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29 July 2023 - 8 minutes

Is Data Science Right for Me?

Data is a growing field, but what’s the right step for you? 

Ironhack - Changing The Future of Tech Education

Data Science & Machine Learning

The decision to make a career change (or even choose a career in the first place!) is a big one--how can you be sure you’re making the right decision? One of the most popular topics in tech right now is data and we know why: data drives practically every choice that we make. No matter the industry, data is necessary and to properly use data, companies need skilled data professionals and even within the field of data, there are tons of different roles. 

Data analysts differ from data scientists and data engineers have diverse responsibilities.   Data scientists are one of the most common roles in tech, but it isn’t for everyone. In this article, we’ll explain what data science is, what your role as a data scientist would be like, and how to decide if it’s the right choice for you. 

What is Data Science?

This branch of tech combines math, statistics, specialized programming, analytics, artificial intelligence, and machine learning. Whew! That’s a lot–and that’s because data is becoming increasingly important and we’re relying more and more on data every day. Data scientists take on a more research-focused role when it comes to data, looking at large amounts of data and raising questions to improve business growth. 

There are five main stages of data science: 

  1. Capturing: the first step is clear: you need data to work with! Collecting your data should be your first priority and once you’ve figured out what you want to achieve with your data analysis, you can start obtaining your data–both raw and unstructured. 

  2. Maintaining: now that you have your data, it’s time to make sure that it’s actually useful, which you can do through data cleansing, staging, and organizing. 

  3. Processing: your data is well-organized and sorted; so you must figure out exactly how you want to analyze it so that you can prepare it for the next stage. 

  4. Analyzing: here’s the big part! It’s time to analyze the data, using predictive or exploratory methods to get the answers you’re looking for. 

  5. Communicating: while analyzing your data might seem like the most important part, your findings are useless if you can’t properly communicate them to your team. Prioritizing data visualization can help the rest of your team understand the results. 

Okay, sounds clear, right? Let’s dive right into why data science is a great career choice for many and the benefits of the field. 

Benefits of using data science 

We hope that you’re already clear on why data science is so crucial, but just in case you’ve stumbled onto this article by chance, let’s recap: especially with large amounts of data, it’s easy to miss certain patterns or trends that could be key to the decision-making process. Data science can help you see and evaluate these patterns in your analysis, ensuring you’re making the most of your data. In addition, lots of industries are using data science to better the client experience: 

  • Healthcare: imagine if doctors could compare your symptoms and information to other patients, using past data from a wide range of worldwide records to better diagnose and treat you. Well, data science in healthcare is allowing doctors to do just that, improving overall healthcare. 

  • Streaming sites: Netflix does know what you’re looking for next and no, it’s not because they’re listening to you! Streaming services such as Netflix, Amazon Prime, and Hulu use the massive amounts of customer data to suggest new content to you once you finish a show; without data science tools, the sheer amount of data would be impossible to organize. 

  • Speech and image recognition: these two technologies use different techniques but process immense amounts of past data to recognize either speech patterns and words or images to properly identify them for the user. 

It’s clear that properly processing and analyzing data can have incredible effects on your company’s overall success, using your data to make better business decisions. And using data may even open up doors you thought were previously nailed shut: 

  • Reopening schools after the COVID-19 pandemic seemed like an impossible challenge. How could schools, especially those with lots of students, keep track of the sheer number of students, their contacts, and any possible symptoms? Or who had already contracted the virus? Through collecting data about symptoms and contact tracing, many towns were able to streamline the process of both reporting cases and informing students about possible contact with the virus, allowing schools to open up faster and safely. 

  • Autonomous vehicles seem like a thing of the future, right? After all, how could we reach a point where cars can anticipate human actions or even react properly in a split second? Well, data science is helping cars learn what happens on the roads around it, using data from surrounding cars to keep passengers safe. 

Now that we’ve convinced you of the importance of data scientists, let’s head a bit deeper into data scientists’ responsibilities and what you can anticipate as a future data scientist. 

Responsibilities of Data Scientists

As a data scientist, you’ll be tasked with both collecting and analyzing large amounts of data to help your company make better decisions. Sounds a bit broad, right? Here’s an example situation to help you picture being a data scientist yourself:

Your business is doing well, but you’ve realized that the majority of male-identifying visitors make one purchase and never return to your site. You’re happy that they’re encouraged to buy once, but want to understand why they’re not returning and how you can improve that in the future. 

  • First things first: you’ll work with colleagues to highlight problem areas. From these selected areas, you’ll create a series of questions to which you want answers; in this case: why men aren’t returning customers. 

  • With your goal in mind, you’ll focus on creating the proper data sets and variables so that you’re able to answer your questions. For example, if you’re worried about men not returning to make a second purchase, you’ll decide to collect data about your male customer base, the pages they’re looking at, their searches, and the products they’re buying. 

  • After you have this data, you’ll pick the right format for analysis, looking at the data to best determine the setup. You’ll also ensure the data is clean and complete, avoiding any mistakes that could compromise the integrity of your data. 

  • You’ll probably have lots of data, so you’ll use machine learning and big data tools such as analytic systems to run the analysis for you. 

  • Once you have your analysis, you’ll take a look and draw conclusions. You can see that many men have returned their product, citing sizing problems. 

  • Now that you have your answer, you can properly visualize the results for the rest of your team and communicate your concerns to the sizing department, helping ensure that the sizing becomes accurate and hopefully ensuring men will soon become return customers. 

Seems straightforward, right? Data science is incredibly valuable and more and more companies are prioritizing it, meaning there are lots of job opportunities out there for you. But before you head to a data analytics course, let’s clarify the main differences between the two. 

Data science v. data analytics 

The two are frequently conflated–and listen, we get it. They’re quite similar and do overlap; here’s a quick and simple guide of the areas in which they differ: 

  • Job description: data scientists typically need to have neural learning and machine learning experience and are responsible for the entire timeline when it comes to data analytics, from defining the problem to presenting the solution to stakeholders. Data analysts focus primarily on the actual analysis, although they might be tasked with more responsibilities in smaller companies. 

  • Required skills: both roles need to have some coding experience, but data scientists should also be familiar with big data frameworks while data analysts are typically fine with data analytic tool knowledge and a few programming languages. 

  • Career opportunities: both fields are growing at incredible rates and are predicted to reach new heights by 2030; no matter which you choose, you’ll be able to check out various job postings. 

How to Become a Data Scientist

If you’ve made it this far it’s definitely because you’re totally set on becoming a data scientist–we’re so happy you’ve decided to make the jump into both tech and such a crucial sector. There are lots of ways to become a data scientist, of course, but here are some of our top recommendations: 

  • Brush up on your math and stats skills: data scientists need to be quite comfortable with data and mathematical functions; preparing yourself with some math review can help you land your dream job.

  • Take a bootcamp: as we mentioned, there are lots of available jobs in data science and employers are looking for specific skill sets; check out what job postings are looking for and choose a bootcamp or online course that focuses exactly on that, preparing you to enter the job market immediately after graduation. 

  • Build up your secondary skills: data science encompasses lots of different areas, meaning a degree in just one area won’t prepare you for the workforce. Taking online courses or bootcamps to learn supporting skills, such as additional programming languages or to become familiar with machine learning or data visualization, can help set your candidacy apart from others. 

At Ironhack, we’re committed to providing you with exactly what you need to land a dream job after graduation with courses in web development, UX/UI design, data analytics, and cybersecurity, so no matter if you’re beginning your tech career or looking to add a new skill to your resume, our bootcamps are a perfect fit. We can’t wait to see you in class! 

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