Newton Institute of Technology · Migori, Kenya
Data Science and Analytics Course in Kenya
Build practical skills in Excel, Python, SQL, data visualisation, machine learning and responsible analytical reporting.
Course at a Glance
Data is everywhere, but useful decisions do not come from raw figures alone. Someone has to organise the information, correct errors, study the patterns and explain what the results mean.
Newton Institute of Technology offers practical training for learners who want to develop competence in data analysis, Python programming, statistics, databases, visualisation, machine learning and artificial intelligence. The programme is structured to take a beginner from computer-based analysis to an end-to-end project without rushing the foundations.
| Course information | Confirmed details |
|---|---|
| Institutional training | 6 months |
| Industrial attachment | Compulsory 3 months |
| Complete pathway | Approximately 9 months |
| School fee | KSh 60,000 |
| Study options | Physical and online |
| Class schedule | Monday to Friday |
| Daily training time | 3 hours |
| Evening classes | Available for busy learners |
| Weekend classes | Not available |
| Maximum class size | 25 learners |
| Admissions | Continuously open |
| Minimum KCSE grade | No formal minimum grade |
| D and D+ applicants | Accepted |
| Programming experience | Not required |
| Basic computer skills | Required |
| Personal laptop | Strongly recommended |
| Accommodation assistance | Available |
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What the Course Is Designed to Achieve
Many organisations collect large amounts of information but struggle to use it well. A school may have student-performance records. A hospital may hold staffing and attendance data. A business may have years of sales, stock and customer information. Without proper analysis, those records remain underused.
This course trains learners to turn such information into useful findings. Students begin with spreadsheets, statistics and basic data handling before progressing to Python, databases, visualisation and machine learning. The sequence builds confidence gradually instead of introducing advanced tools before the learner understands the foundations.
By the end of institutional training, a committed learner should be able to clean a dataset, analyse it, create understandable charts, query a database, build an introductory machine-learning model and present the findings professionally.
Prepare reliable data
Identify missing values, duplicates, inconsistent categories, incorrect dates and unsuitable formats before analysis begins.
Find useful patterns
Use statistics, spreadsheet functions, Python and SQL to explore records and answer defined analytical questions.
Explain the result
Create appropriate charts, document limitations and communicate conclusions to technical and non-technical audiences.
Effective data science training should take learners through the complete data lifecycle—from collecting and cleaning information to analysing patterns, visualising findings, developing models and communicating conclusions responsibly. At NIT, students build practical competence in Python, databases, statistics and machine learning so that they can approach real data problems with accuracy, discipline and sound professional judgement.
Engineer Wilfred Mwendia
Software Engineer and Principal, Newton Institute of Technology
Six-Month Curriculum
The curriculum follows a month-by-month progression. Each stage builds on the knowledge and practical skills developed in the previous month.
Excel, Data and Statistical Foundations
- Data types, sources, accuracy and organisation
- Excel formulas, sorting, filtering and lookup functions
- Pivot tables, charts and data validation
- Mean, median, mode, percentages, ratios and range
- Standard deviation, frequency distributions and basic probability
Outcome: Organise a dataset, identify obvious errors and prepare a simple analytical summary.
Python Programming
- Variables, data types, conditions and loops
- Functions, file handling and error correction
- Lists, dictionaries, strings and numerical values
- Jupyter Notebook for code, explanations and results
- Git and GitHub for file organisation and portfolio development
Outcome: Write, test and explain foundation-level Python programs for data work.
Data Cleaning, NumPy, Pandas and SQL
- Import spreadsheet and CSV files
- Handle missing values and remove duplicates
- Correct categories, dates and data types
- Group, summarise and merge related datasets
- Use SELECT, WHERE, ORDER BY, GROUP BY and table joins
Outcome: Prepare untidy data and retrieve relevant records from a relational database.
Visualisation and Reporting
- Bar charts, line graphs, histograms and scatter plots
- Box plots, heat maps and interactive visualisations
- Chart selection, titles, labels and scales
- Matplotlib, Seaborn and Plotly
- Analytical summaries, limitations and presentation of findings
Outcome: Select appropriate visuals and explain analytical findings clearly.
Machine Learning and AI Foundations
- Regression, classification and clustering
- Training and testing datasets
- Feature preparation and model comparison
- Accuracy, precision, recall and confusion matrices
- Overfitting, underfitting, bias and responsible use
Outcome: Build and evaluate an introductory model without treating its output as automatically reliable.
Capstone Work and Career Preparation
- Define a clear analytical problem
- Clean, document and explore a dataset
- Write SQL queries and create visualisations
- Build and evaluate an introductory model
- Prepare a technical report and present conclusions
Outcome: Complete an end-to-end analytical assignment and organise it for a professional portfolio.
Tools Used During Training
The programme combines spreadsheet, programming, database, visualisation and machine-learning tools used in modern data work.
Analysis foundations
Tools for organising records, working with numerical information and documenting an analysis.
- Microsoft Excel
- Jupyter Notebook
- Statistics
- Git
- GitHub
Programming and databases
Tools for cleaning, transforming, automating and retrieving information.
- Python
- NumPy
- Pandas
- SQL
- MySQL
Visualisation and modelling
Tools for presenting patterns and introducing supervised, unsupervised and neural-network workflows.
- Matplotlib
- Seaborn
- Plotly
- Scikit-learn
- TensorFlow
- Define the question
- Collect and inspect
- Clean and organise
- Analyse and model
- Visualise and explain
Physical and Online Learning
Learners may attend physical classes at Newton Institute of Technology in Migori or participate online through the institution’s learning arrangements. Programming and analysis improve through regular practice, so students should expect to spend additional time on assignments and exercises outside scheduled lessons.
Accommodation assistance
Assistance is available for learners travelling from outside Migori. Contact admissions early to discuss suitable arrangements.
Entry and Laptop Requirements
KCSE entry
There is no formal minimum KCSE grade. Applicants with D and D+ grades may enrol if they are willing to practise and complete the required work.
Previous experience
Programming experience is not required. Python is taught from the foundation level. Basic computer competence is required.
Personal laptop
Owning a laptop is strongly recommended because regular access supports practice, assignments and portfolio development.
Minimum recommended laptop specification
Processor: Intel Core i5 or equivalent AMD Ryzen · Memory: 8 GB RAM · Storage: 256 GB SSD
Learners without adequate computer skills may first take NIT’s two-month Basic Computer and Computer Packages Course.
Compulsory Industrial Attachment
After the six-month institutional programme, learners undertake a compulsory three-month industrial attachment. The purpose is to expose students to real workplace procedures and expectations.
Depending on the organisation, an attachment learner may assist with spreadsheet records, database information, reporting, monitoring and evaluation data, research records, attendance systems or inventory information.
The attachment also develops professional habits that are difficult to learn in the classroom alone, including accuracy, confidentiality, teamwork, time management and communication with supervisors.
The complete pathway is approximately nine months
Six months are spent in institutional training, followed by the compulsory three-month industrial attachment.
Career Pathways
The course prepares graduates for entry-level roles involving data handling, reporting and analysis.
Data analysis and quality
Junior data analyst, data-quality assistant or operations-analysis assistant.
Reporting and visualisation
Reporting assistant, database-reporting assistant or data-visualisation assistant.
Research and monitoring
Research-data assistant or monitoring and evaluation data assistant.
Business intelligence support
Business-intelligence support assistant in administrative, finance, ICT or operations teams.
Machine learning
Machine-learning trainee or AI project assistant under appropriate technical supervision.
Further development
Build deeper competence through continued projects, industrial experience and further study.
A realistic career pathway: This course provides a strong foundation. Progression into advanced data-science roles requires continued practice, further learning and professional experience.
Who Can Join?
- School leavers: KCSE graduates, including applicants with D and D+ grades.
- Students: College and university learners who want practical analytical skills.
- Working professionals: Administrative, research, monitoring and evaluation, finance, ICT and operations staff.
- Entrepreneurs: Business owners who want to understand sales, stock, customer or operational records.
- Career changers: Learners moving toward data analysis, machine learning or further study in artificial intelligence.
Frequently Asked Questions
How much is the Data Science and Analytics Course?
The school fee is KSh 60,000.
How long does the course take?
Institutional training takes six months, followed by a compulsory three-month industrial attachment. The complete pathway is approximately nine months.
Are online classes available?
Yes. Learners may study physically at NIT in Migori or online through the institution’s learning arrangements.
What is the class schedule?
Classes run Monday to Friday for three hours per day. Evening sessions are available for busy learners.
Are weekend classes available?
No. Weekend classes are not available for this programme.
What KCSE grade is required?
There is no formal minimum grade. Applicants with D and D+ grades may enrol.
Do I need programming experience?
No. Python is taught from the beginning. Basic computer competence is required.
Is a laptop required?
Personal laptop ownership is strongly recommended. The minimum recommended specification is an Intel Core i5 or equivalent AMD Ryzen processor, 8 GB RAM and 256 GB SSD storage.
When can I apply?
Admissions are continuously open. Contact NIT to confirm the current reporting arrangements.
Admissions continuously open
Apply for the Data Science and Analytics Course
Build practical skills in data analysis, Python, SQL, visualisation and machine learning at Newton Institute of Technology.
Primary email
newtoninstituteoftechnology@gmail.com · Secondary email
Along the Migori–Sirare Highway, approximately 0.3 km opposite Rubis Filling Station.
- Written by
- Engineer Wilfred Mwendia
- Technical review
- Engineer Simon Barongo
- Institution
- Newton Institute of Technology
- Last reviewed
- July 2026
- Review cycle
- Whenever the syllabus or course arrangements change, and at least once every six months