Data scientists qualifications: How to Become a Data Scientist

How to Become a Data Scientist

A subfield of computer science, data science is the study of large quantities of data.

A relatively new and quickly growing field, data science offers excellent career opportunities. Glassdoor ranks data scientist as the third best job in the U.S. for 2022, citing high job satisfaction, top salaries, and abundant job openings.

This page explains how to become a data scientist. We cover experience and education requirements, look at certifications and job search strategies, and outline the steps to become a data scientist.

What Education Do Data Scientists Need?

Education requirements for data science professionals vary by position, employer, and industry. Data scientists typically need at least a bachelor’s degree in computer science, data science, or a related field. However, many employers in this field prefer a master’s degree in data science or a related discipline.

Data analysts and data engineers usually need a bachelor’s degree. Becoming a data scientist or computer and information research scientist usually requires a master’s.

Some data science professionals hold a mix of education levels. For example, someone might earn a bachelor’s in computer science and complete a data science bootcamp. Or, they might complete a bachelor’s in an unrelated field and then earn a master’s in data science.

In general, career opportunities and salaries increase as people earn higher degree levels. A graduate degree can also help job applicants stand out from candidates with just a bachelor’s.

Requiermenst to Become a Data Scientist

There is more than one way to enter the data science field. Data scientist requirements differ by employer, industry, and location. Aspiring professionals can meet data scientist job requirements in various ways.

The typical process to become a data scientist includes at least a four-year college degree in computer science, data science, or a related field. Many data scientists also pursue graduate education, professional certifications, and bootcamps.

Most data scientist jobs require some relevant professional experience. Students can gain experience while still in school through internships, capstone projects, and fellowships.

Below, we outline in detail some of the steps to become a data scientist.

Steps to Become a Data Scientist

Several paths can lead to a career as a data scientist. Below, we list the steps to become a data scientist based on different paths.

Bachelor’s Degree Path

1. Earn a bachelor’s degree. Most data science jobs require at least a four-year bachelor’s degree. Consider majoring in data science, computer science, or mathematics. Take classes in computer science, business, and statistics.
2. Complete an internship. Getting internship experience develops career-relevant skills and can lead to job offers.
3. Pursue professional certifications. Earning a professional certification is not required for becoming a data scientist, but it can help you prove your skills to potential employers.
4. Get entry-level professional experience. Apply for jobs like data analyst, data engineer, and market research analyst. Spending several years developing your skills can lead to better job opportunities in the future.

Data Science Bootcamp Path

1. Earn a bachelor’s degree in any field.
2. Complete a data science bootcamp. Data science bootcamps provide intensive career training in less time than most traditional college programs. You may need to complete prerequisites before starting a bootcamp.
3. Complete an internship.
4. Pursue professional certifications.

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Master’s Degree Path

1. Earn a bachelor’s degree in any field.
2. Earn a master’s degree in data science. Data science master’s programs usually take 1-2 full-time years to complete.

What Is a Data Scientist?

New technologies help organizations easily collect large amounts of data. But, they often do not know what to do with this information. Data scientists use advanced methods to help bring value to data. They collect, organize, visualize, and analyze data to find patterns, make decisions, and solve problems.

Data scientists need strong skills in programming, data visualization, communication, and mathematics. Typical job responsibilities include gathering data, creating algorithms, cleaning and validating data, and drafting reports. Nearly any organization can benefit from the contributions of a trained data scientist. Potential work sectors include healthcare, logistics, and banking and finance.

How Much Experience Do Data Scientists Need?

Data scientist requirements for professional experience depend on the role and workplace. In general, data scientist positions are not entry-level jobs. Successful candidates need some relevant experience.

Before becoming a data scientist, some people start out in related information technology positions. These stepping-stone roles may include data analyst, market research analyst, or data engineer.

Data science degree programs and bootcamps often include internships, fellowships, and capstone projects. These experiences provide hands-on practice that can help graduates land a job offer. Some employers let job applicants substitute education for experience, or vice versa.

Professional Certifications

Professional certifications are not a requirement for becoming a data scientist. However, a certification can help you stand out and show your expertise to potential employers. Getting certified can help you earn a higher entry-level salary and open the door to more career advancement opportunities.

In some cases, a data science certification may persuade an employer to hire someone who does not meet all of a job’s stated education or experience requirements.

SAS Data Science Certification

This certification demonstrates the ability to use SAS and open source tools to manipulate big data. Certified professionals know how to use machine learning models to make business recommendations. Applicants complete the SAS data curation professional, advanced analytics professional, and AI & machine learning credentials to earn the data science certification.

Azure Data Scientist Associate

Microsoft’s data scientist associate certification shows knowledge and experience with Azure machine learning and Azure databricks. Applicants must pass the Designing and Implementing a Data Science Solution on Azure exam. The credential demonstrates skill in implementing machine learning, managing Azure resources, and deploying machine learning solutions.

Senior Data Scientist

The senior data scientist certification signifies data science knowledge and data leadership potential. Candidates pay a $775 fee that covers exam prep resources, a digital badge, and a credential kit. The certification requires 3-5 years of analytics and research experience and a bachelor’s degree or higher.

Principal Data Scientist

The principal data scientist credential offers four tracks for different applicants. It indicates high-level knowledge of data science and analytics technologies. Depending on the track they qualify for, candidates must pursue various exams and assessments to earn the credential. Once awarded, the designation never expires.

Professional Spotlight: Interview with a Data Scientist

Why did you choose to become a data scientist?

Prior to data science, I was a professor. But I (and many of my fellow young Ph.D.s) gradually realized that the academic job market has serious problems that prevent it from absorbing and properly utilizing all of the talented candidates who are getting their doctorates. Data science offers a way for people with strong mathematical and statistical backgrounds to apply their industry knowledge and research acumen to problems in the private sector in a much livelier job market (also for substantially better pay than is offered in academia). I also felt that data science, as a fast-growing, dynamic field, would allow me to expand my skills and insights faster than in academia.

Can you describe your path to a career in data science?

My first problem once I decided to switch careers was how, exactly, to transition. While I was highly educated, I had no specific certifications or qualifications that many jobs were looking for. That is why I chose to enroll in The Data Incubator. The Data Incubator specializes in taking candidates with strong academic backgrounds and helping them to learn how to conduct and communicate data science effectively in the private sector. They also help to match their students with prospective employers, which enabled me to get my first job in data science at Cova Strategies (which later transitioned to a role at NNData as senior data scientist).

What are some high and low points for this career? What challenges might a data scientist face?

While I am likely not in a good position to comment on career highs and lows (I have not been in data science for that long), I can say that the biggest challenge I faced in data science was believing myself to actually be qualified. Even after getting my first data science role, I felt much of the same imposter syndrome that plagues many people, especially those coming from academia.

What type of person does well in this role?

People who have a strong grasp of mathematics and statistics and can learn and apply new techniques rapidly. Data science is a rapidly evolving field; methods change, new techniques develop, and there is always something relevant to discover, understand, and integrate into new or even existing projects. No one can stay informed on every topic, so there will inevitably be times when you have to learn on the fly to use the latest or best techniques to solve a problem.

What advice do you have for students considering a career in data science?

First, as I mentioned earlier, data science is much more an exercise in mathematical and statistical reasoning than anything else, so don’t neglect your mathematics! Second, be prepared to be a pioneer. While many people have attempted to solve almost any problem (Stack Overflow is proof of that), few have likely tried to solve the problems you will be facing with the exact intention that you have. Be prepared to combine solutions together, modify code, or apply technologies in ways they may not have been initially intended. That’s what makes data science into a “data art,” and that’s what makes it fun! Third, especially if the student is coming from a graduate program, know your value. If you have (or are about to have) a Ph. D., you probably know something! You are more qualified than you likely give yourself credit for, and should not let yourself forget that. Finally, getting your foot in the door in any industry can be hard. Try and find some certification program, course, or something that signals to companies that you are serious and able to apply your skills to meet their needs. Beyond that, just remember that, like any career, a career in data science is a journey. Be prepared for the unexpected and to find your ideal niche in a company (and the wider industry) where you may not initially expect.

Andrew Graczyk, Ph.D.

Dr. Andrew Graczyk is a graduate of The Data Incubator. He also earned his Ph.D. in economics from the University of North Carolina at Chapel Hill in December 2017. His research specialties in game theoretic modeling, Bayesian statistics, and time series analysis allowed him to synthesize novel models to capture adverse incentives responsible for behavior that other models struggle to explain. Prior to his career in data science, he developed experience working with a wide variety of data and topics from asset bubble formation to housing markets to environmental regulation and agriculture. As a senior data scientist at NNData, Dr. Graczyk applies his multifaceted experience with data and theory to create robust, flexible, and holistic solutions to problems using cutting-edge machine learning and statistical techniques.

The Job Hunt

Places to look for data scientist positions include professional organizations, job fairs, and networking opportunities at annual conferences. Ask for job leads from mentors, alumni associations, and former supervisors and colleagues. Current students and recent graduates can apply for paid and unpaid internships to gain professional experience.

You can also search for openings on job boards. Below, we highlight five of the top job boards for the data science industry.

• DataJobs: This job site posts openings in data science, data analysis, and data engineering. It matches companies with big data talent.
• Open Data Science Job Portal: Job-seekers can find thousands of data science jobs here at over 300 companies. Candidates can submit their resumes and get matched automatically with relevant positions.
• Ai-jobs.net: Find artificial intelligence, machine learning, and big data jobs around the world.
• Digital Analytics Association: Browse for analytics openings by industry, job type, location, and experience level.
• icrunchdata: This website posts analytics, technology, and data-related jobs worldwide. It also provides industry insights and career advancement information.

Questions About Data Science Requirements

Is data science a good career?

Yes, data science offers promising career paths. It is a fast-growing and relatively new field with higher-than-average salaries. The Bureau of Labor Statistics projects above-average job growth for many data science careers, including operations research analysts and computer and information research scientists.

Do data science jobs pay well?

Yes. Data scientists can earn significantly more than the average worker. The Bureau of Labor Statistics reports that data scientists earned a mean annual salary of $108,660 as of May 2021.

What are the education requirements for a data scientist?

Minimum education requirements for data science professionals usually include a bachelor’s degree. Many data scientist positions require or prefer applicants with a master’s degree in data science, computer science, or a relevant field.

How can I become a data scientist?

Various paths can prepare students for a data science career. The steps to become a data scientist may include earning a bachelor’s degree, gaining work experience, and completing a professional certification. Many data scientists earn a master’s degree or attend a bootcamp.

What Is a Data Scientist? Salary, Skills, and How to Become One

Working as a data scientist can be intellectually challenging, analytically satisfying, and put you at the forefront of new advances in technology. Data scientists have become more common and in demand, as big data continues to be increasingly important to the way organizations make decisions. Here’s a closer look at what they are and do—and how to become one.

What does a data scientist do?

Data scientists determine the questions their team should be asking and figure out how to answer those questions using data. They often develop predictive models for theorizing and forecasting.

A data scientist might do the following tasks on a day-to-day basis:

• Find patterns and trends in datasets to uncover insights

• Create algorithms and data models to forecast outcomes

• Use machine learning techniques to improve the quality of data or product offerings

• Communicate recommendations to other teams and senior staff

• Deploy data tools such as Python, R, SAS, or SQL in data analysis

• Stay on top of innovations in the data science field

Data analyst vs data scientist: What’s the difference?

The work of data analysts and data scientists can seem similar—both find trends or patterns in data to reveal new ways for organizations to make better decisions about operations. But data scientists tend to have more responsibility and are generally considered more senior than data analysts. 

Data scientists are often expected to form their own questions about the data, while data analysts might support teams that already have set goals in mind. A data scientist might also spend more time developing models, using machine learning, or incorporating advanced programming to find and analyze data.

Many data scientists can begin their careers as data analysts or statisticians.

Read more: Data Analyst vs. Data Scientist: What’s the Difference?

Data scientist salary and job growth

A data scientist earns an average salary of $122,499 in the United States as of April 2022, according to Glassdoor [1]. 

Demand is high for data professionals—data scientists and mathematical science occupations are expected to grow by 31 percent, and statisticians by 33 percent from 2020 to 2030, says the US Bureau of Labor Statistics (BLS) [2, 3]. That’s much faster than the average growth rate for all jobs, which is 8 percent.

The high demand has been linked to the rise of big data and its increasing importance to businesses and other organizations. 

How to become a data scientist

Becoming a data scientist generally requires some formal training. Here are some steps to consider.

1. Earn a data science degree.

Employers generally like to see some academic credentials to ensure you have the know-how to tackle a data science job, though it’s not always required. That said, a related bachelor’s degree can certainly help—try studying data science, statistics, or computer science to get a leg up in the field.

Already have a bachelor’s degree?

Consider getting a master’s in data science. At a master’s degree program, you can dive deeper into your understanding of statistics, machine learning, algorithms, modeling, and forecasting, and potentially conduct your own research on a topic you care about. Several data science master’s degrees are available online.

2. Sharpen relevant skills.

If you feel like you can polish some of your hard data skills, think about taking an online course or enrolling in a relevant bootcamp. Here are some of the skills you’ll want to have under your belt.

• Programming languages: Data scientists can expect to spend time using programming languages to sort through, analyze, and otherwise manage large chunks of data. Popular programming languages for data science include:

  • Python

  • R

  • SQL

  • SAS

• Machine learning: Incorporating machine learning and deep learning into your work as a data scientist means continuously improving the quality of the data you gather and potentially being able to predict the outcomes of future datasets. A course in machine learning can get you started with the basics.

3. Get an entry-level data analytics job.

Though there are many paths to becoming a data scientist, starting in a related entry-level job can be an excellent first step. Seek positions that work heavily with data, such as data analyst, business intelligence analyst, statistician, or data engineer. From there, you can work your way up to becoming a scientist as you expand your knowledge and skills.

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Advice for New Data Scientists

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4. Prepare for data science interviews.

With a few years of experience working with data analytics, you might feel ready to move into data science. Once you’ve scored an interview, prepare answers to likely interview questions. 

Data scientist positions can be highly technical, so you may encounter technical and behavioral questions. Anticipate both, and practice by speaking your answer aloud. Preparing examples from your past work or academic experiences can help you appear confident and knowledgeable to interviewers.

Here are a few questions you might encounter:

• What are the pros and cons of a linear model?

• What is a random forest?

• How would you use SQL to find all duplicates in a data set?

• Describe your experience with machine learning.

• Give an example of a time you encountered a problem you didn’t know how to solve. What did you do?

Read more: SQL Interview Questions: A Guide for Data Analysts

A data professional at IBM offers his advice for aspiring data scientists:

Getting started

Becoming a data scientist might require some training, but an in-demand and challenging career can be waiting at the end. 

Are you just starting out in data science? Get a crash course in the basics with IBM’s Data Science Professional Certificate.

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Data Scientist – Courses – National Research University Higher School of Economics

Certified program of the Digital Economy national project. Studying all sections of modern data analysis: from programming and basic sections of mathematics to machine learning, applied statistics, working with big data and deep learning.

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About

This is a data analysis and machine learning program that covers all areas of modern data analysis, including deep learning and its applications.

• The program starts from the very basics – learning programming and basic mathematics – and moves on to sections on machine learning, applied statistics and data processing, working with big data, deep learning, its applications to images, texts and signals. When developing the program, we focused on practical work.
• At the end of the program, you will receive the most up-to-date knowledge in one of the most sought-after areas of the 21st century, portfolio projects and a diploma of professional retraining of the standard established by the National Research University Higher School of Economics.
• In December 2019, the “Data Scientist” program became a certified program of the “Digital Economy” national project and won in the “Training Digital Industry Professionals” nomination.

Curriculum

• Python for automation and data analysis 18 lessons

  • Introduction to the Python language. Familiarity with the programming environment. basic operations. Error interpretation.
  • Strings and Lists in Python.
  • The concept of control structures. Conditional statements.
  • for and while loops.
  • The arrangement of functions in Python. Finding errors in the code and debugging.
  • Iterators, generators, list generators. Recursion.
  • Working with files. Advanced work with dictionaries.
  • Libraries for storing and working with data in tabular format: pandas.
  • Data collection: Web scraping with BeautifulSoup.
  • Data collection: Selenium, working with services via API.
  • Object-oriented programming. Classes.
  • Introduction to numpy.
  • Introduction to pandas.
  • Works with missing data.
  • Visualization for data presentation: matplotlib. The main types of charts. The main mistakes when creating visualizations.
  • Create interactive visualizations: plotly.
  • Intelligence data analysis. Features of the study of the text.

• Mathematics for data analysis 19classes

  Discrete mathematics:

  • Sets and logic.
  • Combinatorics and probability.
  • Undirected graphs.
  • Directed graphs and algorithms on graphs.

  Mathematical analysis:

  • Functions of one variable, limits, derivatives.
  • Tangents, critical points, searching for minima and maxima.
  • Integrals, an introduction to calculating integrals.
  • Multivariable functions, gradient, directional derivative, level lines, plane tangent, critical points, search for minimums and maximums.
  • Optimization problems, Lagrangian and its geometric meaning, finding a minimum or maximum with given constraints.

  Linear algebra:

  • Systems of linear equations, matrices, reversibility and nondegeneracy.
  • Determinant, inverse matrix.
  • Vector spaces and subspaces, dimensions, ranks of matrices.
  • Linear mappings and their matrix description. Eigenvalues ​​and vectors, connection with the spectrum.
  • Bilinear and quadratic forms. Dot products, angles and distances. Orthogonalization and QR decomposition. Linear manifolds and linear classifiers, margins.
  • Operators in Euclidean spaces. Singular value decomposition (SVD).

  Probability theory:

  • The space of elementary outcomes. Developments. Probability and its properties. Conditional Probability. Total Probability Formula. Bayes formula.
  • Discrete random variables and their distributions. Independence of random variables. Distribution of a function of a discrete random variable. Mathematical expectation and dispersion.
  • Random variables with densities. Mathematical expectation of a random variable with density. Uniform, exponential, normal distributions.
  • Distribution function. Distribution of a function of a random variable with a density. Multidimensional random variables. covariance and correlation.
  • Concentration inequalities (Markov and Chebyshev inequalities). Distribution of the sum of random variables. The law of large numbers. Central limit theorem.

• Algorithms and data structures 10 lessons

  • Asymptotic analysis.
  • Basic data structures.
  • Sorting.
  • Binary search trees.
  • Hash tables.
  • Algorithms on graphs.
  • Algorithms on strings.
  • Dynamic programming.

• Applied statistics for machine learning 9 lessons

  Estimation theory. Estimation of distribution parameters. Method of moments and maximum likelihood method. Comparison of grades.

  • Estimation theory. Estimation of distribution characteristics. Monte Carlo method.
  • Confidence evaluation. Construction of confidence intervals. Confidence intervals in the normal model. Bootstrap.
  • Hypothesis testing. Introduction to hypothesis testing. Consent Criteria.
  • Hypothesis testing. Homogeneity Criteria and A/B Testing I.
  • Hypothesis testing. Homogeneity Criteria and A/B Testing II.
  • Linear models from a statistical point of view I. Investigation of feature dependence. covariance and correlation.
  • Linear models from a statistical point of view II. Least squares method (LSM). Statistical Properties of OLS Estimates.
  • Time series. SARIMA model and its fit.

• Machine learning14 lessons

  • Introduction and main tasks.
  • Linear regression.
  • Gradient teaching methods.
  • Linear classification and classification quality metrics.
  • Logistic regression and SVM.
  • Multi-class classification, work with categorical features and texts.
  • Decision trees.
  • Bagging and random forests.
  • Gradient boosting.
  • Gradient boosting: implementations.
  • Feature selection and dimension reduction.
  • Clustering.
  • Search for anomalies.
  • Recommender systems.
  • Ranging.

• Industrial Machine Learning on Spark 8 lessons

  • Introduction: how big data works and where.
  • Spark environment. Spark RDD / Spark SQL.
  • Advanced SQL.
  • Spark ML / Spark TimeSeries.
  • Advanced ML and validation of model quality results.
  • Spark GraphX/Spark Streaming.
  • Spark Ecosystem (MLFlow, AirFlow, h3O AutoML).
  • Spark in Project Architecture / Spark CI/CD.

• Deep Learning10 lessons

  • Introduction to deep learning. From linear regression to neural network. We look at the basic capabilities of tensorflow / pytorch, collect the first neural network.
  • Training of neural networks. Backpropagation algorithm.
  • Convolutional neural networks. Image classification.
  • Optimization. Heuristics for training neural networks. Batch normalization, initialization, etc.
  • Convolutional network architectures. What Convolutional Networks See. transfer learning.
  • An overview of computer vision tasks. Detection, segmentation, style transfer, face recognition.
  • Autoencoders and generative models (Generative Adversarial Networks).
  • Deep Learning for NLP. Vector representations of texts: word2vec, fasttext.
  • Recurrent neural networks, work with sequences. ELMO embeddings.
  • Sequence2sequence, attention mechanism architectures. Transformers. BERT.

• Applied Data Analysis 10 lessons

  • Introduction to digital signal processing and sound classification.
  • Automatic speech recognition.
  • Text-to-speech.
  • Introduction to word processing and text classification.
  • Language modeling.
  • Machine translation.
  • Face recognition and metric training.
  • Semantic image segmentation.
  • Detection of objects in images.
  • Deep Learning in Manufacturing: Maintenance and Acceleration.

Certificate of completion

Diploma of professional retraining upon successful completion of training.

We have an educational license.

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Teachers

Experts

Cost and Conditions

• 465 000 ₽

Payment can be divided into 8 parts

submit an application

• Schedule

  14, 2023 – February 24 June 20. on Tuesdays (19:00-22:00) and Saturdays, in person.

• Discount

  5-15%

  For students, graduates and trainees of basic and additional HSE programs.

How to apply for the program

• 01

  Apply for the program. In the application, it is important to indicate the current number and e-mail.

• 02

  Confirm course completion. The manager will contact you at the contacts specified in the application so that you can confirm your participation in the training.

• 03

  Send scanned copies of documents for enrollment (passport, SNILS, diploma, certificate from the university, certificate of name change).

• 04

  Conclude an agreement. The manager will send you an agreement for review and a link to the payment, according to which you will need to pay for the training.

• 05

  Start training. A few days before the start of training, the manager will send an organizational letter with all the important information about the program and a link to the Telegram chat.

Contacts

Where are the classes?

• You will study in the HSE main building on Pokrovsky Boulevard, in computer classes.

• During your studies, you will receive a plastic pass with access to all HSE buildings. You can visit the library at any time to study or work.

• You can come to classes in the building, or you can connect via Zoom.

FAQ

• Why should I choose your program?

  Our short programs pack the experience of undergraduate and graduate programs from the HSE Faculty of Computer Science. Based on our knowledge of the industry, we will give you the necessary base to enter the profession or move to the next level in your current job.
  In addition, most of our programs are full-time. This means that you will be able to communicate with teachers on a weekly basis, receive support from assistants and classmates, this will help keep you motivated.
  By studying in our courses, you get the opportunity to integrate into the HSE community, communicate with our teachers, and participate in faculty and university events: for example, we hosted a Data Analysis Night, we regularly host an IT lecture hall and a scientific colloquium of the Faculty of Computer Science.
  We have a license for educational activities, therefore, based on the results of training, we issue certificates of advanced training and professional retraining diplomas of the standard established at the National Research University Higher School of Economics.

• How is a data scientist different from a data analyst?

  Let’s take an example.

  The manager is interested in what products the users of the online store buy together, with this question he will go to the product analyst. The analyst will help identify these product categories and provide ideas for a prototype recommendation system. For many online stores, such recommendations may be enough to increase the average check. But then there may be a need to automate recommendations and build a model. This is already done by a Data Scientist.

  Sergey Yudin talked about this even more in his interview “Data Analyst and Data Scientist: What’s the Difference?”

• If I am a student, can I apply for the Data Scientist program?

  Yes, but a diploma of vocational retraining can only be obtained after graduation upon presentation of a diploma.

• Can I study on my laptop?

  Yes, you can bring your own laptop. You can also use university computers – all classes are held in computer classes.

Data Scientist – Center for Continuing Education – National Research University Higher School of Economics

Occupation

This is a data analysis and machine learning program that covers all areas of modern data analysis, including deep learning and its applications.

The program starts from the very basics – learning programming and basic mathematics – and moves on to sections on machine learning, applied statistics and data processing, working with big data, deep learning, its applications to images, texts and signals. When developing the program, we focused on practical work.

As a result of the program, you will receive the most up-to-date knowledge in one of the most sought-after areas of the 21st century, projects in the portfolio and a diploma of professional retraining of the standard established by the National Research University Higher School of Economics.

In December 2019, the “Data Science Specialist” program became a certified program of the “Digital Economy” national project and won in the “Training Digital Industry Professionals” nomination.

Program

18 sessions*

5 sessions

19 classes*

Applied statistics for machine learning

9 lessons*

10 Lessons*

Machine Learning

14 Lessons*

Industrial training on SPARK

8 lessons

Deep -legged training

10 classes 10 classes 10 *

Applied Data Analysis

10 lessons

Project

* – this block can be taken as a separate advanced training program

teachers

Jan Pile

Head of the analytics group at VK

Anastasia Maksimovskaya

Data Scientist at Sberbank

Maxim Karpov

Senior Lecturer, Faculty of Computer Science

Elena Kantonistova

Associate Professor, Faculty of Computer Science

Valentin Promyslov

Lecturer in the Faculty of Computer Science

Sergey Abdurakipov

Data Science Team Lead at SAP Labs

Ksenia Lisitsina

DL developer at Yandex Go

Ildar Safilo

Head of recommender systems group at MTS

Ilya Abroskin

Lecturer in the Faculty of Computer Science

Dolma Hurakay

Analyst at X5 Retail Group

Nikita Bekezin

Lead Data Scientist at X5 Retail Group

Sergey Drozhzhin

Senior Data Scientist at OTP Bank

Katerina Kolomeytseva

Data Scientist at Skoltech

Kirill Ovchinnikov

Head of Sberbank

Artem Zabolotny

Lecturer in the Faculty of Computer Science

Timur Petrov

Analyst at Yandex.