Professional programmes
Data Science with AI
Transform data into decisions, predictive models and deployable AI-powered solutions.
- Theory
- 36 hours
- Practical and lab
- 48 hours
- Assessment and capstone
- 12 hours
- Length
- 3 months
- Planned hours
- 96 hours
- Weekly schedule
- Two 4-hour sessions a week
- Level
- Advanced applied
- Delivery
- On campus, or blended where the approved programme permits.
- Location
- Riyadh
- Next start
- To be confirmed
Overview
Participants move from raw datasets to a complete decision-ready solution: a reproducible notebook, a tested predictive or analytical model, an interactive data application or dashboard, and a professional presentation of findings.
This three-month professional program develops the practical ability to collect, prepare, analyse and visualise data; build and evaluate machine-learning models; and communicate evidence-based recommendations. AI-assisted workflows are introduced as productivity tools for coding, exploration, documentation and solution design, while learners remain responsible for data quality, validation, privacy and professional judgement.
Who it is for
Who should join, and what to bring.
Who should join
- Graduates and final-year students seeking applied data and AI capability.
- Working professionals in engineering, finance, operations, marketing, HR, healthcare, education or administration.
- Analysts, developers and researchers who want stronger machine-learning and deployment skills.
- Entrepreneurs and managers who need to evaluate data-driven opportunities and communicate with technical teams.
Recommended before you start
- Comfort with computers, spreadsheets and logical problem-solving.
- Basic Python knowledge is strongly recommended; a bridge module may be assigned where needed.
- Foundational algebra and descriptive statistics are helpful.
- A laptop capable of running the approved software stack and reliable internet access.
What you will learn
By the end, you will be able to:
- Frame a business or research question as a measurable data problem.
- Acquire, inspect, clean, transform and combine structured datasets.
- Use Python, NumPy and pandas for reproducible analysis.
- Write practical SQL queries to retrieve, join, filter and aggregate data.
- Apply statistics and exploratory data analysis to identify patterns, uncertainty and data-quality risks.
- Create clear visualisations and explain insights to technical and non-technical audiences.
- Build baseline supervised and unsupervised machine-learning models.
- Select suitable evaluation metrics, use cross-validation and reduce leakage and overfitting.
- Construct preprocessing and modelling pipelines with documented assumptions.
- Use generative-AI assistants responsibly to accelerate code drafting, debugging and documentation without surrendering verification.
- Prototype and present an interactive data/AI application.
- Deliver an end-to-end capstone with technical documentation and an executive-level story.
- Problem framing
- Data acquisition
- Cleaning and exploration
- Feature design
- Model building
- Evaluation
- Interpretation
- Application prototype
- Decision communication
Curriculum
Twelve weeks, three months.
Foundations, Data Preparation & Analytical Thinking
Weeks 1 to 4
- Data science lifecycle, AI/ML terminology and business problem framing
- Python environment, Jupyter notebooks, reproducibility and project structure
- Data types, files, APIs and databases; responsible dataset selection
Lab: define a measurable use case and create a structured analysis notebook.
- Efficient arrays, tabular data, indexing, filtering, grouping and aggregation
- Reading and writing CSV, Excel, JSON and SQL-connected data
- Missing values, duplicates, data types and transformation logic
Lab: clean and profile a multi-source operational dataset.
- Quality checks, outliers, inconsistent categories and date/time issues
- Merging, reshaping, feature creation and auditable cleaning steps
- Data dictionaries, assumptions logs and validation rules
Lab: produce a reusable cleaning pipeline and quality report.
- SELECT, WHERE, GROUP BY, HAVING, JOIN, subqueries and window-function concepts
- Turning analytical questions into reliable queries
- Query validation and safe handling of sensitive fields
Mini-project 1: SQL-to-Python exploratory analysis with a management summary.
Tools and projects
What you will work with, and what you will make.
Tools
- Python and Jupyter Notebook/JupyterLab or an approved cloud notebook.
- NumPy and pandas for numerical and tabular workflows.
- SQL using an approved relational database environment.
- Matplotlib and Seaborn; Plotly or an approved BI tool for selected visual work.
- scikit-learn for preprocessing, supervised/unsupervised learning, model selection and evaluation.
- Git/GitHub or an approved repository for version control and portfolio delivery.
- Streamlit for an interactive data application prototype.
- Approved generative-AI assistants for supervised productivity exercises. Tool access may vary by batch, licence and institutional policy.
The final set of tools is confirmed for each batch.
Example projects
- Sales or demand forecasting and management dashboard
- Customer churn or retention-risk classification
- Customer segmentation and campaign recommendations
- Operational anomaly or quality-risk detection
- Workforce analytics with privacy-safe, non-discriminatory framing
- Public or institutional dataset analysis with an interactive evidence portal
The capstone: what you hand in
- Problem statement, success metric and data-governance note.
- Clean dataset or reproducible data pipeline with data dictionary.
- Exploratory analysis and validated model or analytical method.
- Evaluation, error analysis, limitations and responsible-use statement.
- Interactive application/dashboard, code repository and technical documentation.
- Executive summary and live presentation/demonstration.
Assessment
Assessed on the work you produce.
- Labs and technical exercises25%Notebook/code quality and correct execution
- Applied assignments20%Analysis, interpretation and communication
- Mini-projects20%End-to-end problem-solving
- Capstone project25%Validated solution and documentation
- Presentation and professional practice10%Defence, clarity, ethics and teamwork
How it is taught
- Instructor-led concept sessions supported by live coding.
- Guided labs followed by independent practice.
- Case studies from multiple professional sectors.
- Code review, peer feedback and mentor checkpoints.
- Portfolio-focused mini-projects and an integrated capstone.
- Guest industry talk or project review where available.
Certificate wording and issuing details will follow the final TVTC approval. No accreditation is claimed before that approval.
Dates and fees
Start dates and fees, on request.
Next start
To be announced
Enquire now and we will tell you the next start date and schedule.
Fees
Request fee details
We send the fee, and any options for paying, with your enquiry reply.
Questions
Questions people ask.
It is designed as an advanced applied program. Basic Python and quantitative familiarity are recommended; an entry diagnostic or bridge support may be offered.
Yes. The recommended structure includes guided labs, two mini-projects and an integrated capstone.
Yes. Machine learning is central, and approved generative-AI assistants are used for supervised productivity exercises such as code drafting, critique and documentation. Human validation remains mandatory.
The proposed stack includes Python, Jupyter, NumPy, pandas, SQL, visualisation libraries, scikit-learn, version control and Streamlit. The final stack is confirmed for each batch.
Yes. TechnoBerg can discuss contextualised cohorts, project mentoring, faculty development and sector-specific cases under a separate approved scope.
Certificate type, eligibility, wording and recognition must match the management-approved offer for the specific batch.
No. The program develops demonstrable skills and project evidence; employment outcomes depend on many factors and are not guaranteed.
Only with written authorisation, appropriate anonymisation, agreed security controls and a defined confidentiality arrangement.
Beyond the course
From learning to a technology career.
Strong performance in labs, projects and capstones can lead to further opportunities with TechnoBerg. They are merit-based, subject to availability, legal eligibility and formal selection, and never guaranteed. See the talent pathway