April 27 – June 5 10:00 – 15:00
Data Science: Math and Python
Online: 20000 uah (10 Seats)
Offline: 25000 uah (15 Seats)
There are no upcoming events at this time.
Data Science: Math and Python
Want to study machine learning or data science, but worried that your math or python skills may not be up to it?
Do words like ‘algebra’, ‘calculus’, ‘pandas’, scikit-learn’, ‘SQL’ fill you with dread?
Has it been so long since you studied math at school that you’ve forgotten much of what you learned in the first place?
You’re not alone. Machine learning and Data Science are built on mathematical principles like Calculus, Linear Algebra, Probability, Statistics, and Optimization; and without Python and data collection skills you are lost.
This course is not designed to make you a mathematician or a Python programmer. Rather, it aims to help you learn some essential foundational concepts and the notation used to express them. The course provides a hands-on approach to working with data and applying the techniques you’ve learned.
This course is the first step in Data Science career.
TOP skill you will learn:
- How to use Python packages for data mining;
- How to read complex math equations that are standing behind the machine learning algorithms;
- How to get data from different sources (files, databases, API requests);
- How to write complex SQL queries;
- How to manipulate data and draw insights from large data sets;
- How to create clear and human readable data visualization;
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
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.
- Variables and data structures
- Conditional statements
- Loops (for, while)
- Functions and methods
- Object-Oriented Programming (OOP)
- Packages NumPy, SymPy, Pandas
- Data visualization: Matplotlib, seaborn, plot.ly
- Coding style guidelines
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.
- Linear algebra
- Differential calculus
- Probability theory
- Bayes theorem
- Distributions of random variables
- Null hypothesis significance testing
- Exploratory data analysis
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.
- Reading and writing files
- Relational databases
- SQL queries
- Internet data (API, HTTP requests)
- Data cleaning
Full price: ₴25000
Do you know that by choosing our course you will not only master Data Science but also save money?
Let’s count a little
The total is 150 working hours at a cost of $ 1000. So, you only pay $ 7 per hour.
It’s a bargain!
Have you seen such prices anywhere? Of course not! And we also offer part payment.
Everything is for your learning.
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 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 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
Hobbies: climbing and hiking
Nikolay is one of the leading instructors at DEVrepublik.
He has 4 years of experience in Data Science and machine learning using algorithms for predictive modeling, data processing, image and video processing, as well as data mining for solving complex business problems.
Nikolay successfully applies machine learning methods in medicine, finance, agriculture, AR and many other areas.
In addition, he worked with a large number of popular technologies, such as: Python, TensorFlow, Scikit, Keras, SciPy.