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Learn how to process and visualize data with NumPy, Pandas and other data visualization libraries. Strengthen your linear algebra, Python programming and scientific computing skills in the process.
Building Foundational Python Skills For Data Analytics
Improving Code Reliability
Cluster Analysis With Python
Containers & REST APIs
Numerical Data with NumPy
Exploratory Data Analysis with Pandas
Data Cleaning & Intermediate Charting
Exploring Data With Charting
Upgrade your knowledge of statistics and leverage your new skills to create and test experimental hypotheses and statistical modeling. Learn SQL to work with data in relational databases.
SQL For Data Analysis
Practical Statistics For Data Science
Linear & Logistic Regression
Multilevel and Marginal Models
Introduction To Bayesian Statistics
Learn how to use various types of supervised and unsupervised machine learning models, such as KNNs, decision trees, random forests, support vector machines, gradient boosted trees, XGBoost, LightGBM, K-Means clustering and more.
Introduction to Machine Learning
Machine Learning Projects
KNNs, Decision Trees, and Random Forests
Support Vector Machines
Gradient Boosted Trees, XGBoost, CatBoost, and LightGBM
Interpretable & Responsible Machine Learning
Maintaining Machine Learning Models
Working with Imbalanced Data
Hyperparameter Turning & Model Selection
Learn how to build and use various neural network architectures with PyTorch. Apply these neural networks to solve tabular data, computer vision and natural language processing problems.
Deep Learning Fundamentals
Introduction to PyTorch
Convolutional Neural Networks
Recurrent neural networks
Advanced Deep Learning
Delivering ML Projects
Practical AI Ethics
Work on our hiring partners projects and build an internship-level portfolio
We train our learners to be market-ready. 100% of our graduates have been hired. Here's why:
Comprehensive training in technical skills
Mock interview led by an HR expert
Human resource specialist led mock interview
Endorsement program Learn more
Salary negotiation support
“Turing College prepares data scientists who not only excel in theory but also possess real-world skills.”
Co-founder of Nord VPN
We have a strict 2 part application process which requires you to first complete a technical challenge & then pass an interview.
Are comfortable with basic probability and statistics
Have active coding experience in a general-purpose programming language (e.g., C++, Java, Python)
Can dedicate at least 15 hours per week
Have good English skills
Aiming to build skills to become a great Data scientist, Machine learning engineer, Data analyst, Decision scientist, or AI engineer
Have no coding and math experience
Can’t dedicate at least 15 hours per week
Are looking for high-level theory (our course is highly technical and hands-on)
Apply by finishing a technical challenge and interview. Our scholarship criteria are:
EU, UK or UA citizens only.
The successful passing of a technical challenge (a.k.a. code review)
A score of at least 90% in your technical challenge
A clear intention to start working full-time as a data professional after the course
Active participation in our discord community
Commitment to complete a course first and only then start looking for a job
The ability to dedicate at least 20 hours per week to your studies
Get a scholarship offer from hiring partners.
Pay upfront and save 12% on tuition.
Divide tuition into smaller payments. Pay in 12 months.
People enrolled from
We encourage you to read. But it can take years to cover even the basics and most importantly, books won’t teach you real-life work experience and skills.
Most bootcamps lack depth and offer little in the way of 1-on-1 feedback. You’ll get a nice certificate, but might still be stuck with only the most basic knowledge.
Certain essential skills can only be taught with real-life projects and feedback provided by experts. Online courses offer a lonely path, which leaves many people struggling to figure out where they want to go once they feel they're job-ready.
Education is great, but most degrees take 2+ years to complete and aren’t designed for practical, real-world application. That’s why many students look for alternatives even after they graduate.
a. During your studies or within 1 month of graduation, you can only apply to any company if Turing College has confirmed that you can do this (this applies for both Hiring Partners and other companies). Turing College is allowed to make the decision as to whether an application is confirmed based on individual criteria for each case. You can apply to any company without confirmation 1 month after your graduation.
b. If you apply to a Hiring Partner and receive an offer that meets your expectations (the salary range for the position is provided by us before you apply), but you then decline that offer without a good reason. An example of a good reason would be that you accepted another offer from a Hiring Partner, or that some new requirements such as relocation were added to the job offer.
For you to graduate from Turing College in a reasonable timeframe, we define a minimum learning pace requirement. We have seen that only by maintaining a consistent pace can students successfully graduate from Turing College. The minimum learning speeds for students who receive a 100% scholarship are as follows:
Violating one of these conditions will lead to termination of your scholarship. If a scholarship is terminated, you will need to return the full amount of the scholarship you have received. We implement these scholarship terms to ensure we attract students who are serious about pursuing careers in a related field to their course.
Yes, you will need to cover a part of the full-tuition fee depending on when you lost your scholarship.
The sum is counted proportionally (12 months = 100% of the Data Science course, 6 months = 100% of the Data Analytics course).
Example: If you withdraw from Turing College or your scholarship is terminated after two months from the beginning of your Data Science Program, then you will have to return a part of the scholarship equal to two monthly Installment Payments or 2/12 of the full price, which is roughly 800 Eur.
1) Your place of work. Salaries for the same position are higher in Silicon Valley than in Lithuania due to higher cost of living, demand/supply dynamics, and other factors.
2) The position you are aiming for. If your plan is to secure a data analytics position after completing a data science course, your salary might be lower.
3) Your experience and skills.
4) Performance on interview and your negotiation skills. Even the best candidates have bad days, when answering questions, no matter how simple, proves difficult. Needless to say, this can have an impact on the initial offer you receive. Your final salary will also to a large extent depend on the success of negotiations after the interview.
If you’re interested in working in another European country, we would advise you to start with Glassdoor’s base pay calculator by choosing the relevant country or city. Please have in mind, though, that base pay estimates can be off by as much as 20% because they average out not only junior, but also mid and senior positions. Despite this margin of error, base pay estimates can still be useful for identifying reasonable offers. You should expect to get a salary close to the base pay level, without feeling pressured to accept offers that fall more than 20% below the base salary in a particular country.
Aside from the skills gained by completing the programme, there are 3 additional ways that we offer support to our students:
1) With our Endorsement Program, you will go through a simulation of a real hiring process from the very beginning to the end. You will also be trained on how to prepare your Linkedin profile, structure your projects on Github, answer interview questions, and apply for jobs in a systematic way.
2) Referrals to our Hiring Partners. Endorsed students will be recommended to Hiring partners, who are always on the lookout for different types of specialists.
3) Referrals from our staff, mentors and alumni. We have mentors from companies like Tesla, Google, IBM, and Vinted. If they feel confident in your skills, they can refer you. If you do receive a referral from them, in most cases you will be invited to the interview stage.