DEVrepublik

Helps expanding careers

Data Science bootcamp

Date: January 13 – April 26

Time: 10:00 – 15:00

Fee: 26000 uah

Registration and payment

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Data science

March 2 – June 14

Time: 16:00 – 21:00

Fee: 52000 uah

Registration

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