Asiman Data School

Modules

5 modules, taught in this order over 26 weeks. Each module is split into topics, one topic per lesson.

1 of 52 topics published. The rest are coming soon.

Module 1

Foundations of Python Programming

Weeks 1-6, 12 topics

  1. Topic 1, week 1, TuesdayPython basics: variables, data types, operators; environment setup (Anaconda/Jupyter) Lesson ready
  2. Topic 2, week 1, ThursdayControl flow: if/else statements
  3. Topic 3, week 2, TuesdayLoops (for/while) and functions (parameters, return values, scope)
  4. Topic 4, week 2, ThursdayData structures: lists and tuples
  5. Topic 5, week 3, TuesdayData structures continued: dictionaries, sets, and comprehensions
  6. Topic 6, week 3, ThursdayFile I/O and error handling (try/except)
  7. Topic 7, week 4, TuesdayModules and packages; introduction to Object-Oriented Programming (classes and objects)
  8. Topic 8, week 4, ThursdayNumPy fundamentals: arrays and indexing
  9. Topic 9, week 5, TuesdayNumPy continued: slicing, broadcasting, and vectorized operations
  10. Topic 10, week 5, ThursdayPandas fundamentals: Series and DataFrames, reading data
  11. Topic 11, week 6, TuesdayPandas data cleaning and wrangling (missing values, filtering, merging, grouping)
  12. Topic 12, week 6, ThursdayData visualization: Matplotlib and Seaborn basics

Module 2

Linear Algebra and Probability/Statistics

Weeks 7-10, 8 topics

  1. Topic 13, week 7, TuesdayLinear Algebra: vectors and vector operations (addition, dot product)
  2. Topic 14, week 7, ThursdayMatrices: operations and matrix multiplication
  3. Topic 15, week 8, TuesdayEigenvalues and eigenvectors; vector norms and their role in machine learning
  4. Topic 16, week 8, ThursdayProbability basics: sample space, events, conditional probability
  5. Topic 17, week 9, TuesdayProbability distributions (normal, binomial, Poisson) and sampling
  6. Topic 18, week 9, ThursdayDescriptive statistics: mean, median, variance, standard deviation
  7. Topic 19, week 10, TuesdayHypothesis testing: p-values, significance, common tests (t-test, chi-square)
  8. Topic 20, week 10, ThursdayConfidence intervals and correlation

Module 3

Comprehensive Machine Learning

Weeks 11-18, 16 topics

  1. Topic 21, week 11, TuesdayIntroduction to Machine Learning: supervised vs. unsupervised learning, the ML workflow, scikit-learn basics
  2. Topic 22, week 11, ThursdayLinear Regression: theory and implementation
  3. Topic 23, week 12, TuesdayRegularization: Ridge and Lasso regression
  4. Topic 24, week 12, ThursdayLogistic Regression
  5. Topic 25, week 13, TuesdayClassification metrics: accuracy, precision, recall, F1-score, ROC-AUC
  6. Topic 26, week 13, ThursdayTrain/test split and cross-validation
  7. Topic 27, week 14, TuesdayOverfitting, underfitting, and the bias-variance tradeoff
  8. Topic 28, week 14, ThursdayDecision Trees
  9. Topic 29, week 15, TuesdayRandom Forests and ensemble learning (bagging)
  10. Topic 30, week 15, ThursdayGradient Boosting and XGBoost
  11. Topic 31, week 16, Tuesdayk-Nearest Neighbors and Naive Bayes
  12. Topic 32, week 16, ThursdaySupport Vector Machines
  13. Topic 33, week 17, TuesdayFeature engineering: scaling and encoding categorical variables
  14. Topic 34, week 17, ThursdayHandling outliers and imbalanced datasets
  15. Topic 35, week 18, TuesdayUnsupervised learning: k-means and hierarchical clustering
  16. Topic 36, week 18, ThursdayDBSCAN clustering and Principal Component Analysis (PCA)

Module 4

Computer Vision

Weeks 19-20, 4 topics

  1. Topic 37, week 19, TuesdayImage data fundamentals: pixels, color channels, images as arrays; overview of computer vision tasks
  2. Topic 38, week 19, ThursdayConvolutional Neural Network (CNN) fundamentals: convolution and pooling operations
  3. Topic 39, week 20, TuesdayBuilding and training a simple CNN image classifier (hands-on)
  4. Topic 40, week 20, ThursdayTransfer learning with pretrained models; overview of object detection and image segmentation

Module 5

NLP, Neural Networks, LLMs and RAG

Weeks 21-26, 12 topics

  1. Topic 41, week 21, TuesdayNLP text preprocessing: tokenization, stemming/lemmatization, stopwords; Bag of Words representation
  2. Topic 42, week 21, ThursdayTF-IDF and an introduction to word embeddings
  3. Topic 43, week 22, TuesdayWord embeddings deep dive (Word2Vec/GloVe, cosine similarity); introduction to Neural Networks (the perceptron)
  4. Topic 44, week 22, ThursdayNeural network training: activation functions, forward pass, loss functions
  5. Topic 45, week 23, TuesdayBackpropagation and gradient descent; building a simple neural network (hands-on)
  6. Topic 46, week 23, ThursdaySequence data and an introduction to RNNs/LSTMs
  7. Topic 47, week 24, TuesdayAttention mechanism and Transformer architecture overview
  8. Topic 48, week 24, ThursdayHow Large Language Models (LLMs) work: pretraining objective and tokenization
  9. Topic 49, week 25, TuesdayPrompt engineering (zero-shot, few-shot, chain-of-thought) and working with LLM APIs (hands-on)
  10. Topic 50, week 25, ThursdayFine-tuning and customization overview (LoRA/PEFT); LLM limitations (hallucination, bias, safety)
  11. Topic 51, week 26, TuesdayRetrieval-Augmented Generation (RAG): embeddings, vector databases, chunking, building a RAG pipeline
  12. Topic 52, week 26, ThursdayAdvanced RAG techniques (re-ranking, hybrid search) and course wrap-up