first minute (30+ days before) - 3%
access to recordings if needed
For those interested, free workshops in 
practical exercises and mini-projects
refreshments included
computer station included
Use Python in data analysis!
A course from scratch that shows you how to analyse data and how to use machine learning in your everyday work
Popular tools, lots of practice, practical skills!
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Topics:Analyst's work environment (Anaconda, Jupyter Notebook), data processing (NumPy, pandas), data acquisition from various sources, preparation and data cleaning (working with Series and DataFrame), data analysis, visualisations (Matplotlib, Seaborn), statistical analysis and inference, prediction and classification thanks to scikit-learn
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Application:management, banking, insurance, telecommunications, industry, trade and services, health care, public administration
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Duration:80 hours for the course + 40 hours working at home with our materials = 120 hours in total
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Class types:
- weekends (Sat-Sun, every 2 weeks on average)
- day classes (a 4-day block and two 3-day blocks)
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Group:Stationary classes - max 12 people in the room.
Remote courses - up to 17 people in total.
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Enrolment:Course from scratch
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Location:Warsaw, Krakow, Katowice, Gdansk, Poznan, Wroclaw and remotely (online live with an instructor and the group)
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Flexibility:a) You can cancel up to 15 days before the start
b) during classes you can:
- switch from on-site to remote mode
- switch from remote to on-site classes
- receive a recording in case of unexpected events
- suspend participation and continue later
(depending on the availability of free spots) -
Price:1190 EUR*
Data analysis and the Python language
Drawing conclusions from available data is increasingly becoming the primary method of decision-making. Until recently, only large analytical companies were hired for this purpose, and only the largest market players could afford their services. The emergence of simple (and often free) tools for data analysis makes it possible for any company or individual to apply the same methods.
You just have to learn them.
Accessible, ready-made solutions for machine learning have allowed even novice analysts to skip the tedious process of searching for hidden regularities and therefore achieve results previously only available to experienced statisticians. As a result, you don't need to be a specialist to process more data and draw more accurate conclusions from it, and you can achieve results faster than if it was done by a human.
Python is one of the most popular languages for data analysis thanks to a wide range of ready-made libraries. It is also very easy to learn, so writing your own dedicated tools is not a problem even for novice programmers.
- Pandas is a library for manipulating data in the form of tables or sequences. It allows you to quickly and easily combine, split and transform data to draw conclusions from it.
- NumPy is a library for scientific computing. It is written in C, therefore it runs much faster than code written in Python and it is easy to use. Complex calculations can be done with a single command!
- Matplotlib is a library for data visualisation, i.e. drawing graphs. With a few commands we can create any graph, and then display it or save it to a file.
- Scikit-Learn is the most popular machine learning library. It is not as powerful as Google's TensorFlow or Facebook's PyTorch, but its ease of use, openness and the many algorithms available, make it the first choice for most analysts using Python.
- Python is a very universal language. Apart from analysing data, it allows you to easily download, process and export it both as a report and as an input file for other applications (e.g. Excel).
This course, starting from scratch, will teach you how to use packages dedicated to data analysis in Python. You do not need to be able to program or know Python (although you will get more out of the course if you know the basics of programming).
During the course, we will focus on learning about data analysis tools, not on Python itself. We will go through just enough of it to get to grips with using the tools. This is not a programming-only course, so if you want to learn how to write programs and read code freely, you can also take our Learning to Program in Python course or our short course Scripting in Python, but this is not required to get started. Knowledge of Python will give you a better understanding of these tools, but is not necessary if you want to learn how to use them. You do not need to be a professional programmer or have experience in data analysis.
This is not a mathematics lecture, but a practical course
We do not focus on teaching theory - we pass on practical knowledge. This is not a university lecture, we will not go through the mathematical basics of the models used, we will simply teach you how to use them! You will learn the work of an analyst - preparing, analysing and interpreting large amounts of data, using the Python programming language and its add-ons. During the classes you will practice the techniques you have learnt on real data sets, similar to those you may encounter in your profession.
What will you learn during the course?
make use of a data handling environment with the use of Python (Anaconda, Jupyter Notebook)
process data using NumPy and pandas
obtain data from various sources, prepare and clean data
prepare visualisations using Matplotlib and Seaborn packages
you will learn the basics of data analysis and statistical inference
you will understand how machine learning works and when to use it
Course program
This is a wide range of material – delivered in a simple and accessible way. The course programme is structured in such a way so that it can convey in 10 days the basics of data analysis using the Python language and its add-ons.
In the case of an on-demand training for your company, it is possible to adapt the programme and duration of the course individually – e.g. starting with an introduction to Python or covering more advanced topics related to data analysis.
Certificate
After completing the course, you will receive a certificate signed by ALX with a detailed list of your acquired skills. Each certificate has a unique identifier and an electronic version (regardless of whether the paper version has also been ordered). If you wish, you can share your certificate by pasting its URL – for example to your profile on a social or professional network or into your CV.
Who is this course for?
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For people who want to work in data analysis.
The programme of this course is arranged in such a way that most of our students can start working in data analysis using the tools we present immediately after completing the course, or even during the course. -
For people who use Python on a daily basis and want to develop their skills.
This course improves your professional skills and opens the way up to a promotion or a better job! -
For managers, executives and business owners
Access to current and accurate analysis is the key to making the right business decisions.
What do you need to know before the course?
Everyone is welcome to the course. Any knowledge of the basics of Python programming is welcome, but absolutely not required. If you are unfamiliar with Python, you will learn all the needed commands during the course. If you know Python, you will practice functions and data structures useful in data analysis and you will also understand better the operation of the discussed tools. If you want to learn programming first, you can take a look at our offer – the longer bootcamp Learning to Program in Python or the short course Scripting in Python.
No experience in data analysis is needed.
No programming experience is needed.
How do we teach?
We focus primarily on practical classes! The course is organised in the form of workshops - this means that there are no lectures like there are at university. We work in small groups, with an instructor at all times. All course modules are filled with practical exercises. Our instructors will present you with a set of the most common problems that arise in real business conditions when working with data - you will see for yourself how important and unique this knowledge is.
Learning at home
The course is 80 hours long and it is very intensive, but you can and should get even more out of it! How to do this? You need to make the effort to study at home as well. Our instructors always encourage you to work independently at home, preparing interesting tasks which you work on in between classes. A large number of exercises will make you consolidate the acquired knowledge and master the technology very quickly. If you have a problem with a task you can always contact your instructor.
Is bootcamp a big expense?
Spread it out in installments at no additional cost.
Participation in the bootcamp is an important investment for many of our students. It is an investment in your skills and a chance to get your dream, well-paid job in the IT industry.
It is also a considerable expense!
We know this and that is why we offer convenient fees for our bootcamps in an installment system without any additional costs.
You pay only as much as the course costs.
How to pay for bootcamp in installments?
The process is very simple - you do not have to contact any bank, you do not need to undergo complex verifications, you only need an ID document - you handle everything with our company. Check it out >>
Testimonials
Read what our customers say about our work.
The training was conducted at a high technical and organizational level and the involvement of the organizers deserves high recognition.
The participants of the course highly rated the training program, teaching materials as well as the competences and commitment of the lecturers. (...) We recommend ALX as a partner guaranteeing the proper performance of the service.
We are very pleased with the organization of the training. All trainings and trainers received high marks in surveys from our employees.
The implementation of the training program was highly appreciated by the course participants. ALX can be recommended as a reliable business partner in the field of IT training, with a staff of lecturers with extensive experience.
Course programme
- Introduction to programming in Python
- Genesis and history of Python
- Applications and possibilities
- Installation and configuration of the environment
- Python interpreter
- Virtual environment (venv)
- Integrated development environment (IDE)
- Basics of Python syntax
- Interaction with the user
- Variables and basic data types
- Data structures
- Conditional statements
- Loops
- Comprehension expressions
- Procedural programming
- Basics of defining functions
- Passing arguments
- Date and time handling (`datetime` module)
- Analyst’s work environment
- Anaconda
- Conda package manager
- pip manager
- Creating a virtual environment
- Jupyter notebook
- Markdown
- Elements of Latex notation
- Anaconda
- Data processing
- Introduction to NumPy
- Creating vectors and matrices
- Transformations, operations in NumPy
- Selection
- Vectorization
- Broadcasting
- Arithmetic and algebra using NumPy
- Solving linear equations
- Introduction to Pandas
- Data series and frames
- Obtaining data from various sources
- Files
- Resources on the Internet
- Databases
- Data Preparation and Cleansing – DataFrame Operations and Transformations
- Deleting columns and rows
- Changing dimensions – reshaping
- Pivoting
- Ranking and sorting data
- Combining frames (concatenate, merge, join)
- Introduction to NumPy
- Data analysis
- Visualizations
- Introduction to matplotlib
- Generating charts from pandas
- Seaborn and other data visualization tools in Python
- Basics of statistical analysis
- Statistical inference
- Visualizations
- Introduction to machine learning
- Review of machine learning methods and algorithms
- Machine learning methods
- Supervised learning
- Unsupervised learning
- Machine learning methods
- Machine learning process
- Data mining
- How to choose the best model for the task
- Data preparation
- Training set
- Test set
- Model training
- Model validation
- Model overfitting
- Data dimensionality reduction techniques
- Overview of machine learning methods
- Regression
- Linear Regression
- Polynomial regression
- Logistic regression
- Classification
- Data grouping
- Dimension reduction
- Artificial Neural Networks
- Regression
- Combining classifiers
- Visualizing results