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Data Science на Английском — онлайн курс
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!
- Linear algebra;
- Differential calculus;
- Probability theory;
- Bayes theorem;
- Null hypothesis significance testing;
- Exploratory data analysis.
- 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
- Variables and data structures;
- Functions and methods;
- Object-Oriented Programming (OOP);
- Packages NumPy, SymPy, Pandas;
- Data visualization: Matplotlib, seaborn, plot.ly;
- Relational databases;
- SQL queries;
- Internet data (API, HTTP requests);
- Data cleaning.
- 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.