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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.

Duration
40h · 5 days
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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 · 4 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
Working with APIs and Databases (as technically feasible)
M04
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

How we handle your data

We process your business data (name, work e-mail, phone number, company, job title) in order to handle your corporate training inquiry and prepare an offer. The data controller is infoShare Academy Sp. z o.o., Al. Grunwaldzka 472B, 80-309 Gdańsk. Providing the data is voluntary but necessary to receive a response. You have the right to access, rectify, erase or restrict the processing of your data and to object to it. Full information is available in our data processing notice.

Optional marketing consents

The controller of your personal data is infoShare Academy sp. z o.o. The rules for processing personal data are set out in the Privacy Policy and the Data Processing Notice.

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