Does your current role involve Excel, reports, MIS, operations or business data, but you find yourself spending more time preparing data than actually understanding it?
Banking, healthcare, retail, telecom, logistics and IT all hire analysts to turn raw data into decisions.
SQL, Python, statistics and BI work in every sector, so your options stay open as you grow.
Analysts who can work end to end, without waiting on an engineering team, are the ones companies compete for.
Grow from Data Analyst to Senior Analyst, Analytics Engineer, Analytics Lead and Head of Data.
Agentic workflows now automate the repetitive parts of analysis. Analysts who can build them move ahead.
Graduate with real projects and a portfolio an employer can open, not just a certificate.
Solve layered database problems using SQL subqueries. Work with IN, NOT IN, EXISTS, NOT EXISTS, ANY and ALL to filter, compare and retrieve data efficiently — the query fluency almost every data analyst interview screens for.
Work with large relational datasets and learn to retrieve meaningful customer information using SQL. Apply tables, data types, functions and operators to perform effective queries at scale.
Take a messy real-world dataset and make it analysis-ready with Pandas. Handle missing values, duplicates, inconsistent formats and outliers, then document a repeatable cleaning workflow other analysts can rerun.
Run a full exploratory analysis on an unfamiliar dataset. Profile distributions, test correlations, surface anomalies and build a findings notebook with Matplotlib and Seaborn.
Segment customers using demographic, geographic and behavioural data. Build distinct groups, validate them statistically, and translate the segments into targeting and offer recommendations a marketing team can act on.
Analyse user journeys from first visit to repeat purchase. Build cohort retention tables and funnel drop-off analysis in SQL, then quantify where and why customers leave.
Build time series forecasts on historical sales data. Decompose trend and seasonality, compare forecasting approaches, evaluate accuracy, and communicate uncertainty to planning stakeholders.
Build analytical models to evaluate credit, fraud and operational risk. Identify unusual patterns and suspicious activity using anomaly detection techniques that help organisations spot threats early.
Design a star-schema warehouse and build the pipeline that feeds it. Extract from multiple sources, transform and load on a schedule, then connect a BI layer on top.
Create interactive dashboards in Power BI and Tableau to monitor revenue, margin, retention and operational health, presenting insight in a form that shortens management decision cycles.
Analyse customer financial data to assess creditworthiness and repayment risk. Build and validate risk models that help lenders make faster decisions and reduce defaults.
From a quick conversation with our team to your first live session.
Book a call to see if Data Analytics & Agentic AI is the right fit for where you want to go.
Complete your registration — pay the full fee or a registration fee to hold your seat.
We'll confirm your batch start date and send everything you need before day one.
Join live, mentor-led sessions and start building your portfolio from week one.
Seats fill by cohort — talk to our team or pay your fee to lock in your batch.