Master the basics of data analysis in Python . Expand your skillset by learning scientific computing with numpy.
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
Dive into data science using Python and learn how to effectively analyze and visualize your data.
Use the world’s most popular Python data science package to manipulate data and calculate summary statistics.
Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
Learn the art of writing your own functions in Python , as well as key concepts like scoping and error handling.
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
Learn to combine data from multiple tables by joining data together using pandas.
Learn how to create, customize, and share data visualizations using Matplotlib.
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
Build the foundation you need to think statistically and to speak the language of your data.
Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.
Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
Learn how to create informative and attractive visualizations in Python using the Seaborn library.
Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Learn to use best practices to write maintainable, reusable, complex functions with good documentation.
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.
Improve your Python data importing skills and learn to work with web and API data.
Learn complex data visualization techniques using Matplotlib and seaborn.
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
Learn about the world of data engineering with an overview of all its relevant topics and tools!
Learn how to explore, visualize, and extract insights from data.
Learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames.
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.
Learn to retrieve and parse information from the internet using the Python library scrapy.
Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
Learn to acquire data from common file formats and systems such as CSV files, spreadsheets, JSON, SQL databases, and APIs.
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.
Learn to build recommendation engines in Python using machine learning techniques.
This course focuses on feature engineering and machine learning for time series data.
You will learn how to tidy, rearrange, and restructure your data using versatile pandas DataFrames.
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
In this course you will learn the details of linear classifiers like logistic regression and SVM.
In this course you'll learn the basics of working with time series data.
Learn how to build a model to automatically classify items in a school budget.
In this course you'll learn how to get your cleaned data ready for modeling.
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Learn about string manipulation and become a master at using regular expressions.
In this course, you'll learn the basics of relational databases and how to interact with them.
Learn all about modularity, documentation, & automated testing to help you solve Data Science problems quicker and more reliably.
Learn how to write unit tests for your Data Science projects in Python using pytest.
Analyze the gender distribution of children's book writers and use sound to match names to gender.
You will explore the market capitalization of Bitcoin and other cryptocurrencies.
Learn to analyze Twitter data and do a deep dive into a hot trend.
Analyze the network of characters in Game of Thrones and how it changes over the course of the books.
Explore Disney movie data, then build a linear regression model to predict box office success.
Import, clean, and analyze seven years worth of training data tracked on the Runkeeper app.
Process ingredient lists for cosmetics on Sephora then visualize similarity using t-SNE and Bokeh.
Load, clean, and explore Super Bowl data in the age of soaring ad costs and flashy halftime shows.
Use pandas to calculate and compare profitability and risk of different investments using the Sharpe Ratio.
Use NLP and clustering on movie plot summaries from IMDb and Wikipedia to quantify movie similarity.
Build a binary classifier to predict if a blood donor is likely to donate again.
Load, clean, and visualize scraped Google Play Store data to understand the Android app market.
Scrape news headlines for FB and TSLA then apply sentiment analysis to generate investment insight.
Build a book recommendation system using NLP and the text of books like On the Origin of Species.
Build a machine learning model to predict if a credit card application will get approved.
Build a deep learning model that can automatically detect honey bees and bumble bees in images.
Plot Google Trends data to find the most famous Kardashian/Jenner sister. Is it Kim? Kendall? Kylie?
Build a convolutional neural network to classify images of letters from American Sign Language.
Play bank data scientist and use regression discontinuity to see which debts are worth collecting.
Use pandas and Bayesian statistics to see if left-handed people actually die earlier than righties.
Flex your pandas muscles on breath alcohol test data from Ames, Iowa, USA.
Build a machine learning classifier that knows whether President Trump or Prime Minister Trudeau is tweeting!
How can we find a good strategy for reducing traffic-related deaths?
Rock or rap? Apply machine learning methods in Python to classify songs into genres.
Explore a dataset from Kaggle containing a century's worth of Nobel Laureates. Who won? Who got snubbed?
Build a model that can automatically detect honey bees and bumble bees in images.
Automatically generate keywords for a search engine marketing campaign using Python .
Use web scraping and NLP to find the most frequent words in Herman Melville's novel, Moby Dick.
Load, transform, and understand images of honey bees and bumble bees in Python .
If you've never done a DataCamp project, this is the place to start!
Use MLB's Statcast data to compare New York Yankees sluggers Aaron Judge and Giancarlo Stanton.
Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.
Analyze an A/B test from the popular mobile puzzle game, Cookie Cats.
Find the true Scala experts by exploring its development history in Git and GitHub.
Use Natural Language Processing on NIPS papers to uncover the trendiest topics in machine learning research.
Check what passwords fail to conform to the National Institute of Standards and Technology password guidelines.
Recreate John Snow's famous map of the 1854 cholera outbreak in London.
Find out about the evolution of the Linux operating system by exploring its version control system.
Analyze the relative popularity of programming languages over time based on Stack Overflow data.
Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.
Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.
Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R .
Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.
In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.
In this course you will learn the basics of machine learning for classification.
Learn to combine data across multiple tables to answer more complex questions with dplyr.
Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.
Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.
Learn the language of data, study types, sampling strategies, and experimental design.
Develop the skills you need to go from raw data to awesome insights as quickly and accurately as possible.
Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.
Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.
Take your R skills up a notch by learning to write efficient, reusable functions.
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.
Shiny is an R package that makes it easy to build interactive web apps directly in R , allowing your team to explore your data as dashboards or visualizations.
Learn the essentials of parsing, manipulating and computing with dates and times in R .
In this course you'll learn to add multiple variables to linear models and to use logistic regression for classification.
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
Learn the core techniques necessary to extract meaningful insights from time series data.
Learn to work with data using tools from the tidyverse, and master the important skills of taming and tidying your data.
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
Learn how to make predictions about the future using time series forecasting in R .
This course will show you how to combine and merge datasets with data.table.
Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.
R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.
The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.
This course covers in detail the tools available in R for parallel computing.
Learn how to pull character strings apart, put them back together and use the stringr package.
Learn about how dates work in R , and explore the world of if statements, loops, and functions using financial examples.
In this course, you'll learn about the concepts of random variables, distributions, and conditioning.
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
This course provides a comprehensive introduction to working with base graphics in R .
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
Get ready to categorize! In this course, you will work with non-numerical data, such as job titles or survey responses, using the Tidyverse landscape.
Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R .
This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R .
Wrangle and visualize musical data to find common chords and compare the styles of different artists.
Apply your importing and data cleaning skills to real-world soccer data.
Discover the top tools Kaggle participants use for data science and machine learning.
Discover how the US bond yields behave using descriptive statistics and advanced modeling.
Use tree-based machine learning methods to identify the characteristics of legendary Pokémon.
Use logistic regression to determine which treatment procedure is more effective for kidney stone removal.
Analyze health survey data to determine how BMI is associated with physical activity and smoking.
Apply hierarchical and mixed-effect models to analyze Maryland crime rates.
Use your logistic regression skills to protect people from becoming zombies!
Predict the impact of climate change on bird distributions using spatial data and machine learning.
Use cluster analysis to glean insights into cryptocurrency gambling behavior.
Apply unsupervised learning techniques to help plan an education program in Argentina.
Use R to make art and create imaginary flowers inspired by nature.
Use data science to catch criminals, plus find new ways to volunteer personal time for social good.
Explore the salary potential of college majors with a k-means cluster analysis.
Analyze admissions data from UC Berkeley and find out if the university was biased against women.
Analyze the dialog and IMDB ratings of 287 South Park episodes. Warning: contains explicit language.
Experiment with clustering algorithms to help doctors inform treatment for heart disease patients.
Write functions to forecast time series of food prices in Rwanda.
Apply text mining to Donald Trump's tweets to confirm if he writes the (angrier) Android half.
Use regression trees and random forests to find places where New York taxi drivers earn the most.
Apply your skills from Working with Dates and Times in R to breathalyzer data from Ames, Iowa.
Create and explore interactive maps using Leaflet to determine where to open the next Chipotle.
Get ready for Halloween by digging into a FiveThirtyEight dataset with all your favorite candy!
Examine the relationship between heart rate and heart disease using multiple logistic regression.
Examine the network of connections among local health departments in the United States.
Classify patients with suspected infections using data.table and electronic health records.
Analyze athletics data to find new ways to scout and assess jumpers and throwers.
Compare life expectancy across countries and genders with ggplot2.
Learn to analyze Twitter data and do a deep dive into a hot trend.
Use pandas to calculate and compare profitability and r isk of different investments using the Sharpe R atio.
How can we find a good strategy for r educing traffic- r elated deaths?
R ecreate John Snow's famous map of the 1854 cholera outbreak in London.
Build a book r ecommendation system using NLP and the text of books like On the Origin of Species.
Build a convolutional neural network to classify images of letters from American Sign Language.
Import, clean, and analyze seven years worth of training data tracked on the R unkeeper app.
Use pandas and Bayesian statistics to see if left-handed people actually die earlier than r ighties.
Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.
Join two or three tables together into one, combine tables using set theory, and work with subqueries in PostgreSQL.
Master the complex SQL queries necessary to answer a wide variety of data science questions and prepare robust data sets for analysis in PostgreSQL.
Become proficient at using SQL Server to perform common data manipulation tasks.
Learn how to create one of the most efficient ways of storing data - relational databases!
Learn how to explore what's available in a database: the tables, relationships between them, and data stored in them.
Learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon!
In this course, you will use T- SQL , the flavor of SQL used in Microsoft's SQL Server for data analysis.
Learn the most important PostgreSQL functions for manipulating, processing, and transforming data.
Explore ways to work with date and time data in SQL Server for time series analysis
Learn to write SQL queries to calculate key metrics that businesses use to measure performance.
Learn the most important functions for manipulating, processing, and transforming data in SQL Server.
In this course, students will learn to write queries that are both efficient and easy to read and understand.
Learn to write scripts that will catch and handle errors and control for multiple operations happening at once.
Learn how to analyze a SQL table and report insights to management.
Learn how to design and implement triggers in SQL Server using real-world examples.
Learn how to write recursive queries and query hierarchical data structures.
Learn how to build your very own dashboard by applying all the SQL concepts and functions you have learned in previous courses.
Master SQL Server programming by learning to create, update, and execute functions and stored procedures.
Find tables, store and manage new tables and views, and write maintainable SQL code to answer business questions.
Develop the skills you need to clean raw data and transform it into accurate insights.
This course teaches you the skills and knowledge necessary to create and manage your own PostgreSQL databases.
Learn how to structure your PostgreSQL queries to run in a fraction of the time.
Learn to tame your raw, messy data stored in a PostgreSQL database to extract accurate insights.
Ensure data consistency by learning how to use transactions and handle errors in concurrent environments.
Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.
Learn to manipulate and analyze flexibly structured data with MongoDB.
Learn powerful command-line skills to download, process, and transform data, including machine learning pipeline.
Write SQL queries to answer interesting questions about international debt using data from The World Bank.
Learn all about how DataCamp builds the best platform to learn and teach data skills.
Learn to easily summarize and manipulate lists using the purrr package.
Find the true Scala experts by exploring its development history in Git and Git Hub.
The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds.
Bash scripting allows you to build analytics pipelines in the cloud and work with data stored across multiple files.
Learn how to write Conda recipes and share them on Anaconda Cloud.
In this course you'll learn how to leverage statistical techniques for working with categorical data.
Use RNNs to classify text sentiment, generate sentences, and translate text between languages.
Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.
Learn how to analyze data with spreadsheets using functions such as SUM(), AVERAGE(), and VLOOKUP().
Explore the world of Pivot Tables within Google Sheets, and learn how to quickly organize thousands of data points with just a few clicks of the mouse.
Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.
Expand your spreadsheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.
Learn the fundamentals of data visualization using spreadsheets .
Learn the basics of spreadsheets by working with rows, columns, addresses, and ranges.
Learn how to build a graphical dashboard with spreadsheets to track the performance of financial securities.
Learn basic business modeling including cash flows, investments, annuities, loan amortization, and more using Sheets.
Learn how to ensure clean data entry and build dynamic dashboards to display your marketing data.
Learn how to use conditional formatting with your data through built-in options and by creating custom formulas.
Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.
Learn how to build an amortization dashboard in spreadsheets with financial and conditional formulas.
Learn how to price options contracts and visualize payout of various options strategies using spreadsheets .
Learn how to effectively and efficiently join datasets in tabular format using the Python Pandas library.
This course is all about the act of combining, or merging, DataFrames, an essential part your Data Scientist's toolbox.
Use your knowledge of common spreadsheet functions and techniques to explore Python!
Discover how data engineers lay the groundwork that makes data science possible. No coding involved!
Learn about data science and how can you use it to strengthen your organization.
Understand the fundamentals of Machine Learning and how it's applied in the business world.
Prepare for your upcoming machine learning interview by working through these practice questions that span across important topics in machine learning.
Advance you R finance skills to backtest, analyze, and optimize financial portfolios.
Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.
Explore latent variables, such as personality using exploratory and confirmatory factor analyses.
In this course you'll learn techniques for performing statistical inference on numerical data.
Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.
Sharpen your knowledge in machine learning, and prepare for any potential question you might get in a machine learning interview in Python.
Begin your journey with Scala , a popular language for scalable applications and data engineering infrastructure.
Learn how to write scala ble code for working with big data in R using the bigmemory and iotools packages.
Learn how to visualize big data in R using ggplot2 and trelliscopejs.
Learn how to take the Python workflows you currently have and easily scale them up to large datasets without the need for distributed computing environments.
Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.
Learn to use the Bioconductor package limma for differential gene expression analysis.
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.
Design surveys to get actionable insights via reviewing of survey design structures and visualizing and analyzing survey results.
Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.
In this course, you'll learn how to import and manage financial data in Python using various tools and sources.
Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.
Learn to connect Tableau to different data sources and prepare the data for a smooth analysis.
Learn to develop a set of principles for your data science and software development projects.
Learn to use essential bioconductor packages using datasets from virus, fungus, human and plants!
Get started with Tableau , a widely used business intelligence (BI) and analytics software to explore, visualize, and securely share data.
Take your Tableau skills up a notch with advanced analytics and visualizations.
Dashboards are a must-have in a data-driven world. Increase your impact on business performance with Tableau dashboards.
Gain a 360° overview of how to explore and use Power BI to build impactful reports.
Learn to solve increasingly complex problems using simulations to generate and analyze data.
Imitate Shakespear, translate language and autocomplete sentences using Deep Learning in Python.
Learn efficient techniques in pandas to optimize your Python code.
Learn strategies for answering probability questions in R by solving a variety of probability puzzles.