Live Online Fundamentals of Data Science & Machine Learning in Practice Training Course
Live online, instructor-led · next cohort Oct 19, 2026 · from USD 1,700
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.
At a Glance
How Live Online Training Works
Live online training, led by a trainer in real time, for participants anywhere.
More about live online training →- Format
- Live online, instructor-led
- Duration
- Mon - Fri (10 Days)
- Fee
- USD 1,700 (KES 160,000)
- Next start dates
- Oct 19, 2026, Nov 16, 2026, Dec 14, 2026
- Certificate
- Certificate of Completion
- What you need
- A computer with a reliable internet connection, a webcam and a headset
About this course
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.
Training Objectives
On completing the ten days, participants will 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.
- Summarise data with descriptive statistics and present it in clear Matplotlib and Seaborn charts.
- Build regression and classification models with scikit-learn and evaluate them with suitable metrics.
- Apply train-test splits, cross-validation and simple pipelines to avoid overfitting and data leakage.
- Group records into meaningful segments using k-means clustering.
- Explain model results, limitations and ethical risks in plain language to non-specialist colleagues.
What you'll learn
Key Takeaways
By the final session, each participant takes away:
- A working Python and Jupyter setup, plus a starter notebook template for new analyses.
- A pandas reference sheet covering the cleaning and reshaping steps used most often.
- A completed exploratory analysis with charts and written findings on a realistic dataset.
- Trained regression and classification models with their evaluation reports.
- A customer or beneficiary segmentation built with k-means clustering.
- A simple checklist for spotting overfitting, data leakage and biased training data.
- A finished end-to-end mini-project, presented to the group, to start a personal portfolio.
- A 90-day learning plan naming the next skills, datasets and resources to tackle.
Training Methodology
The pace is set for people who are new to code, and every idea is practised straight away. The approach includes:
- Live coding demonstrations that participants follow line by line in their own notebooks
- Short practice tasks after each concept, with the facilitator on hand to troubleshoot
- Pre-built starter notebooks so that time goes on understanding rather than typing
- Realistic datasets from survey, banking, health and retail settings
- Small-group exercises that pair participants with different levels of confidence
- A guided capstone project built step by step and presented on the last day
Day 1: Data Science, Machine Learning and the Analytics Workflow
- What data science, machine learning and artificial intelligence mean, and how they differ
- Supervised, unsupervised and reinforcement learning explained with everyday examples
- The project workflow: question, data, analysis, model and decision
- Types of data: structured and unstructured, numeric and categorical
- Roles in a data team: analyst, data scientist, data engineer and domain expert
- Use cases in banking, health, agriculture, telecoms and public services
- Installing Anaconda and launching Jupyter Notebook or Google Colab
- Lab: running a first notebook and reading a CSV file
Day 2: Python Essentials for Data Work
- Variables, numbers, strings and Boolean values
- Lists, dictionaries and when to use each
- Conditions and loops for repetitive tasks
- Writing and reusing simple functions
- Importing libraries and reading their documentation
- Introduction to NumPy arrays and vectorised calculations
- Common error messages and how to fix them
- Lab: translating familiar Excel calculations into Python
Day 3: Working with Data in pandas
- Series and DataFrames: the building blocks of pandas
- Reading CSV and Excel files and checking data types
- Selecting, filtering and sorting rows and columns
- Detecting and handling missing values and duplicates
- Creating new columns and recoding categories
- Grouping and aggregating with groupby and pivot tables
- Merging and joining tables from different sources
- Lab: cleaning a messy household survey extract
Day 4: Exploring Data with Statistics and Charts
- Measures of centre and spread: mean, median, range and standard deviation
- Distributions, skewness and outliers
- Correlation and why it does not prove causation
- Samples, populations and the idea of uncertainty
- Bar charts, histograms, box plots and scatter plots in Matplotlib and Seaborn
- Line charts for trends and seasonal patterns over time
- Choosing the right chart for the question and the audience
- Lab: an exploratory analysis of retail sales data with written findings
Day 5: Building a First Predictive Model
- How a model learns from examples: features, target and training
- Splitting data into training and test sets
- Simple and multiple linear regression with scikit-learn
- Reading coefficients and what they say about each feature
- Error measures: MAE, RMSE and R-squared
- Underfitting, overfitting and the bias-variance idea
- Lab: predicting house rents or crop yields from basic features
Day 6: Classification and Model Evaluation
- Classification problems: loan default, customer churn, fraud and disease screening
- Logistic regression and predicted probabilities
- Decision trees and how they split data
- k-nearest neighbours as an intuitive baseline
- Confusion matrix, accuracy, precision, recall and F1 score
- Why accuracy misleads when one class is rare
- Choosing a decision threshold to match the cost of errors
- Lab: predicting loan default on a microfinance dataset
Day 7: Feature Preparation, Pipelines and Validation
- Encoding categorical variables with one-hot encoding
- Scaling numeric features and when it matters
- Creating useful features from dates, ratios and counts
- Spotting and preventing data leakage
- Building scikit-learn pipelines for repeatable preparation
- k-fold cross-validation for more reliable performance estimates
- Random forests and a first look at hyperparameter tuning with GridSearchCV
- Lab: improving the Day 6 model and comparing results fairly
Day 8: Clustering and Dimensionality Reduction
- When there is no target: finding structure in unlabelled data
- k-means clustering and choosing the number of clusters
- The elbow method and silhouette scores
- Hierarchical clustering and reading a dendrogram
- Principal component analysis to simplify and visualise many variables
- Profiling clusters so that they make business sense
- Simple anomaly detection to flag unusual records
- Lab: segmenting mobile money customers or programme beneficiaries
Day 9: First Steps in Time Series, Text and Neural Networks
- Time series basics: trend, seasonality and moving averages
- A first forecast and how to check it against held-out months
- Turning text into numbers with word counts and TF-IDF
- Classifying short text such as customer complaints
- How neural networks learn, explained without heavy mathematics
- Where deep learning and large language models fit, and their limits
- Lab: forecasting monthly sales and tagging feedback comments
Day 10: Responsible Practice and Guided Capstone Project
- Bias in data and models, and who can be harmed by it
- Data protection principles, consent and anonymisation
- Explaining a model with feature importance in plain language
- Saving and reloading a trained model with joblib
- Guided capstone: from question to cleaned data, model and recommendation
- Presenting results and limitations to a non-technical audience
- Peer feedback, course review and a personal learning roadmap
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
- Marketing and customer insight staff in telecoms and retail
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.
Choose Where You Want to Learn
Fundamentals of Data Science & Machine Learning in Practice Training Course Venues
KenyaNairobiNext Nov 9· 13 dates$3,000- KenyaMombasaNext Oct 26· 13 dates$3,200
- KenyaNaivashaNext Nov 2· 13 dates$3,000
- KenyaKisumuNext Oct 26· 13 dates$3,000
- KenyaMachakosNext Nov 2· 13 dates$3,200
UgandaKampalaNext Nov 2· 13 dates$3,800
RwandaKigaliNext Oct 26· 13 dates$3,800
TanzaniaArushaNext Nov 9· 13 dates$3,800- TanzaniaDodomaNext Oct 26· 13 dates$3,600
- TanzaniaDar es SalaamNext Oct 26· 13 dates$4,000
- TanzaniaZanzibarNext Nov 16· 13 dates$5,200
NigeriaLagosNext Nov 16· 13 dates$5,600- NigeriaAbujaNext Nov 9· 13 dates$5,600
South AfricaPretoriaNext Nov 2· 13 dates$6,400- South AfricaJohannesburgNext Nov 2· 13 dates$6,800
- South AfricaCape TownNext Oct 26· 13 dates$7,600
UAEDubaiNext Oct 26· 13 dates$7,600
GhanaAccraNext Nov 16· 13 dates$9,000
Scroll sideways to see all 18 cities →
Frequently Asked Questions About Live Online Fundamentals of Data Science & Machine Learning in Practice Training Course
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 November 16, 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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