Python for Data Science: Advanced topics including list comprehensions, lambda functions, decorators, generators. Focus on PEP 8 standards and efficient code.

Libraries:

NumPy

Array creation: np.array(), np.zeros(), np.ones(), np.arange()
Indexing/slicing: array[0:5], array[:, 1]
Element-wise operations: +, -, *, /
Broadcasting rules
Linear algebra: np.dot(), np.matmul(), np.linalg.inv()
Statistics: np.mean(), np.median(), np.std()
Random: np.random.rand(), np.random.randint()

SciPy:(optimization, signal processing).

Data Structures: Efficient use of dictionaries, sets, tuples, and custom classes for data handling (e.g., average O(1) lookup for hash tables).
Version Control: Git and GitHub for collaborative development (branching, merging, pull requests, contributing to open source).

Descriptive Statistics: Measures of central tendency (mean, median, mode), dispersion (variance, standard deviation, IQR), distribution shapes (skewness, kurtosis).
Inferential Statistics: Hypothesis testing (Z-test, T-test, ANOVA, Chi-squared, p-values, significance levels), confidence intervals (95% CI common standard).
Probability Theory: Bayes’ Theorem, conditional probability, joint and marginal distributions.
Regression Analysis: Simple and multiple linear regression (R-squared, adjusted R-squared, RMSE), logistic regression for binary classification.
Experimental Design: A/B testing methodologies, power analysis (achieve 80% power at 0.05 significance level).

SQL for Data Science: Complex queries (JOINs, subqueries, window functions), database design (normalization, indexing for performance: 10-100x faster queries).

CREATE INDEX
DROP INDEX, ALTER INDEX
CREATE VIEW
DROP VIEW, ALTER VIEW
CREATE SEQUENCE
CREATE PROCEDURE / CREATE FUNCTION: Defining parameterized routines
Control flow (IF/ELSE, WHILE loops, CASE statements)
ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE()
LEAD(), LAG(), FIRST_VALUE(), LAST_VALUE()
Aggregate window functions (SUM() OVER(…), AVG() OVER(…))

Data Acquisition: APIs (RESTful, SOAP, OAuth authentication), web scraping (BeautifulSoup, Scrapy for 1000s of pages/hour), data lakes/warehouses.

Pandas for Data Manipulation: DataFrames, Series,GroupBy operations, merging, reshaping (pivot, melt), handling missing data (imputation techniques: mean, median, mode, K-NN), outlier detection (IQR, Z-score).

Create DataFrame: pd.DataFrame()
Import/export: pd.read_csv(), df.to_csv()
Data inspection: df.head(), df.info(), df.describe()
Missing values: df.isnull(), df.fillna(), df.dropna()
Duplicates: df.drop_duplicates()
Selection: df[‘col’], df.loc[], df.iloc[]
Filtering: df[df[‘col’] > 10]
Transformations: df.apply(), df.map(), df.transform()
Grouping: df.groupby()
Merge/join: pd.merge(), pd.concat()
Pivot/melt: df.pivot_table(), pd.melt()

Feature Engineering: Creating new variables from existing ones (e.g., polynomial features, interaction terms, date/time features), encoding categorical variables (one-hot, label, target encoding).

Exploratory Data Analysis (EDA): Univariate, bivariate, multivariate analysis, correlation matrices (Pearson, Spearman coefficients), distribution plots, scatter plots, box plots.

Supervised Learning:

Classification: k-Nearest Neighbors, Naive Bayes, Support Vector Machines (SVMs), Decision Trees, Random Forests (often 5-10% accuracy boost over single trees), Gradient
Boosting (XGBoost, LightGBM, CatBoost: often top performers in Kaggle competitions).
Regression: Ridge, Lasso, Elastic Net regularization.

Unsupervised Learning:

Clustering: K-Means, DBSCAN, Hierarchical Clustering (evaluating with Silhouette score, Elbow method).
Dimensionality Reduction: Principal Component Analysis (PCA: e.g., reducing 100 features to 10 while retaining 95% variance), t-SNE, UMAP.

Model Evaluation: Cross-validation (K-fold), performance metrics (accuracy, precision, recall, F1-score, ROC-AUC for classification; MAE, MSE, R-squared for regression), confusion matrices.

Model Selection & Hyperparameter Tuning: Grid Search, Random Search, Bayesian Optimization (e.g., using Optuna or Hyperopt, can find optimal hyperparameters 2-5x faster).

Neural Networks: Perceptrons, Multi-Layer Perceptrons (MLPs), activation functions (ReLU, sigmoid, tanh), backpropagation.
Frameworks: TensorFlow 2.x, Keras, PyTorch.
Convolutional Neural Networks (CNNs): Image classification (e.g., ResNet, VGG for ImageNet-scale tasks), object detection (YOLO, Faster R-CNN).
Recurrent Neural Networks (RNNs): LSTMs, GRUs for sequence data (NLP, time series).
Natural Language Processing (NLP): Text preprocessing (tokenization, stemming, lemmatization), Word Embeddings (Word2Vec, GloVe), Transformers (BERT, GPT-3 for SOTA NLP tasks).

Model Deployment: Flask/Streamlit for web apps, Docker for containerization, cloud platforms (AWS Sagemaker, Google AI Platform, Azure ML).
MLOps: CI/CD for ML pipelines, model monitoring (data drift, concept drift), logging, versioning.
Big Data Tools: Apache Spark (processing petabytes of data 100x faster than Hadoop MapReduce), Hadoop, Hive, Kafka for real-time data streams.
Data Visualization & Storytelling: Matplotlib, Seaborn, Plotly, Tableau/Power BI. Effective communication of insights to non-technical stakeholders (e.g., “storytelling with data”).
Ethics in AI: Bias in data and models (e.g., racial bias in facial recognition), fairness, accountability, transparency, privacy (GDPR, CCPA compliance).
Case Studies & Projects: End-to-end projects demonstrating problem definition, data acquisition, model building, evaluation, and deployment.

Capstone Project: A comprehensive, real-world project demonstrating proficiency across all syllabus modules, leading to a deployable solution and presentation.