Python is a general-purpose programming language that is becoming ever more popular for data science. Companies worldwide are using Python to harvest insights from their data and gain a competitive edge. Unlike other Python tutorials, this course focuses on Python specifically for data science. In our Introduction to Python course, you’ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses. Start DataCamp’s online Python curriculum now.

Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.

Discover how to use Python for data science, learning about the ways to store and manipulate data in the environment to begin conducting your own analyses.

Introduction to Python

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

In this introduction to R course, you'll master the basics of this widely used open source language&mdash;including vectors, factors, lists, and data frames. With the coding skills you'll gain in this course, you'll be ready to undertake your own data analysis in R. There are millions of R users worldwide, cementing it as a leading programming language in statistics and data science. More and more organizations are using R, so begin your coding journey in one of DataCamp's most popular courses today!

Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.

DataCamp's Into to R training course teaches you how to use R programming for data science at your own pace with video tutorials & interactive challenges.

Introduction to R Online

Introduction to R

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

SQL is an essential skill for data scientists, and Microsoft SQL Server is one of the world's most popular database systems. In this course, you'll start with simple SELECT statements, and refine these queries with ORDER BY and WHERE clauses. You'll learn how to group and aggregate your results, and also how to work with strings.  You'll also cover the most common way to join tables, how to create tables, and inserting and updating data. You'll mainly work with the Chinook digital media database, representing sales of various artists and tracks. The music theme continues with the classic rock and Eurovision datasets, while we also look at trends in power outages in the US. Overall, you'll become proficient in the most common data manipulation tasks in SQL Server and build a solid foundation for the upcoming T-SQL courses here on DataCamp!

Become proficient at using SQL Server to perform common data manipulation tasks.

Learn how to use SQL Server to do common data manipulation tasks, like insert data, joining tables,  group and aggregate results, and work with strings.

Introduction to SQL Server

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

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.

Learn how to create one of the most efficient ways of storing data - relational databases! 

Learn how to create tables and specify their relationships in SQL, as well as how to enforce data integrity and other unique features of database systems.

Introduction to Relational Databases in SQL

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

Version control is one of the power tools of programming. It allows you to keep track of what you did when, undo any changes you decide you don't want, and collaborate at scale with other people. This course will introduce you to Git, a modern version control tool that is very popular with data scientists and software developers, and show you how to use it to get more done in less time and with less pain.

This course is an introduction to version control with Git for data scientists. 

Discover Git, a popular and modern version control tool for data scientists, and how you can use it work more efficiently, quickly, and with fewer errors.

Introduction to Git

Have you heard people talk about data engineers and wonder what it is they do? Do you know what data engineers do but you're not sure how to become one yourself? This course is the perfect introduction. It touches upon all things you need to know to streamline your data processing. This introductory course will give you enough context to start exploring the world of data engineering. It's perfect for people who work at a company with several data sources and don't have a clear idea of how to use all those data sources in a scalable way. Be the first one to introduce these techniques to your company and become the company star employee.

Learn about the world of data engineering with an overview of all its relevant topics and tools!

Introduction to Data Engineering

A majority of data is stored in databases and knowing the necessary tools needed to analyze and clean data directly in databases is indispensable. This course focuses on T-SQL, the version of SQL used in Microsoft SQL Server, needed for data analysis. You will learn several concepts in this course such as dealing with missing data, working with dates, and calculating summary statistics using advanced queries. After completing this course, you will have the skills needed to analyze data and provide insights quickly and easily.

In this course, you will use T-SQL, the flavor of SQL used in Microsoft's SQL Server for data analysis.

Learn concepts for working in  T-SQL, such as dealing with missing data, working with dates, and calculating summary statistics using advanced queries.

Intermediate SQL Server

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

In this course, you will learn how to build a logistic regression model with meaningful variables. You will also learn how to use this model to make predictions and how to present it and its performance to business stakeholders.

In this course you'll learn to use and present logistic regression models for making predictions.

Learn how to build a logistic regression model in Python with meaningful variables and how to use this model to make predictions for business stakeholders.

Introduction to Predictive Analytics in Python

Experimental design is a crucial part of data analysis in any field, whether you work in business, health or tech. If you want to use data to answer a question, you need to design an experiment! In this course you will learn about basic experimental design, including block and factorial designs, and commonly used statistical tests, such as the t-tests and ANOVAs. You will use built-in R data and real world datasets including the CDC NHANES survey, SAT Scores from NY Public Schools, and Lending Club Loan Data. Following the course, you will be able to design and analyze your own experiments!

In this course you'll learn about basic experimental design, a crucial part of any data analysis.

Learn about basic experimental design, including block and factorial designs, and commonly used statistical tests, such as the t-tests and ANOVAs in R.

Experimental Design in R

Losing time on slow queries? Hesitant to share your queries with more seasoned coworkers? In this course, you will learn how to structure your PostgreSQL to run in a fraction of the time. Exploring intertwined data relating Olympic participation, country climate, and gross domestic product, you will experience firsthand how changes in filtering method and using subqueries impact query performance. You will learn the properties of a row oriented database while also seeing how Hawaii's volcanos impact air quality. Restructuring your queries with the query planner and the SQL order of operations, you will soon be dazzling your coworkers with your effortless efficiency.

Learn how to structure your PostgreSQL queries to run in a fraction of the time.

Discover how you can structure your PostgreSQL and how changes in filtering methods and subqueries used can have an impact on query performance and speed.

Improving Query Performance in PostgreSQL

Everything happens somewhere, and increasingly the place where all these things happen is being recorded in a database. There is some truth behind the oft-repeated statement that 80% of data have a spatial component. So what can we do with this spatial data? Spatial statistics, of course! Location is an important explanatory variable in so many things - be it a disease outbreak, an animal's choice of habitat, a traffic collision, or a vein of gold in the mountains - that we would be wise to include it whenever possible. This course will start you on your journey of spatial data analysis. You'll learn what classes of statistical problems present themselves with spatial data, and the basic techniques of how to deal with them. You'll see how to look at a mess of dots on a map and bring out meaningful insights.

Learn how to make sense of spatial data and deal with various classes of statistical problems associated with it.

Spatial Statistics in R

When working with data, we often want to create models to predict future events, but we also want an even deeper understanding of how our data is connected or structured. In this course, you will explore the connectedness of data using using structural equation modeling (SEM) with the R programming language using the lavaan package. SEM will introduce you to latent and manifest variables and how to create measurement models, assess measurement model accuracy, and fix poor fitting models. During the course, you will explore classic SEM datasets, such as the Holzinger and Swineford (1939) and Bollen (1989) datasets. You will also work through a multi-factor model case study using the Wechsler Adult Intelligence Scale. Following this course, you will be able to dive into your data and gain a much deeper understanding of how it all fits together.

Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.

Structural Equation Modeling with lavaan in R

Learn the fundamental skills necessary to interact with SQL Server. The skills you learn by taking these courses will empower you to join tables, summarize data, and answer your data analysis and data science questions.

Learn the fundamental skills necessary to interact with SQL Server.

SQL Server Fundamentals

Learn the fundamental skills necessary to interact with the majority of dialects of SQL -- PostgreSQL, MySQL, and others! The skills you learn by taking these courses will empower you to join tables, summarize data, and answer your data analysis and data science questions. 

Learn the fundamental skills necessary to interact with the majority of dialects of SQL -- PostgreSQL, MySQL, and others!

SQL Fundamentals

Intermediate R is the next stop on your journey in mastering the R programming language. In this R training, you will learn about conditional statements, loops, and functions to power your own R scripts. Next, make your R code more efficient and readable using the apply functions. Finally, the utilities chapter gets you up to speed with regular expressions in R, data structure manipulations, and times and dates. This course will allow you to take the next step in advancing your overall knowledge and capabilities while programming in R.

Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.

Discover conditional statements, loops, and functions to power your own R scripts, and learn to make your R code more efficient using the apply functions.

Intermediate R

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

After all of the hard work of acquiring data and getting them into a form you can work with, you ultimately want to make clear, succinct conclusions from them. This crucial last step of a data analysis pipeline hinges on the principles of statistical inference. In this course, you will start building the foundation you need to think statistically, speak the language of your data, and understand what your data is telling you. The foundations of statistical thinking took decades to build, but can be grasped much faster today with the help of computers. With the power of Python-based tools, you will rapidly get up-to-speed and begin thinking statistically by the end of this course.

Build the foundation you need to think statistically and to speak the language of your data.

Statistical Thinking in Python (Part 1)

Where should you buy a house to get the most value for your money? Your first step might be to make a map, but spatial analysis in R can be intimidating because of the complicated objects the data often live in.This course will introduce you to spatial data by starting with objects you already know about, data frames, before introducing you to the special objects from the sp and raster packages used to represent spatial data for analysis in R. You'll learn to read, explore, and manipulate these objects with the big payoff of being able to use the tmap package to make maps.By the end of the course you will have made maps of property sales in a small town, populations of the countries of the world, the distribution of people in the North East of the USA, and median income in the neighborhoods of New York City.

Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.

Visualizing Geospatial Data in R

From online social networks such as Facebook and Twitter to transportation networks such as bike sharing systems, networks are everywhere—and knowing how to analyze them will open up a new world of possibilities for you as a data scientist. This course will equip you with the skills to analyze, visualize, and make sense of networks. You'll apply the concepts you learn to real-world network data using the powerful NetworkX library. With the knowledge gained in this course, you'll develop your network thinking skills and be able to look at your data with a fresh perspective.

This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.

Taught by Eric Ma from the Runstadler Lab at MIT, this course will equip you with the skills to analyze and visualize networks such as Facebook and Twitter.

Introduction to Network Analysis in Python

Probability is the study of making predictions about random phenomena. In this course, you'll learn about the concepts of random variables, distributions, and conditioning, using the example of coin flips. You'll also gain intuition for how to solve probability problems through random simulation. These principles will help you understand statistical inference and can be applied to draw conclusions from data.

In this course, you'll learn about the concepts of random variables, distributions, and conditioning.

Learn about random variables, distributions and conditioning, while gaining intuition for how to solve probability problems through random simulation.

Foundations of Probability in R

This beginner-level introduction to machine learning covers four of the most common classification algorithms. You will come away with a basic understanding of how each algorithm approaches a learning task, as well as learn the R functions needed to apply these tools to your own work.

In this course you will learn the basics of machine learning for classification.

Supervised Learning in R: Classification

The beauty of R is that it is built for performing data analysis. The downside is that sometimes R can be slow, thereby obstructing our analysis. For this reason, it is essential to become familiar with the main techniques for speeding up your analysis, so you can reduce computational time and get insights as quickly as possible.

Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.

Writing Efficient R Code

There has never been a better time to use R for spatial analysis! The sf package has made working with vector data in R a breeze and the raster package provides a set of powerful and intuitive tools to work gridded data like satellite imagery. Instead of the painful process of performing your spatial analysis in GIS systems like ArcGIS or QGIS and then shuffling your results into another system for analysis, you can move your entire spatial analysis workflow into R. In this course you will learn why the sf package is rapidly taking over spatial analysis in R. You will read in spatial data, manipulate vectors using the dplyr package and learn how to work with coordinate reference systems. You'll also learn how to perform geoprocessing of vectors including buffering, spatial joins, computing intersections, simplifying and measuring distance. With rasters, you will aggregate, reclassify, crop, mask, and extract. The last chapter of the course is devoted to showing you how to make maps in R with the ggplot2 and tmap packages and performing a fun mini-analysis that brings together all your new skills.

Analyze spatial data using the sf and raster packages. 

Learn how to work with vector data in R using the SF package. You'll learn how to perform geoprocessing of vectors like buffering, spatial joins, and more

Spatial Analysis with sf and raster in R

In this course you'll learn how to work with and visualize network data. You'll use the <code>igraph</code> package to create networks from edgelists and adjacency matrices. You'll also learn how to plot networks and their attributes. Then, you'll learn how to identify important vertices using measures like betweenness and degree. Next, this course covers network structures, including triangles and cliques. Next, you'll learn how to identify special relationships between vertices, using metrics like assortativity. Finally, you'll see how to create interactive network plots using threejs.

In this course you'll learn to analyze and visualize network data with the igraph package.

Network Analysis in R

Cluster analysis is a powerful toolkit in the data science workbench. It is used to find groups of observations (clusters) that share similar characteristics. These similarities can inform all kinds of business decisions; for example, in marketing, it is used to identify distinct groups of customers for which advertisements can be tailored. In this course, you will learn about two commonly used clustering methods - hierarchical clustering and k-means clustering. You won't just learn how to use these methods, you'll build a strong intuition for how they work and how to interpret their results. You'll develop this intuition by exploring three different datasets&#58; soccer player positions, wholesale customer spending data, and longitudinal occupational wage data.

Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data. 

Cluster Analysis in R

Categorical data is all around us. It's in the latest opinion polling numbers, in the data that lead to new breakthroughs in genomics, and in the troves of data that internet companies collect to sell products to you. In this course you'll learn techniques for parsing the signal from the noise; tools for identifying when structure in this data represents interesting phenomena and when it is just random noise.

In this course you'll learn how to leverage statistical techniques for working with categorical data.

Inference for Categorical Data in R

HR analytics, people analytics, workforce analytics -- whatever you call it, businesses are increasingly counting on their human resources departments to answer questions, provide insights, and make recommendations using data about their employees. In this course, you'll learn how to manipulate, visualize, and perform statistical tests on HR data through a series of HR analytics case studies.

Manipulate, visualize, and perform statistical tests on HR data. 

Learn how you can gain insights, answer questions, and make predictions from your HR data by using R to manipulate, visualize, and perform statistical tests.

Human Resources Analytics: Exploring Employee Data in R

The ability to build tools capable of retrieving and parsing information stored across the internet has been and continues to be valuable in many veins of data science. In this course, you will learn to navigate and parse html code, and build tools to crawl websites automatically. Although our scraping will be conducted using the versatile Python library scrapy, many of the techniques you learn in this course can be applied to other popular Python libraries as well, including BeautifulSoup and Selenium. Upon the completion of this course, you will have a strong mental model of html structure, will be able to build tools to parse html code and access desired information, and create a simple scrapy spiders to crawl the web at scale.

Learn to retrieve and parse information from the internet using the Python library scrapy.

Web Scraping with Python

Web Scraping in Python

Take your SQL Server programming to the next level. First, we demystify how to manipulate datetime data by performing temporal exploratory data analysis with the Washington DC BikeShare transactional dataset. Then, you’ll master how to create, update, and execute user-defined functions and stored procedures. You will learn the proper context for each modular programming tool and best practices. In the final chapter, you will apply all of your new skills to solve a real-world business case identifying the New York City yellow taxi utilization for each borough, and which pickup locations should be scheduled for each driver shift.

Master SQL Server programming by learning to create, update, and execute functions and stored procedures.

Writing Functions and Stored Procedures in SQL Server

The most successful companies today are the ones that know their customers so well that they can anticipate their needs. Data analysts play a key role in unlocking these in-depth insights, and segmenting the customers to better serve them. In this course, you will learn real-world techniques on customer segmentation and behavioral analytics, using a real dataset containing anonymized customer transactions from an online retailer. You will first run cohort analysis to understand customer trends. You will then learn how to build easy to interpret customer segments. On top of that, you will prepare the segments you created, making them ready for machine learning. Finally, you will make your segments more powerful with k-means clustering, in just few lines of code! By the end of this course, you will be able to apply practical customer behavioral analytics and segmentation techniques.

Learn how to segment customers in Python.

Learn techniques to segmment your customer data in Python, including behaviour, transaction, and cohort data. You will learn about RFM analysis and more.

Customer Segmentation in Python

Building good models only succeeds if you have a decent base table to start with. In this course you will learn how to construct a good base table, create variables and prepare your data for modeling. We finish with advanced topics on the matter.

Learn how to prepare and organize your data for predictive analytics.

Learn how to prepare and organize your data for predictive analytics using data preparation and advanced base table concepts with variables in Python.

Intermediate Predictive Analytics in Python

Have you ever wondered how data professionals use SQL to solve real-world business problems, like generating rankings, calculating moving averages and running totals, deduplicating data, or performing time intelligence? If you already know how to select, filter, order, join and group data with SQL, this course is your next step. By the end, you will be writing queries like a pro! You will learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon! Using flights data, you will discover how simple it is to use window functions, and how flexible and efficient they are.

Learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon! 

PostgreSQL Summary Stats and Window Functions

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

We live in a busy world with tight deadlines. As a result, we fall back on what is familiar and easy, favoring GUI interfaces like Anaconda and RStudio. However, taking the time to learn data analysis on the command line is a great long-term investment because it makes us stronger and more productive data people.
 
 
In this course, we will take a practical approach to learn simple, powerful, and data-specific command-line skills. Using publicly available Spotify datasets, we will learn how to download, process, clean, and transform data, all via the command line. We will also learn advanced techniques such as command-line based SQL database operations. Finally, we will combine the powers of command line and Python to build a data pipeline for automating a predictive model.

Learn powerful command-line skills to download, process,  and transform data, including machine learning pipeline.

Learn powerful command-line skills to download, process, and transform data, including a machine learning pipeline.

Data Processing in Shell

In this course, you will learn how to make use of the most important functions for manipulating data provided by SQL Server.  You can use these functions for processing and transforming data to get the results you want.

Learn the most important functions for manipulating, processing, and transforming data in SQL Server.

Learn how to use the most important functions for manipulating data in SQL Server to manipulate, process, and transform data to get the results you want.

Functions for Manipulating Data in SQL Server

Data is all around us and can help us to understand many things. Making a pretty graph is great, but how can we tell the difference between a few outliers on a graph and a real, reliable effect? Is a trend that we see on a graph a reliable result or just random chance playing tricks? In this course, you will learn how to interrogate datasets in a rigorous way, giving clear answers to your questions. You will learn a range of statistical tests, how to apply them, how to understand their results, and how to deal with their shortcomings. Along the way, you will explore Olympic athlete data and the differences between populations of continents.

Learn about experimental design, and how to explore your data to ask and answer meaningful questions. 

Experimental Design in Python

SQL Server has a robust set of tools to prepare, aggregate, and query time series data. This course will show you how to build and work with dates, parse dates from strings (and deal with invalid strings), and format dates for reporting. From there, you will see how SQL Server's built-in aggregation operators and window functions can solve important business problems like calculating running totals, finding moving averages, and displaying month-over-month differences using realistic sample data sets. You will also see how taking a different perspective on your data can solve difficult problems.

Explore ways to work with date and time data in SQL Server for time series analysis

Time Series Analysis in SQL Server

Shiny is an R package that makes it easy to build highly interactive web apps directly in R. Using Shiny, data scientists can create interactive web apps that allow your team to dive in and explore your data as dashboards or visualizations. If you want to bring your data to life, Shiny is the way to go! Using data about baby names, food ingredients, and UFO sightings, you'll build a variety of different Shiny apps that leverage different inputs and outputs. You’ll also learn the basics of reactive expressions. By the end of this course, you’ll have the Shiny skills you need to build your first app in R.

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. 

Building Web Applications with Shiny in R

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.

Database Design

pandas is the world's most popular Python library, used for everything from data manipulation to data analysis. In this course, you'll learn how to manipulate DataFrames, as you extract, filter, and transform real-world datasets for analysis. Using pandas you’ll explore all the core data science concepts. Using real-world data, including Walmart sales figures and global temperature time series, you’ll learn how to import, clean, calculate statistics, and create visualizations—using pandas to add to the power of Python!

Use the world’s most popular Python data science package to manipulate data and calculate summary statistics.

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