Employers in banking, health, agriculture, telecoms and public service increasingly expect staff to read data critically and to know what a predictive model can and cannot do. Yet many capable professionals have never written a line of code and find most machine learning material written for specialists. Ten patient, guided days start from zero. You install Python with Anaconda and Jupyter, learn the handful of language features data work really needs, then load, filter, clean and summarise tables with pandas. Charts in Matplotlib and Seaborn and descriptive statistics follow, then your first linear regression in scikit-learn.
The second week builds on that base with logistic regression, decision trees and random forests, honest evaluation through confusion matrices and cross-validation, clustering with k-means, and short introductions to forecasting, text data and neural networks. Bias, privacy and explaining results to colleagues are covered before a guided project brings everything together. Excel-based analysts, M&E officers, finance and operations staff, researchers, graduates and managers who commission data work are all welcome. Afterwards you can prepare a dataset, build and judge a simple model, and talk confidently with data teams. Sessions run in the classroom, online or in-house, with a CPD-accredited certificate.
Data now informs decisions in almost every organisation, from loan approvals and stock planning to disease surveillance and crop advisory services. Professionals who cannot work with it directly are left relying on others to interpret it, and those who manage data projects without understanding the basics struggle to tell a sound model from a misleading one. A solid grounding in how data is prepared, analysed and modelled has become a core workplace skill rather than a specialist one.
The Fundamentals of Data Science & Machine Learning in Practice Training Course assumes no programming background. Days one to five cover the data science workflow, Python essentials, data cleaning and exploration with pandas, descriptive statistics, visualisation and a first regression model. Days six to ten go further: classification and model evaluation, feature preparation and pipelines, ensemble methods, clustering and dimensionality reduction, plus tasters in time series, text analysis and neural networks. Responsible practice, including bias, data protection and clear communication of uncertainty, runs through the programme and is drawn together on the final day.
Teaching moves at a steady pace with live coding demonstrations, short practice tasks after every concept and plenty of support from the facilitator. Datasets are drawn from familiar settings such as household surveys, mobile money transactions, clinic records and retail sales. Participants finish by completing a small project end to end and presenting it, leaving with working notebooks they can reuse and a realistic plan for continuing to learn.
On completing the ten days, participants will be able to:
By the final session, each participant takes away:
The pace is set for people who are new to code, and every idea is practised straight away. The approach includes:
Day 1: Data Science, Machine Learning and the Analytics Workflow
Day 2: Python Essentials for Data Work
Day 3: Working with Data in pandas
Day 4: Exploring Data with Statistics and Charts
Day 5: Building a First Predictive Model
Day 6: Classification and Model Evaluation
Day 7: Feature Preparation, Pipelines and Validation
Day 8: Clustering and Dimensionality Reduction
Day 9: First Steps in Time Series, Text and Neural Networks
Day 10: Responsible Practice and Guided Capstone Project
No coding experience is required. The programme suits newcomers to data work such as:
A CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy is awarded to each participant who attends the full ten days and completes the daily lab exercises and the guided capstone project.
The certificate records the course title, dates and delivery format. Together with the notebooks and project produced in class, it documents a first step into data science for appraisals or further study.
Upcoming cohorts
CPD-Accredited
Official invoice & confirmation letter provided
Team discount for 3+ seats
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Everything you need to know about this course before you register.
By the end of the Fundamentals of Data Science & Machine Learning in Practice programme, you'll be able to describe the data science workflow and where machine learning fits within it, write basic python code in jupyter notebooks to load, inspect and manipulate data, clean, filter, group and merge tabular data using pandas, and summarise data with descriptive statistics and present it in clear matplotlib and seaborn charts. The full breakdown of topics is covered session by session in the Course Outline tab above.
No coding experience is required. The programme suits newcomers to data work such as: Business and data analysts who currently rely on Excel, Monitoring and evaluation officers in NGOs and development programmes, Finance, credit and operations staff in banks, SACCOs and microfinance institutions, Researchers, statisticians and graduate students moving into machine learning, Health records and health information officers, Government planning, statistics and policy officers, IT and systems staff planning a move into data science, Managers and project leads who commission or oversee data projects, and Marketing and customer insight staff in telecoms and retail.
Fundamentals of Data Science & Machine Learning in Practice Training Course typically runs as 10 Days. It's available as in-person classroom, live virtual, and in-house corporate training — every course can also be delivered on-site for your team on dates that suit you.
Fundamentals of Data Science & Machine Learning in Practice Training Course is scheduled in-classroom in Nairobi, Kenya, Mombasa, Kenya, Naivasha, Kenya, and Kisumu, Kenya, and 14 other locations, plus a live interactive virtual classroom you can join from anywhere. Check the schedule panel above for exact upcoming dates and fees in each location.
The next live virtual cohort of Fundamentals of Data Science & Machine Learning in Practice starts October 19, 2026, with new classroom cohorts also running on a rolling basis. Pick a date and location in the schedule panel above, then click "Register for the Course" — it takes a few minutes and your seat is confirmed once payment or a signed purchase order is received.
Yes — delegates who meet the attendance requirement receive a Certificate of Completion for Fundamentals of Data Science & Machine Learning in Practice Training Course from Vision Reach Global Consultancy, issued in the name you register with, so double-check the spelling at checkout.
Fundamentals of Data Science & Machine Learning in Practice Training Course is pitched at beginner professionals. If you're unsure whether it's the right fit for your current role or background, message our training advisors before you register and they'll help you confirm.
Fees for Fundamentals of Data Science & Machine Learning in Practice Training Course vary by delivery location and format and are shown in real time in the schedule panel above once you pick a date. Register 3 or more delegates on the same course together and a 5% team discount is applied automatically — larger cohorts can request a custom corporate quote.
Yes — Fundamentals of Data Science & Machine Learning in Practice Training Course can be delivered on-site at your offices (or virtually for distributed teams), with case studies and examples tailored to your industry and the specific challenges your team is working through. Switch to the "In-House" tab in the schedule panel above to request a proposal.
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