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Lecture 24: 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.

Mar. 15, 2019
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Lecture 23: 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.

Mar. 13, 2019
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Lab 7: Birthday

Mar. 12, 2019
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Lecture 22: Simulation and Analysis

Mar. 11, 2019
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Lecture 21: 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.

Mar. 8, 2019
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Lecture 20: 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.

Mar. 6, 2019
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Lecture 18+19: Probability

Feb. 27, 2019
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Lab 6: Regression

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Lecture 17: Descriptive Statistics and Probability

Feb. 25, 2019
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Lecture 16: Correlation and Regression

Feb. 22, 2019
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Lecture 15: Correlation and Regression

Feb. 20, 2019
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Lab 5: Plots

Feb. 19, 2019
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Lecture 14: Scatter Plots

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.

Feb. 15, 2019
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Lecture 13: 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.

Feb. 15, 2019
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Lecture 12: 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.

Feb. 13, 2019
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Project 1: Confounding Variables among Excellent Teachers

Feb. 12, 2019
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Lecture 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.

Feb. 11, 2019
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Lecture 10: Data Cleaning and Review

Feb. 8, 2019
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Lecture 9: Functions and Data Cleaning

Feb. 6, 2019
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Homework 2: Simpson's Paradox

Homework 2 is available, due Sunday, Feb. 10 at 11:59pm

Feb. 5, 2019
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Lab 4: Similarity

Find your Data Science DISCOVERY twin using Data Science!

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Lecture 8: Developing Algorithms for Complex Problems

Feb. 4, 2019
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Lecture 7: Creating Columns and Groups

Feb. 1, 2019
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Lab 3: Experimental Design

Begin to create your own control and treatment groups with while doing some experimental design!

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Lecture 6: Introduction to Pandas

Time to focus in on data, learning the primary tool we will be using all semester!

Jan. 28, 2019
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Homework 1: Experimental Design and Privacy

Homework 1 is available, due Sunday, Feb. 3 at 11:59pm

Jan. 27, 2019
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Lecture 5: 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!

Jan. 25, 2019
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Lecture 4: 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?

Jan. 23, 2019
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Lab 2: Introduction to Pandas

Use `pandas` for the first time and read/process CSV files!

Jan. 22, 2019
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Lecture 3: Observational Studies & Confounders

Observational studies are done out of necessity. Whenever possible, it’s better to do a randomized controlled experiment. Why?

Jan. 16, 2019
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Lecture: 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.

Jan. 16, 2019
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Lab: Introduction to Data Science

In lab this week, you will get set up with the tools of Data Science (python, jupyter, git, and others) as well as complete your first notebook!

Jan. 15, 2019
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Welcome to Data Science Discovery

First lecture is Monday, Jan. 14 at 9am in G32 FLB. See you there!

Jan. 14, 2019