Upcoming Deadlines


Final grades released:
  • You can view your final course grade in Compass 2g!
  • Have an amazing break!!
Storytelling and Data Visualization

Lecture: Storytelling and Data Visualization

December 11, 2019
Normalization and Neural Networks

Lecture: Normalization and Neural Networks

December 9, 2019
Clustering

Lecture: Clustering

December 6, 2019
Final Project Lab

: Final Project Lab

Distance Metrics

Lecture: Distance Metrics

December 4, 2019
You and Data Science

Projects: You and Data Science

December 2, 2019
2 Sample Z Test

Lecture: 2 Sample Z Test

November 22, 2019
Hypothesis Tests

lab_hypothesis-tests: Hypothesis Tests

Z Tests in Python

Lecture: Z Tests in Python

November 20, 2019
Hypothesis Testing

Lecture: Hypothesis Testing

Hypothesis Tests are statistical tests to see if a difference we observe is due to chance. Many times, we have competing hypotheses about the value of a population parameter. It’s impossible or impractical to examine the whole population to find out which hypothesis is true, so we take a random sample and see which hypothesis better supported by our sample data.

November 18, 2019
Correlation, Regression, and Central Limit

Homework 9: Correlation, Regression, and Central Limit

November 17, 2019
Midterm 2 (CBTF) - No Class :)

Lecture: Midterm 2 (CBTF) - No Class :)

November 15, 2019
K-Means Clustering

lab_kmeans: K-Means Clustering

November 13, 2019
k-means clustering

Lecture: k-means clustering

November 13, 2019
RMSE and Clustering

Lecture: RMSE and Clustering

November 11, 2019
Residuals + RMSE

Lecture: Residuals + RMSE

November 8, 2019
Regression

lab_regression: Regression

November 6, 2019
Residuals, RMSE, Regression in Python

Lecture: Residuals, RMSE, Regression in Python

November 6, 2019
Scatterplots, Correlation, Simple Regression

Lecture: Scatterplots, Correlation, Simple Regression

November 4, 2019
CLT + Polling + Scatterplots

Lecture: CLT + Polling + Scatterplots

November 1, 2019
Lists

lab_lists: Lists

October 30, 2019
Lists and Dictionaries

Lecture: Lists and Dictionaries

October 30, 2019
Confidence Intervals

Lecture: Confidence Intervals

October 28, 2019
Sampling, Random Variables, and Expected Values

Homework 7: Sampling, Random Variables, and Expected Values

October 27, 2019
Sampling

Lecture: Sampling

We take a sample to find out about a larger population. We usually don’t have the resources to gather information on everyone in the whole population so instead, we select a small sample and use it to make inferences about the larger population.

October 25, 2019
Image Mosaic

Projects: Image Mosaic

October 23, 2019
CLT

lab_clt: CLT

October 23, 2019
Central Limit Theorem

Lecture: Central Limit Theorem

The normal approximation for random variables amounts to taking advantage of the Central Limit Theorem. We replace the true probability histogram for the sum, average, or percentage of draws by the normal curve before computing areas.

October 23, 2019
Normal Approximation

Lecture: Normal Approximation

The normal curve is a bell-shaped "ideal" histogram that many histograms resemble. Many histograms are close to the normal curve. For these histograms, you can use the normal curve to estimate percentages for the data.

October 21, 2019
Discrete Random Variables, Bernoulli, and Binomial

Lecture: Discrete Random Variables, Bernoulli, and Binomial

Any outcome that has exactly two outcomes with a fixed probability is called a Bernoulli distribution. The Binomial Distribution with parameters n and p is the discrete probability distribution of the number of successes in a sequence of n independent experiments. For a single trial (n=1), the binomial distribution is a Bernoulli distribution.

October 18, 2019
Random Variable

lab_random-variable: Random Variable

Images + Random Variables

Lecture: Images + Random Variables

October 16, 2019
Simulation Analysis + Images

Lecture: Simulation Analysis + Images

October 14, 2019
Grouping in Python, Center and Spread

Homework 6: Grouping in Python, Center and Spread

October 13, 2019
Bayes Rule

Lecture: Bayes Rule

October 11, 2019
Birthday

lab_birthday: Birthday

October 9, 2019
Functions in Python and Conditional Probability

Lecture: Functions in Python and Conditional Probability

October 9, 2019
Addition Rule + Conditional Probability

Lecture: Addition Rule + Conditional Probability

The conditional probability of an event B is the probability that the event will occur given that an event A has already occurred.

October 7, 2019
Grouping in Python, Center and Spread

Homework 5: Grouping in Python, Center and Spread

October 6, 2019
Midterm 1 (CBTF) - No Class :)

Lecture: Midterm 1 (CBTF) - No Class :)

October 4, 2019
Normal Approximation, Sampling, and Probability

Homework 8: Normal Approximation, Sampling, and Probability

October 3, 2019
Simulation

lab_simulation: Simulation

October 2, 2019
Loops in Python + Addition Rule

Lecture: Loops in Python + Addition Rule

October 2, 2019
Probability, Birthday Problem, and Control Flow

Lecture: Probability, Birthday Problem, and Control Flow

September 30, 2019
Introduction to Probability + Monty Hall

Lecture: Introduction to Probability + Monty Hall

Probability is the likelihood or chance of an event occurring. This begins a multi-week journey discovering probability and how to simulate probabilistic events.

September 27, 2019
Plots

lab_plots: Plots

September 25, 2019
Algorithms to Solve Complex Problems

Lecture: Algorithms to Solve Complex Problems

An algorithm is a step-by-step, detailed set of instructions to solve a problem. An algorithm can be expressed as English sentences (usually as a numbered list) and is a great way to begin solving complex problems.

September 25, 2019
Quartiles and Box Plots

Lecture: Quartiles and Box 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.

September 23, 2019
Bar Graphs and Histograms

Lecture: 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.

September 20, 2019
GPA

lab_gpa: GPA

September 18, 2019
Grouping Data (pandas) II

Lecture: Grouping Data (pandas) II

September 18, 2019
Grouping Data (pandas)

Lecture: Grouping Data (pandas)

A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

September 16, 2019
Measures of Center and Spread and Boolean Logic

Homework 3: Measures of Center and Spread and Boolean Logic

September 15, 2019
Grouping in Python, Center and Spread

Homework 4: Grouping in Python, Center and Spread

September 15, 2019
Boolean Logic and Conditionals

Lecture: Boolean Logic and Conditionals

September 13, 2019
Simpson's Paradox

lab_simpsons-paradox: Simpson's Paradox

September 11, 2019
Measures of Center and Spread

Lecture: Measures of 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.

September 11, 2019
Simpson's Paradox and Stratification

Lecture: Simpson's Paradox and Stratification

Stratification is often called the "blocking of observational studies" and allows us to use stratification to further explore observational studies.

September 9, 2019
Experimental Design II and Privacy

Homework 2: Experimental Design II and Privacy

September 8, 2019
Confounders and Observational Studies

Lecture: Confounders and Observational Studies

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?

September 6, 2019
Getting Started with Pandas

lab_pandas: Getting Started with Pandas

The primary Data Science library we will be using this semester is pandas. This lab explores the basic usage of the pandas library and gets you ready for the Data Science challenges we will be beginning next week!

September 4, 2019
Blocking and Conditionals

Lecture: Blocking and Conditionals

Random assignment to treatment and control works best to make the groups as alike as possible. With enough subjects, random differences average out. But what do you do if you have a small sample? Blocking first, then randomizing ensures that the treatment and control group are balanced with regard to the variables blocked on. We can use conditionals in pandas to help us do this!

September 4, 2019
Experimental Design and Basic Python

Homework 1: Experimental Design and Basic Python

August 30, 2019
Experimental Design and Row Selection (pandas)

Lecture: Experimental Design and Row Selection (pandas)

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.

August 30, 2019
Introduction to Data Science

lab_intro: Introduction to Data Science

Data scientists use powerful tools to help learn about data. In this first lab, you will set up your account and computer for Data Science Discovery and begin to play around with your very first Python notebook!

August 28, 2019
Data Science Tools

Lecture: 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!

August 28, 2019
Welcome to Data Science Discovery

Lecture: Welcome to Data Science Discovery

The next BIG thing at Illinois is Data Science and it starts with Discovery!

August 26, 2019
Welcome to Data Science Discovery!

Welcome to Data Science Discovery!

Our first lecture is Monday, Aug. 26 at 12:00noon in Lincoln Hall Theater. See you there! :)

August 15, 2019