About This Specialization The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have basic a python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate. 5 courses Follow the suggested order or choose your own Projects Follow the suggested order or choose your own Certificates Follow the suggested order or choose your own
Introduction to Data Science in Python Upcoming Session: Dec 18 Subtitles English, Vietnamese, Chinese (Traditional) About the Course This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. The course will end with a statistics primer, showing how various statistical measures can be applied to pandas DataFrames. By the end of the course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.
Week 1 Week 1 In this week you'll get an introduction to the field of data science, review common Python functionality and features which data scientists use, and be introduced to the Coursera Jupyter Notebook for the lectures. All of the course information on grading, prerequisites, and expectations are on the course syllabus, and you can find more information about the Jupyter Notebooks on our Course Resources page. Video Introduction to Specialization Video Python Demonstration: Reading and Writing CSV files Reading Syllabus Video Python Dates and Times Reading Help us learn more about you! Video Advanced Python Objects, map() Video Data Science Reading 50 years of Data Science, David Donoho (optional) Video The Coursera Jupyter Notebook System Reading Notice for Auditing Learners: Assignment Submission Other Week 1 Lectures Jupyter Notebook Video Python Functions Video Python Types and Sequences Video Python More on Strings Video Advanced Python Lambda and List Comprehensions Video Advanced Python Demonstration: The Numerical Python Library (NumPy) Quiz Week One Quiz
Week 2 Week 2 In this week of the course you'll learn the fundamentals of one of the most important toolkits Python has for data cleaning and processing -- pandas. You'll learn how to read in data into DataFrame structures, how to query these structures, and the details about such structures are indexed. The module ends with a programming assignment and a discussion question. Video Introduction Other Week 2 Lectures Jupyter Notebook Video The Series Data Structure Video Querying a Series Video The DataFrame Data Structure Video DataFrame Indexing and Loading Video Querying a DataFrame Video Indexing Dataframes Video Missing Values Other Hacked Data Other Assignment 2 Programming Assignment Assignment 2 Submission
Week 3 Week 3 In this week you'll deepen your understanding of the python pandas library by learning how to merge DataFrames, generate summary tables, group data into logical pieces, and manipulate dates. We'll also refresh your understanding of scales of data, and discuss issues with creating metrics for analysis. The week ends with a more significant programming assignment. Other Week 3 Lectures Jupyter Notebook Video Merging Dataframes Video Pandas Idioms Video Group by Video Scales Video Pivot Tables Video Date Functionality Other Goodhart's Law Other Assignment 3 Programming Assignment Assignment 3 Submission
Week 4 Week 4 In this week of the course you'll be introduced to a variety of statistical techniques such a distributions, sampling and t-tests. The majority of the week will be dedicated to your course project, where you'll engage in a real-world data cleaning activity and provide evidence for (or against!) a given hypothesis. This project is suitable for a data science portfolio, and will test your knowledge of cleaning, merging, manipulating, and test for significance in data. The week ends with two discussions of science and the rise of the fourth paradigm -- data driven discovery. Other Week 4 Lectures Jupyter Notebook Video Introduction Video Distributions Video More Distributions Video Hypothesis Testing in Python Other End of Theory Other Science Isn't Broken: p-hacking activity Other Assignment 4 - Project Programming Assignment Assignment 4 Submission Reading Post-course Survey
Applied Plotting, Charting & Data Representation in Python Upcoming Session: Dec 11 Subtitles English, Vietnamese, Chinese (Traditional) About the Course This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will describe the gamut of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.
Week 1 Module 1: Principles of Information Visualization In this module, you will get an introduction to principles of information visualization. We will be introduced to tools for thinking about design and graphical heuristics for thinking about creating effective visualizations. All of the course information on grading, prerequisites, and expectations are on the course syllabus, which is included in this module. Video Introduction Video Graphical heuristics: Chart junk (Edward Tufte) Reading Syllabus Reading Help us learn more about you! Reading Useful Junk?: The Effects of Visual Embellishment on Comprehension and Memorability of Charts Video About the Professor: Christopher Brooks Video Tools for Thinking about Design (Alberto Cairo) Reading Notice for Coursera Learners: Assignment Submission Video Graphical heuristics: Lie Factor and Spark Lines (Edward Tufte) Video The Truthful Art (Alberto Cairo) Other Must a visual be enlightening? Other Hands-on Visualization Wheel Video Graphical heuristics: Data-ink ratio (Edward Tufte) Reading Graphics Lies, Misleading Visuals Peer Review Graphics Lies, Misleading Visuals Reading Dark Horse Analytics (Optional)
Week 2 Module 2: Basic Charting In this module, you will delve into basic charting. For this week s assignment, you will work with real world CSV weather data. You will manipulate the data to display the minimum and maximum temperature for a range of dates and demonstrate that you know how to create a line graph using matplotlib. Additionally, you will demonstrate the procedure of composite charts, by overlaying a scatter plot of record breaking data for a given year. Other Module 2 Jupyter Notebook Video Line Plots Video Introduction Video Bar Charts Video Matplotlib Architecture Video Dejunkifying a Plot Reading Matplotlib Other Plotting Weather Patterns Reading Ten Simple Rules for Better Figures Peer Review Plotting Weather Patterns Video Basic Plotting with Matplotlib Video Scatterplots
Week 3 Module 3: Charting Fundamentals In this module you will explore charting fundamentals. For this week s assignment you will work to implement a new visualization technique based on academic research. This assignment is flexible and you can address it using a variety of difficulties - from an easy static image to an interactive chart where users can set ranges of values to be used. Other Module 3 Jupyter Notebook Practice Peer Review Practice Assignment: Understanding Distributions Through Sampling Video Subplots Other Building a Custom Visualization Video Histograms Reading Assignment Reading Reading Selecting the Number of Bins in a Peer Review Building a Custom Visualization Histogram: A Decision Theoretic Approach (Optional) Video Box Plots Video Heatmaps Video Animation Video Interactivity Other Practice Assignment: Understanding Distributions Through Sampling
Week 4 Module 4: Applied Visualizations In this module, then everything starts to come together. Your final assignment is entitled Becoming a Data Scientist. This assignment requires that you identify at least two publicly accessible datasets from the same region that are consistent across a meaningful dimension. You will state a research question that can be answered using these data sets and then create a visual using matplotlib that addresses your stated research question. You will then be asked to justify how your visual addresses your research question. Other Module 4 Jupyter Notebook Video Plotting with Pandas Video Seaborn Reading Spurious Correlations Video Becoming an Independent Data Scientist Other Project Description Peer Review Becoming an Independent Data Scientist Reading Post-course Survey
Applied Machine Learning in Python Upcoming Session: Dec 18 Subtitles English About the Course This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.
Week 1 Module 1: Fundamentals of Machine Learning - Intro to SciKit Learn This module introduces basic machine learning concepts, tasks, and workflow using an example classification problem based on the K-nearest neighbors method, and implemented using the scikit-learn library. Reading Course Syllabus Video Introduction Reading Help us learn more about you! Video Key Concepts in Machine Learning Video Python Tools for Machine Learning Reading Notice for Auditing Learners: Assignment Submission Other Module 1 Notebook Video An Example Machine Learning Problem Video Examining the Data Video K-Nearest Neighbors Classification Reading Zachary Lipton: The Foundations of Algorithmic Bias (optional) Quiz Module 1 Quiz Other Assignment 1 Programming Assignment Assignment 1 Submission
Week 2 Module 2: Supervised Machine Learning - Part 1 This module delves into a wider variety of supervised learning methods for both classification and regression, learning about the connection between model complexity and generalization performance, the importance of proper feature scaling, and how to control model complexity by applying techniques like regularization to avoid overfitting. In addition to k-nearest neighbors, this week covers linear regression (least-squares, ridge, lasso, and polynomial regression), logistic regression, support vector machines, the use of cross-validation for model evaluation, and decision trees. Other Module 2 Notebook Video Multi-Class Classification Video Introduction to Supervised Machine Learning Video Kernelized Support Vector Machines Video Overfitting and Underfitting Video Cross-Validation Video Supervised Learning: Datasets Video Decision Trees Video K-Nearest Neighbors: Classification and Regression Reading A Few Useful Things to Know about Machine Learning Video Linear Regression: Least-Squares Reading Ed Yong: Genetic Test for Autism Refuted (optional) Video Linear Regression: Ridge, Lasso, and Polynomial Regression Quiz Module 2 Quiz Video Logistic Regression Other Classifier Visualization Playspace Video Linear Classifiers: Support Vector Machines Other Assignment 2 Programming Assignment Assignment 2 Submission
Week 3 Module 3: Evaluation This module covers evaluation and model selection methods that you can use to help understand and optimize the performance of your machine learning models. Other Module 3 Notebook Video Multi-Class Classification Video Model Evaluation & Selection Video Kernelized Support Vector Machines Video Confusion Matrices & Basic Evaluation Metrics Video Cross-Validation Video Classifier Decision Functions Video Decision Trees Video Precision-recall and ROC curves Reading A Few Useful Things to Know about Machine Learning Video Multi-Class Evaluation Video Regression Evaluation Reading Practical Guide to Controlled Experiments on the Web (optional) Reading Ed Yong: Genetic Test for Autism Refuted (optional) Quiz Module 2 Quiz Other Classifier Visualization Playspace Video Model Selection: Optimizing Classifiers for Different Evaluation Metrics Other Assignment 2 Quiz Module 3 Quiz Programming Assignment Assignment 2 Submission Other Assignment 3 Programming Assignment Assignment 3 Submission
Week 4 Module 4: Supervised Machine Learning - Part 2 This module covers more advanced supervised learning methods that include ensembles of trees (random forests, gradient boosted trees), and neural networks (with an optional summary on deep learning). You will also learn about the critical problem of data leakage in machine learning and how to detect and avoid it. Other Module 4 Notebook Programming Assignment Assignment 4 Submission Video Naive Bayes Classifiers Other Unsupervised Learning Notebook Video Random Forests Video Introduction Video Gradient Boosted Decision Trees Video Dimensionality Reduction and Manifold Learning Video Neural Networks Video Clustering Reading Neural Networks Made Easy (optional) Reading How to Use t-sne Effectively Reading Play with Neural Networks: TensorFlow Playground (optional) Reading How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms Video Deep Learning (Optional) Video Conclusion Reading Deep Learning in a Nutshell: Core Concepts (optional) Reading Post-course Survey Reading Assisting Pathologists in Detecting Cancer with Deep Learning (optional) Video Data Leakage Reading The Treachery of Leakage (optional) Reading Leakage in Data Mining: Formulation, Detection, and Avoidance (optional) Reading Data Leakage Example: The ICML 2013 Whale Challenge (optional) Reading Rules of Machine Learning: Best Practices for ML Engineering (optional) Quiz Module 4 Quiz Other Assignment 4
Applied Text Mining in Python Upcoming Session: Dec 18 Subtitles English About the Course About the Course This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling). This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python
Week 1 Module 1: Working with Text in Python In this module, you will delve into basic charting. For this week s assignment, you will work with real world CSV weather data. You will manipulate the data to display the minimum and maximum temperature for a range of dates and demonstrate that you know how to create a line graph using matplotlib. Additionally, you will demonstrate the procedure of composite charts, by overlaying a scatter plot of record breaking data for a given year. Reading Course Syllabus Reading Help us learn more about you! Video Introduction to Text Mining Video Handling Text in Python Reading Notice for Auditing Learners: Assignment Submission Other Working with Text Video Regular Expressions Other Regex with Pandas and Named Groups Video Demonstration: Regex with Pandas and Named Groups Practice Quiz Practice Quiz Video Internationalization and Issues with Non-ASCII Characters Other Introduce Yourself Reading Resources: Common issues with free text Quiz Module 1 Quiz Other Assignment 1 Programming Assignment Assignment 1 Submission
Week 2 Module 2: Basic Natural Language Processing Video Basic Natural Language Processing Other Module 2 (Python 3) Video Basic NLP tasks with NLTK Video Advanced NLP tasks with NLTK Practice Quiz Practice Quiz Other Finding your own prepositional phrase attachment Quiz Module 2 Quiz Other Assignment 2 Programming Assignment Assignment 2 Submission
Week 3 Module 3: Classification of Text Video Text Classification Video Identifying Features from Text Video Naive Bayes Classifiers Video Naive Bayes Variations Video Support Vector Machines Video Learning Text Classifiers in Python Other Case Study - Sentiment Analysis Video Demonstration: Case Study - Sentiment Analysis Quiz Module 3 Quiz Other Assignment 3 Programming Assignment Assignment 3 Submission
Week 4 Module 4: Topic Modeling Video Semantic Text Similarity Video Topic Modeling Video Generative Models and LDA Practice Quiz Practice Quiz Video Information Extraction Reading Additional Resources & Readings Quiz Module 4 Quiz Other Assignment 4 Programming Assignment Assignment 4 Submission Reading Post-Course Survey
Applied Social Network Analysis in Python Upcoming Session: Jan 1 Subtitles English About the Course TThis course will introduce the learner to network analysis through the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness.. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
Week 1 Why Study Networks and Basics on NetworkX Module One introduces you to different types of networks in the real world and why we study them. You'll learn about the basic elements of networks, as well as different types of networks. You'll also learn how to represent and manipulate networked data using the NetworkX library. The assignment will give you an opportunity to use NetworkX to analyze a networked dataset of employees in a small company. Reading Course Syllabus Other Loading Graphs in NetworkX Reading Help us learn more about you! Video TA Demonstration: Loading Graphs in NetworkX Video Networks: Definition and Why We Study Them Quiz Module 1 Quiz Video Network Definition and Vocabulary Other Assignment 1 Video Node and Edge Attributes Programming Assignment Assignment 1 Submission Video Bipartite Graphs Reading Notice for Auditing Learners: Assignment Submission
Week 1 Why Study Networks and Basics on NetworkX Module One introduces you to different types of networks in the real world and why we study them. You'll learn about the basic elements of networks, as well as different types of networks. You'll also learn how to represent and manipulate networked data using the NetworkX library. The assignment will give you an opportunity to use NetworkX to analyze a networked dataset of employees in a small company. Reading Course Syllabus Other Loading Graphs in NetworkX Reading Help us learn more about you! Video TA Demonstration: Loading Graphs in NetworkX Video Networks: Definition and Why We Study Them Quiz Module 1 Quiz Video Network Definition and Vocabulary Other Assignment 1 Video Node and Edge Attributes Programming Assignment Assignment 1 Submission Video Bipartite Graphs Reading Notice for Auditing Learners: Assignment Submission
Week 2 Network Connectivity In Module Two you'll learn how to analyze the connectivity of a network based on measures of distance, reachability, and redundancy of paths between nodes. In the assignment, you will practice using NetworkX to compute measures of connectivity of a network of email communication among the employees of a mid-size manufacturing company. Video Clustering Coefficient Video Distance Measures Video Connected Components Video Network Robustness Other Simple Network Visualizations in NetworkX Video TA Demonstration: Simple Network Visualizations in NetworkX Quiz Module 2 Quiz Other Assignment 2 Programming Assignment Assignment 2 Submission
Week 3 Influence Measures and Network Centralization In Module Three, you'll explore ways of measuring the importance or centrality of a node in a network, using measures such as Degree, Closeness, and Betweenness centrality, Page Rank, and Hubs and Authorities. You'll learn about the assumptions each measure makes, the algorithms we can use to compute them, and the different functions available on NetworkX to measure centrality. In the assignment, you'll practice choosing the most appropriate centrality measure on a real-world setting. Video Degree and Closeness Centrality Video Betweenness Centrality Video Basic Page Rank Video Scaled Page Rank Video Hubs and Authorities Video Centrality Examples Quiz Module 3 Quiz Other PageRank and Centrality in a real-life network Other Assignment 3 Programming Assignment Assignment 3 Submission
Week 4 Network Evolution In Module Four, you'll explore the evolution of networks over time, including the different models that generate networks with realistic features, such as the Preferential Attachment Model and Small World Networks. You will also explore the link prediction problem, where you will learn useful features that can predict whether a pair of disconnected nodes will be connected in the future. In the assignment, you will be challenged to identify which model generated a given network. Additionally, you will have the opportunity to combine different concepts of the course by predicting the salary, position, and future connections of the employees of a company using their logs of email exchanges Video Preferential Attachment Model Reading Power Laws and Rich-Get-Richer Phenomena (Optional) Video Small World Networks Video Link Prediction Other Extracting Features from Graphs Quiz Module 4 Quiz Reading The Small-World Phenomenon (Optional) Other Assignment 4 Programming Assignment Assignment 4 Submission Reading Post-Course Survey