Machine Learning Conference

On September 27 at the Sound Garden Hotel will take place
the DataWorkshop Alumni Day as well.

This meeting is dedicated to all of DataWorkshop's online courses graduates. This is a unique opportunity to exchange experiences, meet other graduates and learn how to successfully start a career in ML.

DW Alumni Day will be held in Polish and the number of places is limited. If you are a graduate of Data Workshop Courses, you get extra access to this meeting by buying a regular conference ticket (September 28), but remember that we are limited by space, so the early bird catches the worm.

Dołącz do spotkania DW Alumni Day, które odbędzie się 27 Września 2019 w Sound Garden Hotel w ramach konferencji DWCC2019.
Spotkanie poprowadzimy w języku polskim. Wpisz swoje dane i zgłoś chęć uczestnictwa.
Tylko dla absolwentów online kursu DataWorkshop.

Uwaga! Potraktuj to jako formularz do zgłoszenie chęci i następnie czekaj na potwierdzenie z naszej strony. Bardzo prosimy o przemyślane zgłoszenie (uwzględniające Twoją dostępność w piątek, 27 września) w związku z tym, że ilość miejsc jest ograniczona.

Administratorem danych osobowych niezbędnych w procesie przetwarzania, w tym podanych powyżej, jest Biznes Myśli – Uladzimir Aliakseichanka, ul. Grodzka 42/1, 31-044 Kraków, NIP: 6751364881.
Chcesz dołączyć do spotkania DW Alumni Day, ale nie jesteś absolwentem DataWorkshop?
Zarezerwowaliśmy kilka miejsc dla osób, które chcą dołączyć do kursu DataWorkshop w przyszłości oraz poznać nasze unikalne community.
Spotkanie poprowadzimy w języku polskim.
Wpisz swoje dane i odezwiemy się do Ciebie w najbliższym czasie.
Czy brałe(a)ś udział w wyzwaniu #5DWCHALLENGE

Administratorem danych osobowych niezbędnych w procesie przetwarzania, w tym podanych powyżej, jest Biznes Myśli – Uladzimir Aliakseichanka, ul. Grodzka 42/1, 31-044 Kraków, NIP: 6751364881.
How it works
DW Course finished
Buy DataWorkshop Club Conf ticket (Sept. 28)
Get access to DW Alumni Day
(Sept. 27) for FREE
Ask if You can Join
If you are not a graduate yet but would like to be, write to us.
We have 10 tickets to DW Alumni Day for people who will join the next edition of Practical Machine Learning Course by DataWorkshop in October this year.
Meet the DataWorkshop Alumni
who will share their knowledge with you
Rafal Bodziony
Rafał Bodziony is currently a junior Data Scientist at DHI Polska and he was a participant in the first edition of Practical machine learning course by DataWorkshop. He is using deep learning techniques to predict floods based on water sensors and weather radar images.

He is still trying to build an AI-based startup in many ways. The first project related to computer vision and UAV failed. His interests include time-series prediction, Kaggle competition, and image recognition.
Agnieszka Kaczmarczyk
Agnieszka is a data analyst. Her journey with data started at AGH studying Biomedical Engineering, focusing mostly on biomedical data processing. Later on, she joined a hardware startup, Estimote, where she worked as a data scientist on research projects involving sensor fusion (e.g. designing algorithms for indoor positioning).

Currently, Agnieszka is a data analyst at Tourlane, a Berlin-based startup from the travel industry. She's building a new interdisciplinary data team there. Agnieszka graduated the last year edition of the Data Workshop. Privately, sports amateur, traveller and huge nature lover. She can't imagine holidays without mountains and hiking as much as daily life without a bicycle.
Kamil Krzyk
During his 6 year long IT career, he has been able to work in different positions and look at the product building from various perspectives. From Software Engineering through QA Engineering, R&D to Business Analysis. Since last year he has been working as a Machine Learning Engineer at Cosmose, building products for the Chinese market in a retail area. He's involved in projects connected to geolocation, sensor fusion as well as recommendation engines.

He is a graduate of 1st edition of "Practical Machine Learning" course at DataWorkshop. His motto is "Never stop learning". You can frequently find him on stage as a speaker, organizing workshops, blogging, sharing open-source or attending hackathons.
Miroslaw Mamczur
For over 10 years he has been associated with retail banking. He has been working as a Data Scientist at Santander Bank in Poland as a member of a team responsible for calculating credit offers for a year and a half. In his job, he uses advanced analytics and machine learning to provide a process as simple as possible and the best-suited offer for customers. Earlier, he used to analyze data and build credit processes, creditworthiness algorithms, risk estimation models and purchase propensity models.

As far as his family is concerned, He is a happy husband and father of two daughters aged 4 and 1,5. Current hobbies: fun with dolls and ponies :) Participant of the 1st edition.
Mariusz Rokita
Mariusz is a machine learning engineer who specializes in building, architecting intelligent applications leveraging the Microsoft Azure cloud. His recent interests are mainly focused on MLOps process and productionalization of ML models. The backbone of his professional experience is a .NET software development however, Mariusz became interested in machine learning already during his studies at the Wroclaw University of Technology.

His final thesis was on the usage of data mining methods to improve medical diagnostics. After a long break, to name a few, in 2017 he got back in the game he completed the Data Workshop's "Practical machine learning" course. In 2019 he graduated from the postgraduate studies "Data Science" at the Wroclaw University of Economics.
Grzegorz Szopiński
A self-taught data scientist, coming from the seemingly irrelevant field. He currently works at LogicAI – a machine learning consultancy startup, founded by Kaggle Grandmasters, where he develops data science solutions ranging from price optimization to product recommendation. Before finally entering the industry, Grzegorz held various position in analytics, including market research, business intelligence, and ETL.

Outside of his daily job, he is exploring non-mainstream approaches to deep learning, including Swift for Tensorflow and trying to apply machine learning to psychology and social sciences by participating in various non-profit projects. Grzegorz is an alumnus of the first edition of DataWorkshop course.
Zbigniew Pioch
He graduated from the Warsaw School of Economics with a Major in Finance and Quantitative Methods. Later he worked 5 years in Citibank in the Technology Infrastructure department in which he had taken multiple responsibilities, including Business Analysis, Problem Management, Project Management, Project Governance, and Inventory Management.

He started learning and working with ML outside of his working hours and as part of his learning he completed the 4th edition of the Practical Machine Learning course. Currently, he is working on a ML project which is focused on classifying customers and clustering products to facilitate product recommendation. For this, they are utilizing Python, Pandas, Numpy, Jupyter as well as word2vec and XGBoost.
Aleksandra Możejko
Aleksandra Możejko - Machine Learning Engineer at Sigmoidal with over 2,5 years of experience in Machine Learning, co-organizer of the PL in ML conference. Her main field of interest is Natural Language Processing. At her day-to-day work, she designs and develops AI systems for business clients from all over the world, in fields such as e-commerce, finance, and compliance.
Mariusz Grisgraber
He is currently a Coordinator of Robotics and Continuous Improvement, he works for Veolia Shared Services Center. His daily activities include learning about processes and their centralization, standardization, and optimization. Frequently, the examination of processes ends with their automation. Currently, together with colleagues, he is working on the large and ambitious project, which will result in the establishment of a Center of Excellence in the Veolia Poland Group.

One of the pillars of CoE, besides RPA and lean, is machine learning. He was the participant of the 3rd edition of the course "Practical machine learning for programmers". Outside of work, he is a husband and a father, but still not yet a grandfather, in love with sports; young at heart and in mind.
Krzysztof Sopyła
CEO and scientist in the field of machine learning with 12 years of experience. I believe that artificial intelligence is a way to create superhuman people and will accelerate the evolution of our species. I co-create startups using the latest NLP achievements, currently working on polish grammarly and book recommendation engine I write my thoughts on the blog

Alumni Day Program

All speakers are DataWorkshop Courses graduates. Each of them will share their experience and tell you about the choices and activities that helped to enter the professional world of Machine Learning or DataScience after the course.
Connect your dots too!
09:00 – 09:30
09:00 – 09:30
09:30 - 09:40
09:30 - 09:40
Vladimir Alekseichenko / Founder & CEO/ DataWorkshop
09:40 - 10:05
09:40 - 10:05
Can you change careers from a non-technical role to a Data Scientist?
Zbyszek Pioch / Data Scientist / DataWorkshop
As a person, who spent most of his career in "soft" and administrative roles (e.g. as a project manager or an inventory manager), I asked myself multiple times: "is it possible to change careers into a more technical role? And even if yes, then at what cost and is it possible for ME?". And for a long time the answer I gave was "no, I won't be able to do it".

During this presentation, I'll share my experience of what it was like to transition (which took around 1,5 year) to the role of a Data Scientist. I'll briefly describe my journey, and how I got to where I am right now. I'll also say a few words about things that were, and still, are, challenges for me, what hindered my progress, what helped me, what I would change if I could turn back time.
10:05 - 10:30
10:05 - 10:30
The adventures of a data analyst - 5 the most important principles I learned while mastering the secrets of data science.
Mirosław Mamczur / Data Scientist / Santander
Not so long ago, behind a seven mountains, behind seven cities, in the stronghold being the seat of the Lower Silesian lived an experienced data analyst. He studied the numbers with interest and eagerly talked about them. One could say that he lived a peaceful life. It would be so today, if not the day when a powerful wizard appeared before him. He gathered daredevils to travel together into the unknown for a magic orb. It was to give access to powerful might - power over all data. The young adept did not even think for a moment. He wanted to change something. He wanted adventures and knowledge.

During the presentation I will tell about my adventures related to data science, which last for a year and a half. As from a data analyst, I have transformed into a data scientist. In addition, I will share the 5 most important principles that I learned through projects that use machine learning in retail banking: credit risk prediction, income estimation, price calculation.
10:30 - 10:55
10:30 - 10:55
Mirror - processes versus data
Mariusz Grisgraber / Coordinator of Robotics and Continuous Improvement / Veolia Poland
I'll tell you about my path from performing statistical analysis in Excel to the present day. This road ultimately took shape thanks to my involvement in subsequent projects at Veolia SSC. The common denominator of project activities and their effects is the improvement of processes. Continuous improvement is inscribed in the DNA of shared service centers, and automation is the key to the development and competitive advantage of a company. I'll put forward the postulate that in the pursuit of machine learning the path you follow is not important, but there are various obstacles to overcome depending on where you are going from. I will show that, as in the proverbial mirror, we can perceive the process approach similar to the approach using data research - the culmination of efforts is still the launch of the appropriate machine learning model.
10:55 - 11:10
10:55 - 11:10
Coffee break
11:10 - 11:35
11:10 - 11:35
How to enter the Machine Learning industry from a seemingly unrelated field and not get lost?
Grzegorz Szopiński / Data Scientist / LogicAI
Maybe you to would like to try your chances in the machine learning, but something is holding you back? How to start, without experience in the industry or academia? How to harness knowledge from ml/ds courses to boost your career? From over a year, I work as a data scientist, providing machine learning-based solutions, mostly for fintech and e-commerce clients, ranging from price optimization to churn prediction. However, not long ago, I also had to deal with similar difficulties. I would like to tell you about the challenges I encountered in my machine learning journey to help you deal with such problems. There is no silver bullet here - but some of the things that worked for me may also work for you.
11:35 - 12:00
11:35 - 12:00
Different Faces of Data Science
Kamil Krzyk / Machine Learning Engineer / Cosmose Inc
In this talk, I want to share my story about chasing the Data Scientist/Machine Learning Engineer position. At that time, I have been struggling to find out what skill set should I acquire to be ready to take the job. I have asked myself this question for a long time. Over one year has passed since I had joined the field professionally, and my point of view about the whole process has changed. Paradoxically, I've been put in the role of a recruiter a few times. I had to think and decide what is important for the team I work with. That's why I want to share with you my understanding of Data Science. Especially to make a comparison between my past believes, and their verification by the working environment surrounding me. I hope it will help some of you to decide on the direction of your personal development.
12:00 - 12:25
12:00 - 12:25
From being a software developer to becoming a machine learning engineer
Mariusz Rokita / Machine Learning Engineer / Objectivity
My career in data science started, unknowingly, already in my childhood (in the 1990s), when I did my first analyses of football games and on this basis, I tried to predict results of the next games. Millions of PLN were so close! My contact with machine learning came several years later during my studies at the Wroclaw University of Technology. My master's thesis concerned the methods of data mining in medical data. Fast forward - I went to IT, where I had the opportunity to work as a tester, a software engineer, a project manager (short episode), and finally - a machine learning engineer.

During my presentation, I will tell you about the path I followed to work in data science. I will talk about what worked in my case, what I could have done better. I will also talk about my next steps to provide even move value to others. I hope my hints will speed up your career in data science.
12:25 - 12:50
12:25 - 12:50
Game Networking
The efficient way to meet people with whom you can do a something job, project or even business.
12:50 - 13:40
12:50 - 13:40
13:40 - 14:05
13:40 - 14:05
You think you're ML-ready? Would the DS Hierarchy of needs agree?
Agnieszka Kaczmarczyk / Data Analyst / Tourlane GmbH
My career in machine learning started already at university. After graduation, I joined Estimote, a hardware startup, as a data scientist. At the moment, I don't work too much with data science topics. Does it mean that I neglect my experience? Not at all! As a data analyst and an early member of the data team in a startup from the travel industry, I currently create a space for machine learning projects. Freshly joined, I realized that in order to be able to start playing with such topics here, firstly we need to build a solid foundation - trustful data infrastructure storing the essential information. All of this requires mostly working on data mentality and assuring data quality which means lots of communication and providing support for our data engineers. Join me to learn about kicking off projects in machine learning area in the startup world, especially if you're seeking a fresh perspective.
14:05 - 14:30
14:05 - 14:30
How to learn from the mistakes of others, just start in ML
Rafał Bodziony / Junior Data Scientist / DHI
Most of the people like to ask questions, it is the easiest way to get information, but for me not the best. In this talk,I will discuss how starting from scratch, that is without knowledge of statistics and programming, I came to my first job as a junior data scientist. Why get the first job could be hard and how I boost my chance. Why my first ML startup didn't succeed and what I learned. Finally, I also discuss what kind of feeling is it when everyone in the room believes you know that rocket-science technique which is ML, but in fact, you know where to look and how to use information.
14:30 - 14:40
14:30 - 14:40
Coffee break
14:40 - 15:10
14:40 - 15:10
How to jump-start your career in data science and what to expect along the way?
Aleksandra Możejko / Machine Learning Engineer / Sigmoidal
The answer is: I don't really know, and I am not sure if there exists a one-fits-all answer. Nevertheless, I have explored some options myself and learned quite a lot en route. In my presentation, I would like to share my experience with you. I will talk about some common mistakes that I, as a young data scientist made, and how I think one can prevent them. I started my career in data science 3 years ago, fresh out of the university, not knowing much about the field. I had no idea what is important and what is not and how to make career choices. And data science is all about making choices, isn't it? ;) I believe, that my struggles from that period can be familiar to some of you, so I would like to share what I have already learned.
15:10 - 15:40
15:10 - 15:40
5 things I pay attention to when hiring and working with ML engineers
Krzysztof Sopyła / CEO - Chief Data Scientist / Ermlab Software
During the presentation, I will share my experience related to employing people for ML positions. What competences I pay attention to and what I check with the candidates. I will tell the story of two of our employees who started their careers in machine learning. Each of them has a different set of skills. I will reveal what mistakes they made and what mistakes I made in managing them.
15:40 - 16:00
15:40 - 16:00
Closing Conference
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