Want to be a professional programmer? You’re going to want to get familiar with Bash.

Bash (Bourne Again Shell) is a command-line interface shell program that makes it easier to navigate and control your computer’s operating system. Learning how to use Bash will help you improve your file and database management, automate tasks, and better integrate your scripts in different programming languages.

To get started, check out our Learn Bash Scripting course. Then once you’ve mastered the basics, use the 10 Bash script code challenges below to apply your new skills.

1. Creating commands

While Bash comes with plenty of useful built-in commands, a key skill every Bash programmer should know is how to create their own.

Write a command that lists the contents of your usr/local directory. Your script can set up variables, but you’re not allowed to call any commands within the script directly. When you run your command, the output should show the directory’s contents in a single column.

For an extra challenge, have your command accept a single argument that lists the contents of the directory contained in the argument.

2. Pick your favorite

Think of three things you like and create a Bash script that prompts the user to respond based on these things. It could be something like “Which would you choose: …”

Using case statements, have the script output different responses based on the chosen selection. For example, if cake is a choice and someone picks cake, a sample output response could be “Great choice. Enjoy your cake!”

To make things a bit more challenging, add an appropriate response message if someone picks something that isn’t on the list. For example, “Sorry, but that’s not on the list. Pick x, y, or z, please.”

You can also have your Bash script prompt for more information about the chosen thing. For example, if someone picks cake, another prompt can ask “What kind of cake?” And that prompt could be followed by a final response based on this input, such as, “Great choice. Enjoy your chocolate cake!”

3. Listing and sorting directories

Create a Bash script that accepts directory paths as arguments. For each argument, list the contents of each directory with the name of each listed at the top.

If you want to give yourself an added challenge, have your script accept an additional argument, “a” or “d,” that sorts the contents of each directory in either ascending or descending order. For even more of a challenge, create a Bash script that sorts the contents of the directories based on an “a” or “d” argument that directly precedes it. In other words, you should be able to sort one directory in ascending order, a second directory in descending order, and so on.

4. SED commands

The stream editor (SED) command is widely used by Bash programmers to process and modify text.

Write a Bash script that includes an SED command that takes the files in your home directory and changes their owner from your username to the reverse of your username. So, for example, if your username is Codecademy, then your Bash script should change the owner to ymedacedoC.

For an extra challenge, have your script accept a numerical input, n, that changes the owner of just the first n files in your home directory.

5. AWK commands

Another way to work with text in Bash is with AWK commands. For this challenge we’ll use the following data set of the world’s countries and their capitals.

Create a Bash script that outputs this dataset into two columns: one for the country and the other for its capital.

For an added challenge, have your script accept a letter as input. The output should be just the countries that start with the input letter. For another challenge, create another Bash script that outputs a two-column list sorted alphabetically by capital city.

6. TPS reports

Using a Bash script, generate an email message that reminds the user to submit a TPS report by a certain day. Your script should accept a date or day as an input argument. For example, if the user inputs “Tuesday,” then the output should look something like:

“Please submit your TPS reports by EOD Tuesday. Your cooperation in this matter is much appreciated.”

For an extra challenge, have your script accept arguments for:

• Name of recipient(s)
• Due date for TPS reports
• Motivational closing
• Sender

If any of the arguments are blank, the email message should output a default word or phrase.

7. Alarm clock

Create a Bash script that “sleeps” for a given number of seconds before beeping after the time has elapsed. You can make this exercise more challenging by adding features to your alarm clock, such as:

• An input argument that accepts time rather than the number of seconds.
• A snooze function that lets you reset the alarm clock to go off again after a given number of minutes.
• An option that enables you to choose from different beeping sounds in your computer system.
• An option that plays an audio file stored on your computer.

8. Detect mounted disks

Programmers who work in Bash are often System Administrators who manage computer and server networks. So, it’s important to know which disks and file systems are mounted — that is, accessible by the computer’s file system. Note that just because a disk or computer is physically attached with cables doesn’t always mean that it’s mounted and accessible.

Come up with a Bash script that detects whether a given mount point or file system is mounted. If so, the script should return the amount of used space on the file system and the amount of free space. If the file system isn’t mounted, your script should return an error message.

Looking for an extra challenge? Add a prompt to your script that asks the user if they want to attempt to mount the file system if it’s unmounted. When the file system mounts, the script should then output the amount of used space and free space in the file system.

9. Recursion countdown

Recursion is when a function calls itself to perform the same task until some condition is met. This is most commonly done in Bash with loops.

Create a countdown generator in Bash that accepts a positive integer as input. The output should show numbers decreasing, starting with the input number until 0. After displaying 0, the output should also display “BLAST OFF!”

Looking for something a little more challenging? Try creating a Bash function that accepts any number of arguments. The output should print out each argument on a new line. Your Bash script should use the echo command only once.

10. Sales tax calculator

While Bash is commonly used to work with file systems and applications, it can also perform basic math.

Create a Bash script that functions like a sales tax calculator using your home state’s sales tax rate. If you live in a state with no sales tax, use the rate of a nearby state. Your Bash script should accept a decimal number as input and output the item’s total price rounded to the nearest cent.

Here are a few ways to make your sales tax calculator more challenging:

• Create a script that accepts both base price and state as input and outputs the total price based on the tax rate in that state. Here’s a good reference for sales tax rates in the US by state.
• Create a script that accounts for local sales tax within a state. It should accept either a city/town name or ZIP code as input to calculate the local sales tax.
• Include input that accounts for the type of item being purchased. For example, many states don’t charge sales tax on groceries, and some don’t charge sales tax on clothing if the total purchase is less than a certain amount.

Or, for an ultra-challenge, create a tax calculator that incorporates all of these things. You’ll probably want to create a dedicated folder that contains all the data files you need.

Every developer should know Bash

Whether you’re working on web design as a Front-End Developer, analyzing big datasets as a Data Scientist, or managing databases as a Back-End Developer, knowing Bash and how to use the Terminal are valuable skills that can support your work.

Once you understand the basics of Bash scripting, you can keep building your skills with online programming courses. If you’re not sure which programming language or tool to learn next, our career paths will help point you in the right direction to get you started in your new career.

Bash/Shell Courses & Tutorials | Codecademy

We use a mouse or finger to click icons and access files, programs, and folders on our devices. But this is just one way for us to communicate with computers. The command line and the shell make up a quick, powerful, text-based interface developers use to more effectively and efficiently communicate…

Go (Golang) is a programming language used for a variety of purposes, including servers, web development, cloud infrastructure, and command-line interfaces. It’s also beginner-friendly and easy to remember.

Ahead, we’ll explore Go’s uses in different industries, and the pros and cons of using it compared to other languages. Then, we’ll show you how to get started learning and using it.

What is Go?

Go was designed by Google in 2007. Google was rapidly growing then, and the code its engineers were using, C++, was difficult to manage and overly complex. This slowed down the development process.

So Google engineers Robert Griesemer, Rob Pike, and Ken Thompson, developed something easier to manage and learn. This new language was Go.

Go became open source in 2009 and was released publicly in 2012. It quickly gained popularity among developers and engineers for its ease of use.

Today, Go is one of the most popular programming languages. Unlike dynamically typed languages, like JavaScript and Python, Go is statically typed. Statically typed programs won’t start up until errors have been fixed, while dynamically typed languages like JavaScript will start up even if they have errors.

What is Go used for?

If you’re looking for a new language to add to your tech stack, Go is a great choice. There are plenty of reasons to learn Go. For example, it’s versatile and can be used in a variety of settings, including:

Cloud services

Many organizations have migrated to the cloud from traditional IT environments. This means there’s less to keep up on-premises, which lowers costs. One popular cloud service is Google Cloud, which is built on Go and offers scalability and high performance. Other cloud services use Go as well, including Dropbox and SendGrid.

SendGrid is a cloud-based email service that delivers high-volume emails for companies like Uber, Airbnb, and Spotify. SendGrid’s APIs (application programming interfaces) were developed using Go.

Online booking

Uber used Go to build many of its services. For example, Go is used to help Uber load maps more quickly and match riders to drivers. Trivago and Delivery Hero also use Go.

Finance companies

Several finance companies have put Go to work. Capital One used Go to develop their Credit Offers API. American Express used it to modernize its payment and rewards networks. And PayPal uses Go to simplify coding and improve the performance of its payments platform.

Cybersecurity

Go’s usefulness in server and cloud environments makes it a perfect fit for cybersecurity. 1Password, a popular application for saving passwords and other sensitive information, uses Go for its Administrator Tools back-end server. This allows administrators who use 1Password for business to easily manage 1Password and recover accounts.

Keybase, an open-source key directory that lets users verify identities and encrypt messages, uses Go because its libraries work together well.

Music and video

Go is efficient and supports the most frequently used file and encoding formats, which makes it a good choice for streaming music and video. SoundCloud, for example, maintains about six services and over a dozen repositories written in Go.

According to Peter Bourgon, one of SoundCloud’s engineers, “We were one of the early adopters [of Go]. We were using the pre-1.0, actually, and every test we’ve put it up to it passed with flying colors.”

Chatting and messaging

Twitch uses Go for its chat, which delivers hundreds of billions of messages daily. Its Web APIs, Search and Discover services, revenue systems, and administrative tools are also written, at least in part, in Go.

Slack has become a critical part of many of our work lives. Go is used by Slack’s engineers for a variety of projects, including scaling its job queue system. The system is used for every message post, push notification, calendar reminder, and more, processing over 1.4 billion jobs at a rate of 33,000 per second. Incorporating Kafkagate, a stateless service written in Go, was key to ensuring Slack could continue at this pace and beyond.

Gaming

Libraries are tools that simplify writing code. Some of Go’s existing libraries were developed for gaming, including Nano, which is a game server library. This makes Go a popular choice for gaming companies like Riot Games, the makers of League of Legends.

Aaron Torres, an Engineering Manager with Riot Games, says that they chose Go as one of their programming languages because Go code builds fast, has a large and powerful standard library, and has excellent third-party support.

Advantages and disadvantages of using Go

Go is clearly a good fit for many organizations. Let’s take a look at the benefits and drawbacks of writing code with Go.

Advantages

• It’s relatively simple to learn.
• You can easily create high-performance applications.
• Go uses a relatively small amount of memory compared to other languages.
• It’s supported by an enthusiastic community.
• Systems that use Go have a 99.99% uptime.

Disadvantages

• It’s not as streamlined as other languages. You may need to write more lines of code to accomplish the same task that would take a couple of lines in another language.
• It’s relatively new, so developers may need to write their own patches on occasion.

Getting started with Go

Go is a beginner-friendly language, but it’s also well-suited for more experienced developers. Regardless of your coding background, we can get you up to speed with our Learn Go course.

This course covers:

• How to set up a Go environment
• How to create a Go file
• Go’s data types and variables
• Go’s unconditional statements
• Using functions in Go

In the course, you’ll build several projects, including printing out ASCII art, simulating a bank heist, and calling the functions of a space-traveling agency.

Ready to get started? We’re here to support you in reaching your goals. And if you need a little extra help as you work through the course, visit our forums or join our Discord server.

Go Courses & Tutorials | Codecademy

Go, or Golang, is an open source programming language developed at Google. The designers of Go wanted developers to have a programming language that made it quick and easy to develop applications. Go is used on servers, web development, and even command line interfaces.

Natural language processing (NLP) is more important than ever before as computers become more integrated into our daily lives.

In the past, you’d need technical knowledge to interact with computers. But now, thanks to NLP, computers can understand and decode human language to respond to our verbal and written commands.

NLP refers to a range of methods for processing language using artificial intelligence.

Today’s users expect to be able to speak to their devices, which means devices need to be able to understand and accurately interpret speech patterns — including different languages, accents, slang, and regional terms. But NLP’s utility extends far beyond speech recognition. You’ll find it in chatbots, hiring tools like applicant tracking systems (ATS), email filters, and more.

Many programming languages can be used to conduct NLP, but Python, in particular, has many high-quality NLP libraries that are used extensively in the industry. These tools include language models and functions for analyzing language and finding insights.

Why is natural language processing important?

NLP’s importance can’t be overstated. Over the last decade, it’s grown to power many computing interfaces that make daily life more convenient. It also plays a huge role in accessibility, making it easier for people with physical and cognitive impairments to navigate and interact with their devices.

How NLP works

A wide range of tools are used within NLP, ranging from algorithms for processing and analyzing text to large language models. Still, whether you’re using numerical or text-based data, the first step is always to prepare the data by standardizing it. This makes it possible for the software to analyze it and find patterns.

Preprocessing text

Before any analysis can happen, the source data has to be cleaned up to make it optimal for NLP tools and models. Text preprocessing is the term used for the preparation of this data. Key parts of preprocessing text include:

• Formatting and Error Correction: The removal of characters, punctuation, or mistakes that could pollute the analysis derived from the text.
• Tokenization: Breaking inputted text into separate words or sentences.
• Stop Word Removal: Normalizing text by removing stop words such as articles and prepositions.

All language includes filler words that don’t help determine a statement’s intent, such as “the” or “me.” Removing these words helps focus the analysis or modeling on the words with the most significance or predictive value. You can use libraries like pandas to automate this process to some extent if you’re working with a large dataset.

Parsing Text

Text segmentation, the grouping of text into meaningful units, plays a huge role in a computer’s analysis. This can be achieved by parsing statements to identify speech, verbs, and proper names.

Prioritizing these high-value words (in lieu of considering each word in a given statement) can streamline text processing. For example, by parsing text, an application could identify the proper name “The Empire State Building” and the verb “walking,” which would indicate a query regarding directions to that location on foot.

Language modeling

We train applications to understand our language, speech patterns, and the structure of our commands through a process called language modeling. Language models allow a system to predict which words will be used and in what order they’ll be introduced, improving the accuracy of NLP. Commonly used models include:

• Unigram or bag-of-words: This model uses a count of each word used to draw conclusions about the statement or command without considering grammar or syntax. The model simply organizes the words in order of most to least used to suggest intent or meaning that can be drawn from analyzing the words used most often.
• N-gram: More advanced than the bag-of-words model, n-gram considers which words are placed next to each other and how they subsequently impact the meaning of the statement. The n-gram model works best on longer sentences or statements because a wider sample of words results in natural-sounding language and presents a more accurate prediction of what comes next.
• Neural language models (NLMs): NLMs are based on neural networks and go deeper than bag-of-words or N-gram to offer an analysis that goes beyond simple sentence structure or word usage frequency.

Topic Modeling

Language modeling can help devices process simple commands and straightforward statements, but it becomes harder to use these models as the commands grow longer. This is where topic modeling comes in. Rather than focusing on the order of the words, topic modeling tries to find hidden topics and meanings within a statement.

Unlike language models that count the frequency of each word and use this count to assign importance, topic models prioritize the words that are used less frequently. This kind of topic modeling is known as term frequency-inverse document frequency (TF-IDF).

Another form of topic modeling is called latent Dirichlet allocation (LDA). This model, based on statistical analysis, determines which words are often used in the same context.

NLP Key Issues and Considerations

Working with NLP, programmers and developers are likely to run into issues surrounding privacy and other hot button topics. Collecting data that powers NLP can be seen as invasive, particularly if it’s then shared with (or sold to) third parties.

For example, prediction software can be powered by location-based data, leading users to wonder how much the app developers know about their movements. And some language models contain memory cells with sensitive information that can identify a user if programmers aren’t careful.

How to learn NLP

The possibilities for a career in NLP are only growing as smart devices become more popular — and more complex. Interested in working in this exciting field? Start by learning Python, then jump into our natural language processing courses like:

You can also check out our Data Scientist: Natural Language Processing Specialist career path. Codecademy Data Science Domain Manager Michelle McSweeney says that NLP Specialists hold a unique role compared to other types of Data Scientists. “This is the entry point for artificial intelligence,” she says. “Working with chatbots and taking data science to the next level of what’s possible in this new world of NLP and language and getting computers to act more like humans.”

In our NLP Specialist career path, you’ll gain all the skills you’ll need to launch your new career. You’ll learn programming with SQL and Python, the fundamentals of supervised and unsupervised learning, text preprocessing, language parsing, and more, as you build your own chatbots and other projects you can use to build a portfolio that’ll help you land a job.

Ready to get started? Sign up now!

Data Science Courses & Tutorials | Codecademy

Data Scientists try to make sense of the data that’s all around us. Learning Data Science can help you make informed decisions, create beautiful visualizations, and even try to predict future events through Machine Learning. If you’re curious about what you can learn about the world using the data p…

Data is revolutionizing the world as we know it. As we produce more and more data every day, businesses are finding new ways to put it to good use. But in order to utilize data, you need to know how to analyze it to find data-based insights. That’s why data analysis is so important — and why Data Analysts and Data Scientists are in such high demand.

Ahead, we’ll take a closer look at the different types of data analysis, how it’s used, how it’s performed, and the different careers that use it. (Or if you’d rather learn how to analyze data yourself, check out our Data Scientist: Analytics Specialist career path).

What is data analysis?

Data analysis is the process of collecting and analyzing data for insights. Many businesses use these insights to improve their systems and products — boosting operational efficiency, improving how services are delivered to customers, and refining products.

A Data Analyst is able to examine data and discover how to use it to boost the bottom line of an organization. Because businesses today generate so much data, a Data Analyst has a lot to work with. As a result, they often play a key role in the operation of an organization.

“We’re seeing data transform our society and everything we do,” says Codecademy Data Science Domain Manager Michelle McSweeney, “whether it’s measuring how well something performed or deciding what we’re going to do next.”

The role of big data

As the name suggests, big data involves large amounts of data, and it’s often used to both improve basic processes and generate machine learning models. Machine learning is a branch of artificial intelligence that enables Data Scientists to predict outcomes and mimic the learning processes of humans in machines. The collection of big data gives Machine Learning Engineers enough information to build very accurate prediction models.

For instance, the artificial intelligence systems that control self-driving cars learn based on huge storehouses of image data. Images are aligned with categories, such as “people,” “vehicle,” “animal,” and “road element,” allowing the vehicle to decide what to do based on the kind of object its cameras are viewing.

Big data also plays a critical role in figuring out how customers use products, their buying habits, and how they may react to the release of services or products. For example, a company with a rewards card program can collect data regarding the kinds of products customers purchase, when they do, and where. They can then use that information to create promotions aimed at giving customers exactly what they want, when they want it.

Real-time data

Real-time data refers to data that is collected as it’s generated. When real-time data is used in a process, it gives the application using it more agility, allowing it to adapt to changing circumstances.

A common example of a source of real-time data is the stock market. As the prices of different stocks rise and fall, the data has to be received, processed, and analyzed in order to ensure investors can make the best decisions possible.

Real-time data also plays a central role in certain medical care environments, allowing computer systems to tell doctors and nurses whether they need to respond to a situation immediately or if a patient’s needs don’t necessarily have to be tended to right away.

Machine data

Machine data is data generated by machines, but this includes a wide variety of industrial and personal tools. Machine data can be generated by:

• Robots on a production floor
• Handheld devices in a warehouse
• Your smartphone
• Computers and monitoring systems in hospitals
• Applications run by a business
• Cybersecurity systems

Machine data is one of the most diverse categories of data, and analyzing it can improve operations, safety, and the quality of services.

Qualitative vs. quantitative data analysis

Quantitative data analysis involves examining data that can be measured. For example, say a spreadsheet contained a collection of customer ratings for a given product. A Data Analyst could use this data to produce tangible, measurable insights.

Qualitative data analysis is different in that it’s based on data that cannot be easily measured using numbers. For example, if a customer responds to a survey asking them to describe their experience, their responses would be qualitative data. Even though qualitative data analysis involves, in some ways, a very different process than the analysis of quantitative data, the insights produced are just as valuable.

Using machine learning, qualitative data can be transformed into quantitative data when the machine learning algorithm studies patterns in the data sets. For example, if 30% of customers included the word “great” in their reviews in March, and that number progressively decreased over the next six months, a machine learning algorithm could observe the trend and even provide recommendations. A customer service team could start implementing the suggestions in time for the holiday season and significantly boost sales revenue.

Why is data analysis important?

Data analysis is important because it gives decision-makers tangible information on which to base their strategy. This information has a wide range of applications, from improving systems and processes to better understanding clients and even human behavior.

For instance, if a high-end coffee chain were selling a new flavor and customers were buying 35% less of it than the company projected, they could take action to scrap the new flavor in favor of a different one. They could even use customer comments and buying habits to craft new flavors.

Safety

In some cases, data analysis can mean the difference between a safe environment and one that threatens people’s health or well-being. For instance, you can use data analysis to study the movement patterns of people on a factory floor.

The distances they get from dangerous machinery can be studied in relation to the person’s speed, the time of day, and even the habits of specific employees. You can then use this information to set up boundaries and guidelines that keep people out of harm’s way.

Objective information

Data analysis also plays an important role because it provides objective information as opposed to subjective, emotional perspectives. While emotions are a crucial element of the decision-making process, they can sometimes get in the way, especially when somebody is personally invested in an outcome.

For example, if an executive is personally invested in the success of a product they helped conceive, they may fail to see some of its flaws. On the other hand, with data analysis, the reactions of end-users can provide concrete numbers that can produce decisions motivated by facts instead of feelings.

Note that while data analysis can provide objective information, data professionals need to be mindful of biases in the data that can influence analysis and insights and lead to poor outcomes.

There are several techniques you can use to analyze data and a variety of technological tools that can make the process faster.

Data analysis techniques

There are many types of data analysis techniques, but the most popular include regression analysis, Monte Carlo simulation, and cohort analysis.

Regression analysis

Regression analysis refers to finding the relationship between different sets of variables. Whenever you do any kind of regression analysis, you’re trying to see a connection between independent and dependent variables. A dependent variable is a factor you’re trying to predict or measure, and an independent variable is one that may affect the dependent variable.

For instance, if you’re studying people’s voting habits in a certain town, one of your dependent variables may be the percentage of registered voters that show up to the polls. Your independent variable could be the temperature outside on voting day. You may discover a correlation between how hot or cold the day was and the number of people that went to the polls.

Monte Carlo simulation

Monte Carlo simulation involves a computer analyzing a set of data and then producing a report that outlines the chance of different outcomes happening. In most situations, the data a computer is analyzing has been organized into a spreadsheet, and the computer figures out the percentage of times a certain outcome occurs and then uses that to predict what may happen in the near future.

For instance, if a city’s traffic light system has been set according to configuration A, and there are 165 accidents over the course of two months, but with configuration B, there are 232 accidents, the computer can use this data to predict which configuration is safest. Configuration B produced about 29% fewer accidents than configuration A, perhaps making it the better choice.

Cohort analysis

Cohort analysis focuses on separating large data sets into smaller groups that are then examined individually. This helps Data Analysts study the behavior and tendencies of specific subjects.

For example, suppose a university has a freshman class of 2,000 people. You can divide these into several smaller cohorts. For instance, you could group the students according to their high school grade point averages. Data Analysts at the university could then follow students in each grade point average cohort and observe how successful they are in different kinds of classes.

You could use this information to provide tailored support services to students based on their cohort. You could also design support systems around specific types of classes or within certain majors, all using the data gathered from the cohort analysis.

Data analysis tools

Computerized tools make data analysis faster, easier, and more accessible. Some of the most common data analysis tools include Microsoft Excel and the programming languages Python, R, and SQL. Our courses can provide you with a strong foundation to build a career as a Data Analyst. Some of our best courses for data analysis are:

Using these tools, you can create your own databases, complete with rules and configurations that allow you to generate data-based insights. You can also incorporate these kinds of databases into other programs.

For instance, you could design a database using SQL that allows users of an eCommerce clothing app to search through products, specifying elements such as size, color, and style. The end user’s search experience can then be tailored in any way you want, ensuring they get the search options they need and stay engaged with the app and the company’s products.

Who uses data analysis?

In reality, nearly any career that brings you into contact with substantial amounts of data can use data analysis. Even if your role doesn’t specifically require you to analyze data, having some background in it can position you to:

• Be a more effective leader — one that makes decisions based on data.
• Offer better insights to executives and managers.
• Come up with more actionable solutions.

That being said, there are some professions where data analysis is a must. These include:

• Data Scientist: A Data Scientist uses algorithms and scientific methods to derive discoveries from vast amounts of apparently random data.
• Data Analyst: A Data Analyst focuses on exploring and visualizing data, applying statistical methods to generate insights, and communicating their findings.
• Data Engineer: A Data Engineer collects and observes data for a wide range of disciplines, setting up pipelines and data warehouses.
• Business Analyst: A Business Analyst is essentially a Data Analyst focused on improving business outcomes.
• Product Manager: A Product Manager uses insights gained from data analysis to strategize the design of a specific product or a sequence of products. These could be physical or digital products, such as software applications.
• Digital Marketer: A Digital Marketer uses data analysis to choose the most effective marketing channels for a product or service.

Getting started with data analysis

The best way to work with data analysis is to get comfortable using the tools and techniques that power data analytics. You can get started today with our Data Scientist: Analytics Specialist career path. In this course, you’ll learn how to use SQL, Python, and various libraries including pandas and Matplotlib, to analyze and visualize data and share your discoveries. Sign up today to get started for free.

Data Analytics Courses & Tutorials | Codecademy

Data analytics is the process of taking raw data and turning it into something meaningful we can understand. By finding trends and patterns, you can make predictions and uncover new information that helps inform decisions. There’s a great demand for Data Analysts in healthcare, marketing, retail, i…