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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 |