THE COMPLETE COURSE ON DATA SCIENCE & BIG DATA ANALYTICS
Transform Raw Data into Actionable Intelligence with End‑to‑End Data Science Skills
Course Schedule
| Date | Venue | Fees (Face-to-Face) |
|---|---|---|
| 02 – 06 Mar 2026 | Dubai, UAE | USD 3495 per delegate |
| 09 – 13 Aug 2026 | Riyadh, KSA | USD 3495 per delegate |
Course Introduction
Data is the lifeblood of modern organizations, yet turning vast, complex datasets into clear business value remains a challenge. This intensive, hands‑on masterclass bridges the gap between theory and real‑world application, giving participants a complete toolkit for every stage of the data life‑cycle—from acquisition and cleaning to modeling, visualization, and strategic decision‑making.
Across five immersive days, you’ll explore Python, SQL, and cloud‑native analytics while mastering machine‑learning workflows that drive competitive advantage. Whether you’re a beginner looking to break into data science or an experienced professional keen to sharpen big‑data skills, this program will accelerate your journey.
Course Objectives
By the end of this course, participants will be able to:
- Frame Business Problems as Data Questions
- Collect, clean, and transform structured & unstructured data with Python & SQL
- Build, validate, and interpret supervised & unsupervised machine‑learning models
- Leverage big‑data ecosystems (Hadoop, Spark) for large‑scale processing
- Communicate insights with compelling dashboards & storytelling techniques
- Apply MLOps principles for model deployment, monitoring, and governance
Key Benefits of Attending
- Gain a comprehensive understanding of the full data analytics lifecycle
- Stay competitive in today’s data-driven business environment
- Learn practical skills from industry-experienced trainers
- Work on real-world projects and datasets
- Enhance your career prospects with cross-industry analytical skills
- Network with peers in analytics and business intelligence fields
Intended Audience
This program is designed for:
- Data analysts and scientists
- Business intelligence professionals
- IT and software engineers
- Statisticians and mathematicians
- Decision-makers seeking data-driven strategies
- Anyone interested in pursuing a career in data science or big data
Individual Benefits
Key competencies that will be developed include:
- Data exploration and interpretation skills
- Ability to create machine learning models
- Proficiency in Python, R, or similar tools
- Confidence in managing and analyzing large datasets
- Skill in translating data into business insights
Organization Benefits
Upon completing the training course, participants will demonstrate:
- Improved business decision-making through data analysis
- Enhanced forecasting and performance measurement
- Ability to leverage big data tools for cost-effective insights
- Stronger innovation driven by analytics-based strategies
- Reduced risks and data-driven problem-solving approaches
Instructional Methdology
Course Outline
Detailed 5-Day Course Outline
Training Hours: 7:30 AM – 3:30 PM
Daily Format: 3–4 Learning Modules | Coffee Breaks: 09:30 & 11:15 | Lunch Buffet: 01:00 – 02:00
Day 1: Introduction to Data Science and Analytics
- Module 1: The Data Science Lifecycle
- Data science and big data overview
- Analytics process models (CRISP-DM)
- Introduction to tools: Python, R, Jupyter Notebooks
- Module 2: Data Collection and Cleaning
- Data types and sources
- Data wrangling and preprocessing techniques
Day 2: Statistical Analysis and Visualization
- Module 1: Descriptive and Inferential Statistics
- Central tendency, distributions, correlation
- Hypothesis testing and confidence intervals
- Module 2: Data Visualization Tools
- Visual storytelling with Tableau/Power BI
- Python/R visualization libraries (matplotlib, seaborn, ggplot2)
Day 3: Predictive Modeling and Machine Learning
- Module 1: Supervised Learning
- Regression and classification algorithms
- Linear regression, logistic regression, decision trees
- Module 2: Model Evaluation
- Accuracy, precision, recall, ROC curves
Day 4: Unsupervised Learning and Big Data Technologies
- Module 1: Clustering and Dimensionality Reduction
- K-means, PCA, association rules
- Module 2: Big Data Architecture
- Hadoop ecosystem overview
- Apache Spark for distributed computing
Day 5: Capstone Project and Real-World Applications
- Module 1: Industry Use Cases
- Healthcare, finance, retail analytics
- Fraud detection, customer segmentation, recommendation engines
- Module 2: Final Project Presentation
- Team project: Data analysis from start to finish
- Instructor feedback and improvement insights
Certification
Participants will earn a Certificate of Completion in Data Science & Big Data Analytics, attesting to their capability to design, build, and operationalize data‑driven solutions that deliver measurable business impact.