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Data

Data analysis and machine learning

This training presents a sample program that can be tailored to the group’s expectations and skill level.

Ask about the program → See all
Who it's for

Ideal for teams that…

1 People developing toward machine learning and artificial intelligence
2 Data analysts needing tools to implement and automate their own analyses and algorithms
3 Python programmers looking to expand their competencies in data analysis and machine learning
Outcomes after the program

Hands-on AI and data analytics workshops — built around your team's real cases.

✓

Perform data analysis and machine learning with Python libraries: Pandas, NumPy, SciPy, matplotlib, seaborn

✓

Acquire data, perform analysis, handle missing data, and apply cleaning procedures

✓

Use visualization techniques (matplotlib, seaborn), export results, and save visualizations

✓

Build models in Scikit-learn: training, hyperparameter tuning, solving classification, regression, and clustering problems

✓

Work with neural networks in TensorFlow and Keras: building, training, fine-tuning, transfer learning, and applying models for image and language processing

✓

Learn about model productionization: theory of monitoring and daily operations with machine learning models

Program · 3 modules

What we actually do

M01
Computational and Algorithmic Tools (Pandas, NumPy, SciPy)
  • · Data acquisition
  • · Data analysis and functions
  • · Data operations – handling missing data
  • · Data cleaning procedures
M02
Visualization (matplotlib, seaborn)
  • · Data visualization and presentation methods
  • · Exporting and saving visualizations
M03
Machine Learning and Deep Learning in Python
  • · Model creation in Scikit-learn (training, hyperparameters, classification and regression problems)
  • · Model creation in Scikit-learn (regression, clustering, model comparison)
  • · Neural networks in TensorFlow and Keras (building, training, fine-tuning, transfer learning, architectures for image and language processing)
  • · Model productionization – theoretical aspects of monitoring and day-to-day ML operations
Every module is adapted to your stack and context. The above is a starting point — not a fixed agenda.
How we work

From brief to retro in 30 days.

01

Brief & diagnosis

A call with the team lead + a short survey for participants. We define goals, gap and context.

02

Program customization

We adapt modules, case studies and code examples to your stack. Approval in 5 days.

03

Workshop

Trainer-led sessions, hands-on, code review. Mentor available between sessions too.

04

Retro + report

Outcome report for the team and lead. 30 days of consulting included.

Inquiry

Send a brief. We'll reply within 1 day.

After a short brief we'll prepare a program and a quote. No obligations — it's just a starting point.

✓Quote within 48h of the brief
✓First session within 30 days
✓Pilot before the full decision
✓VAT invoice, payment in instalments possible

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