Introduction to Data Science for Complete Beginners

Data science blends computer science, math, statistics, business expertise, and machine learning to store, analyze, and extract insights from diverse data for informed business decisions.

Discover the differences between artificial intelligence, machine learning, deep learning, and data science, with examples from self-driving cars, image and video data, and neural networks.

Explore the basics of machine learning by contrasting supervised and unsupervised learning, with explanations of binary classification, classification, regression, multiclass classification, semi supervised learning, and reinforcement learning.

Explore supervised learning with labeled data, featuring binary classification of cats and dogs using neural networks and features, plus the difference between classification and regression.

Explore unsupervised learning through real-world clustering examples, grouping items by color and customer buying behavior into segments. See how clustering drives recommendations and anomaly detection in trends and events.

Explore core machine learning terms through a recipe analogy, showing how algorithms form models from training data and features, with logistic regression for binary classification and train-test splits.

Turn a business problem into a data problem, gain data access, collect and prepare a dataset, assess features and missing values, and later train, test, and predict with a model.

Explore the data science workflow by detailing data collection and cleaning, exploratory data analysis, and machine learning model building and deployment as features in apps and products.

Distinguish data engineer, data analyst, data scientist, and machine learning engineer roles; describe data pipelines, data preparation, descriptive analysis, and basic model deployment in practice.

Develop critical thinking to turn business problems into data problems. Master math and statistics, Python or R, data visualization, and storytelling and communication to persuade stakeholders in real projects.

Learn how to study data science from theory to boot camps and tracks, through Python, Pandas, statistics, machine learning, webinars, books, and competitions.