio presentation drills
4. Resume and LinkedIn optimisation90 min
   • Highlighting project metrics on resumes
   • Structuring LinkedIn profiles for recruiter reach
   • Tailoring cover letters for analytics roles
5. Certification and next steps90 min
   • Final capstone evaluation
   • Receiving the Squarebrackets certification
   • Transitioning into job search execution

Hands-on project: Complete a capstone project taking a raw uncurated dataset, cleaning it, building a multi-tool dashboard, and presenting strategic recommendations to a panel of mentors.