Six months | Flexible 40h per week
An immersive online course that provides you the tools and training you need to become the best Data Scientist you can be. In this course we will cover the following:
+ Statistics & Modelling
+ Machine Learning
+ Computer Vision
+ Natural Language Processing
+ Supervised Learning
+ Unsupervised Learning
+ Career Acceleration
Next Data Science cohort begins this August
Data science is a complex and intricate field. It is comprised of structuring and analyzing large-scale volumes of data, applying machine learning to make predictions, identifying patterns, and drawing conclusions. Data scientists are needed in business, manufacturing, and science. They work with essential tools such as Python and its libraries, including Scikit-Learn and XGBoost, Jupyter Notebook, and SQL. Our mission is to teach you how to use these tools.
During the first month you will get an overview of different professions in the field of data science. After learning the basics and demonstrating your skills and strength, you will be able to either continue with the data science route, or divert to data analysis.
Free introductory course
Pre-course: Python and data analysis basics
The process and stages of the data scientist’s work — essential terms, methods, and tools of data analysis. Data preparation. Python programming language and its Pandas library. Jupyter development environment.
Learning what it means to be a data scientist. An overview of spheres where data scientists can find work. Organisational aspects of the training process.
Data Preprocessing 40h
Compensating for less-than-perfect data. Handling missing and duplicate values. Changing data types. Systems thinking for analysts.
Exploratory Data Analysis 40h
Performing initial scans to detect patterns in data. Building basic graphs and generating your first hypotheses.
Statistical Data Analysis 40h
Probability theory, the most common distributions, and statistical methods in Python. Sampling and statistical significance. Identifying and handling anomalies.
Integrated Project 1 20h
Identify patterns to help you determine whether a given video game will succeed or not.
Data Collection and Storage (SQL) 40h
How databases are organised and how to pull data from them using SQL queries. Finding data online.
Introduction to Machine Learning 40h
Mastering the basics of machine learning. How the scikit-learn library works and how to use it in order to complete your very first machine learning project.
Supervised Learning 40h
Diving into the most highly demanded area of machine learning. Understanding how to tune machine learning models, improve metrics, and work with imbalanced data.
Machine Learning for Business 40h
Applying the acquired machine learning knowledge to business tasks. Discover business metrics, A/B testing, the Bootstrapping technique, and data labelling.
Integrated Project 2 20h
Prepare a prototype of a machine learning model to help the company develop efficiency solutions for heavy industry.
Linear Algebra 40h
Taking a more in-depth look at some algorithms you’ve already learned and understanding how to apply them. Get a hands-on feel for the main concepts behind linear algebra: vectors, matrices, and linear regression.
Numerical Methods 20h
Pulling apart a number of algorithms that use numerical methods and applying them to practical tasks. Learning about gradient descent, gradient boosting, and neural networks.
Time Series 40h
Exploring the time series. Understanding trends, seasonality, and feature creation.
Machine Learning for Texts 40h
Applying machine learning to text data. Finding out how to convert text into numbers and how to use bag-of-words, TF-IDF, as well as embeddings and BERT.
Computer Vision 40h
Learning how to handle simple computer vision tasks using pre-made neural networks and the Keras library. Taking a quick look at deep learning.
Unsupervised Learning 20h
Figuring out what to do when you have no target features. Handling the clustering tasks and looking for anomalies.
Final Project 40h
Apply everything you’ve learned to a two-week intensive project that simulates the experience of working as a junior data scientist.
The track for data analysts
Analysis of Business Indicators 40h
Even closer to business, we take a detailed look at metrics and essential tools like cohort analysis, sales funnels, and unit economics.
Making Data-Informed Business Decisions 40h
A/B testing: when to use it. Designing and identifying the sample size. Getting and validating results.
How to Tell a Story Using Data 40h
How to correctly present research results using graphics, key numbers, and solid interpretation.
Integrated Complex Project 20h
Pulling data from a database. Data set preprocessing and overview. Formulating hypotheses in light of business specifics. Checking hypotheses and preparing conclusions formatted as an analytical report. Event-based Analytics Project.
Automating data analysis processes. Scripting routine and regular tasks. Creating dashboards for different audiences and company needs.
Forecasts and Predictions 40h
Basics of machine learning, project dealing with churn rate prediction.
Final project 40h
Independent solution of an analytical problem of your choice. All the stages of data analysis project flow. Bootcamp sprint checking and evaluation additional tasks sending in your project.
Throughout the six-month course, you will master the skills required to become a data professional and build a portfolio of projects on topics such as these:
Predict traffic levels
Train an algorithm that predicts the severity of daily traffic jams.
Weather affects taxi services
Suggest the probable causes of customer churn and test your hypotheses.
Courier services rush times
Train a courier service's algorithm to predict which time slots will be used on a given day.
Video games sales analysis
Help a game service identify the most popular trends.
Ad selection and display
If the community has multiple sponsors, you’ll want each user to see the ad they’re most likely to click on. Your algorithm will predict how likely each click is.
Real estate analysis
Determine the real estate market value in a city with a population of over one million.