Professional programmes
Data Analytics with AI
تحليل البيانات باستخدام الذكاء الاصطناعي
Turn business data into trusted insight, dashboards and AI-assisted decisions.
- Theory
- 36 hours
- Practical and lab
- 48 hours
- Assessment and capstone
- 12 hours
- Length
- 3 months
- Planned hours
- 96 hours
- Weekly schedule
- To be confirmed
- 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 operational data to a trusted analytical product: clean and documented data, defensible insights, an interactive dashboard, a tested predictive or decision-support element, and a clear stakeholder presentation.
This three-month professional program develops the ability to collect, prepare, analyse, visualise and communicate data for real decisions. It combines spreadsheet analytics, SQL, Python, statistics, Power BI-style dashboarding, predictive methods and responsible AI-assisted workflows through practical labs and an end-to-end capstone.
Who it is for
Who should join, and what to bring.
Who should join
- Graduates and final-year students seeking practical analytics capability.
- Professionals in finance, operations, sales, marketing, HR, supply chain, healthcare, education or administration.
- MIS, reporting and business-analysis staff who want Python, SQL and AI-assisted workflow skills.
- Managers, entrepreneurs and institutional teams who need reliable, decision-ready reporting.
Recommended before you start
- Comfort with spreadsheets, percentages and basic business calculations.
- Basic statistics and computer skills; prior Python is useful but not compulsory if bridge support is approved.
- A suitable laptop and reliable internet access.
- Willingness to complete practical assignments and a capstone.
- An entry diagnostic is recommended to place learners correctly and preserve the advanced applied pace.
What you will learn
By the end, you will be able to:
- Translate stakeholder needs into measurable analytical questions and KPIs.
- Acquire, clean, validate, combine and document data from common sources.
- Use spreadsheets, SQL and Python/pandas for repeatable analysis.
- Apply descriptive statistics and exploratory analysis responsibly.
- Build accessible dashboards and communicate findings to decision-makers.
- Create reliable data models, measures and interactive reports in an approved BI environment.
- Apply introductory predictive analytics and select suitable evaluation metrics.
- Use generative-AI assistants for drafting and critique while independently validating every output.
- Recognise data leakage, bias, privacy, security and quality risks.
- Deliver a complete capstone with notebook/query work, dashboard and executive briefing.
- Question
- Data
- Quality
- Analysis
- Visualisation
- AI-supported insight
- Validation
- Decision
- Monitoring
Curriculum
Twelve weeks, three months.
Foundations & Data Preparation
Weeks 1 to 4
- Business questions, KPIs, dimensions, measures and stakeholder needs
- Files, databases, APIs and data ethics; analytics project structure
Lab: convert a vague management request into an analysis brief
- Advanced spreadsheet tables, lookups, pivots and quality checks
- Missing values, duplicates, formats, validation and audit trail
Lab: clean and reconcile an operational workbook
- Jupyter workflow, Series/DataFrame, filtering, grouping and aggregation
- Reading CSV/Excel/JSON, joins, reshaping and derived fields
Lab: reproducible cleaning and profiling notebook
- SELECT, filtering, CASE, aggregation, joins and subqueries
- CTEs and window-function concepts; query validation
Mini-project 1: SQL-driven KPI analysis
Tools and projects
What you will work with, and what you will make.
Tools
- Excel or an approved spreadsheet platform.
- SQL using PostgreSQL or another approved relational database.
- Python, Jupyter, NumPy, pandas, Matplotlib and Seaborn.
- Power BI Desktop/Service or an approved BI alternative; licence availability is batch-specific.
- scikit-learn for selected predictive analytics exercises.
- Git/repository and approved generative-AI assistant tools where permitted.
The final set of tools is confirmed for each batch.
Example projects
- Sales performance and demand dashboard
- Customer retention/churn risk analysis
- Inventory, procurement or operational-efficiency analysis
- Workforce or training analytics with privacy-safe framing
- Marketing campaign performance and segmentation
- Finance variance, cash-flow or profitability insight model
The capstone: what you hand in
- Problem brief, KPIs, success criteria and governance note.
- Cleaned data, data dictionary and transformation record.
- SQL/Python analysis with reproducible evidence.
- Interactive dashboard and validated measures.
- Predictive/AI-assisted component where appropriate, with limitations.
- Executive report, live presentation and technical handover.
Assessment
Assessed on the work you produce.
- Labs and exercises25%Correct, reproducible work
- Applied assignments20%Analysis and communication
- Mini-projects20%Integrated analytics outputs
- Capstone25%End-to-end decision product
- Presentation/practice10%Defence, ethics and teamwork
How it is taught
- Instructor-led concepts with live analysis demonstrations.
- Guided labs, practice datasets and structured reflection.
- Business cases across multiple sectors.
- Code/query review, dashboard critique and mentor checkpoints.
- Two mini-projects and one integrated capstone.
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 advanced applied training. Spreadsheet confidence is expected; diagnostics and an approved bridge may support learners who lack Python or statistics.
Yes—guided labs, two mini-projects and a capstone are recommended.
Yes. Predictive analytics and verified AI-assisted analyst workflows are included.
Power BI or an approved BI alternative may be used; licence and platform details are confirmed per batch.
No. It develops demonstrable skills and portfolio evidence; employment is not guaranteed.
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