Covers the three perspectives that are fundamental to their ability to reason with data: critical thinking, inferential thinking, and computational thinking. Through data modeling and visualization, students will construct and communicate arguments that are rooted in data. The course teaches critical concepts and skills in computer (Python) programming, linear regression, and statistical inference, and delves into dilemmas surrounding data analysis such as balancing individual privacy and social utility.

Students will use Python to explore fundamental CS concepts and processes used in data science with a focus on descriptive data analysis, including data structures, development of functions and more advanced recursion, object- oriented programming, data processing and data visualization. Numpy, pandas, and matplotlib will be used to analyze real-world data.

Introduction to key concepts from Calculus (differentiation and integration), Probability (discrete and continuous random variables) and Linear Algebra (vector spaces, matrices, and linear systems). The course links mathematics and computational thinking through problem sets requiring students to answer mathematically- posed questions using computation.

Covers an introduction to key concepts from Linear Algebra (vector space, independence, orthogonality and matrix factorizations). The DS theme running through the course is exploratory data analysis, enabling a better understanding of the data at hand. The course will link mathematical concepts with computational thinking, specifically through the use of problem sets that require students to answer mathematically-posed questions using computation.

Covers topics in probability (including common probability distributions, conditional probability, independence, Bayes Theorem, prior and posterior distributions, sampling, and the central limit theorem), statistics (including maximum likelihood), basic numerical optimization (including gradient descent methods), and topics in calculus (including sequences and series). Knowledge of a programming language (such as Python) is expected.

First half continues the programming experience begun in DS110, with enhanced focus on machine learning applications. Later, the course introduces students to compiled programming languages, such as Rust, Go and Java, suitable for building large projects. Basic data structures (stacks, queues, priority queues, binary search trees), techniques for representing graphs, and basic graph algorithms will be explored. Concepts are developed and reinforced through data-driven inquiries in real-world settings.

Focused on developing capacity to design and implement data flows and the computational workflows meant to inform online/offline decision- making within large systems. Data science lifecycle, including question formulation, data collection and cleaning, exploratory data analysis and visualization, statistical inference and prediction, and decision-making. Relational (SQL) and MapReduce (noSQL) paradigms used to assemble analysis, optimization, and decision-making algorithms to track and scale data.

Fundamental principles underlying the design and analysis of algorithms. Including classical design methods, such as greedy algorithms, design and conquer, and dynamic programming, focusing on applications in data science. Algorithmic methods more specific to data science and machine learning. Emphasis on algorithmic efficiency, crucial with large and/or streaming data sets, for which multiple scans of data are infeasible, including the use of approximation and randomized algorithms.

This course instructs students in key algorithms for classic artificial intelligence (AI) and modern machine learning (ML). Along the way, we seek to explore what kinds of problems these techniques are good and bad at, and build intuition for what makes a good search or machine learning problem. The primary assessment tools will be programming problem sets in Python, using Jupyter notebooks.