Python for Data and ML
Students learn core Python data‑science tools—NumPy, Pandas, visualization libraries, Scikit‑Learn, and PyTorch—through hands‑on projects that emphasize idiomatic code, performance, real‑world data handling, and reproducible ML pipelines.
Who Should Take This
Data analysts, software engineers, and aspiring machine‑learning practitioners who have basic Python knowledge and want to transition to end‑to‑end data science will benefit. The course fits professionals seeking practical, production‑ready skills to clean, explore, visualize, and model data efficiently, and to integrate PyTorch for deep‑learning experiments.
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
60 learning goals
1
NumPy for Scientific Computing
6 topics
Describe NumPy array fundamentals including ndarray creation, data types, shape, strides, and how contiguous memory layout enables vectorized operations that outperform Python loops
Apply array operations including indexing, slicing, boolean masking, fancy indexing, and broadcasting rules for performing element-wise operations on arrays of different shapes
Apply linear algebra operations including matrix multiplication, determinants, eigendecomposition, SVD, and solving linear systems using NumPy's linalg module
Apply random number generation and statistical operations including sampling from distributions, computing descriptive statistics, and performing element-wise mathematical functions
Analyze NumPy performance optimization including memory layout considerations, avoiding unnecessary copies, using views versus copies, and when to use specialized libraries over raw NumPy
Apply structured arrays and record arrays for heterogeneous data including defining custom dtypes, accessing fields by name, and interoperating with pandas for performance-critical operations
2
Pandas for Data Analysis
9 topics
Describe pandas data structures including Series, DataFrame, and Index and explain how labeled data and alignment simplify data manipulation compared to raw arrays
Apply data loading and inspection including reading CSV, JSON, Parquet, and SQL sources, examining shape, dtypes, missing values, and generating summary statistics with describe
Apply data selection and filtering including loc, iloc, boolean indexing, query method, and method chaining for readable and composable data transformations
Apply data cleaning operations including handling missing values with fillna and dropna, type conversion, string manipulation, duplicate removal, and outlier treatment
Apply groupby and aggregation including split-apply-combine, multiple aggregation functions, transform, apply, and pivot tables for summarizing data across categories
Apply merging and joining including inner, outer, left, and right joins on single and multiple keys, concatenation, and handling index alignment during merge operations
Analyze pandas performance including identifying slow operations, using vectorized operations over apply, leveraging categorical types, and when to switch to Polars or Dask for large datasets
Apply time series operations in pandas including date range generation, resampling, rolling windows, expanding windows, and shift operations for temporal data analysis and feature engineering
Apply categorical data handling including Categorical dtype, ordered categories, category-aware operations, and how proper categorical encoding reduces memory usage and improves performance
3
Data Visualization Libraries
5 topics
Apply matplotlib fundamentals including figure and axes objects, plot types, customization of labels, titles, legends, and the object-oriented versus pyplot interface distinction
Apply seaborn statistical visualization including distribution plots, categorical plots, regression plots, heatmaps, and how seaborn integrates with pandas DataFrames for concise plotting
Apply interactive visualization with Plotly including scatter, bar, line, and 3D plots, hover tooltips, animations, and creating dashboards for exploratory data analysis
Analyze visualization best practices including choosing appropriate chart types for data relationships, avoiding misleading visual encodings, and designing figures for publication and presentation
Apply geospatial visualization including folium for map-based plots, choropleth maps, point plots, and how to visualize location-based data for spatial analysis and presentation
4
Scikit-Learn for Machine Learning
7 topics
Describe the scikit-learn API design including estimators, transformers, predictors, the fit-predict-transform pattern, and how consistent interfaces enable composable ML workflows
Apply data preprocessing with scikit-learn including StandardScaler, MinMaxScaler, LabelEncoder, OneHotEncoder, SimpleImputer, and ColumnTransformer for heterogeneous data preparation
Apply classification and regression models including LogisticRegression, RandomForestClassifier, GradientBoostingRegressor, SVM, and KNN with appropriate hyperparameter configurations
Apply model evaluation including train_test_split, cross_val_score, GridSearchCV, RandomizedSearchCV, and Pipeline construction for preventing data leakage during hyperparameter tuning
Apply clustering and dimensionality reduction including KMeans, DBSCAN, PCA, and t-SNE for unsupervised analysis, feature reduction, and data visualization in high-dimensional spaces
Analyze scikit-learn pipeline design including custom transformers, feature union, column-specific preprocessing, and building end-to-end ML pipelines that serialize for production deployment
Apply anomaly detection with scikit-learn including IsolationForest, LocalOutlierFactor, and One-Class SVM for identifying unusual observations in datasets
5
PyTorch Fundamentals
6 topics
Describe PyTorch tensor fundamentals including tensor creation, data types, device placement, and how PyTorch tensors relate to NumPy arrays with automatic differentiation support
Apply autograd and computational graphs including requires_grad, backward pass, gradient accumulation, and how PyTorch's dynamic computational graph enables flexible model architectures
Apply nn.Module for model building including defining layers, forward methods, parameter registration, and organizing model components into reusable and composable modules
Apply the PyTorch training loop including DataLoader, loss computation, optimizer steps, learning rate scheduling, model checkpointing, and GPU training with device management
Analyze PyTorch debugging techniques including gradient checking, tensor shape tracking, common error patterns, memory profiling, and strategies for diagnosing training issues
Apply transfer learning with PyTorch including loading pretrained models from torchvision and Hugging Face, freezing layers, replacing heads, and fine-tuning on custom datasets
6
Development Environment
5 topics
Apply Jupyter notebook workflows including cell execution, magic commands, kernel management, and best practices for organizing exploratory analysis in reproducible notebooks
Apply Python environment management including virtual environments, conda environments, pip requirements, and dependency resolution for reproducible data science project setups
Analyze the transition from notebooks to production code including refactoring exploratory code into modules, testing strategies, and project organization patterns for ML projects
Apply project organization for ML including directory structure conventions, configuration management, logging setup, and how cookie-cutter templates standardize ML project scaffolding
Apply Python profiling for data science including cProfile, line_profiler, memory_profiler, and how to identify and resolve performance bottlenecks in data processing and model training code
7
Feature Engineering
6 topics
Apply feature creation techniques including polynomial features, interaction terms, binning, log transforms, and domain-specific feature derivation from raw data using pandas and NumPy
Apply text feature extraction including CountVectorizer, TfidfVectorizer, and integration with scikit-learn pipelines for text classification and NLP preprocessing workflows
Apply datetime feature engineering including extracting temporal components, computing rolling features, lag features, and cyclical encoding of periodic time features
Analyze feature selection methods including univariate selection, recursive feature elimination, L1 regularization, and tree-based feature importance for reducing dimensionality while preserving predictive signal
Apply target encoding and leave-one-out encoding for high-cardinality categorical features and explain how proper cross-validation prevents data leakage when using target-based encodings
Apply geospatial feature engineering including distance calculations, clustering geographic points, and deriving location-based features from coordinates for spatial prediction tasks
8
Data Formats and I/O
5 topics
Apply file I/O for data science including reading and writing CSV, JSON, Parquet, HDF5, and pickle files and explain the performance and compatibility trade-offs between formats
Apply database interaction from Python including SQLAlchemy, psycopg2, and pandas read_sql for querying databases and loading results directly into DataFrames for analysis
Apply API data collection including requests library usage, JSON response parsing, pagination handling, and rate limiting for building data collection scripts that feed ML pipelines
Analyze data loading performance including chunked reading for large files, memory-mapped arrays, lazy evaluation frameworks, and strategies for processing data that exceeds available memory
Apply web scraping with Python including BeautifulSoup, Scrapy, and Selenium for collecting training data from websites with proper rate limiting, robots.txt compliance, and error handling
9
Gradient Boosting Libraries
5 topics
Apply XGBoost for classification and regression including DMatrix creation, parameter configuration, early stopping, and interpreting training output for model tuning
Apply LightGBM and CatBoost including categorical feature handling, GPU training, and the performance characteristics that distinguish each gradient boosting implementation
Apply SHAP values for model interpretability including TreeExplainer, force plots, summary plots, and dependence plots to explain individual predictions and global feature importance
Analyze gradient boosting hyperparameter optimization including learning rate, tree depth, regularization, subsampling, and Optuna-based Bayesian optimization for efficient search
Apply Optuna for hyperparameter optimization including defining search spaces, pruning unpromising trials, multi-objective optimization, and integrating Optuna with scikit-learn and boosting libraries
10
Hugging Face Ecosystem
6 topics
Apply Hugging Face Transformers including AutoModel, AutoTokenizer, pipeline API, and loading pretrained models for text classification, summarization, and question answering tasks
Apply Hugging Face Datasets including loading, filtering, mapping transformations, and creating custom datasets for fine-tuning transformer models on domain-specific tasks
Apply sentence embeddings with sentence-transformers including model selection, encoding text for semantic search, and building similarity-based retrieval systems
Analyze the Hugging Face ecosystem including model hub navigation, model card evaluation, choosing between model variants, and integrating Hub models into production ML pipelines
Apply Hugging Face Trainer for fine-tuning including training arguments, evaluation strategies, callbacks, mixed-precision training, and gradient accumulation for training on limited GPU memory
Apply tokenizer usage including encoding and decoding, special token handling, padding and truncation strategies, and batch encoding for preparing text data for transformer model input
Hands-On Labs
15 labs
~410 min total
Console Simulator
Code Sandbox
Practice in a simulated cloud console or Python code sandbox — no account needed. Each lab runs entirely in your browser.
Scope
Included Topics
- NumPy (arrays, broadcasting, linear algebra), pandas (DataFrames, groupby, merging, time series), matplotlib and seaborn visualization, Plotly interactive charts, scikit-learn (preprocessing, classification, regression, clustering, pipelines), PyTorch (tensors, autograd, nn.Module, training loops), feature engineering, data I/O (CSV, Parquet, SQL, APIs), XGBoost/LightGBM/CatBoost, SHAP interpretability, Hugging Face transformers and datasets
Not Covered
- General Python programming fundamentals (covered in Python Fundamentals domain)
- Web development frameworks (Django, Flask, FastAPI)
- Advanced deep learning architectures (covered in Deep Learning domain)
- DevOps and deployment tooling (covered in DevOps and MLOps domains)
- R programming and its data science ecosystem
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