Data Science with Python

Data Science with Python Foundation Training and Certification

Learn the best practice for effective data analysis and machine learning with this practical training about data science with Python. Explore how Python can help you take the next step in your Data & AI career, guided by Data Science experts in this 3-day course..

The Xebia Data Science with Python Foundation course and exam provides guidance on the principles and practices of loading, analysing and visualising data with libraries such as pandas. It also teaches participants how predictive models work and how Scikit-learn can be used to train and fit these models.

Who is the certification aimed at?

The course and certification is for those with a basic understanding of Python, looking to learn about the Data Science ecosystem, including libraries and the most used algorithms.

This training is aimed at budding data scientists who want to get a grip on the most popular and essential data science tools. If you want to invest in your data science skills and learn best practices for setting up projects in Python to kickstart your venture into data science, then this is the course for you.

The Data Science with Python Foundation certification has been created by APMG and Xebia. Xebia is an IT consultancy company based in the Netherlands, delivering high-quality services to cover all aspects of digital transformation. They specialise in Data & AI, Cloud, DevOps, Business Agility, Security, Professional Development, Software Development, Product Management and Quality Improvement.

Training is available directly from Xebia and the exam (booked by Xebia on completion of training) can be taken online at a date & time convenient to the candidate, using a secure, online proctoring service via APMG.

Who is the Data Science with Python Certification for?

You have some basic experience with Python (or any other programming language)
You would like to apply best practices to your data science projects
You want to get certified in data science
You hope to better communicate and collaborate with your data science colleagues

What will I learn?

Perform exploratory data analysis on your datasets with pandas
Identify a suitable machine learning algorithm and metric for your data problem
Apply best practices for data wrangling and model building
Train and evaluate machine learning models with scikit-learn
Prepare complex data for machine learning with techniques such as scaling, encoding, and imputing

Benefits

For Individuals

Gain confidence working with real‑world data by exploring and uncovering insights using pandas, helping you make smarter, evidence‑based decisions.
Build practical machine learning skills as you learn to train and evaluate models with scikit‑learn—making you more effective in data‑driven roles.
Choose the right algorithm for the right problem, improving your ability to solve business challenges efficiently and deliver high‑quality analytical results.
Handle complex datasets with ease through mastering essential techniques such as scaling, encoding, and imputing—skills that employers value highly.
Work like a professional data scientist by applying best practices in data wrangling and model building, boosting both the quality and reliability of your analyses.

For Organizations

Faster, better decisions across teams - Employees who can perform exploratory data analysis (EDA) with pandas surface trends and risks earlier, enabling quicker, evidence‑based decisions and reducing time spent debating opinions.
Higher ROI from data initiatives - Staff trained to train and evaluate ML models with scikit‑learn can prototype and validate ideas internally, cutting reliance on external vendors and accelerating time‑to‑value for analytics projects.
Solutions that fit real business problems - The ability to select the right algorithm and performance metric means teams solve the actual business question, improving operational outcomes and stakeholder trust.
More reliable pipelines, fewer data blockers - Mastery of scaling, encoding, and imputing improves data quality and model stability, reducing defects in production and lowering unplanned engineering rework.
Consistent, repeatable analytics practices - Applying best practices for data wrangling and model building establishes shared standards, improving collaboration across data, product, and engineering—and making models easier to audit and maintain.

Useful links

Data Science with Python Training and Certification

https://academy.xebia.com/training/data-science-with-python/?_gl=1*elp228*_gcl_au*NzI1OTgwNTQwLjE3NjkwODU2NzY.*_ga*MTY1MTc4MDYyNS4xNzI3Njg5Mzc1*_ga_KPY6TP6BEV*czE3NjkwODU5MTIkbzEkZzEkdDE3NjkwODU5MzAkajQyJGwwJGgw

The SFIA Framework is the global common reference for skills and competency for the digital world.

This APMG certification has been informally mapped against the SFIA Framework to indicate the skills that are addressed and referenced by the certification. Although it is not yet possible to claim this digital badge, the indicative skills can be used to plan your professional development through assessing the skills you have and the skills you need for the role you want.

SFIA website

Foundation

SFIA elements this certification confirms (endorsement)

Generic attribute Knowledge up to level 3, Data Science up to level 3, Machine Learning up to level 3, Data Visualisation up to level 4

SFIA elements this would be useful for (development)

Same as above

FAQs

Once you’ve been notified that you’ve passed your exam, you will have the option to create a digital badge in APMG's Candidate Portal.

Visit APMG's Candidate Portal, view your exam results and select 'Create Badge'.

This takes you to the Credly website where the digital badges are hosted. You will be guided through the Credly account creation process.

Once you have created an account with Credly, log into the account and accept your pending badge.

Data Science with Python