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The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets.

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Data mining foundations and practice specialization.

Launch Your Career in Data Science. Master core data mining concepts, techniques, and hands-on skills.

Specialization - 3 course series

The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets. This specialization consists of three courses: (1) Data Mining Pipeline, which introduces the key steps of data understanding, data preprocessing, data warehouse, data modeling and interpretation/evaluation; (2) Data Mining Methods, which covers core techniques for frequent pattern analysis, classification, clustering, and outlier detection; and (3) Data Mining Project, which offers guidance and hands-on experience of designing and implementing a real-world data mining project.

Data Mining can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder .

Specialization logo image courtesy of Diego Gonzaga, available here on Unsplash: https://unsplash.com/photos/QG93DR4I0NE

Applied Learning Project

There are programming assignments that cover specific aspects of the data mining pipeline and methods. Furthermore, the Data Mining Project course provides step-by-step guidance and hands-on experience of formulating, designing, implementing, and reporting of a real-world data mining project.

• Jupyter Notebook 100.0%

Homework Assignments

We will plan to have 8 short homework assignments, roughly covering each main topic in the class. The homeworks will usually consist of an analytical problems set, and sometimes a programming exercise. A preferred programming language for the class is Python. Homework assignments make up 40% of the final grade .

Standard due date times for homeworks are 2:45 PM .

All homework assignments must be submitted to the designated area of Canvas . Do not submit assignments via email.

I recommend using LaTex for writing up homeworks. It is something that everyone should know for research and writing scientific documents. This overleaf project Links to an external site.contains a sample .tex file, as well as what its .pdf compiled outcome looks like. It also has a figure .pdf to show how to include figures.

Late Assignemnts

To get full credit for an assignment, it must be turned in through Canvas by the due time. Standard due date times for homework assignments are 2:45 PM . You are allowed to be late with at most two HOMEWORK (not project!) assignments no more than 48H, without penalty . You don’t need to ask for permission. After two homework assignments have been submitted late, once the deadline is missed, those turned in late will lose 10% . Every subsequent 24 hours until it is turned another 10% is deducted. Assignments will not be accepted more than 48 hours late , and will be given a 0.

Assignments will be posted far enough ahead of time that I will not be able to make additional exceptions if a student falls ill. The exception will be prolonged illness accompanied by a doctor’s note.

If you believe there is an error in grading, you may request a regrading within one week of receiving your grade. Requests must be made by email to the instructor, explaining clearly why you think your solution is correct.

You can earn extra points on some homework assignments.

Policy Statement on Academic Misconduct

The class operates under the School of Computing’s policies and guidelines . Among other things, we will adhere to the academic misconduct policy . More information about the policy can be found on the linked page, but we highlight this:

Violations of this policy are recorded as “strikes” by the SoC. A failing grade sanction in an SoC course counts as one “strike” in a student’s academic record. Two lesser sanctions in SoC courses count as one “strike” in a student’s academic record. Any student with two strikes due to academic misconduct will be subsequently barred from registering for any additional SoC courses, immediately dropped from their respective degree program, and will not be admitted to any future SoC program.

You are encouraged to discuss class materials with your peers. If you want you can form study groups because discussions help understanding. You are also welcome to discuss assignments.

However, you must write your own solutions, proofs, and code and submit your own solution. Do not copy or ask for assignments from other students or the internet. Do not let someone else copy your submissions either.

You should cite all sources that you refer to. This includes personal communication, books, papers, websites, etc. Doing so reflects academic integrity.

Statistics 36-462/662: Data Mining

Spring 2020.

Prof. Cosma Shalizi Tuesdays and Thursdays 1:30--2:50 Porter Hall 100

Prerequisites

Graduate students and 36-662, instructors.

Topics and Techniques to be Covered

Methods of pattern discovery (a.k.a. "heuristics"), methods of pattern validation, case studies and concerns, course mechanics and grading, time expectations.

Electronics

Missing class and emergencies, r, r markdown, and reproducibility, format requirements for homework, canvas, gradescope and piazza, office hours.

Collaboration, Cheating and Plagiarism

Accommodations for Students with Disabilities

Schedule, lecture notes and readings, lecture 1 (tuesday, 14 january): introduction to the course, lecture 2 (thursday, 16 january): lightning review of linear regression, sunday, 19 january at 10 pm, lecture 3 (tuesday, 21 january): nearest neighbors i: mostly theoretical, lecture 4 (thursday, 23 january): nearest neighbors ii: mostly statistical, sunday, 26 january, lecture 5 (tuesday, 28 january): trees and ensembles i, lecture 6 (thursday, 30 january): trees and ensembles ii, lecture 7 (tuesday, 4 february): linear classifiers and logistic regression, lecture 8 (thursday, 6 february): kernel methods, support vector machines, and random kitchen sinks, sunday, 9 february, lecture 9 (tuesday, 11 february): information measures i: scene-setting, lecture 10 (thursday, 13 february): information measures ii: the information-theoretic view of prediction, feature selection, and summarization, sunday, 16 february, no class on tuesday, 18 february, lecture 11 (thursday, 20 february): prelude to dimension reduction, sunday, 23 february, lecture 12 (tuesday, 25 february): linear dimension reduction, lecture 13 (thursday, 27 february): linear dimensionality reduction, once more with feeling, sunday, 1 march, lecture 14 (tuesday, 3 march): prelude to clustering, lecture 15 (thursday, 5 march): clustering i: clusters without probability, 10 and 12 march: no class, monday, 16 march, lecture 16 (tuesday, 17 march): no class, lecture 17 (thursday, 19 march): clustering ii, lecture 18 (tuesday, 24 march): how big are the errors of our predictive models, lecture 19 (thursday, 26 march): what kinds of errors do our predictive models make, saturday, 28 march, lecture 20 (tuesday, 31 march): cross-validation, lecture 21 (thursday, 2 april): bootstrapping, lecture 22 (tuesday, 7 april): recommender systems i: broad ideas and technical issues, lecture 23 (thursday, 9 april): recommender systems ii: what's not to love, lecture 24 (tuesday, 14 april): information retrieval, thursday, 16 april: optional lecture on epidemic modeling, lecture 25 (tuesday, 21 april): fairness in prediction, especially classification, lecture 26 (thursday, 23 april): waste, fraud and abuse i: when is our data-mining project going to be a technical failure, saturday, 25 april, lecture 27 (tuesday, 28 april): waste, fraud, and abuse ii: more sources of technical failure, lecture 28 (thursday, 30 april): waste, fraud and abuse iii: what are we doing to ourselves should we be doing this , friday, 1 may.

Homework assignment

There are 5-6 homework assignments that will be assigned in class. Assignments are due as scheduled, and grades on late work (except for final project paper) will be decreased by 10% per day late. All written work should be submitted in R Notebook format. You will submit your work through courseweb.pitt.edu .

Browse Course Material

Course info, instructors.

• Prof. Cynthia Rudin
• Allison Chang
• Dimitrios Bisias

Departments

• Sloan School of Management
• Institute for Data, Systems, and Society

As Taught In

• Data Mining
• Probability and Statistics

Learning Resource Types

Statistical thinking and data analysis, assignments, computer assignments.

There are three computer homework assignments, which comprise a total of 20% of the course grade and should be completed independently. The MATLAB ® tutorial below covers the basics of MATLAB.

MATLAB Tutorial ( PDF )

Optional Assignments

The optional homework assignments correspond to problems from the course textbook:

Tamhane, Ajit C., and Dorothy D. Dunlop. Statistics and Data Analysis: From Elementary to Intermediate . Prentice Hall, 1999. ISBN: 9780137444267.

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Recently Asked data mining Questions

• Q 1 : Use the dataset for Airplanes, Motorbikes, Schooners your goal is improving the average accuracy of classification See Answer
• Q 2 : Programming Assignment Explanation • Fortune Cookie Classifier¹ You will build a binary fortune cookie classifier. This classifier will be used to classify fortune cookie messages into two classes: messages that predict what will happen in the future (class 1) and messages that just contain a wise saying (class 0). For example, "Never go in against a Sicilian when death is on the line" would be a message in class 0. "You will get an A in Machine learning class" would be a message in class 1. Files Provided There are three sets of files. All words in these files are lower case and punctuation has been removed. 1) The training data: traindata.txt: This is the training data consisting of fortune cookie messages. trainlabels.txt: This file contains the class labels for the training data. 2) The testing data: testdata.txt: This is the testing data consisting of fortune cookie messages. testlabels.txt: This file contains the class labels for the testing data. See Answer
• Q 3 : Q1. (10 points) Answer the following with a yes or no along with proper justification. a. Is the decision boundary of voted perceptron linear? b. Is the decision boundary of averaged perceptron linear? See Answer
• Q 4 : Q2. (10 points) Consider the following setting. You are provided with n training examples: (T₁, 9₁, h₁), (2, 92, h₂),, (In, Yn, hn), where z, is the input example, y, is the class label (+1 or -1), and h₁> 0 is the importance weight of the example. The teacher gave you some additional information by specifying the importance of each training example. How will you modify the perceptron algorithm to be able to leverage this extra information? Please justify your answer. See Answer
• Q 5 : Q3. (10 points) Consider the following setting. You are provided with n training examples: (₁, ₁), (2, 2), (In, Yn), where zi is the input example, and y, is the class label (+1 or -1). However, the training data is highly imbalanced (say 90% of the examples are negative and 10% of the examples are positive) and we care more about the accuracy of positive examples. How will you modify the perceptron algorithm to solve this learning problem? Please justify your answer. See Answer
• Q 6 : Q4. You were just hired by MetaMind. MetaMind is expanding rapidly, and you decide to use your machine learning skills to assist them in their attempts to hire the best. To do so, you have the following available to you for each candidate i in the pool of candidates Z: (i) Their GPA, (ii) Whether they took Data Mining course and achieved an A, (iii) Whether they took Algorithms course and achieved an A, (iv) Whether they have a job offer from Google, (v) Whether they have a job offer from Facebook, (vi) The number of misspelled words on their resume. You decide to represent each candidate i € I by a corresponding 6-dimensional feature vector f(z)). You believe that if you just knew the right weight vector w R you could reliably predict the quality of a candidate i by computing w- f(z). To determine w your boss lets you sample pairs of candidates from the pool. For a pair of candidates (k, 1) you can have them face off in a "DataMining-fight." The result is score (k > 1), which tells you that candidate k is at least score (k> 1) better than candidate 1. Note that the score will be negative when I is a better candidate than k. Assume you collected scores for a set of pairs of candidates P. Describe how you could use a perceptron based algorithm to learn the weight vector w. Make sure to describe the basic intuition; how the weight updates will be done; and pseudo-code for the entire algorithm. See Answer
• Q 7 : Please create a K-means Clustering and Hierarchical Clustering with the line of code provided. The line of code should include a merger of the excel files. The excel files will also be provided See Answer
• Q 8 : Discussion - Data Mining, Text Mining, and Sentiment Analysis Explain the relationship between data mining, text mining, and sentiment analysis. Provide situations where you would use each of the three techniques. Respond to the following in a minimum of 230 words: See Answer
• Q 9 : Assignment #3: DBSCAN, OPTICS, and Clustering Evaluation 1. If Epsilon is 2 and minpoint is 2 (including the centroid itself), what are the clusters that DBScan would discover with the following 8 examples: A1=(2,10), A2=(2,5), A3=(8,4), A4=(5,8), A5=(7,5), A6=(6,4), A7=(1,2), A8=(4,9). Use the Euclidean distance. Draw the 10 by 10 space and illustrate the discovered clusters. What if Epsilon is increased to sqrt(10)? (30 pts) See Answer
• Q 10 : 2. Use OPTICS algorithm to output the reachability distance and the cluster ordering for the dataset provided, starting from Instance 1. Use the following parameters for discovering the cluster ordering: minPts =2 and epsilon =2. Use epsilonprime =1.2 to generate clusters from the cluster ordering and their reachability distance. Don't forget to record the core distance of a data point if it has a dense neighborhood. You don't need to include the core distance in your result but you may need to use them in generating clusters. (45 pts) 2 16 14 12 10 015 05 20 06 04 021 026 016 027 022 025 019 023 09 024 07 018 08 011 070 030 029 028 012 013 014 2 01 0 03 0 2 4 8 10 12 14 16 017 Dataset visualization Below are the first few lines of the calculation. You need to complete the remaining lines and generate clusters based on the given epsilonprime value: Instance (X,Y) Reachability Distance Instance 1: (1,1) Undefined(or infinity) Instance 2: (0, 1) 1.0 Instance 3: (1, 0) 1.0 Instance 16: (5,9) Undefined Instance 13: (9,2) Undefined Instance 12: (8,2) 1 See Answer
• Q 11 : 3. Use F-measure and the Pairwise measures (TP, FN, FP, TN) to measure the agreement between a clustering result (C1, C2, C3) and the ground truth partitions (T1, T2, T3) as shown below. Show details of your calculation. (25 pts) Ground Truth T, TT, Cluster C, CC3 See Answer
• Q 12 : 1. We will use Flower classification dataset a. https://www.kaggle.com/competitions/tpu-getting-started 2. Your goal is improving the average accuracy of classification. a. You SHOULD use google collab as the main computing. (Using Kaggle is okay) b. You SHOULD create a github reposit for the source code i. Put a readme file for execution c. You SHOULD explain your source code in the BLOG. d. Try experimenting with various hyperparameters i. Network topology 1. Number of neurons per layer (for example, 100 x 200 x 100, 200 x 300 x 100...) 2. number of layers (For example, 2 vs 3 vs 4 ... ) 3. shape of conv2d ii. While doing experiments, make sure you record your performance such that you can create a bar chart of the performance iii. An additional graph idea might be a training time comparison Do some research on ideas for improving this. iv. e. You can refer to the code or tutorial internet. But the main question you have to answer is what improvement you made over the existing reference. i. Make sure it is very clear which lines of code is yours or not. When you copy the source code, add a reference. 3. Documentation is the half of your work. Write a good blog post for your work and step-by-step how to guide. a. A good example is https://jalammar.github.io/visual-interactive-guide-basics-neural-networks/ 4. Add a reference a. You add a citation number in the contents and put the reference in the separate reference section See Answer
• Q 13 : This tutorial will guide you how to do homework in this course. 1. Goto https://www.kaggle.com/c/titanic and follow walkthrough as https://www.kaggle.com/alexisbcook/titanic-tutorial B 2. Submit your result to Kaggle challenge. 3. Post jupyter notebook to your homepage as blog post. A good example of blog post is. https://jalammar.github.io/visual-interactive-guide-basics-neural-networks/ 4. Submit your homepage link and screenshot pdf in the canvas. 5. Doing 1-4 will give you 8 points. To get additional 2 points, create a section as "Contribution" and try to improve the performance. I expect one or two paragraph minimum (the longer the better). Show the original score and improved score. See Answer
• Q 14 : 1. Use the insurance fraud dataset. Consider the data quality issues (e.g., missing data) and preprocess the data. Split the data into a 10% train and 90% test set using random_state = 1. Create a decision tree with a max depth of 3 using a gini measure. Print the accuracy on the test set and the tree. Is this a good approach? Why or why not? 2. Create a decision tree on the same data with max depth of 3 and an entropy measure. Does the accuracy change? Does the tree change? Discuss which measure you think is better. 3. Now split the data into 70% train and 30% test using random_state = 1. Redo 2 and 3. Have the trees and accuracy changed? Are the trees more or less similar now? Discuss which split you think is better and why. 4. Evaluate how the accuracy changes with the depth of the tree with the 70-30 data. Look at the accuracy for a max depth of 1, 2, 3, ... 10, 15, 20. Plot the curve of changing. Do you see underfitting? Do you see overfitting? 5. What variable provides the most information gain in the insurance fraud data (for the 70-30 split)? 6. Decision trees are a "white box" method. What do you observe about the insurance fraud data using decision trees? See Answer
• Q 15 : You are required to write a 1 page proposal for your project as a pdf. Your proposal must include the following pieces of information: 1. Data Mining Task: What is your data mining task? This task could be a series of exploratory questions that you want to investigate or analyze. What is your motivation behind choosing this task for your project? 2. Dataset: What is the source of your data? Provide a link to your data source if you acquired it online. 3. Methodology: How will you solve the data mining task? You should have some idea of the algorithms or software tools you plan to investigate. Please feel free to use existing data mining and machine learning tool kits (e.g., Weka, Scikit-Learn) as needed for your project. 4. Final product: What will be the outcome of this project? How will you measure the success of your course project? Will this project help you explore or learn something new? See Answer

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