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    Python for Data Science
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    DURATION
    54 hours
    calendar
    HOW OFTEN
    Tue, Thu 18:00-21:00, Sat 10:00-13:00
    shuttle
    START
    16 February
    money
    FEE:
    $700

    Python for Data Science

    Python is data scientists’ preferred programming language. If machine learning researchers decide to open source their work they will most likely do it in python. Therefore, the course starts by introducing python concepts and packages that are useful for data analysis. This part of the program also describes data structures, relational and non-relational databases, means of interacting with databases, manipulating data, and merging datasets from different sources.

    We offer employment support to our graduates.

    ПІБ платника:
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    Curriculum Overview
    • Variables and data structures;
    • Conditional statements;
    • Loops (for, while);
    • Functions and methods;
    • Object-Oriented Programming (OOP);
    • Packages NumPy, SymPy, Pandas;
    • Data visualization: Matplotlib, seaborn, plot.ly;
    • Git/GitHub;
    • Coding style guidelines;
    • Reading and writing files;
    • Relational databases;
    • SQL queries;
    • Workbench;
    • Internet data (API, HTTP requests);
    • Data cleaning.
    Учебный план картинка
    TOP skill you will learn:
    • The basics of Python programming language;
    • How to use Python packages for data mining;
    • How to manipulate data and draw insights from large data sets;
    • How to create clear and human-readable data visualization.
    • How to get data from different sources (files, databases, API requests);
    • How to write complex SQL queries;
    • How to clean the data and perform exploratory data analysis (EDA).
    Учебный план картинка

    This is exactly for you if you are:

    • A person looking for a career change
    • A graduate from universities looking for a job in Data Science
    • A developer with a mathematical mindset who would like to get career growth
    • A business owner who would like to utilize data analysis and implement data-driven and AI projects

    FAQ