The role of a data scientist is to turn raw data into actionable insights. Much of the world's raw data—from electronic medical records to customer transaction histories—lives in organized collections of tables called relational databases. To be an effective data scientist, you must know how to wrangle and extract data from these databases using a language called SQL . This course teaches syntax in SQL shared by many types of databases, such as PostgreSQL, MySQL, SQL Server, and Oracle. This course teaches you everything you need to know to begin working with databases today!

Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.

Learn the basics of syntax in SQL, how to extract and wrangle data, and use querying tables in relational databases like MySQL, SQL Server, and PostgreSQL.

Introduction to SQL

Begin your journey into Data Science! Even if you've never written a line of code in your life, you'll be able to follow this course and witness the power of Python to perform Data Science. You'll use data to solve the mystery of Bayes, the kidnapped Golden Retriever, and along the way you'll become familiar with basic Python syntax and popular Data Science modules like Matplotlib (for charts and graphs) and pandas (for tabular data).

Dive into data science using Python and learn how to effectively analyze and visualize your data. No coding experience or skills needed.

Dive into data science using Python and learn how to effectively analyze and visualize your data using Pandas and Matplotlib. No coding experience needed.

Introduction to Data Science in Python

Learning Python is crucial for any aspiring data science practitioner. Learn to visualize real data with Matplotlib's functions and get acquainted with data structures such as the dictionary and the pandas DataFrame. After covering key concepts such as boolean logic, control flow, and loops in Python, you'll be ready to blend together everything you've learned to solve a case study using hacker statistics.

Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.

In this intermediate Python course, learn to visualize real data with Matplotlib's functions and get to know new data structures such as the pandas DataFrame.

Intermediate Python

Now that you've learned the basics of SQL in our <a href="https://learn.datacamp.com/courses/introduction-to-sql">Introduction to SQL</a> course,  it's time to supercharge your queries using joins and relational set theory. In this course, you'll learn all about the power of joining tables while exploring interesting features of countries and their cities throughout the world. You will master inner and outer joins, as well as self joins, semi joins, anti joins and cross joins—fundamental tools in any PostgreSQL wizard's toolbox. Never fear set theory again after learning all about unions, intersections, and except clauses through easy-to-understand diagrams and examples. Lastly, you'll be introduced to the challenging topic of subqueries. You will be able to visually grasp these ideas by using Venn diagrams and other linking illustrations.

Join two or three tables together into one, combine tables using set theory, and work with subqueries in PostgreSQL.

Discover all the fundamental in PostgreSQL, like inner, outer self, semi, anti, and cross joins, as well as unions, intersections, and except clauses.

Joining Data in SQL

So you've learned how to aggregate and join data from tables in your database—now what? How do you manipulate, transform, and make the most sense of your data? This intermediate-level course will teach you several key functions necessary to wrangle, filter, and categorize information in a relational database, expand your SQL toolkit, and answer complex questions. You will learn the robust use of CASE statements, subqueries, and window functions—all while discovering some interesting facts about soccer using the European Soccer Database.

Master the complex SQL queries necessary to answer a wide variety of data science questions and prepare robust data sets for analysis in PostgreSQL.

Intermediate SQL

There’s a lot more to Microsoft Excel than SUM and COUNT. Companies and institutions around the world use Excel’s powerful functions to efficiently transform mountains of raw data into clear insights, and now you can too.

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In this course, you’ll develop employable analyst skills as you learn how to use time-saving keyboard shortcuts, convert and clean data types including text, times and dates, and build impressive logic functions and conditional aggregations. Through hands-on practice, you’ll learn over 35 new Excel functions, including CONCATENATE, VLOOKUP, and AVERAGEIF(S), and work with real-world Kickstarter data as you use your new-found Excel skills to analyze what makes a successful project.

Discover time-saving shortcuts in Excel to help you convert and clean your data. You will learn 35+ new Excel functions including CONCAT, VLOOKUP, and more.

Data Analysis in Excel

It's commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time analyzing it. The time spent cleaning is vital since analyzing dirty data can lead you to draw inaccurate conclusions.

Data cleaning is an essential task in data science. Without properly cleaned data, the results of any data analysis or machine learning model could be inaccurate. In this course, you will learn how to identify, diagnose, and treat a variety of data cleaning problems in Python, ranging from simple to advanced. You will deal with improper data types, check that your data is in the correct range, handle missing data, perform record linkage, and more!

Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights! 

Cleaning Data in Python

You have access to a database. Now what do you do? Building on your existing skills joining tables,  using basic functions, grouping data, and using subqueries, the next step in your SQL journey is  learning how to explore a database and the data in it.  Using data from Stack Overflow, Fortune 500 companies, and 311 help requests  from Evanston, IL, you'll get familiar with numeric, character, and date/time data types. You'll use  functions to aggregate, summarize, and analyze data without leaving  the database. Errors and inconsistencies in the data won't stop you!  You'll learn common problems to look for  and strategies to clean up messy data. By the end of this course, you'll be ready to start exploring your  own PostgreSQL databases and analyzing the data in them.

Learn how to explore what's available in a database: the tables, relationships between them, and data stored in them.

Learn what's available in a database in SQL, including the tables, relationships, and data stored in them so you can explore your own PostgreSQL databases.

Exploratory Data Analysis in SQL

How do we get from data to answers?  Exploratory data analysis is a process for exploring datasets, answering questions, and visualizing results.  This course presents the tools you need to clean and validate data, to visualize distributions and relationships between variables, and to use regression models to predict and explain.  You'll explore data related to demographics and health, including the National Survey of Family Growth and the General Social Survey.  But the methods you learn apply to all areas of science, engineering, and business. You'll use Pandas, a powerful library for working with data, and other core Python libraries including NumPy and SciPy, StatsModels for regression, and Matplotlib for visualization.  With these tools and skills, you will be prepared to work with real data, make discoveries, and present compelling results.

Learn how to explore, visualize, and extract insights from data. 

Learn with real-world examples how to clean, explore, visualize, and extract insights from data and use regression models to make predictions in Python.

Exploratory Data Analysis in Python

Every day we interact with the cloud—whether it’s using Google Drive, apps like Salesforce, or accessing our favorite websites. Cloud computing has become the norm for many companies, but what exactly is the cloud and why is everyone rushing to adopt it? Designed for absolute novices, this course breaks down what the cloud is and explains terminology such as scalability, latency, and high-availability. You'll learn about the many advantages including ease of remote collaboration, how there are no hardware limitations, and reliable disaster recovery. You'll also discover the range of tools provided by major cloud providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. By the end of this course, you'll be able to confidently explain how cloud tools can increase productivity and save money, as well as ask the right questions about how to optimize your use of cloud tools.

A non-coding introduction to the world of cloud computing.

Discover what the cloud is, how it works, and what its benefits are. You’ll also learn about terminology like scalability, latency, and high availability.

Cloud Computing for Everyone

Spreadsheet software is one of the most popular and powerful tools in data analysis. Millions of people use tools like Google Sheets or Microsoft Excel on a daily basis. Even the most experienced data scientists often started their careers with spreadsheets and still use it to test assumptions or to look at data for the first time. In this course, you will learn the basics of spreadsheets by working with rows, columns, addresses, and ranges. You will create your own formulas and learn how to use references.

Learn the basics of spreadsheets by working with rows, columns, addresses, and ranges. 

Discover the basics of spreadsheets by working with rows, columns, addresses, and ranges, then create your own formulas and learn how to use references.

Introduction to Spreadsheets

What is data science and how can you use it to strengthen your organization? This course will teach you about the skills you need on your data team, and how you can structure that team to meet your organization's needs. Data is everywhere! This course will provide you with an understanding of data sources your company can use and how to store that data. You'll also discover ways to analyze and visualize your data through dashboards and A/B tests. To wrap up the course, we'll discuss exciting topics in machine learning, including clustering, time series prediction, natural language processing (NLP), deep learning, and explainable AI! Along the way, you'll learn about a variety of real-world applications of data science and gain a better understanding of these concepts through practical exercises.

Learn about data science and how can you use it to strengthen your organization. 

Data Science for Business

This course will expand your Google Sheets vocabulary. You'll dive deeper into data types, practice manipulating numeric and logical data, explore missing data and error types, and calculate some summary statistics. As you go, you'll explore datasets on 100m sprint world records, asteroid close encounters, benefit claims, and butterflies.

Expand your spreadsheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.

Learn about Google Sheets by practicing manipulating numeric and logical data, exploring missing data and error types, and calculating summary statistics.

Intermediate Spreadsheets

Working with large quantities of data in spreadsheets can be difficult and time-consuming. Have you ever wished there was a quick and efficient way to organize and evaluate your data within seconds? Pivot Tables are your answer! In this course we will explore the world of Pivot Tables within Google Sheets, and learn how to quickly organize thousands of datapoints with just a few clicks of the mouse. We will analyze the Average rainfall across multiple US cities, the Top 10 of the Fortune Global 500, and a selection of Films released between 2010 and 2016. You will learn techniques such as sorting, subtotaling, and filtering your data using these real world examples. By the end of the course, you will be able to create your own custom pivot tables with datasets of any size!

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.

Pivot Tables in Spreadsheets

Statistics is the science that deals with the collection, analysis, and interpretation of data. Having a solid foundation in statistics will help you effectively work with your data to test hypotheses and uncover insights that can help solve your problems. This course is designed to give you that foundation in statistics. Using Spreadsheets functions, you'll dive into averages, distributions, hypothesis testing, and conclude the course by applying your newfound knowledge in a case study. Along the way, you'll work with a variety of datasets ranging from eBay auctions to train ridership to historical presidential approval ratings. Enjoy!

Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.

Introduction to Statistics in Spreadsheets

Businesses track data on everything, from operations to marketing to HR. Leveraging this data enables businesses to better understand themselves and their customers, leading to higher profits and better performance. In this course, you’ll learn about the key metrics that businesses use to measure performance. You'll write SQL queries to calculate these metrics and produce report-ready results. Throughout the course, you'll study data from a fictional food delivery startup, modeled on data from real companies.

Learn to write SQL queries to calculate key metrics that businesses use to measure performance.

Analyzing Business Data in SQL

A picture can tell a thousand words - but only if you use the right picture! This course teaches you the fundamentals of data visualization with Google Sheets. You'll learn how to create common chart types like bar charts, histograms, and scatter charts, as well as more advanced types, such as sparkline and candlestick charts. You will look at how to prepare your data and use Data Validation and VLookup formulas to target specific data to chart. You'll learn how to use Conditional Formatting to apply a format to a cell or a range of cells based on certain criteria, and finally, how to create a dashboard showing plots and data together. Along the way, you'll use data from the Olympics, sharks attacks, and Marine Technology from the ASX.

Learn the fundamentals of data visualization using spreadsheets. 

Learn how to create different chart types, from bar charts, histograms, and scatter charts, to advanced sparkline and candlestick charts in Google Sheets.

Data Visualization in Spreadsheets

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.

Learn how to build your very own dashboard by applying all the SQL concepts and functions you have learned in previous courses.

Reporting in SQL

Now that you’ve learned the basic tools of SQL you are ready to synthesize them into practical, real-world skills. In this course, you will work with a database of a fictional movie rental company. The size and complexity of this database will allow you to experience the challenges of working with databases firsthand. Throughout this course, you will use SQL to answer business-driven questions. You will learn new skills that will empower you to find the tables you need. You will then learn how to store and manage this data in tables and views that you create. Best of all you will also learn how to write code that not only clearly conveys your intent but is also legible.

Find tables, store and manage new tables and views, and write maintainable SQL code to answer business questions.

Applying SQL to Real-World Problems

So what is all this AI fuss about? Machine Learning, Deep Learning, Predictive Analytics -- what is the reality behind the hype? How do machines actually learn and what are their limits? How can we use Machine Learning to recognize written digits, predict customer churn and find structure in Elon Musk's tweets? All this -- and much more -- is the topic of this course, which will introduce you to the world of AI in a gentle, but firm and very practical manner.

Learn the fundamentals of AI. No programming experience required!

Learn to work with labeled data, train regression models, divide data into clusters, detect anomalies, select the right model, and create neural networks.

AI Fundamentals

This course will introduce the key elements of machine learning to the business leaders. We will focus on the key insights and base practices how to structure business questions as modeling projects with the machine learning teams. You will understand the different types of models, what kind of business questions they help answer, or what kind of opportunities they can uncover, also learn to identify situations where machine learning should NOT be applied, which is equally important. You will understand the difference between inference and prediction, predicting probability and amounts, and how using unsupervised learning can help build meaningful customer segmentation strategy.

Understand the fundamentals of Machine Learning and how it's applied in the business world.

Machine Learning for Business

In this course, you will learn how to use SQL to support decision making. It is based on a case study about an online movie rental company with a database about customer information, movie ratings, background information on actors and more. You will learn to apply SQL queries to study for example customer preferences, customer engagement, and sales development. This course also covers SQL extensions for online analytical processing (OLAP), which makes it easier to obtain key insights from multidimensional aggregated data.

Learn how to analyze a SQL table and report insights to management.

Data-Driven Decision Making in SQL

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.

Learn how to write recursive queries and query hierarchical data structures.

Learn how to write recursive queries as well as query hierarchical data structures using Common Table Expressions (CTE) and the recursion principle.

Hierarchical and Recursive Queries in SQL Server

Say you have a collection of customers with a variety of characteristics such as age, location, and financial history, and you wish to discover patterns and sort them into clusters. Or perhaps you have a set of texts, such as Wikipedia pages, and you wish to segment them into categories based on their content. This is the world of unsupervised learning, called as such because you are not guiding, or supervising, the pattern discovery by some prediction task, but instead uncovering hidden structure from unlabeled data. Unsupervised learning encompasses a variety of techniques in machine learning, from clustering to dimension reduction to matrix factorization. In this course, you'll learn the fundamentals of unsupervised learning and implement the essential algorithms using scikit-learn and SciPy. You will learn how to cluster, transform, visualize, and extract insights from unlabeled datasets, and end the course by building a recommender system to recommend popular musical artists.

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

Unsupervised Learning in Python

A good database design is crucial for a high-performance application. Just like you wouldn't start building a house without the benefit of a blueprint, you need to think about how your data will be stored beforehand. Taking the time to design a database saves time and frustration later on, and a well-designed database ensures ease of access and retrieval of information. While choosing a design, a lot of considerations have to be accounted for. In this course, you'll learn how to process, store, and organize data in an efficient way. You'll see how to structure data through normalization and present your data with views. Finally, you'll learn how to manage your database and all of this will be done on a variety of datasets from book sales, car rentals, to music reviews.

Learn to design databases using star and snoflake schemas, and OLTP and OLAP. You will gain essential skills for creating and managing databases in SQL.

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