Schedule

What is data science? Why is it important? Who are we? Course overview and syllabus.

Lecture Video

Running a Python program, IPython, Jupyter notebooks, variables and data types, operations, functions, scope. Version Control with GIT

Lecture Video

Data types and operators, conditions, lists, loops.

Lecture Video

Variable types, basic summary statistics and plotting, covariance and correlation, confounders, probability: Bernoulli, Binomial, and Normal distributions.

Lecture Video

Sets, dictionaries, pandas series, working with modules.

Lecture Video

Reading and writing data from files, pandas data frames, basic plotting.

Lecture Video

Introduction to Hypothesis Testing, Central Limit Theorem, A/B testing.

Lecture Video

Downloading, cleaning, analyzing, and visualizing stock data.

Lecture Video

Introduction to simple linear regression, multiple linear regression, exploratory vs. inferential viewpoints

Lecture Video

Model generalizability, cross validation, and using categorical variables in regression

Lecture Video

Data Visualization in Python with Matplotlib, Seaboarn, Altair.

Lecture Video

Principles of Data Visualization.

Lecture Video

Scrape HTML websites with Beautiful Soup. Data Cleanup with Pandas. Connect to APIs such as Twitter, Reddit. JSON, REST.

Lecture Video

Introduction to classification, k-nearest neighbors, generalizability, bias-variance, cross validation, discussion of course projects

Lecture Video

Spring Break Light

Spring Break Light

Decision Trees and Support Vector Machines (SVM), generalizability and cross validation

Lecture Video

Guest Lecture by Vivek Srikumar. What are the challenges in understanding natural language? How can we build statistical models of language?

Lecture Video

NLP in Python with NLTK. Parsing strings with regular expressions.

Lecture Video

Introduction to Clustering, supervised vs. unsupervised learning, k-means method

Lecture Video

Give and receive feedback on your project proposal from a peer group.

Hierarchical clustering, dendogram plots, clustering in practice

Lecture Video

Principal Component Analysis (PCA), using PCA for visualization

Lecture Video

What are the social impacts of computing technology such as personal privacy, intellectual property, interface usability, accessibility, and reliability. What are scenarios where pervasive use of automated systems can and has disproportionately and negatively impacted some groups more than others? What are solutions to mitigate these effects?

Classification and regression with neural networks. Network architectures. Using Tensor Flow.

Classification and regression with neural networks. Network architectures. Using Tensor Flow.

Working with relational databases in Python. Introduction to the Structured Query Language.

Basics about Networks. Visualization methods for general graphs and trees. Graph algorithms - path search, centrality, pagerank.

What did we learn, what else is out there, what can you learn next?