Data Science & Analytics

Overview

Here is a structured course overview covering those key topics:

1. Exploratory Data Analysis (EDA)

• Data Wrangling & Cleaning: Techniques for handling missing values, identifying outliers, filtering data, and restructuring messy datasets.

• Feature Engineering: Transforming raw attributes into meaningful numerical features, standardizing scaling, and encoding categorical variables.

• Data Profiling: Utilizing statistical summaries and distribution checks to uncover initial patterns and data quality issues.

2. Statistical Inference

• Probability Foundations: Key probability distributions (Normal, Binomial, Poisson) and Bayes’ Theorem.

• Hypothesis Testing: Designing and executing $t$-tests, Chi-Square tests, and ANOVA to validate assumptions and measure significance.

• Confidence Intervals & Sampling: Estimating population parameters, understanding the Central Limit Theorem, and evaluating statistical power.

3. Data Visualization

• Visual Storytelling: Principles of effective data communication, choosing appropriate charts, and designing clean, intuitive dashboards.

• Core Libraries & Tools: Hands-on visualization using Python tools (Matplotlib, Seaborn) and interactive BI platforms (Tableau, Power BI).

• Exploratory Visuals: Heatmaps, correlation matrices, scatter plots, and multi-dimensional charts for pattern discovery.

4. Machine Learning Fundamentals

• Supervised Learning: Building predictive models using Linear/Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines.

• Unsupervised Learning: Pattern extraction via $k$-Means Clustering, Hierarchical Clustering, and Principal Component Analysis (PCA).

• Model Evaluation: Performance metrics including Precision, Recall, $F1$-Score, ROC-AUC, Mean Squared Error (MSE), and cross-validation techniques.

5. Big Data Technologies

• Distributed Computing: Handling datasets that exceed single-machine memory limits using frameworks like Apache Spark and Hadoop.

• Data Pipelines & Storage: Basics of NoSQL databases (MongoDB, Cassandra), relational data warehouses, and ETL (Extract, Transform, Load) pipelines.

• Query Optimization: Writing efficient SQL queries at scale for massive analytical workloads.

6. Cloud Platforms for Data Science

• Cloud Infrastructure: Leveraging major cloud providers (AWS, Google Cloud, Microsoft Azure) for scalable compute and storage resources.

• Managed Data Tools: Working with enterprise solutions such as AWS SageMaker, Google BigQuery, or Snowflake.

• Collaborative Notebooks: Utilizing cloud-hosted environments (Databricks, Google Colab) for team workflows and model training.

7. Deployment of Models (MLOps)

• Model Containerization: Packaging data pipelines and models using Docker to guarantee environment consistency.

• API Development: Serving models as REST APIs using lightweight web frameworks like Flask or FastAPI.

• Continuous Monitoring: Tracking model drift, performance latency, and retraining requirements in production environments.

8. Ethics in AI and Data Science

• Bias & Fairness: Detecting, auditing, and mitigating systemic biases within training datasets and algorithmic outputs.

• Data Privacy & Compliance: Navigating global regulations (GDPR, CCPA), anonymization techniques, and user consent frameworks.

• Model Interpretability: Implementing Explainable AI (XAI) frameworks like SHAP and LIME to ensure transparency in algorithmic decisions.