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DB-004

Certificate in Data Science (CDS)

Intensive 5-day course · English, Arabic & European languages

Specialists and managers using data to address business or research problems examine data science in a 5 day course with 6 modules. Delegates assess statistical and machine learning methods, compare technologies and map data workflows. Forecasting, project management and the Internet of Things (IoT) connect with big data systems through practical cases and software comparisons.

Classroom

Public courses in our cities

  • 19–23 Oct 2026Riyadh£4,200 + VAT
  • 2–6 Nov 2026Doha£4,200 + VAT
  • 16–20 Nov 2026Barcelona£3,080 + VAT
  • 7–11 Dec 2026London£3,360 + VAT
  • 21–25 Dec 2026Abu Dhabi£4,200 + VAT
  • 11–15 Jan 2027Frankfurt£2,600 + VAT
Show 23 more dates
  • 25–29 Jan 2027Amman£3,400 + VAT
  • 8–12 Feb 2027Rome£3,400 + VAT
  • 22–26 Feb 2027Madrid£3,400 + VAT
  • 1–5 Mar 2027Kuala Lumpur£1,800 + VAT
  • 15–19 Mar 2027Berlin£3,400 + VAT
  • 5–9 Apr 2027Milan£3,400 + VAT
  • 19–23 Apr 2027Barcelona£3,080 + VAT
  • 3–7 May 2027London£3,360 + VAT
  • 17–21 May 2027Malaga£3,080 + VAT
  • 14–18 Jun 2027Frankfurt£2,600 + VAT
  • 28 Jun – 2 Jul 2027Sarajevo£3,400 + VAT
  • 12–16 Jul 2027Rome£3,400 + VAT
  • 26–30 Jul 2027Dubai£4,200 + VAT
  • 2–6 Aug 2027Kuala Lumpur£1,800 + VAT
  • 16–20 Aug 2027Riyadh£4,200 + VAT
  • 6–10 Sep 2027Doha£4,200 + VAT
  • 20–24 Sep 2027Barcelona£3,080 + VAT
  • 4–8 Oct 2027London£3,360 + VAT
  • 18–22 Oct 2027Geneva£4,200 + VAT
  • 8–12 Nov 2027Frankfurt£2,600 + VAT
  • 22–26 Nov 2027Abu Dhabi£4,200 + VAT
  • 13–17 Dec 2027Rome£3,400 + VAT
  • 27–31 Dec 2027Cairo£2,280 + VAT

Group discount: 2 delegates 20% · 3 delegates 35% · 5 or more delegates 50%

Can't make these dates? We can run this course for your team on dates that suit you. Ask for custom dates · In-house options

Online (live)

With a trainer, on dates to suit your team

This course can be taught live online by a trainer, in English, Arabic or any European language, for one person or a whole team.

£1,850 + VAT

Group discount: 2 delegates 20% · 3 delegates 35% · 5 or more delegates 50%

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Certificate in Data Science (CDS)

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Course overview

Data science involves several disciplines. CDS examines how to structure data for analysis, judge analytical validity and choose between conventional analysis and machine learning solutions. Predictive and pattern-detection models receive attention alongside the selection of proprietary or open-source technology. Step-by-step cases and practical work address advanced methods and the interpretation of findings, with documentation comparing SAS, SPSS, STATISTICA, Excel, R and Python directly.

The workflow links raw sources with reports. Descriptive statistics and visualisation precede supervised and unsupervised learning, business intelligence forecasting and comparison of R and Python in statistical tests and algorithms. Project management covers integration, scope, cost, time and quality as well as communication, risk, procurement and stakeholders. A final module connects IoT platforms and embedded systems with the big data ecosystem, providing context for collaboration with professionals in advanced data management.

Course objectives

By the end of the course, delegates will be able to:

  1. Structure data for effective analysis
  2. Evaluate data analysis and machine learning solutions
  3. Distinguish predictive models from models for pattern detection
  4. Choose between proprietary and open-source technologies
  5. Map the data workflow between raw sources and completed reports
  6. Manage data science projects using project management good practice

Target competencies

  • Business data analysis
  • Assessment of analytical validity
  • Evaluation of artificial intelligence algorithms
  • Evaluation of IoT platforms
  • Comparison of big data results

Who should attend

This course suits:

  • Specialists combining data science components to address data, business or research problems
  • Managers
  • Professionals in marketing and customer relationship management (CRM)
  • Researchers
  • Manufacturing professionals
  • Quality control professionals
  • Application developers
  • IT analysts
  • Professionals in banking, insurance, retail, government, manufacturing, healthcare, telecommunications, transport and distribution

Training methodology

  • Step-by-step case studies explain the analytical methods and solutions through practical work.
  • Documentation covers the analytical topics and directly compares SAS, SPSS, STATISTICA, Excel, R and Python.

Course modules

  1. Module 1

    Analysing and presenting data

    • Data types and methods of visualisation
    • Evaluating whether data is representative
    • Descriptive statistics for summarising data
    • Statistical tests to profile multiple groups
    • Advanced visualisations with smart charts
    • Simple linear regression and logistic regression methods
    • Detecting and addressing outliers
  2. Module 2

    Supervised learning methods

    • Multiple linear and logistic regression methods
    • Discriminant analysis functions and probability models
    • Decision trees using CART, CHAID and random forests
    • Support vector machines and K-nearest neighbours
    • Using Naïve Bayes
    • Neural networks, deep learning and artificial intelligence applications
  3. Module 3

    Forecasting and business intelligence with R and Python

    • Basic concepts of business intelligence
    • Collecting data and using database sources
    • Extract, transform and load (ETL) processes
    • Storage in data warehouses, marts and lakes
    • BI platforms, OLAP, dashboards and other analytical tools
    • Methods for forecasting and analysing trends
    • Additive and multiplicative exponential smoothing
    • Time series methods and ARIMA models
    • Comparing R and Python for statistical tests and machine learning algorithms
  4. Module 4

    Unsupervised learning methods

    • Applying Principal Component Analysis (PCA)
    • Hierarchical clustering and K-means methods
    • Using simple correspondence analysis
    • Using multidimensional scaling
    • Applying quadrant analysis
  5. Module 5

    Project management in data science (PMP)

    • Introducing PMP for data science projects
    • Managing project integration, scope and costs
    • Managing time, quality and communication
    • Managing risk and procurement and engaging stakeholders
  6. Module 6

    Connected systems and big data technologies

    • Foundations of IoT, M2M and embedded systems
    • Basic protocols for IoT communication
    • Big data foundations: where and when
    • Distributed file systems using HDFS
    • Comparing MapReduce with Spark for data sharing
    • Big data ecosystem tools: Spark, MongoDB, Cassandra, Flume, Cloudera, Oozie and Mahout

Certificate

Every delegate who completes the course receives an ARUK Training Certificate of Attendance.

Frequently asked questions

How do I register?

Choose a date and city in the schedule and press Register, or send us an enquiry. Our team confirms your place and sends an invoice.

Are fees shown with VAT?

Fees are shown per delegate and exclude VAT, which is added to the invoice where applicable.

Can this course be delivered for our team only?

Yes. Any ARUK course can be delivered in-house at your premises, in a meeting room we book for you, or 1-to-1.

Is this course available online?

Yes. We run it live online with a trainer, on dates that suit your team, for £1,850 + VAT per delegate.

Is there a group discount?

Yes. 20% for 2 delegates, 35% for 3 delegates and 50% for 5 or more delegates on the same course.

Can the course be delivered in Arabic or another language?

Yes. Our courses can be delivered in English, Arabic or any European language, by a trainer who teaches in that language. Mention your preferred language when you register or enquire.

What certificate will I receive?

An ARUK Training Certificate of Attendance.

Related courses

Register

Certificate in Data Science (CDS)

Key booking terms (read before you register)
  • The full fee is paid in advance, before the course starts.
  • Cancel in writing 30 or more days before the start date (45 for in-house) for a refund. Later than that, the full fee is payable.
  • No refund for non-attendance or partial attendance.
  • Places are for the named delegate only; another person cannot attend instead.
  • Moving to another date or city is only possible if ARUK agrees in writing.
  • Travel, accommodation and visas are your responsibility. A refused visa does not change the cancellation terms.

booking terms and conditions

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Certificate in Data Science (CDS)