About course

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Abstract

This program is designed for novice programmers and data analysts. The material provides a wealth of advanced data analytics information, challenges, and examples using SQL data queries and the Python programming language for advanced data analysis.Here, in addition to gaining a general understanding of what data analysis is, real data analysis solutions are being developed that use the tools from this course.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

Design typical relational and non-linear (NoSQL) databases

Program server data requests.

Create big data analytics projects

Content of the learning program

Topic name	Brief description of the topic
Topic name Create structured data repositories	Brief description of the topic • Creating a MySQL server: • Installing a MySQL server on a computer; • CREATING SQL Server users; • Setting up a convenient connection to SQL Server; • Setting consumer protection parameters; • Familiarization with the mySQL server infrastructure: • Using MySQL Workbench; • Database structure on a MySQL server; • Principles of creating new databases on MySQL servery; • Differences between active and inactive databases[ • Importing and viewing database models on a MySQL server; • Creating and updating tables on a MySQL server without using code; • Introduction to viewsViews) and their conception; • Introduction to MySQL procedures; • Introduction to MySQL functions; • Uploading data to a MySQL server: • Uploading data tables to a MySQL server; • Filling data tables on a MySQL server; • Creating a data model on a MySQL server.
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 Create and update structured databases	Brief description of the topic • SQL query syntax C.R.U.D.Create Read Update Delete) concept; • Automatic creation of databases using SQL code; • Automatic creation of tables with different data types using SQL code; • Creating tables with keys from several columns; • Creation of temporary tables on SQL servers; • Features of temporary tables and practice of use; • ViewsView) creation using SQL code; • Principles and limitations of deleting data on SQL servers; • Deleting columns in relational tables using SQL code; • Deleting relational tables using SQL code; • Restrictions on deleting relational tables when using relationships; • Deleting SQL Server databases using SQL code; • Automation of data recording to SQL Server using INSERT INTO syntax; • Automatic data refresh in SQL Server using update set where syntax; • Data refresh restrictions on SQL Servers; • Editing columns in a database table using ALTER TABLE.
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 Create data queries	Brief description of the topic • Basic sql server data types; • Data formats; • SQL queries: • SQL code syntax; • STRUCTURE OF SQL queries; • Selection of columns with the help of SELECT queries; • Selection of tables using FROM; • Renaming columns and tables using AS; • Sorting the query result using ORDER BY; • Data aggregation using SQL queries; • SQL functions: • Standard functionsbuilt-in) SQL functions; • Text transformation; • Management of numeric data using SQL functions; • Arithmetic operators of SQL queries; • Comparative operators of SQL queries; • Data filtering using WHERE syntax; • Multifunctional filtering using logical operators; • SQL syntax for data grouping; • Changing if logic to a more convenient CASE syntax; Combining tables: • Principles of data queries using multiple tables; • Vertical connection of tables using UNION syntax; • Horizontal connection of tables using the syntax JOIN.
Topic name Identify data sources	Brief description of the topic Data sources: • SQL databases; • Excel files; • CSV files; • JSON files; • XML files; • Text files.• Descriptive analysis; • Diagnostic analysis; • Predictive analysis; • Prescriptive analysis.• Structured data; • Unstructured data; • Data flows; • Big data and its formats; • Cloud data technologies; • On-premises servers; • SQL Servers: • Relational data tables; • Automatic data update; • NoSQL technologies; • Data warehousesDate Warehouse); • Data Lakes Date Lake); • Artificial intelligence services; • Data modeling; • Data cleaning; • Data integration.
Topic name Understanding data infrastructure	Brief description of the topic Data transformation: • Automatic data collection from data sources; • ETA Process (ETA Process)Extract Transform Load); • Data cleaning and preparation for analysis; • Database structures: • Structured data tables; • Representation of relational data tables; • SQL Server ViewsViews); • Using SQL data queries to extract data from the server; • Data aggregation: • Data modeling using SQL queries; • SQL functions; • Application of mathematical models using SQL code; • Creating new tables and columns by pulling data from the server; • Table relationships: • Types of relational table relationships; •Advantages and disadvantages of a one-to-one table relationship; • Advantages and disadvantages of the "one-to-many" table connection; • Advantages and disadvantages of the "many-to-many" table connection; • Types of table connection: • Table keys: • Principles of combining structural data; • Primary keys for connecting structural tables; • Foreign keys.
Topic name Generate analytical insights and recommendations	Brief description of the topic • Drawing graphs using the Pandas module: • Principles of drawing graphs in different code editors; • Features of drawing graphs in jupyter notebook environment; • Drawing complex graphs with matplotlib module: • Copying complex Matplotlib graphs; • Drawing graphs with Seaborn module: • Seaborn module overview; • Standard data packets in the Seaborn module; • Seaborn Graph Library; • Different types of graphs in the Seaborn module; • displot graph drawing; • drawing a jointplot graph; • pairplot graph drawing; • drawing a scatterplot graph; • drawing a countplot graph; • boxplot graph drawing; • violinplot graph drawing; • stripplot graph drawing; • drawing a regplot graph; • drawing a heatmap graph; • 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.
Topic name Development of analytical projects	Brief description of the topic • Preparation of the final project: • Raising hypotheses and sequential verification of them; • Setting project goals; • Description of the project conclusions; • Application of Python functions in the project; • The use of Python cycles in project algorithms; • Substantiation of data analysis with graphs; • Application of Python modules; • Application of machine learning algorithms.