Tutorial Lab

Core Python syntax, data types, and basic operations

Understanding Python variables and type system

Master string manipulation and methods

If statements, loops, and flow control

Function definition, parameters, and return values

Lists, tuples, sets, and dictionaries in depth

Classes, inheritance, and OOP principles

Reading, writing, and managing files

Advanced error handling and custom exceptions

List, dict, set comprehensions and generator expressions

Organizing code with modules and packages

Map, filter, reduce, and functional concepts

Function decorators, class decorators, and metaprogramming

Asynchronous programming and concurrent execution

Creating and using context managers

Iterator protocol and custom iterators

Profiling, optimization, and performance best practices

Unit testing, debugging, and test-driven development

Common software design patterns in Python

Walrus operator, match/case structural pattern matching, positional-only parameters

Counter, defaultdict, deque, namedtuple, OrderedDict, ChainMap

Python dataclasses, field(), frozen=True, slots=True, __post_init__, ordering, inheritance

pathlib.Path, path operations, glob, reading/writing files, cross-platform compatibility

f-string format specifiers, = debug syntax, alignment, number formatting, nested expressions, multiline

Arrays, broadcasting, vectorization and numerical computing

Series, DataFrames, indexing, groupby, merging and data manipulation

Matplotlib, Seaborn, chart types, figure layout and best practices

Descriptive statistics, probability, hypothesis testing, and statistical thinking for data science

Missing data, outliers, type conversion, encoding, and data quality for ML pipelines

Core ML concepts, terminology, and the learning process — no math required

Linear models, decision trees, random forests, boosting and the sklearn API

Precision, recall, ROC-AUC, regression metrics, cross-validation, leakage and hyperparameter tuning

Clustering, dimensionality reduction, K-means, DBSCAN, PCA, t-SNE and evaluation without labels

Neurons, layers, activations, backpropagation, gradient descent and training fundamentals

Convolutional networks, regularisation, transfer learning, and advanced training techniques