Interested in learning more about R, Python, or Data Visualization with these tools? DataCamp is your best bet for comprehensive, clear and easy to access online training to help you learn these technologies at your own pace. While you can take some of the introductory courses for free, or pay for more yourself, Swarthmore College has arranged for a number of seats that we're happy to share with faculty and staff who would like to learn more within the DataCamp environment. Students can have access to DataCamp in the context of a course that is teaching R or something related that might be covered by DataCamp. Interested faculty need to set this up on a course by course basis with DataCamp directly. Contact Doug Willen (willen@swarthmore.edu, x7787) for more information, or for help with access to this resource.

DataCamp compliments our current offerings through LinkedIn Learning, which are generally geared towards a general software curriculum of the most popular software tools, with more specialized content on the R Data Analysis tool set, R Studio and R Studio Server (which Swarthmore also licenses for use with your classes) as well as Python and Data Visualization using these tools.

The general topics covered here include:

- Programming
- Importing & Cleaning Data
- Data Manipulation
- Data Visualization
- Probability & Statistics
- Machine Learning
- Applied Finance
- Reporting
- Case Studies
- and a few others

At the time of this writing, the full list of courses included these titles, for the full list, go to this link (https://www.datacamp.com/courses/all):

Course | Description |
---|---|

Intro to Python for Data Science | Master the basics of data analysis in Python. Expand your skill set by learning scientific computing with numpy. |

Introduction to R | Master the basics of data analysis by manipulating common data structures such as vectors, matrices and data frames. |

Intro to SQL for Data Science | Master the basics of querying databases with SQL, the world's most popular databasing language. |

Intermediate Python for Data Science | Level up your data science skills by creating visualizations using matplotlib and manipulating data frames with Pandas. |

Intermediate R | Continue your journey to become an R ninja by learning about conditional statements, loops, and vector functions. |

Deep Learning in Python | Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0. |

Introduction to Data Visualization with Python | Learn more complex data visualization techniques using Matplotlib and Seaborn. |

Python Data Science Toolbox (Part 1) | Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling. |

Introduction to Machine Learning | Learn to train and assess models performing common machine learning tasks such as classification and clustering. |

Importing Data in R (Part 1) | In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table. |

Data Visualization with ggplot2 (Part 1) | Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics. |

Writing Functions in R | Learn the fundamentals of writing functions in R so you can make your code more readable and automate repetitive tasks. |

Importing Data in Python (Part 1) | Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web. |

Python Data Science Toolbox (Part 2) | Continue to build your modern Data Science skills by learning about iterators and list comprehensions. |

pandas Foundations | Learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames. |

Cleaning Data in R | Learn to explore your data so you can properly clean and prepare it for analysis. |

Introduction to Spark in R using sparklyr | Learn how to analyze huge datasets using Apache Spark and R using the sparklyr package. |

Data Manipulation in R with dplyr | Master techniques for data manipulation using the select, mutate, filter, arrange, and summarise functions in dplyr. |

Intermediate R - Practice | Strengthen your knowledge of the topics you learned in Intermediate R with a ton of new and fun exercises. |

Network Analysis in Python (Part 1) | This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library. |

Cleaning Data in Python | This course will equip you with all the skills you need to clean your data in Python. |

Supervised Learning with scikit-learn | Learn how to build and tune predictive models and evaluate how well they will perform on unseen data. |

Data Visualization in R | This course provides a comprehensive introduction to working with base graphics in R. |

Statistical Thinking in Python (Part 1) | Build the foundation you need to think statistically and to speak the language of your data. |

Importing Data in Python (Part 2) | Improve your Python data importing skills and learn to work with web and API data. |

Importing Data in R (Part 2) | Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web. |

3Introduction to Data | Learn the language of data, study types, sampling strategies, and experimental design. |

Machine Learning Toolbox | This course teaches the big ideas in machine learning like how to build and evaluate predictive models. |

Joining Data in R with dplyr | This course will show you how to combine data sets with dplyr's two table verbs. |

Manipulating DataFrames with pandas | You will learn how to tidy, rearrange, and restructure your data using versatile pandas DataFrames. |

Introduction to Databases in Python | In this course, you'll learn the basics of relational databases and how to interact with them. |

Unsupervised Learning in Python | Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy. |

Correlation and Regression | Learn how to describe relationships between two numerical quantities and characterize these relationships graphically. |

Importing & Cleaning Data in R: Case Studies | In this series of four case studies, you'll revisit key concepts from our courses on importing and cleaning data in R. |

Introduction to R for Finance | Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples. |

Reporting with R Markdown | Learn to create interactive analyses and automated reports with R Markdown. |

Interactive Data Visualization with Bokeh | Learn how to create versatile and interactive data visualizations using Bokeh. |

Exploratory Data Analysis | Learn how to use graphical and numerical techniques to begin uncovering the structure of your data. |

Introduction to Time Series Analysis | Learn the core techniques necessary to extract meaningful insights from time series data. |

Forecasting Using R | Learn how to make predictions about the future using time series forecasting in R. |

Data Analysis in R, the data.table Way | Master core concepts in data manipulation such as subsetting, updating, indexing and joining your data using data.table. |

Merging DataFrames with pandas | This course is all about the act of combining, or merging, DataFrames, an essential part your Data Scientist's toolbox. |

Data Visualization with ggplot2 (Part 2) | Take your data visualization skills to the next level with coordinates, facets, themes, and best practices in ggplot2. |

Manipulating Time Series Data in R with xts & zoo | The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free. |

Statistical Thinking in Python (Part 2) | Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing. |

Text Mining: Bag of Words | Learn the bag of words technique for text mining with R. |

Credit Risk Modeling in R | Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk. |

Exploratory Data Analysis in R: Case Study | Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly. |

Unsupervised Learning in R | This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective. |

Statistical Modeling in R (Part 1) | This course was designed to get you up to speed with the most important and powerful methodologies in statistics. |

Machine Learning with the Experts: School Budgets | Learn how to build a model to automatically classify items in a school budget. |

Foundations of Inference | Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference. |

ARIMA Modeling with R | Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R. |

String Manipulation in R with stringr | Learn how to pull character strings apart, put them back together and use the stringr package. |

Data Visualization with ggplot2 (Part 3) | This course covers some advanced topics including strategies for handling large data sets and specialty plots. |

Working with the RStudio IDE (Part 1) | Learn the basics of the important features of the RStudio IDE. |

Importing and Managing Financial Data in R | Learn how to access financial data from local files as well as from internet sources. |

Intermediate R for Finance | Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples. |

Object-Oriented Programming in R: S3 and R6 | Manage the complexity in your code using object-oriented programming with the S3 and R6 systems. |

Introduction to Portfolio Analysis in R | Apply your finance and R skills to backtest, analyze, and optimize financial portfolios. |

Financial Trading in R | This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies. |

Visualizing Time Series Data in R | Learn how to visualize time series in R, then practice with a stock-picking case study. |

Network Analysis in Python (Part 2) | Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics. |

Writing Efficient R Code | Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming. |

Bond Valuation and Analysis in R | Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes. |

Working with Geospatial Data in R | Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R. |

Data Visualization in R with ggvis | Learn to create interactive graphs to display distributions, relationships, model fits, and more using ggvis. |

Quantitative Risk Management in R | Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk. |

Working with the RStudio IDE (Part 2) | Further your knowledge of RStudio and learn how to integrate Git, LaTeX, and Shiny |

Manipulating Time Series Data in R: Case Studies | Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data. |

Data Types for Data Science | Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them t... |

Statistical Modeling in R (Part 2) | In this follow-up course, you will expand your stat modeling skills from part 1 and dive into more advanced concepts. |

Data Visualization in R with lattice | Learn to visualize multivariate datasets using lattice graphics. |

Intermediate Portfolio Analysis in R | Advance you R finance skills to backtest, analyze, and optimize financial portfolios. |

Co-author of PortfolioAnalytics R package | Exploring Pitch Data with R |