SoftPro9 runs Data Science with Machine Learning & AI training as live online batches in UK time, for learners in the City, Canary Wharf, Shoreditch and across London.
Last updated: 28 October 2026
London's finance and AI sectors want data scientists who can explain models and handle data responsibly. The course is split into three levels. You can join at the level that matches your experience after a short assessment at counselling.
London practice: UK GDPR and consent practice in web and data exercises.
London practice: Accessibility to WCAG 2.2 AA, as UK public-sector sites require.
London practice: UK date formats, postcodes and the April–April tax year.
Freshers usually start in junior or support versions of these roles.
Data Science with Machine Learning & AI – full course details and fee →
Every tool below is used hands-on in class or in a project, not just mentioned on a slide.
A customer-churn model with feature engineering, explainability and a business write-up
A demand-forecasting model compared against a simple baseline
A document question-answering assistant built with RAG and evaluated on real questions
A capstone deployed as an API and Streamlit app, with experiment tracking in MLflow
London project: A London house-price model using public Land Registry data, using data and conventions from London.
New batches start every Monday and Thursday, with no more than 15 learners in each. Weekday class slots in UK time: 8 AM / 3:30 PM / 6 PM. Weekend slots: 10 AM / 5 PM. Times shown are for winter; they move one hour when clocks change.
The early-morning and evening UK-time slots fit around a standard working day.
| Start date | Course | Mode | Batch size |
|---|---|---|---|
| Thu, 29 Oct 2026 | Data Science (all levels) | Live online | Up to 15 |
| Mon, 2 Nov 2026 | Data Science (all levels) | Live online | Up to 15 |
| Thu, 5 Nov 2026 | Data Science (all levels) | Live online | Up to 15 |
| Mon, 9 Nov 2026 | Data Science (all levels) | Live online | Up to 15 |
Message us with the start date and slot you want, and we will confirm your seat.
Data science uses statistics, programming and machine learning to answer questions and make predictions from data: which customers will churn, how much stock to order, whether a transaction looks fraudulent. A data scientist frames the question, prepares the data, builds and evaluates models, and explains the result to people who will act on it.
The field now overlaps heavily with AI engineering, so alongside classic machine learning (scikit-learn, XGBoost) we cover deep learning with PyTorch, natural language processing with Hugging Face transformers, building on large language models with retrieval-augmented generation (RAG), and the MLOps needed to put a model into production.
The three fields share tools and are often confused in job titles. The simplest way to tell them apart is by the question each one answers.
| Aspect | Data Analytics | Business Analytics | Data Science |
|---|---|---|---|
| Main question | What happened, and why? | What should the business do? | What will happen, and can we automate the decision? |
| Focus | Cleaning data, reporting, dashboards | Turning analysis into business decisions and plans | Predictive models, machine learning and AI |
| Core tools | Excel, SQL, Power BI, Tableau | Excel, SQL, Power BI, forecasting, BRDs and process maps | Python, statistics, scikit-learn, PyTorch, MLOps |
| Maths needed | Basic statistics | Business statistics and forecasting | Statistics, linear algebra and probability |
| Coding | Low (SQL, some Python) | Low (SQL, light Python) | High (Python every day) |
| Typical output | Dashboards and reports | Recommendations, business cases, requirement documents | Models, predictions and AI features |
| Typical first roles | Data analyst, MIS analyst, Power BI developer | Business analyst, BI analyst, strategy analyst | Junior data scientist, ML engineer |
| Best starting point for | Anyone new to data, any degree | MBA, BBA and commerce graduates, managers | Engineering, maths and statistics graduates, developers |
| Course length here | 14–16 weeks | 14–16 weeks | 24–28 weeks |
Which should you choose? If you are new to data, start with Data Analytics: it is the foundation the other two build on. Choose Business Analytics if you enjoy the business side and want to influence decisions. Choose Data Science if you like programming and maths and want to build predictive models.
London is Europe's largest financial centre and one of its biggest technology hubs: banks and insurers in the City and Canary Wharf, fintech and startups around Shoreditch and King's Cross, and public-sector digital teams. Employers value clean, tested code, accessibility and a clear explanation of your work.
Banking, insurance and fintech
Technology and startups
Public sector digital
Retail, media and consulting
Generic courses use generic datasets, and interviewers notice. Our exercises include:
| Your background | Start with | Why |
|---|---|---|
| Engineering / maths / statistics graduate | Full course | The maths will feel familiar; the tooling is what you need |
| Data / business analyst | ML modules, then deep learning | You already know SQL and the business; add prediction |
| Software developer | ML basics, then GenAI and MLOps | AI engineering roles value your production skills |
| Commerce / non-technical graduate | Data Analytics first | Build SQL, Excel and statistics before ML |
| Domain expert (finance, healthcare, supply chain) | Full course with a domain capstone | Domain knowledge is what makes models useful |
Common pairings: Data Science + Data Analytics (if you are starting without SQL or statistics), Data Science + Python (if you are new to programming), and Data Science + Web Development (for AI-product roles). Learn one course well before adding a second.
Master the python, maths and data handling before moving on.
Build and publish your projects, including the London capstone.
Apply for entry roles such as data scientist or machine-learning engineer with employers like the banking, insurance and fintech above.
Honest note: freshers usually start in junior or trainee roles, and salaries in London vary too widely for a headline figure. Ask us which openings we are seeing.
Data science has no single official certification. Cloud vendors run optional role exams such as Microsoft Azure Data Scientist Associate (DP-100) and AWS Certified Machine Learning, paid directly to the vendor and not part of our fee; we point out which modules map to them. SoftPro9 issues a course completion certificate. Hiring managers look mainly at your projects and how clearly you explain them.
A 2026 syllabus: look for Streamlit, Docker, PySpark, Databricks, not only the basics.
Projects on your own accounts, a trainer you can meet first, and a published fee with nothing hidden.
No "global certification" or "100% placement" claims: vendors run their own exams, and nobody can promise a job.
Taught from Bangalore
Live, with recordings and reviews
Interview preparation
| Area | Where | Most learners choose |
|---|---|---|
| The City, Canary Wharf | Central / east | Live online, evening slots |
| Shoreditch, King's Cross, Camden | Central / north | Live online |
| Croydon, Wimbledon, Richmond | South / west | Live online, weekend batches |
| Stratford, Ilford, Harrow, Wembley | Outer London | Live online |
London batches are live online in UK time (times shift with GMT/BST); there is no classroom in London.
London batches are live online. They are run from our Bangalore centre, which you are welcome to visit if you are ever in the city.
SoftPro9 IT Services – Bangalore
22/1, Opp Infosys, BHBCS Layout, BTM Layout 2nd Stage, BTM Layout, Bengaluru, Karnataka – 560076
Phone: +91 95354 51414
We are in BTM Layout 2nd Stage, opposite the Infosys office. The nearest metro is Jayadeva Hospital station, about a 10-minute walk away, and BMTC buses on the Outer Ring Road and Bannerghatta Road stop nearby.
Monday–Friday 9:30 AM–7:30 PM · Saturday 9:30 AM–3:30 PM · Sunday closed
The current training fee is listed on the Data Science with Machine Learning & AI course page: /course/data-science-course. It covers live classes, the practice environment, projects and interview preparation. Any optional cloud ML exam (DP-100, AWS ML) is optional, not included, and paid directly to the exam provider.
You need school-level maths and a willingness to learn. We teach the statistics, linear algebra and calculus ideas ML relies on, with intuition and code rather than proofs.
Yes. You work with LLM APIs, build a retrieval-augmented generation (RAG) assistant, and learn how to evaluate it, alongside classic ML.
Data analytics explains what happened and why, using SQL, Excel and BI tools. Data science predicts what will happen and builds models. If you are new to data, analytics is the easier start.
No. Deep-learning work runs in GPU-enabled cloud notebooks we provide during the course.
No. London learners join live online batches in UK time, with recordings and a 15-learner cap.
No. It is a private professional course and not eligible for UK student finance.
New Data Science batches start every Monday and Thursday. See the batch table on this page for the next dates and UK time class slots.
No. We provide interview preparation, resume help and referrals to openings we hear about in London, but no institute can honestly guarantee a job.
Tell us your degree, your current job (if any) and where in London you are. We will suggest a track and a batch that fits.
We use cookies to measure visits and our ads on Google, Meta (Facebook, Instagram) and LinkedIn. No analytics or ad cookies are set, and nothing is sent to Meta or LinkedIn, unless you accept. Privacy Policy