course | Data Science for Business Intelligence
Join our Data Science for Business Intelligence course in Dubai. Gain practical data science business intelligence skills with expert guidance, real cases, and
DIGTR-498 | Data Science for Business Intelligence
Course Sector : Digital Transformation and Innovation
| Duration | Date from | Date to | Course Venue | Course fees | Book a course |
|---|---|---|---|---|---|
| 5 Days | 07/09/2026 | 11/09/2026 | Dubai | $4,250 | Book now |
| 5 Days | 09/11/2026 | 13/11/2026 | Barcelona | $4,950 | Book now |
| 5 Days | 05/04/2027 | 09/04/2027 | Dubai | $4,250 | Book now |
| 5 Days | 10/05/2027 | 14/05/2027 | Abu Dhabi | $4,250 | Book now |
Course Introduction
Data and effective management of it are critical for any business as it helps them make decisions based on trends, statistical numbers, and facts. Due to the importance of data, data science as a multi-disciplinary field has developed. It utilizes scientific approaches, frameworks, algorithms, and procedures to extract insight from a massive amount of data.
This course is designed to provide participants with the essential concepts and techniques for data mining and its uses in business applications to enable business intelligence. The course highlights topics such as data mining tools to model business problems and interesting patterns for decision support, as well as several cases that discuss strategies, outcomes, and impact on organizations when using data mining.
Course objective
- Effectively deploy data science and how to build your organisation's capability to support this
- Recognize the latest developments and make proactive and evidence-based business
- decisions instead of reactive, trial-and-error-based ones
- Understand what data is and create insights gathered from data solutions and all other available data sets
- Manage the entire process of using data to make better business decisions: extraction, cleaning, understanding, modeling, and presenting.
- Develop an organised framework to capitalise on data opportunities and maximise its immense untapped value.
- Lead and manage a skilled team to innovate and harness the value of data in your business
Course audience
- Business and technology leaders
- Business Unit Managers
- Business Development Consultants
- General Managers / Regional Managers
- Senior and mid-level leaders
- Individual leaders of all levels in the organization
- Art Director
- Marketing Consultants
- Marketing Development Manager
Course Outline | Day 01
Data Visualization
- How to use cutting-edge software to crunch large data sets into powerful and informative visualisations.
- Transform, understand, and simplify data, using it to move your organisation from
- ‘What is data?’ to knowing why, when, and how to use it for competitive advantage.
Introduction to Predictive Analytics
- Simple linear regression
- Multiple linear regression, interpretation, and basic inference
Predictive Modelling
- How to use data mining and probability to forecast outcomes.
- Identifying features that are likely to influence future results and successes using historical data
Course Outline | Day 02
Classification
-
Predict future successes and solve problems before they occur.
Model Accounting and Multicollinearity
- Extra and partial sums of squares, R-squared
- New Food and Quality Control cases
- Multicollinearity
- Quality control case
- Residual, QQ, and influence plots
Diagnostics and Transformations
- Transformations, the multiplicative model, and polynomials
- Business failure and purifier cases
Course Outline | Day 03
Categorical Predictor Variables, Interactions, and Logistic Regression
- Dummy variables
- Interactions
- Logistic regression
Model Evaluation, Selection, and Regularization
- Confusion tables, ROC curves, AUC
- Penalized measures of fit
- Test sets and k-fold cross-validation
- Variable subset selection
- Ridge regression and the lasso
Course Outline | Day 04
Midterm and Smoothing
- In-class midterm, 80 minutes, covers chapters 3 and 4 (not 5 and 6)
- Bin smoothers, k-nearest neighbors
- Step functions, piecewise linear models, and cubic splines
GAMS and Trees
- Generalized additive models
- CART
Course Outline | Day 05
Bagging, Random Forests, Principal Components
- Bagging and random forests
- Stumps, shrubs, and boosted trees, as time permits
- Principal component analysis
Clustering and Recommendation Systems
- K-means and hierarchical clustering
- Distance metrics
- Overview of recommendation systems: popularity, user-based, item-based, SVD, as time permits
- How to develop meaningful segmentations or clusters with similarities.
- Develop more targeted customer engagement strategies or identify similar products and build recommendation engines.
BOOST’s Professional Attendance Certificate “BPAC”
BPAC is always given to the delegates after completing the training course,and depends on their attendance of the program at a rate of no less than 80%,besides their active participation and engagement during the program sessions.
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