Overview

Details

Applied Data Science with Python Specialization

University of Michigan, 2025-03-01

PDF | External Link | Return to overview

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 a basic 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.

What you'll learn

• Conduct an inferential statistical analysis

• Discern whether a data visualization is good or bad

• Enhance a data analysis with applied machine learning

• Analyze the connectivity of a social network

External link | Return to overview

Applied Social Network Analysis in Python

University of Michigan, 2025-03-01

PDF | External Link | Return to overview

This course will introduce the learner to network analysis through tutorials using 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.

What you'll learn

• Represent and manipulate networked data using the NetworkX library

• Analyze the connectivity of a network

• Measure the importance or centrality of a node in a network

• Predict the evolution of networks over time

External link | Return to overview

Applied Text Mining in Python

University of Michigan, 2025-02-17

PDF | External Link | Return to overview

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).

What you'll learn

• Understand how text is handled in Python

• Apply basic natural language processing methods

• Write code that groups documents by topic

• Describe the nltk framework for manipulating text

External link | Return to overview

Applied Machine Learning in Python

University of Michigan, 2025-02-11

PDF | External Link | Return to overview

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 through a tutorial. 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.

What you'll learn

• Describe how machine learning is different than descriptive statistics

• Create and evaluate data clusters

• Explain different approaches for creating predictive models

• Build features that meet analysis needs

External link | Return to overview

Applied Plotting, Charting, and Data Representation in Python

University of Michigan, 2025-01-24

PDF | External Link | Return to overview

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 be a tutorial 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.

What you'll learn

• Describe what makes a good or bad visualization

• Understand best practices for creating basic charts

• Identify the functions that are best for particular problems

• Create a visualization using matplotlb

External link | Return to overview

Introduction to Data Science in Python

University of Michigan, 2024-10-19

PDF | External Link | Return to overview

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

What you'll learn

• Understand techniques such as lambdas and manipulating csv files

• Describe common Python functionality and features used for data science

• Query DataFrame structures for cleaning and processing

• Explain distributions, sampling, and t-tests

External link | Return to overview

Algebra and Differential Calculus for Data Science

University of Colorado Boulder, 2025-03-06

PDF | External Link | Return to overview

Are you interested in Data Science but lack the math background for it? Has math always been a tough subject that you tend to avoid? This course will teach you the most fundamental Calculus concepts that you will need for a career in Data Science without a ton of unnecessary proofs and techniques that you may never use. Consider this an expressway to Data Science with approachable methods and friendly concepts that will guide you to truly understanding the most important ideas in Differential Calculus. We will review some algebra basics, talk about what a derivative is, compute some simple derivatives and apply the basics of derivatives to graphing and maximizing functions.

This course is designed to prepare learners to successfully complete Statistical Modeling for Data Science Application, which is part of CU Boulder's Master of Science in Data Science (MS-DS) program.

What you'll learn

• Practice working with logarithm properties and how logarithm functions behave graphically.

• Identify the difference between a continuous and non-continuous function.

• Solidify an understanding of what a derivative is calculating.

• Understand how to use derivatives to create graphs of functions.

External link | Return to overview

Data Science Workflows Using ArcGIS Notebooks

Esri Training, 2024-04-18

PDF | Return to overview

GIS professionals use various tools to help answer complicated questions about patterns and relationships that exist in data. Using ArcGIS Notebooks, you can easily access GIS data and perform analysis using many of the geoprocessing tasks that already exist in ArcGIS. This course introduces you to the process of performing data engineering tasks using ArcGIS Notebooks in ArcGIS Pro.

External link | Return to overview

ArcGIS Notebooks Basics (2024)

Esri Training, 2024-04-12

PDF | Return to overview

You can use ArcGIS Notebooks in ArcGIS Pro to create notebooks to query features, perform analysis, geocode a location, and perform many other tasks. This course introduces you to the ArcGIS Notebooks interface and functionality for creating Python notebooks that perform spatial data analysis tasks.

External link | Return to overview

Introduction to the Tidyverse

Johns Hopkins University, 2024-01-09

PDF | External Link | Return to overview

This course introduces a powerful set of data science tools known as the Tidyverse. The Tidyverse has revolutionized the way in which data scientists do almost every aspect of their job. We will cover the simple idea of "tidy data" and how this idea serves to organize data for analysis and modeling. We will also cover how non-tidy can be transformed to tidy data, the data science project life cycle, and the ecosystem of Tidyverse R packages that can be used to execute a data science project.

If you are new to data science, the Tidyverse ecosystem of R packages is an excellent way to learn the different aspects of the data science pipeline, from importing the data, tidying the data into a format that is easy to work with, exploring and visualizing the data, and fitting machine learning models. If you are already experienced in data science, the Tidyverse provides a power system for streamlining your workflow in a coherent manner that can easily connect with other data science tools.

What you'll learn

• Distinguish between tidy and non-tidy data

• Describe how non-tidy data can be transformed into tidy data

• Describe the Tidyverse ecosystem of packages

• Organize and initialize a data science project

External link | Return to overview

Wrangling Data in the Tidyverse

Johns Hopkins University, 2024-01-08

PDF | External Link | Return to overview

Data never arrive in the condition that you need them in order to do effective data analysis. Data need to be re-shaped, re-arranged, and re-formatted, so that they can be visualized or be inputted into a machine learning algorithm. This course addresses the problem of wrangling your data so that you can bring them under control and analyze them effectively. The key goal in data wrangling is transforming non-tidy data into tidy data.

This course covers many of the critical details about handling tidy and non-tidy data in R such as converting from wide to long formats, manipulating tables with the dplyr package, understanding different R data types, processing text data with regular expressions, and conducting basic exploratory data analyses. Investing the time to learn these data wrangling techniques will make your analyses more efficient, more reproducible, and more understandable to your data science team.

What you'll learn

• Apply Tidyverse functions to transform non-tidy data to tidy data

• Conduct basic exploratory data analysis

• Conduct analyses of text data

External link | Return to overview

The Data Scientist's Toolbox

Johns Hopkins University, 2024-01-03

PDF | External Link | Return to overview

In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

What you'll learn

• Set up R, R-Studio, Github and other useful tools

• Understand the data, problems, and tools that data analysts use

• Explain essential study design concepts

• Create a Github repository

External link | Return to overview

R Programming

Johns Hopkins University, 2023-12-31

PDF | External Link | Return to overview

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

What you'll learn

• Understand critical programming language concepts

• Configure statistical programming software

• Make use of R loop functions and debugging tools

• Collect detailed information using R profiler

External link | Return to overview

Spatial Data Science The New Frontier in Analytics, MOOC

Esri Training, 2022-11-22

PDF | Return to overview

Spatial data science allows analysts to extract deeper insight from data using a comprehensive set of analytical methods and spatial algorithms, including machine learning and deep learning techniques. This course explores the application of spatial data science to uncover hidden patterns and improve predictive modeling. You'll work with powerful analytical tools in Esri's ArcGIS software and learn how to integrate popular open data science packages into your analyses.

SECTION 1 • Introduction to Spatial Data Science

Explore how spatial data, tools, and analysis techniques augment traditional data science. Understand that “spatial” means more than x,y coordinates and that place-based context reveals patterns in data that otherwise may be hidden. Start applying data engineering and visualization techniques in ArcGIS Pro and ArcGIS Notebooks.

SECTION 2 • The Spatial Approach to Predictive Analysis

Prediction is fundamental to data science. See how incorporating spatial properties into modeling workflows deepens understanding of data and adds a widely used machine learning approach, to solve problems. Train and evaluate a model, then use it to generate robust predictions.

SECTION 3 • Finding Optimal Locations Using Suitability Models

Apply widely used spatial analysis techniques to answer this universal question asked by all kinds of organizations: Where is the best location for ? Perform a weighted overlay analysis that considers and ranks multiple suitability criteria. Learn how to transform data using functions to more completely represent suitability impact.

SECTION 4 • Pattern Detection and Clustering

Does a pattern have meaning or is it a product of random chance? ArcGIS includes a suite of tools to help analysts identify patterns and clusters in data and determine if they are meaningful. Learn how to apply statistical clustering methods to analyze patterns in space as well as time. Create a spacetime cube, then use space-time pattern mining tools to explore spatiotemporal trends and determine where and when high and low clusters occur.

SECTION 5 • Object Detection with Deep Learning

Take a deep dive into extracting information from massive data using deep learning. Learn how to automate the process of detecting objects and identifying features from imagery. Practice preparing training sample data, then use a neural network to train an object detection model.

External link | Return to overview

Advanced SQL for Data Scientists

LinkedIn Learning, 2022-09-18

PDF | External Link | Return to overview

Many data scientists know how to work with SQL—the industry-standard language for data analysis. But as data sizes grow, you need to know how to do more than simply read and write from a database. This course provides a more sophisticated approach to designing data models and optimizing queries in SQL. Instructor Dan Sullivan begins with the logical and physical design of tables—with particular focus on very large databases—and then presents a deep dive review of indexes, including specialized indexes and when to use them. The next section introduces query optimization and shows how to optimize basic, multi-join, and more complex queries. The course also covers SQL extensions, including user-defined functions and specialized data types. The techniques taught here enable more efficient analysis of large data sets using SQL, statistics, and custom business logic.

External link | Return to overview