DEVrepublik

Helps expanding careers

Become TOP specialist within 15 weeks and join top data-driven companies. This is the right place for you if you have a desire for an impactful work. Get knowledge that can change the future of humanity!

November 1 – February 14
13000uah

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January 13 – April 24
26000uah
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TBD
52000uah

Our Kyiv Campus

Our Kyiv campus located in the historical center of the city combines both classroom atmosphere and lounge self-studying zone. You are here to dive into deep learning process and we will help you get one of the most required jobs on the market.

 
 
 
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FLOW OF EACH DAY

Mornings in the classroom // 9:00am – 12:00pm

  • Pair programming exercises
  • Interactive lectures

60-minute lunch // 12:00 – 1:00pm Eat lunch with your fellow students and rest your brain

Working afternoons // 1:00pm – 5:00pm*

  • Investigation presentations
  • Challenges and project work
  • Instructors and TAs remain on campus to provide support

More throughout each week

  • Career support curriculum
  • Guest speakers from various industries
  • On-campus Meetup events
  • English classes
No limits

No age or background limits. You don’t have to be a computer scientist to launch your career in Data Science.

Total immersion

Bootcamp is an intensive 15 weeks program which equals 2 years of university course. Perceive information quickly and get results in a short amount of time.

Free pre-course workshop

Some high school mathematics level is required, so we offer free pre-course trainings to pull up your knowledge

We guarantee support with employment. If you don’t get a job we will refund you the money*

*You are required to participate in at least 95% of class time and completed 100% of the homework and score between 90-100 points

Data Science

In Kyiv campus we offer Data Science course designed with the needs of the employer. You will be taught knowledge through practice and you’ll learn how to learn.

Transform your career with the most in-demand data science bootcamp. This is an intensive course, where you can learn the advanced topics of Data Science, Machine Learning and AI. You will get acquainted with technology trends and learn up-to-date information from well known Data Science guru.

TOP skill you will learn:

  • Experience using computer languages (Python, SQL, etc.) to manipulate data and draw insights from large data sets.
  • Deep knowledge of fundamentals of machine learning
  • Data mining, and statistical predictive modeling
  • Experience applying these methods to real-world problems.
  • Experience of driving and delivering analytics models and solutions

This is exactly for you if you are:

  • a person looking for a career change
  • a graduate from universities looking for a job in Data Science
  • a developer with a mathematical mindset who would like to get career growth
  • a business owner who would like to utilize data analysis and implement data-driven and AI projects
  • a Data Scientist practitioner who wants to systematize the knowledge and to master Deep Learning

This is exactly for you if you are:

  • a person looking for a career change
  • a graduate from universities looking for a job in Data Science
  • a developer with a mathematical mindset who would like to get career growth
  • a business owner who would like to utilize data analysis and implement data-driven and AI projects
  • a Data Scientist practitioner who wants to systematize the knowledge and to master Deep Learning

Curriculum Overview

Module 1: Python

Python is data scientists’ preferred programming language. If machine learning researchers decide to open source their work they will most likely do it in python. Therefore, the course starts by introducing python concepts and packages that are useful for data analysis.

Topics covered:

  • Variables
  • Booleans and Conditionals
  • Lists
  • Dictionaries
  • Looping
  • Functions
  • Reading and Writing Files
  • Pandas
  • NumPy
  • Matlotlib/Seaborn for Data Visualization
  • Git/Github

Module 2: Math for Machine Learning

Machine learning is a technical science and like any technical subject, it uses mathematical language to formulate ideas. There is an increasing number of solutions that try to automate the whole machine learning process but if one does not understand mathematical formalism behind algorithms it is impossible to test and debug models which can lead to spurious insights.

In this course, students learn those concepts of linear algebra, probability theory, and statistics that are essential for exploratory data analysis, understanding and designing machine learning algorithms.

Topics covered:

  • Linear Algebra
  • Differential Calculus
  • Probability Theory
  • Bayes Theorem
  • Statistical Quantities
  • Distributions

Module 3: Data Collection

“More data beats better algorithms” – this quote by Peter Norvig emphasizes the great importance of data in machine learning. This part of the program describes data structures, relational and non-relational databases, means of interacting with databases, manipulating data and merging datasets from different sources.

Topics covered:

  • Data structures
  • Relational Databases
  • SQL
  • JSON
  • HTML/XML
  • Accessing Data Through APIs
  • CSS
  • Web Scraping

Module 4: Intro to Machine Learning and Pre-Training Phase

This module starts with an introduction to machine learning: how it is organized, what are the sub branches of machine learning, fundamental differences between these approaches and types of problems they are designed to solve.

Next, students get familiar with framing a machine learning problem, picking up appropriate objective function and algorithm according to a given problem. It is well known that data wrangling and feature engineering takes most of the time of model development. Students learn techniques to effectively deal with missing values, outliers, categorical variables and design new features.

Topics covered:

  • Intro
  • Formulating an ML problem
  • Data Cleaning
  • Data Preparation
  • Feature engineering

Module 5: Supervised Learning

This course covers algorithms that are used when the target variable which has to be predicted is known. It starts with simple KNN and ends with fully connected feed-forward neural networks. Proper testing of a model is essential to build a reliable product. Students are introduced to various testing methods and parameters that help to build generalizable and stable models.

Topics covered:

  • Lazy Learner – K Nearest Neighbo8urs
  • Linear Regression
  • Loss Function
  • Optimizing Loss Function
  • Regularization: L1 and L2
  • Data Partitioning
  • Logistic Regression
  • Multiclass Classifier: Softmax Regression
  • Ranking
  • Classification and Regression Trees
  • Boosting: Adaboost, Gradient Boosting Machine
  • Model Ensembling
  • Intro to Bayesian Learning
  • Intro to Neural Networks
  • Training Neural Networks

Module 6: Unsupervised Learning

Most of the time values of the target variable are unknown and that is when unsupervised learning techniques are needed. They enable us to identify hidden structures in multidimensional datasets.

Topics covered:

  • Dimensionality Reduction
  • Clustering: K-Means
  • Anomaly Detection

Module 7: Final Project

To test and assess students’ knowledge each of them picks a machine learning problem after completing all modules and tries to find a solution by going through the data preparation, model training and testing phases.

Module 8: Bonus Topics *

This module will introduce some advanced topics and instruments for data analysis that will boost and enhance your knowledge in Data Science.

Our instructors

Iryna Lazarenko

One of the Lead Instructors at DEVrepublik boot camp and PhD in Mathematics
Iryna is a Senior Lecturer at the Department of Mathematical Modeling for Economic Systems at the National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute. She is also a Scientist at the Laboratory for computer modelling and intelligent data analysis at the World Data Center for geoinformatics and sustainable development
Her research interests are Data Analysis, Data Mining, Operation Research, Optimal Control, Sustainable Development, Theory of Integral and Differential Equations.
She loves travelling a lot.

Ivan Luchko

Ivan is one of the Lead Instructors at DEVrepublik.
Won a Bronze Medal in International Physics Olympiad, Mexico. Received a Master degree in Applied Physics at Technical University of Munich. Later performed some research in the Heisenberg antiferromagnets using Qauntum Monte Carlo simulations and machine learning technics in Geneva (and PSI), Switzerland. Has a diverse experience in business process automation, advanced analytics, mathematical modeling, optimization and machine learning. During the last two years Ivan works as Data Science team lead at Boosta. Recent projects are related to dynamic price optimization and recommendation system in e-commerce.
Loves sport, traveling and hiking.

Ruslan Klymentiev

Ruslan is a curriculum writer at DEVrepublik and Practice Instructor.
He graduated from Odessa National Polytechnic University, a specialist in "Radio-electronic devices."
2.5 years of experience in Data Science.
Interests: statistics, Data Visualization, CNN models and Computer Vision
2 times received at Weekly Kernels Award on Kaggle.com
Winner.
Hobbies: climbing and hiking

Our trainers are qualified experts with at least 5 years of experience in IT, extensive real-world knowledge and coaching skills. They are knowledgeable practitioners, who want to give you the most value and practical knowledge. Our tutors are ready to reply to essentially all of your queries.

The price for full course
100 000 UAH ($4000) / per course
DEVrepublik offers scholarships for students to contribute to the development of IT community in Ukraine
price depends on the date of the course
  • 15 weeks of immersive learning
  • 300 hours of lectures
  • 400 hours of practical experience
  • Free IT English courses
  • Leadership & communication skills workshops
  • Employment assistance
  • New career within 3 months
  • Fresh start into prosperous future

Frequently Asked Questions

Do I need to bring laptop to classes?

Yes, you need to bring your own laptop to classes so that to be able to work on it after the course.

Is there an admission test?

Yes, there will be an admission test to measure each student’s background. 

What is the schedule of the classes?

You will have to work hard to master a new profession, thus, bootcamp presupposes hard work Monday to Saturday, 9 am – 5 pm. A more detailed schedule of the day can be found on the course page.

Will I get employment after the course?

Our career counselors are ready to help each student find a good job, but it also depends on you. You need to work hard to be able to master a new profession within 3 months. In case if you participated in all the lectures and submitted all the practical assignments for 95-100 scores, and you will not get a job within 3 months after graduation, we are ready to reimburse you money.

Will I get lectures texts and/or any other additional materials?

Yes, lectures will be provided and additional materials will be recommended as well.

Is there any online platform for learning?

Yes, we are using online learning management platform where every student will have their own online account with the progress of studying, all the materials and scores.

Is there any certificate after the course?

Yes, you will get the certificate confirming you have completed the course, where the number of hours and your score will be stated.

Still have questions? Contact us