Course Schedule

Date Event Links
2019-01-14 Welcome to Data Science Discovery
2019-01-16 Ideal Experimental Design
Does the death penalty have a deterrent effect? Is chocolate good for you? What causes breast cancer? All of these questions attempt to assign a cause to an effect. A careful examination of data can help shed light on questions like these.
2019-01-18 Confounders and Observational Studies
Observational studies are done out of necessity. Whenever possible, it’s better to do a randomized controlled experiment. Why?
2019-01-21 MLK Day
2019-01-23 Observational Studies & Simpson’s Paradox
For years observational studies have shown that people who carry lighters are more likely to get lung cancer. However, this does not mean that carrying lighters causes you to get cancer. Smoking is an obvious confounder! If we weren’t sure about this, how can we determine whether it’s the lighters or the confounders or (maybe some combination of both) that is causing the lung cancer?
2019-01-25 Data Science Tools
\"Data\", \"Science\", and \"Tools\" all have meaning in their own, explore how one relates to another and how they all related to Data Science DISCOVERY!
2019-01-28 Introduction to Pandas
Time to focus in on data, learning the primary tool we will be using all semester!
2019-01-30 Arctic Vortex
2019-02-01 Pandas - Creating Columns and Groups
2019-02-04 Algorithms for Complex Problems
2019-02-06 Functions
2019-02-08 Data Cleaning
2019-02-11 Bar Graphs and Histograms
Large tables of numbers can be difficult to interpret, no matter how organized they are. Sometimes it is much easier to interpret graphs than numbers.
2019-02-13 Center and Spread
Parameters are numerical facts about the population. In this lecture, we will look at parameters such as the average (µ) and standard deviation (σ) of a list of numbers. Later, we will start talking about statistics. Statistics are estimates of parameters computed from a sample.
2019-02-15 Boxplots
Just like histograms, box plots are used as a way to visually represent numerical data. They do this through selected percentiles which are given special names.
2019-02-18 Scatter Plots
2019-02-20 Correlation and Regression
2019-02-22 Correlation and Regression II
2019-02-25 Descriptive Statistics and Probability
2019-02-27 Probability
2019-03-01 Probability II
2019-03-04 Midterm Exam (CBTF)
2019-03-06 Simulation
Simulation is an imitation of a real-world event within a computer program. We can use millions of simulations and observe the distribution of outcomes to help us understand the answer to a problem that may be difficult to model mathematically.
2019-03-08 Binary Event Simulation
As we work towards simulating events using Python, we need to first develop an understanding of different types of events to simulate. The first type of events are events with exactly two outcomes, or binary outcome events.
2019-03-11 Simulation and Analysis
2019-03-13 Control Flow in Python - Conditionals and Loops
In nearly every programming language, every program runs from top-to-bottom, one line at a time. In addition to running from top-to-bottom, there are three control flow commands in Python that allows us to control the flow of a Python program.
2019-03-15 Control Flow in Python - Loops and Functions
In nearly every programming language, every program runs from top-to-bottom, one line at a time. In addition to running from top-to-bottom, there are three control flow commands in Python that allows us to control the flow of a Python program.
2019-03-18 Spring Break
2019-03-20 Spring Break
2019-03-22 Spring Break
2019-03-25 Random Variables, EV, SE
2019-03-27 Discrete Random Variables, Bernoulli, Binomial
2019-03-29 Continuous Random Variables and the Normal Distribution
2019-04-01 The Central Limit Theorem
2019-04-03 Confidence Intervals for means and proportions
2019-04-05 Choosing a Sample Size
2019-04-08 Hypothesis Testing - One Sample Z Test for means and proportions
2019-04-10 Hypothesis Testing - Two Sample Z Test for means and proportions
2019-04-12 Hypothesis Testing - One and 2 Sample t tests
2019-04-15 Hypothesis Testing - Chi Square Test for Goodness of Fit
2019-04-17 Regression Inference
2019-04-19 Decisions and Type I & Type 2 Errors
2019-04-22 Bootsrapping/Resampling
2019-04-24 A/B Testing
2019-04-26 Classifiers
2019-04-29 Case Studies
2019-05-01 Final Exam Review
2019-05-02 Reading Day and Final Exam