Datacamp Courses

Data Visualization for Everyone

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An introduction to data visualization with no coding involved.Visualizing data using charts, graphs, and maps is one of the most impactful ways to communicate complex data. In this course, you’ll learn how to choose the best visualization for your dataset, and how to interpret common plot types like histograms, scatter plots, line plots and bar plots. You'll also learn about best practices for using colors and shapes in your plots, and how to avoid common pitfalls. Through hands-on exercises, you'll visually explore over 20 datasets including global life expectancies, Los Angeles home prices, ESPN's 100 most famous athletes, and the greatest hip-hop songs of all time.

Dimensionality Reduction in Python

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Learn to reduce dimensionality in Python.High-dimensional datasets can be overwhelming and leave you not knowing where to start. Typically, you’d visually explore a new dataset first, but when you have too many dimensions the classical approaches will seem insufficient. Fortunately, there are visualization techniques designed specifically for high dimensional data and you’ll be introduced to these in this course. After exploring the data, you’ll often find that many features hold little information because they don’t show any variance or because they are duplicates of other features. You’ll learn how to detect these features and drop them from the dataset so that you can focus on the informative ones. In a next step, you might want to build a model on these features, and it may turn out that some don’t have any effect on the thing you’re trying to predict. You’ll learn how to detect and drop these irrelevant features too, in order to reduce dimensionality and thus complexity. Finally, you’ll learn how feature extraction techniques can reduce dimensionality for you through the calculation of uncorrelated principal components.

Reporting in SQL

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Learn how to build your very own dashboard by applying all the SQL concepts and functions you have learned in previous courses.Become a master at building complex reports!In this course, you will apply all the SQL concepts and functions you have learned in previous courses to build out your very own dashboard.By navigating through an Olympics database, you will become an expert data explorer and learn how to understand novel database quickly and effectively.Since data is never perfect, you will gain valuable strategies to deal with real-world issues commonly found with SQL, including how to remove data duplication and how to turn messy data into clean, organized reports.Lastly, you’ll conquer complex calculations using window functions and layered calculations, all within the same report.This is a perfect class for anyone who will be commonly pulling data from databases and is a great complement for those who use R or Python for data science.

Improving Query Performance in SQL Server

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In this course, students will learn to write queries that are both efficient and easy to read and understand.A mission critical assignment is depending on your SQL coding skills. You’ve been given some code to fix.It is giving the results you need but it’s running too slow, and it’s poorly formatted making it hard to read.The deadline is tomorrow.You’ll need to reformat the code and try different methods to improve performance.The pressure is on!!!In this course we’ll be using SQL on real world datasets, from sports and geoscience, to look at good coding practices and different ways how we can can improve the performance of queries to achieve the same outcome.

Case Studies: Building Web Applications with Shiny in R

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Practice your Shiny skills while building some fun Shiny apps for real-life scenarios!After learning the basics of using Shiny to build web applications, this course takes you to the next level by putting your newly acquired skills into practice. You'll get experience developing fun and realistic Shiny apps for different common use cases, such as using Shiny to explore a dataset, generate a customized plot, and even create a word cloud. With all this practice and new knowledge, you will be well-equipped to develop Shiny apps for your own use.

Introduction to Relational Databases in SQL

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Learn how to create one of the most efficient ways of storing data - relational databases! You’ve already used SQL to query data from databases. But did you know that there's a lot more you can do with databases? You can model different phenomena in your data, as well as the relationships between them. This gives your data structure and consistency, which results in better data quality. In this course, you'll experience this firsthand by working with a real-life dataset that was used to investigate questionable university affiliations. Column by column, table by table, you'll get to unlock and admire the full potential of databases. You'll learn how to create tables and specify their relationships, as well as how to enforce data integrity. You'll also discover other unique features of database systems, such as constraints.

Exploratory Data Analysis in R

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Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.When your dataset is represented as a table or a database, it's difficult to observe much about it beyond its size and the types of variables it contains. In this course, you'll learn how to use graphical and numerical techniques to begin uncovering the structure of your data. Which variables suggest interesting relationships? Which observations are unusual? By the end of the course, you'll be able to answer these questions and more, while generating graphics that are both insightful and beautiful.

Course Creation at DataCamp

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Learn all about how DataCamp builds the best platform to learn and teach data skills.Welcome to the DataCamp family! You are about to begin creating a course that, in just a few months, will be available to over 3 million students worldwide! If you're new to eLearning, you'll soon learn that teaching an online course is very different from teaching in a classroom. But we're here to help! This course will provide a guide to the DataCamp Course Creation process; an introduction to the tools we use, including GitHub, Asana, and our very own course editor; and the different types of exercises and slides you can use, and how to make sure you're reaching students at the other end of the screen. While creating your course, you will find you have other questions, such as, "How will my course be marketed?", "How do I recommend other instructors to DataCamp?", or "When do I get paid?". This course will also provide you with direction on where to find answers to all your questions. Following this course, you should be familiar with the DataCamp Course Creation process and be ready to start your very own DataCamp course. Have fun and see you in the course!

Introduction to PySpark

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Learn to implement distributed data management and machine learning inSpark using the PySpark package.In this course, you'll learn how to use Spark from Python! Spark is a tool for doing parallel computation with large datasets and it integrates well with Python. PySpark is the Python package that makes the magic happen. You'll use this package to work with data about flights from Portland and Seattle. You'll learn to wrangle this data and build a whole machine learning pipeline to predict whether or not flights will be delayed. Get ready to put some Spark in your Python code and dive into the world of high-performance machine learning!

Equity Valuation in R

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Learn the fundamentals of valuing stocks. How do we know when a stock is cheap or expensive? To do this, we need to compare the stock's price with its value. The price of the stock can be obtained by looking at various public sources, such as Yahoo Finance or Google Finance. The value of the stock though is much harder to identify. Every investor has to form his or her valuation of the stock. In this course, you will learn the fundamentals of valuing stocks using present value approaches, such as free cash flow to equity and dividend discount models, and valuation multiples. By the end of this course, you will be able to build your own valuation models.

Introduction to Financial Concepts in Python

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Using Python and NumPy, learn the most fundamental financial concepts. Understanding the basic principles of finance is essential for making important financial decisions ranging from taking out a student loan to constructing an investment portfolio. Combining basic financial knowledge with Python will allow you to construct some very powerful tools. You'll come out of this course understanding the time value of money, how to compare potential projects and how to make rational, data-driven financial decisions.

Intermediate Interactive Data Visualization with plotly in R

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Learn to create animated graphics and linked views entirely in R with plotly.The plotly package enables the construction of interactive and animated graphics entirely within R. This goes beyond basic interactivity such as panning, zooming, and tooltips. In this course, you will extend your understanding of plotly to create animated and linked interactive graphics, which will enable you to communicate multivariate stories quickly and effectively. Along the way, you will review the basics of plotly, learn how to wrangle your data in new ways to facilitate cumulative animations, and learn how to add filters to your graphics without using Shiny.

Survey and Measurement Development in R

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Design surveys to get actionable insights via reviewing of survey design structures and visualizing and analyzing survey results.How can we measure something like “brand loyalty?” It’s an obvious concept of interest to marketers, but we can’t quite take a ruler to it. Instead, we can design and analyze a survey to indirectly measure such a so-called “latent construct.”In this course, you’ll learn how to design and analyze a marketing survey to describe and even predict customers’ behavior based on how they rate items on “a scale of 1 to 5.” You’ll wrangle survey data, conduct exploratory & confirmatory factor analyses, and conduct various survey diagnostics such as checking for reliability and validity.

Python for Spreadsheet Users

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Use your knowledge of common spreadsheet functions and techniques to explore Python! Are you looking for a better solution than the one you’ve built in a spreadsheet? If so, then Python for Spreadsheet Users is a great introduction to the Python language, and will put you on the right path towards automating repetitive work, diving deeper into your data, and widening the scope of what you are capable of accomplishing. Throughout the course, we’ll draw parallels to common spreadsheet functions and techniques, so you’ll always have a familiar reference point as you dive head first into Python.

Hierarchical and Recursive Queries in SQL Server

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Learn how to write recursive queries and query hierarchical data structures.Do you want to query complex data structures in an iterative way? Do you have access to hierarchical data structures that need to be queried? This course will teach you the tools required to solve these questions. You will learn how to write recursive queries and query hierarchical data structures. To do this, you will use Common Table Expressions (CTE) and the recursion principle on a wide variety of datasets. You will, for example, dig into a flight plan dataset and learn how to find the best and cheapest connection between two airports. After completing this course, you will understand the principle of recursion, and be able to identify and create hierarchical data models.