Technology

Master of Science in Data Analytics

The Master of Science in Data Analytics is a postgraduate programme that trains graduates in big data analytics, data science, and business intelligence. The programme prepares graduates for advanced careers in data analytics, data-driven decision-making, and data science across industries.

Core areas include big data analytics, statistical analysis, data mining, machine learning, data visualisation, predictive analytics, business intelligence, data management, programming for analytics, and research methods. Students engage with theoretical foundations and practical data analytics applications using modern tools and technologies.

The programme is offered by KCA University through its School of Technology, Department of Networks and Applied Computing. KCA University is a leading ICT training institution in Kenya with established data science and analytics programmes. The programme is suitable for both specialist and non-specialist graduates who want to transform business decision-making through data.

Students develop competencies in data analysis, statistical modelling, machine learning, data visualisation, predictive analytics, big data technologies, programming, and research. The programme includes coursework, laboratory practicals, examinations, dissertation, and project work over five trimesters.

The programme is delivered over two years (five trimesters) with full-time and part-time study modes. Intakes are in January, May, and September. KCA University fees are KES 20,000 per unit with total programme cost approximately KES 540,000. Applicants need a Bachelor's degree with a significant mathematical component.

Graduates pursue careers as data analysts, data scientists, business intelligence analysts, analytics managers, researchers, and lecturers in technology companies, financial institutions, consulting firms, government agencies, and universities.

Skills Required

Big Data Analytics and Processing
Statistical Analysis and Modelling
Machine Learning and Predictive Analytics
Data Mining and Pattern Discovery
Data Visualisation and Dashboarding
Business Intelligence and Reporting
Programming for Data Analytics (Python/R)
Data Management and Database Analytics
Data-Driven Decision Making
Analytics Research Methods

Key Subjects

Big Data Analytics
Statistical Analysis
Data Mining
Machine Learning
Data Visualisation
Predictive Analytics
Business Intelligence
Data Management
Programming for Analytics
Analytics Research Methods

Certifications

CSK Professional Membership
IEEE Big Data Certification

Specializations

Big Data Analytics

Focuses on big data analytics, processing vast datasets, big data technologies, Hadoop, Spark, and managing and analysing large-scale data.

Machine Learning and Predictive Analytics

Examines machine learning, predictive modelling, classification, regression, clustering, and building predictive models from data.

Business Intelligence

Covers business intelligence, BI tools, dashboards, reporting, KPIs, and using data for business intelligence and decision-making.

Data Visualisation

Focuses on data visualisation, visual analytics, dashboards, interactive visualisations, and communicating insights through visual representations.

Statistical Analysis

Examines statistical analysis, statistical modelling, hypothesis testing, regression analysis, and applying statistical methods to data analysis.

Data Science

Covers data science, the data science lifecycle, data pipelines, data engineering, and the end-to-end process of extracting insights from data.

Duration: 2 years
Public, up to: Ksh 256,200
Private, up to: Ksh 254,518
Job market: Very high

The programme

What you study, how long it takes, and how it is delivered.

Practicalities

Study mode: Full-time, Part-time
Attachment: 0 months
Average class: 25 students
Award: Masters

What you study

10 subjects

Big Data Analytics
Statistical Analysis
Data Mining
Machine Learning
Data Visualisation
Predictive Analytics
Business Intelligence
Data Management
Programming for Analytics
Analytics Research Methods

Modules

12 in the programme

Foundations of Data Analytics
Year 1Semester 13 creditsCore
Examines foundations of data analytics, data science lifecycle, analytics frameworks, and understanding the data analytics process and its applications.
Statistical Analysis and Modelling
Year 1Semester 13 creditsCore
Covers statistical analysis, statistical methods, hypothesis testing, regression analysis, Bayesian analysis, and applying statistics to data analysis.
Data Mining and Pattern Discovery
Year 1Semester 13 creditsCore
Examines data mining, pattern discovery, association rules, clustering, classification, and discovering patterns and insights in data.
Machine Learning for Analytics
Year 1Semester 23 creditsCore
Covers machine learning, supervised learning, unsupervised learning, neural networks, deep learning, and building predictive models from data.
Big Data Technologies
Year 1Semester 23 creditsCore
Examines big data technologies, Hadoop, Spark, distributed processing, NoSQL databases, and managing and analysing large-scale datasets.
Data Visualisation and Communication
Year 1Semester 23 creditsCore
Covers data visualisation, visual analytics, dashboards, interactive visualisations, Tableau, ggplot, and communicating insights through visual representations.
Predictive Analytics and Forecasting
Year 2Semester 13 creditsCore
Examines predictive analytics, forecasting, time series analysis, predictive modelling, and using data for prediction and forecasting.
Business Intelligence and Decision Making
Year 2Semester 13 creditsCore
Covers business intelligence, BI tools, dashboards, KPIs, reporting, data-driven decision-making, and using data for business intelligence.
Programming for Data Analytics
Year 2Semester 13 creditsCore
Examines programming for analytics, Python, R, SQL, data manipulation, data cleaning, data wrangling, and programming for data analysis.
Data Management and Database Analytics
Year 2Semester 13 creditsCore
Covers data management, database analytics, data warehousing, ETL, data pipelines, data governance, and managing data for analytics.
Analytics Research Methods
Year 2Semester 23 creditsCore
Examines research methods for analytics, experimental design, evaluation methods, and conducting analytics research, preparing students for their dissertation.
Dissertation Project
Year 2Semester 26 creditsCore
Original dissertation project on a data analytics topic, demonstrating mastery of analytics methods and tools, assessed through written submission and oral defence.

Specialisations

Big Data Analytics
Focuses on big data analytics, processing vast datasets, big data technologies, Hadoop, Spark, and managing and analysing large-scale data.
Machine Learning and Predictive Analytics
Examines machine learning, predictive modelling, classification, regression, clustering, and building predictive models from data.
Business Intelligence
Covers business intelligence, BI tools, dashboards, reporting, KPIs, and using data for business intelligence and decision-making.
Data Visualisation
Focuses on data visualisation, visual analytics, dashboards, interactive visualisations, and communicating insights through visual representations.
Statistical Analysis
Examines statistical analysis, statistical modelling, hypothesis testing, regression analysis, and applying statistical methods to data analysis.
Data Science
Covers data science, the data science lifecycle, data pipelines, data engineering, and the end-to-end process of extracting insights from data.

A day as a student

A typical day during the MSc Data Analytics programme begins with morning lectures on statistical analysis, data mining, or machine learning. Students engage in theoretical discussions on data science methods, algorithms, and analytics techniques. Big data analytics sessions cover big data technologies, Hadoop, Spark, distributed processing, and managing and analysing large-scale datasets. Statistical analysis sessions examine statistical methods, hypothesis testing, regression, Bayesian analysis, and applying statistics to data analysis. Data mining sessions cover pattern discovery, association rules, clustering, classification, and discovering patterns in data. Machine learning sessions involve supervised learning, unsupervised learning, neural networks, deep learning, and building predictive models. Data visualisation sessions cover visual analytics, dashboards, interactive visualisations, ggplot, Tableau, and communicating insights visually. Predictive analytics sessions examine forecasting, time series, predictive modelling, and using data for prediction. Business intelligence sessions cover BI tools, dashboards, KPIs, reporting, and using data for business intelligence. Programming sessions involve Python, R, SQL, data manipulation, data cleaning, and programming for analytics. Data management sessions cover database analytics, data warehousing, ETL, data pipelines, and managing data for analytics. Laboratory sessions provide hands-on experience with analytics tools, programming, data analysis projects, and real datasets. The programme is delivered over five trimesters with coursework in the first four and dissertation in the final trimesters. Guest lectures from data science industry professionals, analytics consultants, and KCAU alumni provide real-world insights. The two-year programme culminates in a dissertation project.

The trade offs

In its favour

Kenya's fintech growth, digital transformation, data-driven businesses, and government data initiatives create very high demand for qualified data analytics professionals.
KCA University is a leading ICT training institution with established data science and analytics programmes through its School of Technology.
Programme is suitable for both specialist and non-specialist graduates, making it accessible to a wide range of backgrounds.
Flexible study modes with full-time and part-time options, and three intakes per year (January, May, September).

Against it

Programme is offered only at KCA University (private), with total cost approximately KES 540,000, which may be expensive for some students.
Programme requires a Bachelor's degree with significant mathematical component, which may exclude some graduates.
Trimester system may be intensive, requiring continuous coursework without long breaks.
Rapidly evolving field may require frequent curriculum updates to remain current with industry tools and technologies.

What it costs

Tuition at both ends of the market, and how to pay for it.

What it costs, and where

Against 191 technology courses

Public254k to 256k

254k at Jomo Kenyatta University of Agriculture and Technology256k at Technical University of Kenya

Private255k to 255k

255k at KCA University255k at KCA University

Annual tuition in Kenyan shillings, rounded. The upright tick is the median for this field, so a bar sitting entirely to its right is an expensive programme by the standards of its own subject.

The fine print

TUK ICT ~256K/yr (eafinder). JKUAT ~254K/yr (jkuat.ac.ke). KCA MSc Data Analytics ~255K/yr (kcau.ac.ke).

HELB postgraduate loans are available for Kenyan students. KCA University offers scholarships and financial aid. IEEE provides big data research grants. The programme's data science focus attracts industry and technology funding.

Funding options

HELB Postgraduate Loan
KCA University Scholarships
IEEE Big Data Grants

Scholarships

3 recorded

HELB Postgraduate Loan
LoanKsh 200,000Kenyan
Kenyan students pursuing postgraduate data analytics studies at recognised universities.
KCA University Scholarships
ScholarshipKsh 300,000Kenyan
KCA University offers scholarships and financial aid for postgraduate students based on academic merit and financial need.
IEEE Big Data Grants
GrantKsh 300,000
IEEE provides big data research grants for research, conference attendance, and professional development in big data and analytics.

Getting in

The grades, the alternatives, and who accredits the award.

What you need

KCSE mean grade: N/A (Postgraduate)
Alternative entry: The programme is suitable for both specialist and non-specialist graduates. Two-year programme (five trimesters) with full-time and part-time options at KCA University. Intakes in January, May, and September. Application fee KES 3,000. Contact enrollment office for actual subject and work experience requirements. Foreign students pay 20% more on tuition except EAC countries.

How you are assessed

4 components

Coursework and Assignments
Coursework30% of the mark
Continuous assessment through coursework assignments, data analysis projects, programming exercises, case studies, and class participation.
Written Examinations
Examination30% of the mark
Written examinations covering statistical analysis, data mining, machine learning, and big data technologies, conducted at end of each trimester.
Practical and Laboratory Assessment
Practical40% of the mark
Assessment of practical and laboratory skills including data analysis projects, programming, visualisation, machine learning implementation, and project reports.
Dissertation Project
Research100% of the mark
Original dissertation project on a data analytics topic, demonstrating mastery of analytics methods and tools, assessed through written submission and oral defence.

Accreditation

The programme is accredited by the Commission for University Education (CUE). KCA University offers MSc in Data Analytics through its School of Technology, Department of Networks and Applied Computing. The programme meets CUE standards for postgraduate data analytics training. Kenya ICT Authority promotes ICT and data science standards in Kenya.

Accredited by

Commission for University Education (CUE)
Academic accreditationRequired
Programme accredited by CUE. KCA University offers MSc in Data Analytics through its School of Technology, Department of Networks and Applied Computing. The programme meets CUE standards for postgraduate data analytics training.
Kenya ICT Authority
Professional accreditation
Kenya ICT Authority promotes ICT and data science standards in Kenya. The programme aligns with national ICT and data capacity building goals.

Where it leads

The roles it opens, and what you leave with.

Where graduates go

6 roles

Data Analyst
Very high demandKsh 150,000 to Ksh 600,000
Analyses data, creating insights, building dashboards, and supporting data-driven decision-making in organisations across industries.
Data Scientist
Very high demandKsh 180,000 to Ksh 700,000
Builds predictive models, applies machine learning, develops data pipelines, and extracts advanced insights from data for organisations.
Business Intelligence Analyst
High demandKsh 140,000 to Ksh 550,000
Develops BI solutions, creating dashboards, reports, KPIs, and supporting business intelligence and data-driven decision-making.
Analytics Manager
High demandKsh 200,000 to Ksh 800,000
Manages analytics teams, overseeing analytics projects, data initiatives, and driving data-driven culture in organisations.
Data Analytics Consultant
High demandKsh 180,000 to Ksh 700,000
Consults on data analytics projects, providing expert advice on analytics strategy, implementation, and data-driven transformation.
University Lecturer
High demandKsh 130,000 to Ksh 500,000
Teaches data analytics and data science in universities, conducting research and training future data analytics professionals.

Graduate outcomes

Graduates pursue careers as data analysts, data scientists, and business intelligence analysts in technology companies, financial institutions, and consulting firms.

Where these fields lead

8 careers

Tools you will learn

Python
SoftwarePrimary
Python programming language for data analysis, machine learning, data manipulation with pandas, NumPy, scikit-learn, and data analytics libraries.
R
SoftwarePrimary
R statistical programming language for statistical analysis, data visualisation, ggplot, and statistical modelling in data analytics.
Tableau
Software
Data visualisation and business intelligence tool for creating interactive dashboards, reports, and visual analytics.
Apache Spark
Software
Big data processing framework for large-scale data processing, distributed analytics, and handling big data workloads.

Industry links

Common misconceptions

Data analytics is just about making charts and graphs.
Data analytics involves statistical modelling, machine learning, big data processing, predictive analytics, and data-driven decision-making, far beyond charting.
This programme is only for computer science graduates.
KCA University designed the programme for both specialist and non-specialist graduates who want to use data for decision-making, requiring only a mathematical component.
Data analytics is the same as statistics.
While statistics is a core component, data analytics also includes machine learning, big data technologies, programming, visualisation, and business intelligence.
There is limited demand for data analysts in Kenya.
Kenya's fintech growth, digital transformation, data-driven businesses, and government data initiatives create very high demand for qualified data analytics professionals.
Data analytics requires only software skills.
Data analytics requires analytical thinking, statistical knowledge, business understanding, communication skills, and domain expertise in addition to software skills.
A master's in data analytics is redundant after a bachelor's in computer science or statistics.
The master's provides advanced analytics skills, big data technologies, machine learning expertise, and career progression to senior data science and analytics positions.