Agenda

16 sessions of 4 academic hours, 64 academic hours in total, on Mondays and Wednesdays, 10:00 - 13:00 (two 10-minute breaks).

Goal: a smooth move to Python as the main tool for the same daily work with data that you now do in SQL and SAS: reporting, modelling and ad-hoc analysis.

How we work: a lot of practice. Every topic is connected to what you already know from SQL and SAS. All examples and tasks use made-up data in the shape of the data you work with (a loan portfolio: clients, loans, a rating scale) - never real data.

Sessions

The dates are planned and may change.

	Date	Topic
1	Mon 26.10	Introduction, syntax, data types and variables
2	Wed 28.10	Conditional statements and loops
3	Mon 02.11	Lists (basics), tuples and sets
4	Wed 04.11	Functions
5	Mon 09.11	Lists (advanced)
6	Wed 11.11	Dictionaries
7	Mon 16.11	Text processing
8	Wed 18.11	Regular expressions
9	Mon 23.11	Error handling and file handling
10	Wed 25.11	Modules, packages, Git and GitHub
11	Mon 30.11	OOP: first steps, classes and objects
12	Wed 02.12	OOP in practice: nomenclatures
13	Mon 07.12	Working with databases
14	Wed 09.12	NumPy and introduction to pandas
15	Mon 14.12	pandas: selection, aggregation, joins
16	Wed 16.12	pandas: reports and the final project

Session details

1. Introduction, syntax, data types and variables

• Python next to SQL and SAS: what each one is used for.
• Working with VS Code and Jupyter notebooks.
• int, float, str, bool, None; converting between types; operators.
• f-strings; Decimal for money values.

2. Conditional statements and loops

• if / elif / else, logical operators, match.
• for, range, while, break / continue.
• The Python version of CASE WHEN and of the loops in a DATA step.
• Tasks: sorting clients into risk classes.

3. Lists (basics), tuples and sets

• Creating lists, indexing, slicing, the methods of list.
• Tuples as records that cannot change.
• Sets for unique values, and set operations like UNION / INTERSECT / EXCEPT.

4. Functions

• def, parameters and default values, *args / **kwargs, return.
• Scope of variables, docstrings, type hints, lambda.
• Functions instead of SAS macros.

5. Lists (advanced)

• List comprehensions; sorted with key; min / max.
• zip, enumerate, map, filter.
• Nested lists: a table as a list of rows; copies and references.

6. Dictionaries

• Working with dict, looping over it, nested dictionaries, dict comprehensions.
• defaultdict, Counter.
• Grouping and aggregating by hand (GROUP BY without pandas).
• Lookup tables and nomenclatures.

7. Text processing

• The methods of str: split / join, strip, replace, formatting.
• Cleaning text data: names, codes, identifiers.
• Dates from text with datetime.

8. Regular expressions

• The re module: patterns, groups, search / findall / sub / split.
• Validating IBANs, company ids (EIK), e-mails and dates; extracting values from free text.
• The Python version of PRXMATCH (SAS) and LIKE / REGEXP (SQL).

9. Error handling and file handling

• try / except / else / finally, kinds of exceptions, raise, your own exceptions.
• pathlib, with open; text, CSV, JSON and Excel files.
• Handling bad records while loading data.

10. Modules, packages, Git and GitHub

• import, your own modules, the standard library (datetime, collections, statistics, os).
• pip and virtual environments; the structure of a project.
• Git: commit, branch, merge, .gitignore. GitHub: clone, push, pull, pull requests.

11. OOP: first steps, classes and objects

• Class and object, __init__, attributes, methods, self.
• __str__ / __repr__; class attributes; property; dataclasses.

12. OOP in practice: nomenclatures

• A nomenclature as a class: data, metadata, validation rules and processing methods.
• classmethod and staticmethod; inheritance and composition.
• Applying one nomenclature the same way to different datasets.

13. Working with databases

• DB-API and sqlite3: connection, cursor, parameterised queries, transactions.
• SQLAlchemy engine; the result of a query straight into a DataFrame.
• Drivers for Oracle / MS SQL / PostgreSQL (overview).

14. NumPy and introduction to pandas

• ndarray, vector operations, broadcasting, aggregates, np.where.
• Series and DataFrame; reading CSV and Excel files.
• head, info, describe, the types of the columns.

15. pandas: selection, aggregation, joins

• loc / iloc, boolean masks, query, new columns.
• groupby / agg (like GROUP BY and PROC MEANS).
• merge / concat (like JOIN and MERGE in a DATA step); missing values.

16. pandas: reports and final project

• pivot_table, melt, dates and time series, export to Excel with several sheets.
• Final project: load data from a file and a database, nomenclatures as classes, cleaning, aggregation and a finished report.