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    Data Science in English – online course
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    DURATION

    170 hours

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    HOW OFTEN

    Mon Wed Fri

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    START

    7 June

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    FEE:

    € 2000

    Data Science in English – online course

    Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals.

    We have developed our course to reflect the most important points in Data Science and pay special attention to practice, so as not to waste your time and arm you with the most essential skills. Hence, you have 200 working hours, during this period you have the opportunity to learn a new profession and apply all the acquired knowledge in practice within 12 weeks.

    You will pay a lot of attention to math and start learning Python from scratch (the only volume needed for data science), master SQL, touch the basis of machine learning, feel the importance of linear and logistic regressions, be able to understand which algorithm to use to provide best results.

    Register and let’s upgrade together!

    ПІБ платника:
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    Math and Statistics for Data Science
    • Linear algebra;
    • Differential calculus;
    • Probability theory;
    • Bayes theorem;
    • Null hypothesis significance testing;
    • Exploratory data analysis.
    Учебный план картинка
    Python for Data Science
    • Variables and data structures;
    • Functions and methods;
    • Object-Oriented Programming (OOP);
    • Packages NumPy, SymPy, Pandas;
    • Data visualization: Matplotlib, seaborn, plot.ly;
    • Git/GitHub;
    • Relational databases;
    • SQL queries;
    • Internet data (API, HTTP requests);
    • Data cleaning.
    Учебный план картинка
    Machine Learning Course
    • Formulating an ML problem;
    • Feature engineering;
    • Loss functions;
    • Generalization and performance estimation;
    • Hyperparameters optimization;
    • Linear and Logistic regression;
    • k Nearest Neighbours;
    • Tree-based models;
    • Adaboost, XGBoost;
    • Support Vector Machine (SVM);
    • Introduction to neural networks;
    • Recommendation systems;
    • Collaborative filtering.
    Учебный план картинка
    TOP skill you will learn:
    • Mathematical computing using popular Python packages as NumPy or Scikit-Learn
    • How to use linear/non-linear models
    • How to prepare your data for model building (feature engineering)
    • How to train and evaluate the performance of machine learning models
    • How to tune the model’s hyperparameters and select models
    • Expertise in mathematical computing using popular Python packages as NumPy or Scikit-Learn
    • Understand and use linear/non-linear models
    • Obtain an in-depth understanding of supervised and unsupervised learning models such as linear regression, logistic regression, SVM, clustering and K-NN
    • Get understanding about how the magic of neural networks actually works and will be able to write them yourself
    • Build reproducible machine learning pipelines
    • Experience applying these methods to real-world problems
    • Experience of building machine learning model APIs
    Учебный план картинка

    Data Science instructor-led online course

    FAQ

    Do I need to bring laptop to classes?

    Yes, you need to bring your own laptop to classes so that to be able to work on it after the course.

    Is there an admission test?

    Yes, there will be an admission test to measure each student’s background.

    What is the schedule of the classes?

    We have 2 different schedules either 10-15 or 16-21. A more detailed schedule of the day can be found on the course page. Check the course to see which tie suites you most.

    Will I get employment after the course?

    Our career counselors are ready to help each student find a good job, but it also depends on you. You need to work hard to be able to master a new profession within 3 months. In case if you participated in all the lectures and submitted all the practical assignments for 95-100 scores, and you will not get a job within 3 months after graduation, we are ready to reimburse you money.