Data Analytics Bootcamp with AI
in Pakistan

Power BI Fundamentals

Learn how to connect Power BI Desktop to databases, Excel files, and web APIs. Understand the report, data, and model views and how data flows through each layer.

DAX Deep Dive

Write DAX measures and calculated columns to produce time-intelligence calculations (YTD, MTD, rolling 12-month) that would be impossible with basic aggregations alone.

Publishing & Sharing

Publish reports to Power BI Service, set up scheduled refresh, configure row-level security, and share dashboards with stakeholders through workspaces and apps.

Relational Database Foundations

Understand how tables, primary keys, and foreign keys work together. You'll set up a local PostgreSQL instance and populate it with real transactional datasets to query throughout the module.

Advanced Query Writing

Move from basic SELECT statements to window functions, CTEs, and subqueries. You'll solve analytics problems — cohort analysis, funnel drop-off, revenue by segment — using pure SQL.

Performance & Indexing

Learn to read query execution plans, identify bottlenecks, and add the right indexes to turn a 10-second query into a sub-second one. A skill that immediately sets you apart in data roles.

AI-Assisted Analysis

Learn how to use LLM tools (ChatGPT, Copilot) as a force-multiplier for data work — from writing SQL queries faster to summarising findings and generating formula suggestions.

Prompt Engineering for Data

Craft effective prompts to extract structured information from unstructured text, classify customer feedback at scale, and generate synthetic datasets for testing your pipelines.

Automation Workflows

Build no-code/low-code automation flows that route data, trigger alerts, and schedule reports — so your analysis runs itself and you spend time on insight, not repetition.

Python & Pandas Essentials

Start from scratch if needed — cover Python syntax, data types, and control flow before diving into Pandas DataFrames. You'll load, filter, group, merge, and reshape tabular data with confidence.

Data Cleaning in Practice

Real datasets are messy. You'll handle missing values, fix data types, deduplicate records, standardise categorical variables, and build reusable cleaning pipelines with custom functions.

Visualisation with Matplotlib & Seaborn

Go from raw numbers to publication-quality charts. Create distributions, heatmaps, scatter plots, and time-series visuals that communicate clearly to both technical and non-technical audiences.

The EDA Framework

Learn a repeatable 5-step EDA process: understand the data dictionary, assess distributions, identify correlations, detect anomalies, and form hypotheses — applied to a full e-commerce dataset.

Statistical Summaries

Go beyond mean and median — calculate skewness, kurtosis, percentile ranges, and inter-quartile ranges. Know when to log-transform a variable and why it matters for downstream models.

Storytelling with EDA

Turn your exploratory findings into a structured narrative. Build a Jupyter Notebook report that walks stakeholders from raw data to insight in a clear, reproducible format.

Supervised Learning

Train regression and classification models from scratch using scikit-learn. Understand the bias-variance tradeoff, overfitting, and how train/validation/test splits protect your evaluation metrics.

Feature Engineering

Learn to create features that actually help your model — encoding categoricals, scaling numerics, handling class imbalance, and using domain knowledge to create interaction terms.

Model Evaluation & Tuning

Use cross-validation, confusion matrices, ROC-AUC, and RMSE correctly. Apply GridSearchCV and RandomizedSearchCV to tune hyperparameters systematically rather than guessing.

Tool Landscape

Survey the leading no-code data tools — Airtable, Notion databases, Google Looker Studio, and Retool. Understand where each fits and when to reach for them instead of code.

Building with Looker Studio

Connect Google Sheets and BigQuery to Looker Studio, create blended data sources, and publish a live, auto-refreshing dashboard without writing a single line of code.

Automation with Make & Zapier

Design multi-step automation workflows that move data between tools, send alerts, and update dashboards automatically — freeing you to focus on analysis rather than manual data entry.

Probability Foundations

Build intuition for probability distributions — normal, binomial, Poisson. Understand expected value, variance, and how these concepts underpin every statistical test you'll ever run.

Hypothesis Testing

Run t-tests, chi-squared tests, and ANOVA correctly. Learn what a p-value actually means, how to set significance levels without fooling yourself, and how to report results honestly.

Regression & Correlation

Distinguish correlation from causation. Fit linear regression models by hand and with statsmodels, interpret coefficients, and validate assumptions using residual diagnostics.

Intro to GIS & Spatial Data

Learn the difference between raster and vector data, coordinate reference systems, and how to load shapefiles and GeoJSON into Python using GeoPandas.

Spatial Queries & Joins

Run spatial joins to answer questions like 'which customers are within 5km of our stores?' or 'which census tracts overlap this flood zone?' — using both Python and QGIS.

Mapping & Visualisation

Create choropleth maps, heatmaps, and interactive Folium/Leaflet maps that can be embedded in web pages — making your spatial insights accessible to anyone with a browser.