Data Science Basics

Indicative content

1. Introduction to real-world data-related issues - big data, multidimensional data, structured and unstructured data, discussion on the topic of data in practice.
2. Data storage in relational databases - creating and updating the tables with PostgreSQL and understanding different data types, simple querying of one and / or more columns from an SQL table, use of aliases and compound queries, filtering rows using WHERE and logical operators
3. Merging and aggregating data in relational databases - applying the optimization strategy of setting primary and foreign keys, sorting and grouping of data using HAVING and application of aggregation functions, JOIN for joining tables by practicing INNER, LEFT and RIGHT joining. Introducing the key word UNION.
4. Nested data query in relational databases - nested queries inside SELECT, FROM and WHERE clauses, basic arithmetic in nested queries.
5. Reporting and exploratory analysis (EDA) in relational databases - exploring PostgreSQL databases and analysing the data in them, summarize their main characteristics of data set by exploratory data analysis (EDA).
6. Python scripting language and its ecosystem - Python script development in common integrated programming environments (IDEs), variable assignment, basic commands in Python, work with user input, logical operations, basic arithmetic and string formatting, work-flow control using conditions (if, else, elif).
7. Basics of programming in Python - working with data arrays, manipulating mentioned data objects by predefined methods and functions, cycles with controlled run and use of Python-specific keywords, making it easier, runtime optimization using list comprehension.
8. Introduction to data science - working with NumPy library objects, core principle of vectorization seeking optimization of performance, arithmetic operations in NumPy.
9. Data science with the use of Pandas - understanding of basic Pandas-specific object and getting familiar with their specifics, retrieving the values ​​and mastering common data issues - missing values, outliers, and conditional data operations, importing external data sources with Python.
10. Data science with the use of Pandas II. - data grouping and sorting functions using one / more predefined aggregation functions and creating user specific functions, merging tables in Pandas, understanding multi-indexing.
11. Data visualization in Python - visualization using various graphs by predefined libraries and interactive graphs, modifying the chart parameters and style, subplots.
12. Data acquisition -API connections with requests library, interaction with the application interface and formatting of the obtained data. extraction of data from the web by beautifulsoup library, formatting html data, creating a Spider application designed for comprehensive web browsing.
13. Comprehensive summary applied in an economic analysis - comprehensive analysis requiring applying all presented topics.