Data Science for Business Intelligence

Course Code: DIGTR 122

329 Course Visits

Course Sector:

Digital Transformation and Innovation

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Course Dates and Locations

Choose a date and location to book your seat

No.

Date

Days

Location

Fees

Enrollment

01 - 05 Sep 2025

5 Days

Dubai, UAE

$4,250

03 - 07 Nov 2025

5 Days

Barcelona, Spain

$4,950

Introduction

Training course introducion / brief

Data and effective management of it is critical for any business as it helps them make decisions based on trends, statistical numbers and facts. Due to this importance of data, data science as a multi-disciplinary field 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 the 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.

Effectively deploy data science and how to build your organisation 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

-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

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

Classification

Predict future successes and solve problems before they occur.

Model Accounting and Multicollinearity

Extra and partial sums of squares, R-squared
Newfood and Quality Control cases
Multicollinearity
Quality control case
Residual, QQ and influence plots

Diagnostics and Transformations

Transformations, the multiplicative model, polynomials
Business failure and purifier cases

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

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

Bagging, Random Forests, Principal Components

Bagging and random forests
Stumps, shrubs, 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 certificate of completion

BOOST's Professional Attendance Certificate “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.

ENDORSED EDUCATION PROVIDER

Flexible deadlines

Customized dates accordance to your schedule

Shareable Certificate

Earn certificate upon completion

COURSE METHODOLOGY

Our Training programs are implemented by combining the participants' academic knowledge and practical practice (30% theoretical / 70% practical activities).

At The end of the training program, Participants are involved in practical workshop to show their skills in applying what they were trained for. A detailed report is submitted to each participant and the training department in the organization on the results of the participant's performance and the return on training. Our programs focus on exercises, case studies, and individual and group presentations.

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TRAINING COURSE: Data Science for Business Intelligence

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.

Simple linear regressionMultiple linear regression, interpretation, and basic inference