About course

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Abstract

This program is designed for persons without programming experience who want not only to learn how to program, but also to apply software code to data analysis in practice.The material provides a wealth of advanced data analytics information, challenges, and examples using the Python programming language for advanced data analysis.Here, not only a general understanding of what data analysis is, but also real solutions for data analysis are developed.The requirements of employers and the real tasks set for data analysts in the recruitment selections are introduced and demonstrated lively.

Important information

Way of learning

Place

Language

Aukštos pridėtinės vertės programa

Yes

Minimum requirements for the participant

Education

Vidurinis išsilavinimas

Acquired and improved competencies

Ordinary:

Mathematical competence and competence in science, technology and engineering

Digital competence

Professional competencies:

Create typical software

Create big data analytics projects

Apply relevant software development methodologies

Content of the learning program

Topic name	Brief description of the topic
Topic name	Brief description of the topic
Topic name Development of simple machine learning algorithms	Brief description of the topic • Mathematical basis of machine learning algorithms; • Basic machine learning technologies; •Classification; • classification of the nearest neighbors of the KNN; • Types of regressions; • Linear regression; • Logistic regression; • R2 accuracy measure; •Grouping; • Dimensional reduction; • The main components of machine learning algorithms; • Application of machine learning algorithms; • Installation of scikit Learn module; • Scikit Learn module tools; • Splitting the data file into X (data) and y (result) columns; • Import of the linear regression module; • Model training with X data using the fit function; • Prediction of the result of the model y with the predict function; • Representation of results in the graph; • Checking the accuracy of the model R2 with the score function; • Import of the logistic regression module; • Principles of sigmoide function; • Splitting the data package into training and testing data packages.
Topic name Advanced data analysis using specialized Python programming modules	Brief description of the topic • Library of modules created by Python third parties pypi.org; • Principles of importing and renaming module components; • NumPy; • Pandas; • Matplotlib; • Other modules based on the NumPy module; • NumPy module data structures – arraysArrays); • Arrays of different dimensions; • Principles of installation of the Pandas module in different environments; • Data structures of the Pandas module; • DataFrame data structure properties and structure; • NumPy speed; • Array methods; • Array issues with non-numeric values; • Reading and recording data formats with Pandas; • Quick file analysis; • Data file cleaningData Cleaning) using Pandas; • Creation of new data and replacement of existing ones; • Grouping, sorting and merging data; • Reading and saving SQL Server data: • Syntax for specifying login credentials to the server; • Creating a SQL database using Python code.
Topic name Basics of programming using the Python software language	Brief description of the topic • Downloading and installing a Python application; • Downloading and installing the code editor PyCharm; • Downloading and installing the code editor Jupyter Notebook; • Computer requirements for writing software code; • Differences between code editors; • Python file types .py and .ipynb;• Labor market for different IT professions (experience and wages); • Differences and similarities between programming languages: • Bits; •Bytes; • Conversion of text to binary code – ASCII; • Compilation of software code; • Interpretation of software code; • The concept of algorithms; • Basic Python functions; • Python primitive data structures; • Python basic mathematics; • Python non-target data structures; • Python cycles; • Python error management; • Python functions; • Python object-oriented programming (OOP) concept; • Python integrated modules.
Topic name Graphical representation of data using Python modules	Brief description of the topic Subject.Drawing graphs using the Pandas module; • Installation of the necessary modules for drawing graphs; • Principles of drawing graphs in different code editors; • Features of drawing graphs in jupyter notebook environment; • Axis drawing using NumPy module methods; • Axis transformation into DataFrame; • Use of the pandas area function; • Changing the names of graphs and axes; • Principles of choosing different types of graphs; • Drawing cake-type graphs; • Drawing columnar graphs; • Drawing of completed linear graphs; • Drawing graphs for scanned data; • Matplotlib graph library; • Import of the Matplotlib module and its individual parts; • Copying complex Matplotlib graphs; • Features of the representation of Matplotlib graphs using different code editors; • Determination of graph axes; • Changing the names of graphs and axes.
Topic name	Brief description of the topic
Topic name Preparing your computer for programming.	Brief description of the topic Downloading and installing tools for Python programming; • Downloading and installing a Python application; • Downloading and installing the code editor PyCharm; • Downloading and installing the code editor Jupyter Notebook; • Computer requirements for writing software code; • Using Python code on different operating systems; • Running Python code using the command line; • Differences between code editors; • Code editors for application development; • Code editors adapted for data analysis; • Python file types .py and .ipynb; • PyCharm Code Editor Review; • Microsoft Visual Studio Code Code Code Editor Overview; • Jupyter Notebook Code Editor Review; • Google Colab Code Editor Review; • The importance of programming languages in data science; • Labour market for different IT professions (experience and wages).