Data Science, Analytics & BI
A sequenced learning journey across seven programs, from data and SQL foundations through data science, business intelligence, and applied finance analytics to analytics leadership. Follow it in order to build cumulatively, or enter wherever your goals fit.
Seven programs, one sequence
The programs are arranged as a path from data foundations to applied practice and leadership. Each stage builds on the last, but every program is complete in itself.
Four stages, in order or on demand
The journey moves through four stages. Foundations builds fluency with data and SQL and the visual-analysis craft that turns numbers into decisions. Core analytics is the heart of the track: the full data science path, and the strategy to take analytics from vision to production. Applied and domain programs put those skills to work in business intelligence at scale and in banking and markets. And leadership equips those who plan and run analytics to deliver it end to end. You can walk the whole path, or step in exactly where your goals sit.
All seven, grouped by stage
Foundations
Start here. Two programs build the bedrock every analyst needs: how data is stored and queried, and how to communicate what you find so it changes decisions.
Core analytics
The two central disciplines of the field: governed business intelligence, and applied data science and machine learning.
Applied
Take the core skills into a domain. These two programs apply analytics and AI to finance, from banking dashboards to investment models.
Leadership
Rise from doing the work to directing it, with a board-ready approach to analytics strategy and transformation.
What each program is, in brief
A one-paragraph orientation to each of the seven programs and where it sits in the journey, so you can see the whole track at a glance before choosing.
Data Foundations & SQL01 · Foundations
Every analytics career rests on a foundation most people skip: understanding how data is actually stored, and being able to query it fluently. This program builds that bedrock, relational thinking and SQL from first principles to window functions, so that everything you learn afterward has something solid to stand on.
In the journey: This is the first program in the journey and the recommended starting point for anyone without a solid SQL base. Every later program, BI, data science, and the applied finance tracks, assumes the querying and data-shaping skills built here.
Data Visualization & Storytelling02 · Foundations
An analysis nobody understands changes nothing. This program teaches the other half of the analyst's craft: turning findings into visuals and narratives that are understood instantly and act on. It is the difference between insight that sits in a folder and insight that moves a decision.
In the journey: The second foundation program. It pairs directly with Business Intelligence, where these principles are applied to production dashboards, and it strengthens every later program that requires presenting results.
Business Intelligence03 · Core analytics
Business intelligence is where data becomes a daily decision tool for a whole organization. This program covers the full BI craft, dashboard design, semantic modeling, governance, and self-service, using Power BI and Tableau, with domain labs in finance, energy, and retail.
In the journey: The first core-analytics program. It builds directly on the SQL foundation and the visualization program, and its governed-modeling mindset connects to the Analytics Roadmap leadership program later in the journey.
Data Science04 · Core analytics
Data science is where analytics becomes prediction. This program takes you from Python fundamentals through machine learning to MLOps, applied to real finance and energy problems, so you can build models that forecast, classify, and score, and take them toward production.
In the journey: The second core-analytics program and the modeling heart of the track. It builds on the SQL foundation and feeds directly into the two applied finance programs, where its methods are specialized to BFSI use cases.
Data Analytics for Finance05 · Applied
This program applies analytics to finance end to end. Using Python, SQL, Power BI, and real banking datasets, you build dashboards, run portfolio and credit-risk analysis, and automate financial insights, finishing with a bank-performance analytics capstone.
In the journey: The first applied program. It takes the core analytics and data-science skills into banking and markets, and it pairs with the Machine Learning & AI in Finance program, which pushes the same domain into predictive modeling.
Machine Learning & AI in Finance06 · Applied
This program is the applied AI peak of the track. It teaches how machine learning powers modern finance, credit scoring, fraud detection, portfolio optimization, and personalization, using Python and modern AI frameworks, culminating in an AI-driven investment-advisor capstone.
In the journey: The second applied program and the AI peak of the track. It builds on Data Science and Data Analytics for Finance, and its governance and model-risk themes connect to the firm's wider AI-governance and risk offerings.
Analytics Roadmap07 · Leadership
This program is the leadership capstone of the track. It teaches how to shape analytics at the level of strategy: board-ready transformation roadmaps that align business outcomes, data products, governance, and delivery into a plan that actually gets funded and delivered.
In the journey: The leadership capstone of the journey. It assumes the practitioner skills built across the earlier programs and turns them outward into strategy, making it the natural destination for someone moving from doing analytics to leading it.
The disciplines the curriculum spans
Across the seven programs, the curriculum covers every major discipline of modern analytics, from data modeling and SQL to visualization, business intelligence, machine learning, and analytics strategy.
Sequenced, but not rigid
The seven programs are arranged as a deliberate sequence, but the sequence is a recommendation, not a requirement. Someone new to data benefits from walking the whole path: the foundations build fluency with data, SQL, and visual analysis, the core programs deepen that into data science and delivery skill, the applied programs turn it into capability on real finance problems, and the leadership program adds the ability to plan and run analytics work. Taken together, in order, they build a rounded and cumulative command of analytics that no single program provides.
But careers rarely start from zero. A working analyst might skip the foundations and go straight to data science or business intelligence; a finance professional might take only the two finance-focused programs; an analytics lead might go directly to the roadmap. The sequencing exists so that those who want a path have one, and so that those who know their destination can see exactly how a given program relates to everything around it. Every program is complete in itself, with its own syllabus, worked examples, and project, so entering at any point still gives you a whole, finished program rather than a fragment of a larger one.
What ties the track together is a consistent philosophy rather than a forced dependency. Each program is built around real tools and real datasets, extended with worked examples and code, and anchored by a project you can keep and show. That shared approach means the skills compound naturally when you take more than one, without any program assuming you have taken another. The result is a track you can treat as a guided journey or as a library of standalone programs, depending on what your career needs.
Find your starting point
Not sure where to begin? Match your situation to a program.
Cumulative by design
The sequence is built so that each stage rests on the one before, from a shared foundation up to specialist mastery and the frontier.
Practical, practitioner-built, portfolio-driven
Every program in this track shares a philosophy: learning should translate into capability you can demonstrate. Each is built around real tools and real datasets, extended with worked examples and code that show the method rather than just naming the topic. Each ends with a project or capstone you can keep and show, because the point is not only to know the material but to prove you can apply it.
The programs are also built to fit together. Sequencing them as a journey means the foundation you build in one is assumed and extended by the next, so that a learner who follows the path develops a rounded, cumulative command of analytics rather than a collection of disconnected courses. And because every program can be delivered as a private corporate cohort, teams can build the same capability together, aligned to their own data stack and use cases.
Advanced Microsoft Excel
Excel remains the most widely used analytics tool of all. The Advanced Microsoft Excel program builds professional-grade spreadsheet, modeling, and data-analysis skills that complement every program in this track.
Build the capability across your team
Every program can be delivered as a tailored corporate cohort, aligned to your data stack and use cases, whether you are upskilling an analytics team, a BI function, or a group of finance analysts.
Scope a corporate engagementFrequently asked questions
What does this track cover?
The full data-and-analytics journey: data and SQL foundations, visualization and storytelling, business intelligence, data science and machine learning, applied finance analytics, and analytics strategy and delivery.
Do I need prior experience?
No. The track is sequenced so you can start from Data & SQL Foundations with no background, or enter at a later program if you already have the earlier skills. Each program lists its own audience and prerequisites.
In what order should I take them?
The programs are sequenced as a journey: foundations first, then core analytics and data science, then applied and domain programs, with analytics roadmap for those planning or leading the work. You can follow that order or enter wherever your goals fit. The how-to-choose guide on this page helps you decide.
Which program should I choose?
Foundations and visualization suit newcomers; business intelligence suits reporting-at-scale roles; data science suits aspiring data scientists; the finance programs suit banking and markets professionals; and the roadmap suits analytics leaders. The how-to-choose guide maps situations to programs.
Are the programs self-paced or cohort-based?
Both, depending on the program. Most are available self-paced with hands-on labs, and mentor-led cohorts and private corporate delivery are available across the track.
What tools and languages are used?
The mainstream analytics stack: SQL, Python (Pandas, NumPy, scikit-learn), Power BI and Tableau, and modern warehouse platforms such as Snowflake and Databricks, matched to each program's focus.
Do the programs include hands-on practice?
Yes. Every program includes practical labs on real datasets, worked examples and code, and a portfolio-ready project or capstone.
Can my organization run these as private cohorts?
Yes. Every program can be delivered as a tailored corporate cohort, aligned to your data stack and use cases. Contact us to scope it.
Do you offer a brochure?
Yes. A downloadable PDF brochure summarizing the programs is available from the button at the top of the page.
How do the finance-focused programs relate to the rest?
Data Analytics for Finance and Machine Learning & AI in Finance apply the core analytics and data science skills to banking and markets, so they build directly on the foundations, BI, and data science programs earlier in the journey.