• Variables and data types (integers, floats, strings, booleans)
• Type conversion (e.g., int(), str(), float())
• Basic operators (arithmetic, comparison, logical, assignment)
• Input and output functions (input(), print())
• Comments (single-line and multi-line)

• Conditional statements (if, elif, else)
• Loops: for loop (iterating over lists, tuples, strings, etc.) and while loop
• Loop control statements (break, continue, pass)
• Nested loops and conditionals

• Lists: Creation, indexing, slicing, methods (append(), remove(), sort())
• Tuples: Immutability, use cases, and operations
• Dictionaries: Key-value pairs, accessing, modifying, methods (keys(), values(), items())
• Sets: Unique elements, set operations (union, intersection, difference)
• List comprehensions and dictionary comprehensions

• Defining and calling functions
• Parameters and arguments (positional, keyword, default, variable-length)
• Return statements
• Scope and lifetime of variables (global vs. local)
• Lambda functions
• Recursion basics

• String creation and formatting (f-strings, .format(), % operator)
• String methods (upper(), lower(), strip(), split(), join())
• String slicing and indexing
• Working with escape characters and raw strings
• Regular expressions (intro with re module)

• Reading and writing files (open(), read(), write(), close())
• File modes (r, w, a, r+)
• Handling text and CSV files
• Using with statement
• Error handling during file operations

• Understanding exceptions and errors
• Using try, except, else, finally blocks
• Raising exceptions (raise)
• Common built-in exceptions (ValueError, TypeError, etc.)

• Importing standard library modules (math, random, datetime)
• Creating and using custom modules
• Understanding packages and __init__.py
• Installing third-party libraries with pip (e.g., requests)

• Classes and objects
• Attributes and methods
• Constructors (__init__) and instance variables
• Inheritance and polymorphism
• Encapsulation and access modifiers
• Special methods (__str__, __len__)

• math, random, datetime, os, sys
• Optional: Introduction to numpy and pandas

• Using numpy for numerical operations
• Using pandas for data manipulation
• Basic visualization with matplotlib/seaborn
• Reading & analyzing CSV files

• Writing clean and readable code
• PEP 8 styling
• Debugging techniques
• Code documentation (docstrings, comments)
• Basics of Git version control (optional)

• Calculator program
• To-do list application
• File organizer
• Text-based games (Hangman, Tic-Tac-Toe)
• Basic data analysis project

• Advanced Python topics (decorators, generators, multithreading)
• Intro to Flask/Django for web development
• Getting started with data science/machine learning
• Further learning resources (books, courses, docs)

• NamedTuples and Dataclasses for efficient data storage
• Advanced Dictionary Operations: nested dicts, defaultdict, Counter
• Itertools for data processing: permutations, combinations, chain, groupby
• Memory-efficient structures: array module, generators

• Array creation, reshaping, broadcasting, stacking/splitting
• Boolean and fancy indexing, masking
• Universal functions (ufuncs) for performance
• Linear algebra: matrix ops, eigenvalues, SVD
• Handling missing data: np.nan, masked arrays

• Advanced DataFrame and Series operations: indexing, pivoting, multi-indexing
• Data cleaning: missing values, duplicates, type conversions
• GroupBy, transform, apply for aggregation
• Merging and joining data: concat, merge types, joins
• Time series: resampling, rolling, datetime
• Performance tips: vectorization, eval, query

• Customizing Matplotlib: subplots, styles, annotations
• Advanced Seaborn: pair plots, heatmaps, facet grids
• Intro to Plotly for interactive charts
• Handling large datasets: sampling, aggregation

• Chunking large files with pandas and dask
• Working with CSV, JSON, Excel, Parquet, HDF5
• SQL integration with SQLAlchemy, pandas
• Web scraping: requests + BeautifulSoup

• Parallel processing using multiprocessing
• Scalable computing with Dask
• Joblib and parallel loops
• Intro to PySpark for distributed data

• Advanced regex for text processing
• Feature engineering: encoding, transformation
• Outlier handling: detection and scaling
• Normalization techniques: standard, min-max, log

• SciPy for stats: hypothesis testing, distributions
• Statsmodels: regression, ANOVA, time series
• Correlation types: Pearson, Spearman, Kendall
• Non-parametric methods and KDE

• Data prep: train/test split, pipelines
• Feature selection: RFE, MI, variance threshold
• Model evaluation: accuracy, precision, recall, F1, ROC
• Hyperparameter tuning: GridSearchCV
• Handling imbalance: oversampling, undersampling, SMOTE

• Lag features, rolling stats, differencing
• Decomposition using statsmodels
• Forecasting models: ARIMA, SARIMA, ETS
• Irregular time series: alignment, interpolation

• Decorators for pipeline logging
• Generators/iterators for large dataset processing
• Context managers and resource management
• Metaprogramming for automation

• Building ETL workflows
• Task scheduling with schedule/APScheduler
• Pipeline logging and monitoring
• Error handling with retries

• Working with AWS S3, GCP Storage (boto3, google-cloud-storage)
• Cloud computing basics with AWS Lambda
• Fetching data from cloud APIs

• Performance profiling with cProfile, line_profiler
• Reproducibility with virtual environments, requirements.txt
• Documentation and Jupyter notebooks
• Version control with Git

End-to-End Project:

• Ingest data from CSV, API, DB
• Clean and preprocess
• Perform EDA and visualize
• Apply ML or statistical models
• Present results in a dashboard/report

Example Projects:

• Customer churn prediction
• Sales forecasting with time series
• Sentiment analysis on text
• Market basket analysis