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DB-014

Financial Data Analytics with Python

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

Finance and investment professionals use Python to analyse financial data over 5 days. Delegates clean datasets, explore trends and build financial models before applying machine learning to forecasting and risk assessment. Portfolio optimisation, algorithmic trading and sentiment analysis complete the agenda, with coding exercises, industry cases and discussion of ethical and regulatory requirements.

Classroom

Public courses in our cities

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

Special requests

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Financial Data Analytics with Python

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Course overview

Financial data supports decisions. Work in banking, investment, risk management and corporate finance provides the context for processing, analysing and interpreting datasets through Python, using NumPy, pandas and Matplotlib for manipulation, statistical work and visualisation. Data wrangling and Exploratory Data Analysis (EDA) lead into time series, financial modelling and the handling of outliers and missing values, with practical coding work based on financial datasets.

Models connect the data with financial questions. Delegates examine the valuation of bonds, stocks and derivatives, risk frameworks, portfolio analysis and scenario or sensitivity analysis. Supervised and unsupervised learning support predictive work, including artificial intelligence (AI) forecasting and methods for evaluating and validating models. Trading strategies and backtesting provide further applications, together with news and social media sentiment and big data in market decisions. Ethics and industry compliance accompany the technical material, while guided exercises and cases provide practice in interpreting findings for strategic planning, investment and risk management.

Course objectives

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

  1. Use Python to clean, process and visualise financial data
  2. Apply statistics and machine learning to financial forecasts and risk assessment
  3. Develop and interpret valuation, portfolio optimisation and scenario models
  4. Use big data and sentiment analysis in financial decisions and market predictions
  5. Implement algorithmic trading strategies and explain ethical and regulatory considerations

Who should attend

This course suits:

  • Finance professionals and analysts incorporating data analytics into their work
  • Investment and risk managers using Python for portfolios and risk assessment
  • Data analysts and IT professionals working with financial datasets
  • Traders and investment professionals studying algorithmic trading and forecasting
  • Consultants and decision-makers developing financial analysis with data

Training methodology

  • Interactive lectures cover financial analytics and Python programming concepts.
  • Coding sessions use financial datasets for analysis and modelling.
  • Cases and industry examples illustrate the applications.
  • Guided exercises apply machine learning and predictive modelling step by step.
  • Group discussions and Q&A support interpretation and workplace application.

Daily agenda

  1. Day 1

    Python and financial analytics foundations

    • The contribution of data analytics to finance
    • Python programming for financial applications
    • Preparing the Python environment with Jupyter Notebooks
    • Using NumPy, pandas and Matplotlib
    • Loading financial datasets and working with them
  2. Day 2

    Exploring and preparing financial data

    • Cleaning and preprocessing financial datasets
    • Descriptive statistics and summaries of data
    • Displaying trends and patterns in financial data
    • Foundations of time series analysis
    • Finding and handling outliers and missing values
  3. Day 3

    Models, valuation and risk

    • Implementing models in Python
    • Valuing bonds, stocks and derivatives
    • Techniques for risk assessment and management
    • Optimising portfolios and analysing them
    • Scenario and sensitivity methods
  4. Day 4

    Machine learning for financial questions

    • Basic concepts in machine learning
    • Regression and classification in supervised learning
    • Clustering and dimensionality reduction in unsupervised learning
    • Building predictive models for financial forecasts
    • Assessing model performance and applying validation techniques
  5. Day 5

    Trading, sentiment and ethical considerations

    • Algorithmic trading strategies and historical backtesting
    • Analysing sentiment in financial news and social media
    • Applications of big data analytics in finance
    • Ethical issues and regulatory compliance in financial analytics

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

Financial Data Analytics with Python

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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Financial Data Analytics with Python