Master the basics of data analysis in Python. Expand your skill set by learning scientific computing with numpy.
Master the basics of data analysis by manipulating common data structures such as vectors, matrices and data frames.
Level up your data science skills by creating visualizations using matplotlib and manipulating data frames with Pandas.
Master the basics of querying databases with SQL, the world's most popular databasing language.
Continue your journey to become an R ninja by learning about conditional statements, loops, and vector functions.
Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests...
Learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames.
In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.
Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
In this course, you'll learn how to import and manage financial data in Python using various tools and sources.
Learn how to make sense of spatial data and deal with various classes of statistical problems associated with it.
Learn to explore your data so you can properly clean and prepare it for analysis.
In this course, you'll learn about the concepts of random variables, distributions, and conditioning.
Master techniques for data manipulation using the select, mutate, filter, arrange, and summarise functions in dplyr.
Learn the fundamentals of writing functions in R so you can make your code more readable and automate repetitive tasks.
Strengthen your knowledge of the topics you learned in Intermediate R with a ton of new and fun exercises.
This course will equip you with all the skills you need to clean your data in Python.
Learn more complex data visualization techniques using Matplotlib and Seaborn.
Learn to train and assess models performing common machine learning tasks such as classification and clustering.
Learn the language of data, study types, sampling strategies, and experimental design.
Learn how to build and tune predictive models and evaluate how well they will perform on unseen data.
Improve your Python data importing skills and learn to work with web and API data.
Build the foundation you need to think statistically and to speak the language of your data.
Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.
Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.
You will learn how to tidy, rearrange, and restructure your data using versatile pandas DataFrames.
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.
This course provides a comprehensive introduction to working with base graphics in R.
Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.
In this course, you'll learn the basics of relational databases and how to interact with them.
Learn the core techniques necessary to extract meaningful insights from time series data.
Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
This course will show you how to combine data sets with dplyr's two table verbs.
In this series of four case studies, you'll revisit key concepts from our courses on importing and cleaning data in R.
Learn to create interactive analyses and automated reports with R Markdown.
Learn how to create versatile and interactive data visualizations using Bokeh.
Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world...
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
This course is all about the act of combining, or merging, DataFrames, an essential part your Data Scientist's toolbox.
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
Take your data visualization skills to the next level with coordinates, facets, themes, and best practices in ggplot2.
Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Da...
Learn how to build a model to automatically classify items in a school budget.
Master core concepts in data manipulation such as subsetting, updating, indexing and joining your data using data.table.
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Learn how to make predictions about the future using time series forecasting in R.
Learn the bag of words technique for text mining with R.
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.
Learn how to analyze huge datasets using Apache Spark and R using the sparklyr package.
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.
This course covers some advanced topics including strategies for handling large data sets and specialty plots.
This course was designed to get you up to speed with the most important and powerful methodologies in statistics.
Learn the basics of the important features of the RStudio IDE.
Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.
Learn how to pull character strings apart, put them back together and use the stringr package.
In this course, you will the learn principles of sentiment analysis from a tidy data perspective.
Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.
Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.
Learn how to access financial data from local files as well as from internet sources.
This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.
Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.
Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.
Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.
Learn how to visualize time series in R, then practice with a stock-picking case study.
Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.
Learn to create interactive graphs to display distributions, relationships, model fits, and more using ggvis.
Further your knowledge of RStudio and learn how to integrate Git, LaTeX, and Shiny
In this follow-up course, you will expand your stat modeling skills from part 1 and dive into more advanced concepts.
Advance you R finance skills to backtest, analyze, and optimize financial portfolios.
Learn to visualize multivariate datasets using lattice graphics.
Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.
Use a rich baseball dataset from the MLB's Statcast system to practice your data exploration skills.
“Learning R is the most amazing thing I have done in YEARS! Thanks DataCamp.”