This is a list of course on Datacamp.com.
library(rvest)
library(stringr)
library(knitr)
library(kableExtra)
options(Encoding="UTF-8")
url = "https://www.datacamp.com/courses/all"
webpage <- read_html(url, encoding = "UTF-8")
# check items
str_extract_all(webpage, pattern = ".course-block__\\w+\\w*")
table(str_extract_all(webpage, pattern = ".course-block__\\w+\\w*"))
# writing functions
crawl_datacamp <- function(x) {
y <- html_nodes(webpage, str_c('.course-block__', x))
z <- html_text(y)
}
###
title <- crawl_datacamp("title")
author <- crawl_datacamp("author")
description <- crawl_datacamp("description")
description <- str_replace_all(description, "\\n[:blank:]+", "")
length <- crawl_datacamp("length ")
author <- crawl_datacamp("author-name")
author_info <- crawl_datacamp("author-ocupation")
author_info <- str_replace_all(author_info, "\\n[:blank:]+", "")
# language
str_extract_all(webpage, pattern = ".course-block__technology[:graph:]*")
language <- unlist(str_extract_all(webpage, pattern = ".course-block__technology--\\w*"))
language <- toupper(str_replace_all(language, pattern = ".course-block__technology--", replacement = ""))
datacamp <- data.frame(title, author, author_info, description, length, language)| title | author | author_info | description | length | language |
|---|---|---|---|---|---|
| Intro to Python for Data Science | Filip Schouwenaars | Data Science Instructor at DataCamp | Master the basics of data analysis in Python. Expand your skill set by learning scientific computing with numpy. | 4 hours | PYTHON |
| Introduction to R | Jonathan Cornelissen | Co-founder and CEO of DataCamp | Master the basics of data analysis by manipulating common data structures such as vectors, matrices and data frames. | 4 hours | R |
| Introduction to Time Series Analysis in Python | Rob Reider | Consultant at Quantopian and Adjunct Professor at NYU | In this course you’ll learn the basics of analyzing time series data. | 4 hours | PYTHON |
| Intermediate Python for Data Science | Filip Schouwenaars | Data Science Instructor at DataCamp | Level up your data science skills by creating visualizations using matplotlib and manipulating data frames with Pandas. | 4 hours | PYTHON |
| Intro to SQL for Data Science | Nick Carchedi | Director of Content at DataCamp | Master the basics of querying databases with SQL, the world’s most popular databasing language. | 4 hours | SQL |
| Intermediate R | Filip Schouwenaars | Data Science Instructor at DataCamp | Continue your journey to become an R ninja by learning about conditional statements, loops, and vector functions. | 6 hours | R |
| Deep Learning in Python | Dan Becker | Data Scientist and contributor to Keras and TensorFlow libraries | Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0. | 4 hours | PYTHON |
| Supervised Learning with scikit-learn | Andreas Muller | Core developer of scikit-learn; Lecturer at Columbia University | Learn how to build and tune predictive models and evaluate how well they will perform on unseen data. | 4 hours | PYTHON |
| pandas Foundations | Dhavide Aruliah | Senior Python Curriculum Lead at DataCamp | Learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames. | 4 hours | PYTHON |
| Python Data Science Toolbox (Part 1) | Hugo Bowne-Anderson | Data Scientist at DataCamp | Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling. | 3 hours | PYTHON |
| Introduction to Data Visualization with Python | Bryan Van de Ven | Software Engineer at Anaconda and Developer of Bokeh | Learn more complex data visualization techniques using Matplotlib and Seaborn. | 4 hours | PYTHON |
| Building Chatbots in Python | Alan Nichol | Co-founder and CTO of Rasa | Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning. | 4 hours | PYTHON |
| Importing Data in Python (Part 1) | Hugo Bowne-Anderson | Data Scientist at DataCamp | Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web. | 3 hours | PYTHON |
| Data Visualization with ggplot2 (Part 1) | Rick Scavetta | Data Scientist and cofounder of Science Craft | Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics. | 5 hours | R |
| Introduction to Git for Data Science | Greg Wilson | Head of Instructor Training at DataCamp | This course is an introduction to version control with Git for data scientists. | 4 hours | GIT |
| Importing Data in R (Part 1) | Filip Schouwenaars | Data Science Instructor at DataCamp | In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table. | 3 hours | R |
| Python Data Science Toolbox (Part 2) | Hugo Bowne-Anderson | Data Scientist at DataCamp | Continue to build your modern Data Science skills by learning about iterators and list comprehensions. | 4 hours | PYTHON |
| Cleaning Data in R | Nick Carchedi | Director of Content at DataCamp | Learn to explore your data so you can properly clean and prepare it for analysis. | 4 hours | R |
| Cleaning Data in Python | Daniel Chen | Data Science Consultant at Lander Analytics | This course will equip you with all the skills you need to clean your data in Python. | 4 hours | PYTHON |
| Data Manipulation in R with dplyr | Garrett Grolemund | Data Scientist at RStudio | Master techniques for data manipulation using the select, mutate, filter, arrange, and summarise functions in dplyr. | 4 hours | R |
| Introduction to Data | Mine Cetinkaya-Rundel | Associate Professor at Duke University & Data Scientist and Pr… | Learn the language of data, study types, sampling strategies, and experimental design. | 4 hours | R |
| Statistical Thinking in Python (Part 1) | Justin Bois | Lecturer at the California Institute of Technology | Build the foundation you need to think statistically and to speak the language of your data. | 3 hours | PYTHON |
| Importing Data in Python (Part 2) | Hugo Bowne-Anderson | Data Scientist at DataCamp | Improve your Python data importing skills and learn to work with web and API data. | 2 hours | PYTHON |
| Introduction to the Tidyverse | David Robinson | Chief Data Scientist, DataCamp | Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collect… | 4 hours | R |
| Intermediate R - Practice | Filip Schouwenaars | Data Science Instructor at DataCamp | Strengthen your knowledge of the topics you learned in Intermediate R with a ton of new and fun exercises. | 4 hours | R |
| Joining Data in PostgreSQL | Chester Ismay | Curriculum Lead at DataCamp | Join two or three tables together into one, combine tables using set theory, and work with subqueries in PostgreSQL | 5 hours | SQL |
| Introduction to Databases in Python | Jason Myers | Co-Author of Essential SQLAlchemy and Software Engineer | In this course, you’ll learn the basics of relational databases and how to interact with them. | 4 hours | PYTHON |
| Manipulating DataFrames with pandas | Dhavide Aruliah | Dhavide Aruliah is a Senior Python Curriculum Lead at DataCamp… | You will learn how to tidy, rearrange, and restructure your data using versatile pandas DataFrames. | 4 hours | PYTHON |
| Writing Functions in R | Hadley Wickham | Chief Scientist at RStudio, author of ggplot2, dplyr, and tidyr | Learn the fundamentals of writing functions in R so you can make your code more readable and automate repetitive tasks. | 4 hours | R |
| Building Dashboards with shinydashboard | Lucy D’Agostino McGowan | Graduate Researcher in Biostatistics at Vanderbilt University | In this course you’ll learn to build dashboards using the shinydashboard package. | 4 hours | R |
| Data Visualization in R | Ronald Pearson | PhD in Electrical Engineering and Computer Science from M.I.T. | This course provides a comprehensive introduction to working with base graphics in R. | 4 hours | R |
| Network Analysis in Python (Part 1) | Eric Ma | Data Carpentry instructor and author of nxviz package | This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library. | 4 hours | PYTHON |
| Supervised Learning in R: Classification | Brett Lantz | Data Scientist at the University of Michigan | In this course you will learn the basics of machine learning for classification. | 4 hours | R |
| Importing Data in R (Part 2) | Filip Schouwenaars | Data Science Instructor at DataCamp | Parse data in any format. Whether it’s flat files, statistical software, databases, or data right from the web. | 3 hours | R |
| Building Web Applications in R with Shiny | Mine Cetinkaya-Rundel | Associate Professor at Duke University & Data Scientist and Pr… | Build interactive web apps straight from R with shiny! | 4 hours | R |
| Correlation and Regression | Ben Baumer | Assistant Professor at Smith College | Learn how to describe relationships between two numerical quantities and characterize these relationships graphically. | 4 hours | R |
| Natural Language Processing Fundamentals in Python | Katharine Jarmul | Founder, kjamistan | Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from … | 4 hours | PYTHON |
| Unsupervised Learning in Python | Benjamin Wilson | Director of Research at lateral.io | Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy. | 4 hours | PYTHON |
| Introduction to Shell for Data Science | Greg Wilson | Head of Instructor Training at DataCamp | The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs … | 4 hours | SHELL |
| Statistical Thinking in Python (Part 2) | Justin Bois | Lecturer at the California Institute of Technology | Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing. | 4 hours | PYTHON |
| Merging DataFrames with pandas | Dhavide Aruliah | Dhavide Aruliah is a Senior Python Curriculum Lead at DataCamp… | This course is all about the act of combining, or merging, DataFrames, an essential part your Data Scientist’s toolbox. | 4 hours | PYTHON |
| Introduction to R for Finance | Lore Dirick | Data Scientist at DataCamp | Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples. | 4 hours | R |
| Importing & Cleaning Data in R: Case Studies | Nick Carchedi | Director of Content at DataCamp | In this series of four case studies, you’ll revisit key concepts from our courses on importing and cleaning data in R. | 4 hours | R |
| Introduction to Time Series Analysis | David S. Matteson | Associate Professor at Cornell University | Learn the core techniques necessary to extract meaningful insights from time series data. | 4 hours | R |
| Exploratory Data Analysis | Andrew Bray | Assistant Professor of Statistics at Reed College | Learn how to use graphical and numerical techniques to begin uncovering the structure of your data. | 4 hours | R |
| Machine Learning with the Experts: School Budgets | Peter Bull | Co-founder of DrivenData | Learn how to build a model to automatically classify items in a school budget. | 4 hours | PYTHON |
| Data Visualization with ggplot2 (Part 2) | Rick Scavetta | Data Scientist and cofounder of Science Craft | Take your data visualization skills to the next level with coordinates, facets, themes, and best practices in ggplot2. | 5 hours | R |
| Interactive Data Visualization with Bokeh | Bryan Van de Ven | Software Engineer at Anaconda and Developer of Bokeh | Learn how to create versatile and interactive data visualizations using Bokeh. | 4 hours | PYTHON |
| Joining Data in R with dplyr | Garrett Grolemund | Data Scientist at RStudio | This course will show you how to combine data sets with dplyr’s two table verbs. | 4 hours | R |
| Introduction to PySpark | Nick Solomon | Data Scientist at DataCamp | Learn to implement distributed data management and machine learning in Spark using the PySpark package. | 4 hours | PYTHON |
| Machine Learning Toolbox | Zachary Deane-Mayer | Data Scientist at Data Robot and co-author of caret | This course teaches the big ideas in machine learning like how to build and evaluate predictive models. | 4 hours | R |
| Introduction to Machine Learning | Vincent Vankrunkelsven | Data Science Instructor at DataCamp | Learn to train and assess models performing common machine learning tasks such as classification and clustering. | 6 hours | R |
| Manipulating Time Series Data in R with xts & zoo | Jeffrey Ryan | Creator of xts and quantmod | The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free. | 4 hours | R |
| Reporting with R Markdown | Garrett Grolemund | Data Scientist at RStudio | Learn to create interactive analyses and automated reports with R Markdown. | 3 hours | R |
| Importing & Managing Financial Data in Python | Stefan Jansen | Founder & Lead Data Scientist at Applied Artificial Intelligence | In this course, you’ll learn how to import and manage financial data in Python using various tools and sources. | 5 hours | PYTHON |
| Data Visualization with ggplot2 (Part 3) | Rick Scavetta | Data Scientist and cofounder of Science Craft | This course covers some advanced topics including strategies for handling large data sets and specialty plots. | 6 hours | R |
| Communicating with Data in the Tidyverse | Timo Grossenbacher | Data Journalist at SRF Data | Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communica… | 4 hours | R |
| Multiple and Logistic Regression | Ben Baumer | Assistant Professor at Smith College | In this course you’ll lear to add multiple variables to linear models and to use logistic regression for classification. | 4 hours | R |
| Exploratory Data Analysis in R: Case Study | David Robinson | Chief Data Scientist, DataCamp | Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly. | 4 hours | R |
| Text Mining: Bag of Words | Ted Kwartler | Senior Director, Data Scientist at Liberty Mutual | Learn the bag of words technique for text mining with R. | 4 hours | R |
| Forecasting Using R | Rob J. Hyndman | Professor of Statistics at Monash University | Learn how to make predictions about the future using time series forecasting in R. | 5 hours | R |
| Foundations of Inference | Jo Hardin | Professor at Pomona College | Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference. | 4 hours | R |
| Data Analysis in R, the data.table Way | Matt Dowle | Author of data.table | Master core concepts in data manipulation such as subsetting, updating, indexing and joining your data using data.table. | 4 hours | R |
| Unsupervised Learning in R | Hank Roark | Senior Data Scientist, Boeing | This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective. | 4 hours | R |
| ARIMA Modeling with R | David Stoffer | Professor of Statistics at the University of Pittsburgh | Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R. | 4 hours | R |
| Sentiment Analysis in R: The Tidy Way | Julia Silge | Data Scientist at Stack Overflow | In this course, you will the learn principles of sentiment analysis from a tidy data perspective. | 4 hours | R |
| Statistical Modeling in R (Part 1) | Daniel Kaplan | DeWitt Wallace Professor at Macalester College | This course was designed to get you up to speed with the most important and powerful methodologies in statistics. | 4 hours | R |
| Supervised Learning in R: Regression | Nina Zumel | Co-founder, Principal Consultant at Win-Vector, LLC | In this course you will learn how to predict future events using linear regression, generalized additive models, rand… | 4 hours | R |
| Credit Risk Modeling in R | Lore Dirick | Data Scientist at DataCamp | Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk. | 4 hours | R |
| String Manipulation in R with stringr | Charlotte Wickham | Assistant Professor at Oregon State University | Learn how to pull character strings apart, put them back together and use the stringr package. | 4 hours | R |
| Extreme Gradient Boosting with XGBoost | Sergey Fogelson | VP of Analytics and Measurement Sciences, Viacom | Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve… | 4 hours | PYTHON |
| Working with the RStudio IDE (Part 1) | Garrett Grolemund | Data Scientist at RStudio | Learn the basics of the important features of the RStudio IDE. | 3 hours | R |
| Manipulating Time Series Data in Python | Stefan Jansen | Founder & Lead Data Scientist at Applied Artificial Intelligence | In this course you’ll learn the basics of working with time series data. | 4 hours | PYTHON |
| Intermediate R for Finance | Lore Dirick | Data Scientist at DataCamp | Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples. | 5 hours | R |
| Intro to Financial Concepts using Python | Dakota Wixom | Quantitative Analyst and Founder of QuantCourse.com | Using Python and NumPy, learn the most fundamental financial concepts. | 4 hours | PYTHON |
| Machine Learning with Tree-Based Models in R | Erin LeDell | Machine Learning Scientist at H2O.ai and co-author of the h2o … | In this course you’ll learn how to use decision trees for regression and classification. | 4 hours | R |
| Financial Trading in R | Ilya Kipnis | Professional Quantitative Analyst and R programmer | This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies. | 5 hours | R |
| Spatial Statistics in R | Barry Rowlingson | Research Fellow at Lancaster University | Learn how to make sense of spatial data and deal with various classes of statistical problems associated with it. | 4 hours | R |
| Data Types for Data Science | Jason Myers | Co-Author of Essential SQLAlchemy and Software Engineer | Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them t… | 4 hours | PYTHON |
| Network Analysis in Python (Part 2) | Eric Ma | Data Carpentry instructor and author of nxviz package | Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics. | 4 hours | PYTHON |
| Visualizing Time Series Data in Python | Thomas Vincent | Senior Data Science Engineer at DigitalOcean | Visualize seasonality, trends and other patterns in your time series data. | 4 hours | PYTHON |
| Working with Geospatial Data in R | Charlotte Wickham | Assistant Professor at Oregon State University | Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R. | 4 hours | R |
| Cluster Analysis in R | Dima Gorenshteyn | Senior Data Scientist at Memorial Sloan Kettering Cancer Center | Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract in… | 4 hours | R |
| Network Analysis in R | James Curley | Associate Professor at UT Austin | In this course you’ll learn to analyze and visualize network data with the igraph package. | 4 hours | R |
| Importing and Managing Financial Data in R | Joshua Ulrich | Quantitative Analyst & member of R/Finance Conference committee | Learn how to access financial data from local files as well as from internet sources. | 5 hours | R |
| Introduction to Portfolio Analysis in R | Kris Boudt | Professor of Finance and Econometrics at VUB and VUA | Apply your finance and R skills to backtest, analyze, and optimize financial portfolios. | 5 hours | R |
| Introduction to Spark in R using sparklyr | Richie Cotton | Instructor at DataCamp | Learn how to analyze huge datasets using Apache Spark and R using the sparklyr package. | 4 hours | R |
| Hierarchical and Mixed Effects Models | Richard Erickson | Quantitative Ecologist | In this course you will learn to fit hierarchical models with random effects. | 4 hours | R |
| Object-Oriented Programming in R: S3 and R6 | Richie Cotton | Instructor at DataCamp | Manage the complexity in your code using object-oriented programming with the S3 and R6 systems. | 4 hours | R |
| Building Web Applications in R with Shiny: Case Studies | Dean Attali | Founder & Lead R-Shiny Consultant at AttaliTech Ltd | Build interactive web apps using R and shiny! | 4 hours | R |
| Working with Web Data in R | Oliver Keyes | Data Scientist | Learn how to efficiently import data from the web into R. | 4 hours | R |
| Visualizing Time Series Data in R | Arnaud Amsellem | Quantitative Trader and creator of the R Trader blog | Learn how to visualize time series in R, then practice with a stock-picking case study. | 4 hours | R |
| Case Studies in Statistical Thinking | Justin Bois | Lecturer at the California Institute of Technology | Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract ac… | 4 hours | PYTHON |
| Forecasting Product Demand in R | Aric LaBarr | Director and Senior Scientist at Elder Research | Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of pr… | 4 hours | R |
| Foundations of Probability in R | David Robinson | Chief Data Scientist, DataCamp | In this course, you’ll learn about the concepts of random variables, distributions, and conditioning. | 4 hours | R |
| Spatial Analysis in R with sf and raster | Zev Ross | President, ZevRoss Spatial Analysis | Analyze spatial data using the sf and raster packages. | 4 hours | R |
| Sentiment Analysis in R | Ted Kwartler | Senior Director, Data Scientist at Liberty Mutual | Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelli… | 4 hours | R |
| Parallel Computing with Dask | Dhavide Aruliah | Senior Python Curriculum Lead at DataCamp | Learn how to take the Python workflows you currently have and easily scale them up to large datasets without the need… | 4 hours | PYTHON |
| Inference for Numerical Data | Mine Cetinkaya-Rundel | Associate Professor at Duke University & Data Scientist and Pr… | In this course you’ll learn techniques for performing statistical inference on numerical data. | 4 hours | R |
| Bond Valuation and Analysis in R | Clifford Ang | Vice President at Compass Lexecon | Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes. | 4 hours | R |
| Manipulating Time Series Data in R: Case Studies | Lore Dirick | Data Scientist at DataCamp | Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data. | 4 hours | R |
| Quantitative Risk Management in R | Alexander J. McNeil | Professor of Actuarial Science at the University of York. | Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk. | 5 hours | R |
| Intermediate Portfolio Analysis in R | Ross Bennett | Co-author of PortfolioAnalytics R package | Advance you R finance skills to backtest, analyze, and optimize financial portfolios. | 5 hours | R |
| Working with Dates and Times in R | Charlotte Wickham | Assistant Professor at Oregon State University | Learn the essentials of parsing, manipulating and computing with dates and times in R. | 4 hours | R |
| Equity Valuation in R | Clifford Ang | Vice President at Compass Lexecon | Learn the fundamentals of valuing stocks. | 4 hours | R |
| Working with the RStudio IDE (Part 2) | Garrett Grolemund | Data Scientist at RStudio | Further your knowledge of RStudio and learn how to integrate Git, LaTeX, and Shiny | 3 hours | R |
| Writing Efficient R Code | Colin Gillespie | Assoc Prof at Newcastle University, Consultant at Jumping Rivers | Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming. | 4 hours | R |
| Statistical Modeling in R (Part 2) | Daniel Kaplan | DeWitt Wallace Professor at Macalester College | In this follow-up course, you will expand your stat modeling skills from part 1 and dive into more advanced concepts. | 4 hours | R |
| Inference for Linear Regression | Jo Hardin | Professor at Pomona College | In this course you’ll learn how to perform inference using linear models. | 4 hours | R |
| Valuation of Life Insurance Products in R | Roel Verbelen | Postdoctoral researcher, KU Leuven | Learn the basics of cash flow valuation, work with human mortality data and build life insurance products in R. | 4 hours | R |
| Data Visualization in R with ggvis | Garrett Grolemund | Data Scientist at RStudio | Learn to create interactive graphs to display distributions, relationships, model fits, and more using ggvis. | 4 hours | R |
| Scalable Data Processing in R | Michael Kane | Assistant Professor at Yale University | Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages. | 4 hours | R |
| Data Visualization in R with lattice | Deepayan Sarkar | Member of R-Core & the creator of lattice | Learn to visualize multivariate datasets using lattice graphics. | 4 hours | R |
| Supervised Learning in R: Case Studies | Julia Silge | Data Scientist at Stack Overflow | Apply your supervised machine learning skills by working through four case studies using data from the real world. | 4 hours | R |
| Exploring Pitch Data with R | Brian M. Mills | Assistant Professor at the University of Florida | Use a rich baseball dataset from the MLB’s Statcast system to practice your data exploration skills. | 4 hours | R |