Introduction to Data Science and Artificial Intelligence with Python

Target audience

The course is intended for people who want to develop data/AI skills in practice, in particular:

• data analysts, business analysts, and reporting specialists
• people from finance, sales, operations, and marketing teams working with data
• developers and engineers who want to organize ML/LLM fundamentals in Python
• people preparing for a Data Scientist / ML Engineer role at junior/regular level

Prerequisites

• basic knowledge of Python (variables, loops, functions, working in a notebook)
• basic data skills (tables, data types, simple calculations)
• readiness to work on your own computer in a remote environment

Training methods

Practical workshops dominate (approx. 75% of the time). During the sessions, the following are used, among others:

• individual and group exercises in notebooks (Python)
• short theoretical introductions preceding labs
• case studies based on data close to real business applications
• mini-projects summarizing each day and work on pipelines
• Q&A sessions and consultations on participants’ work problems (if data/cases are provided)

Training materials

Participants receive a complete set of materials used during the course, including:

• notebooks (Jupyter) and exercise files
• datasets for labs
• cheat sheets with key Pandas functions and scikit-learn components
• links to recommended sources and documentation

Technical requirements for remote training

To participate, the following are required:

• computer with Windows/macOS/Linux (min. 8 GB RAM, 16 GB recommended)
• stable internet connection (min. 10 Mb/s)
• headset with microphone and a camera (recommended for workshop work)
• access to MS Teams or Zoom
• optional: access to the DaDesktop environment provided by NobleProg (if launched for the group)

Validation and certificates

Learning outcomes are validated based on practical tasks (notebooks), mini-projects, and quality checklists. After completing the course, participants receive a NobleProg certificate in electronic form.

Information document

The program can be adapted to the group’s needs after requirements analysis (PCQ).

Data analysis in Pandas

• Creates and modifies Series/DataFrame objects, performs operations on tables and columns
• Applies filtering, sorting, grouping, and aggregations to solve analytical problems

Verification criterion: Verification: practical tasks in a notebook (calculation results and correctness of data transformations)

Regression models

• Selects and trains a regression model for a given problem
• Evaluates the model with appropriate metrics and interprets the results
• Applies normalization and data preparation, and mitigates overfitting

Verification criterion: Verification: regression mini-project + discussion of metrics and conclusions.

Classification models

• Builds and compares classification models, selecting metrics (e.g. accuracy, precision/recall, F1, ROC-AUC)
• Applies ensemble techniques to improve prediction quality

Verification criterion: Verification: classification task + comparison of at least 2 models and justification of the choice.

Working with LLMs

• Understands basic concepts: prompt, context, embeddings, tokenization, model limitations
• Uses an LLM API to generate and process text and creates embeddings for semantic search
• Knows basic safety and quality principles (data protection, testing, limitations)

Verification criterion: Verification: integration exercise (simple prototype) + data security checklist.

Day 1 - Data analysis in Pandas

• Basic data types: Series and DataFrame (creation, indexing, data types).
• Table operations: loading data, merging (merge/join), concatenation.
• Filtering, sorting, grouping: groupby, aggregations, pivot table.
• Value modification: mapping, replace, missing data (NaN) and filling strategies.
• Column operations: feature creation and transformation, apply/assign functions.

Day 2 - Machine Learning: regression algorithms

• Introduction to regression: problem, data, ML pipeline.
• Model evaluation: metrics (MAE, MSE/RMSE, R2), train/test split.
• Normalization and standardization: when and why.
• Handling categorical variables: One-Hot Encoding and Label Encoding.
• Overfitting: diagnosis and mitigation methods.
• Cross-validation: choosing a validation strategy.
• Grid Search and hyperparameter optimization (including pipeline).

Day 3 - Machine Learning: classification algorithms

• Introduction to classification: binary and multiclass.
• Classification evaluation: confusion matrix, accuracy, precision/recall, F1, ROC-AUC.
• Overview of classification algorithms (e.g. logistic regression, trees, SVM, kNN).
• Ensemble: combining classifiers (bagging, boosting, stacking - overview and practice).

Day 4 - Large language models (LLMs)

• Introduction to LLMs: capabilities, limitations, typical business use cases.
• OpenAI API and other models: integration basics, costs, limits, best practices.
• Multimodal models: working with text and images - scenarios and limitations.
• Embeddings: semantic search, clustering, simple recommendations, RAG basics.