Data Science Methods

What You Will Learn

• 1 Learn the foundations of data science, including the data science lifecycle, key roles, and the problem-framing process used by professional data teams.
• 2 Understand data collection and wrangling techniques, including sourcing structured and unstructured data, handling missing values, and cleaning datasets for analysis.
• 3 Apply exploratory data analysis methods to uncover patterns, distributions, outliers, and relationships using descriptive statistics and visualization techniques.
• 4 Analyze statistical inference concepts including hypothesis testing, confidence intervals, and probability distributions to draw valid conclusions from sample data.
• 5 Build supervised learning models using regression and classification algorithms, including model training, evaluation metrics, and overfitting prevention strategies.
• 6 Explore unsupervised learning methods such as clustering, dimensionality reduction, and anomaly detection to discover hidden structure in unlabeled data.
• 7 Implement feature engineering and selection workflows to improve model performance by transforming, creating, and identifying the most predictive input variables.
• 8 Develop model evaluation and tuning techniques including cross-validation, hyperparameter optimization, and performance benchmarking across multiple model types.
• 9 Understand data storytelling and visualization principles to communicate analytical findings clearly to both technical and non-technical audiences.
• 10 Discover how to deploy and monitor data science solutions in professional environments, including pipeline design, model versioning, and ethical data practices.