Global Learning for Operational Leaders
Classroom
15 sessions
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Online / Live
15 sessions
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Introduction
Course overview
The Certificate in Data Science course offers participants an in-depth understanding of Data Science best practices and provides a foundational overview of the Big Data ecosystem and Artificial Intelligence opportunities. It goes beyond analytics, encompassing all disciplines connected to modern data. By the end of the course, participants will gain expertise in advanced techniques and technologies, enabling them to extract valuable insights from data and collaborate effectively with professionals in advanced data management fields.
What you will achieve
Learning objectives
- Understand and structure data for effective analysis
- Evaluate solutions for Data Analysis versus Machine Learning
- Distinguish between predictive models and pattern-detection models
- Make informed choices between proprietary and open-source technologies
- Map the modern data workflow from raw sources to finalized reports
- Oversee Data Science projects using project management best practices
Who should attend
Target audience
- This course is for specialists who aspire to become accustomed with data science components, and how they can be applied coordinately to solve data and business problems, as well as research issues. The course is specifically suited for managers and persons involved in marketing, CRM, research, manufacturing, quality control, app developers and IT analysts from almost any sector, such as banks, insurance companies, retail, governments, manufacturers, healthcare, telecom, transport and distributors.
- Target Competencies
- Business data analysis
- Data analytic validity
- Judging AI algorithms
- Evaluating IoT platforms
- Comparing big data results
Methodology
Learning approach
- All analytical methods and solutions are elaborated with step-by-step case studies with practical, hands on experiences. An exhaustive documentation will cover analytical topics with an exclusive face-to-face comparison between SAS, SPSS, STATISTICA, Excel, R and Python.
Complete course outline
Course outline and key learning areas
The timings provided are estimates and may vary depending on the number of participants and the discussions taking place during the sessions. They are indicative only and do not constitute a binding commitment.
Module 1
Data Analysis and Visualization
- Understanding data types and visualization techniques
- Assessing the representativeness of data
- Summarizing data using descriptive statistics
- Profiling multiple groups with statistical tests
- Creating advanced visualizations with smart charts
- Simple Linear Regression and Logistic Regression
- Identifying and addressing outliers
Module 2
Machine Learning – Supervised
- Multiple Linear and Logistic Regression
- Discriminant Analysis: Functions and probabilistic models
- Decision Trees: CART, CHAID, and Random Forests
- Support Vector Machines and K-Nearest Neighbors
- Naïve Bayes
- Neural Networks, Deep Learning, and AI applications
Module 3
Business Intelligence Forecasting – R vs. Python
- Fundamentals of Business Intelligence
- Data collection and database sources
- ETL processes (Extract, Transform, Load)
- Data storage: Warehouses, marts, and lakes
- Analytics tools: BI platforms, OLAP, dashboards, etc.
- Forecasting methods and trend analysis
- Exponential smoothing (additive and multiplicative)
- Time Series Analysis and ARIMA models
- Comparison of R and Python in statistical tests and ML algorithms
Module 4
Machine Learning: Unsupervised
- Principal Component Analysis (PCA)
- Clustering techniques: Hierarchical and K-Means
- Simple Correspondence Analysis
- Multidimensional Scaling
- Quadrant Analysis
Module 5
Project Management for Data Scientists (PMP)
- Introduction to PMP for Data Science projects
- Managing integration, scope, and cost
- Handling time, quality, and communication
- Risk management, procurement, and stakeholder engagement
FAQ
Frequently asked questions
What does Certificate in Data Science (CDS) cover?
This course covers Data Management and Business Intelligence through a structured five-day outline focused on practical application, discussion, and implementation planning.
When is the next available session?
The next scheduled session starts on 5 - 9 October 2026. See the course schedules section for the available dates and delivery formats.
Who should attend this course?
This course is for specialists who aspire to become accustomed with data science components, and how they can be applied coordinately to solve data and business problems, as well as research issues. The course is specifically suited for managers and persons involved in marketing, CRM, research, manufacturing, quality control, app developers and IT analysts from almost any sector, such as banks, insurance companies, retail, governments, manufacturers, healthcare, telecom, transport and distributors., Target Competencies, Business data analysis
Is bilingual course delivery available?
Yes. All course materials and presentations are provided in English, while bilingual explanation and discussion support may be available depending on the selected location and trainer availability. For example, when attending a course in Istanbul, you may request a Turkish-speaking trainer to explain the English course content in Turkish. Arabic explanation support may also be requested at this location. Other languages are available in selected cities. You can choose your preferred language support as an optional preference during registration.
How can I register for a session?
Use any Register button next to the available course dates to open the participant registration page and submit your booking request for the selected session.
Is this course available online as well as classroom-based?
Check the course schedules section for currently published classroom and Online / Live sessions.
Where are classroom sessions delivered?
Current classroom venues include Kuala lumpur, Barcelona, London, Frankfurt, Rome, Geneva.
How long is the training programme?
The programme runs for five days, with up to five training hours per day, from 9:00 AM to 2:00 PM. Each programme includes a total of 25 training hours.
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