Big Data, Big Jobs: Get Hired in Data Analytics & Data Science


When it comes to unlocking the potential of big data, data analysts and data scientists are the true keyholders. They play a crucial role in transforming complex datasets into valuable insights which businesses can use to grow in ways they may not have otherwise imagined. With the demand for skilled professionals in data analytics and data science skyrocketing, acquiring expertise in these fields has become vital for the workforce of today and tomorrow. We delve into the world of big data, offering insights on how to land exciting careers in data analytics and data science.


Big Data, Big Deal

Phone sensors, CCTV cameras, Starlink satellites, FitBits, Apple Watches, web browsers – all of these things we interact with every day are constantly collecting our data. More than 2 quintillion – that’s 2,000,000,000,000,000 – bytes of data are created every day.

The question remains: how can that data be used in a way that maximizes productivity and revenue?

The term “Big Data” refers to exceptionally large datasets – like those mentioned above – that grow exponentially over time. This includes information about the production of goods and services, customer feedback, and consumer behavior. Businesses typically leverage this data to improve operations, provide better customer service, and, ultimately, increase their revenue and profits.

Big data operates on the premise that, the more information you have about something or a situation, the more accurate predictions you can make about the future. This is hugely important for any tech field, from software engineering all the way to digital marketing. By understanding consumer behavior, we can predict what consumers will want and strategically position products and services to meet their needs even before they are aware of them.


How Data Analysts and Data Scientists Make Sense of Big Data

Two distinct fields are the key to unlocking the potential of big data. They include data analytics and data science.

A data analyst focuses on analyzing past data to identify trends and build visualizations to help companies make better, more strategic decisions. A data scientist, on the other hand, uses algorithms and predictive models to design and build new processes for modeling new data.

Both are essential, but serve slightly different roles. An analyst identifies trends in old data and presents them, while a scientist thinks of ways to improve their data capture abilities in order to answer questions we haven’t thought to ask yet.

In practice, the most notable distinction between the two lies in the level of coding involved. As a data analyst, your primary focus will be extracting insights from data, whereas as a data scientist, your role will involve building algorithms and predictive models that require a lot of coding to get off the ground.

These two fields of study are becoming increasingly more important, as data continues to grow, creating the opportunity for careers that are not only fruitful, but very much here to stay.


How is Big Data Used in Business?

An analyst looks at code | Big data jobs - ALX Global
Photo by REUTERS/Jim Urquhart via WEF

Data analysts and data scientists are tasked with translating huge datasets into actionable insights that yield positive results for business. Today, most leading companies increasingly rely on data analysis to find out information about their customers in order to increase their company’s efficiency and improve their project management flows.

A data-driven company is 23 times more likely to acquire customers than a business that isn’t data-focused. This is largely because data-driven companies closely monitor their audiences and can quickly respond to their needs, changes in trends, or market demands.

In data-driven companies, data scientists set up the frameworks used to collect user data, while data analysts parse through the collected data and draw insights. The two fields rely on each other in this way to help companies remain relevant and competitive.

For example, let’s say your company makes blankets. On your company’s website, a data scientist sets up a framework for collecting user data based on where they click on the website. The data analyst will then take a look at that data and report on their findings. Let’s say that the blue blanket gets a lot of clicks, while the green one gets very few. The analyst might suggest increasing the stock of the blue blanket, or pushing out a more robust marketing campaign to sell the green one.


The Benefits of Big Data

By empowering data analysts and data scientists, businesses benefit in a variety of ways from leveraging big data. These include:

  • Improved efficiency and productivity
  • Faster, more effective decision-making
  • Better financial performance
  • Competitive advantage
  • Improved customer experiences
  • Improved customer acquisition and retention
  • Identification and creation of new revenue streams

When you become a data analyst or data scientist, you become a key feature in a company’s roster, holding the key to a lot of potential success.


Big Data Jobs

As big data continues to grow bigger, so too does the big data job market. More and more companies are realizing the potential value of data analytics and data science and the demand for the crucial insights these data masters can provide is growing. 

By 2027, the worldwide big data & analytics industry is expected to reach $146.71 billion in market value. This is projected to create an estimated 11.5 million new jobs in data analytics and data science by 2026. 

In order to capitalize on this shift, it is important to learn the skills necessary to dive headfirst into either data analytics or data science. That’s where ALX Global comes in, providing future-first online learning courses for aspiring data analysts or data scientists. Our courses are thoughtfully designed and really updated to keep up with the constantly-changing world of tech.

Let’s delve into the distinct aspects of each career and discover the unique contributions they make in the world of data-driven decision-making.


Careers in Data Analytics: Telling the Story of Data

As explained before, data analysts are the storytellers of data. They find patterns in existing datasets, document trends, and summarize the most interesting insights for businesses to use as they please. These outcomes can help a company make informed decisions to increase their profits and reduce financial losses.

A key role of data analysts is to help companies better understand and target their audience, come up with new innovations for their products, and cut costs all around. They are problem-solvers with a lot of tricks up their sleeves.

As a data analyst, you will learn how to do statistical analysis, database management, and data modeling in order to get the most out of the data being collected. You’ll also learn how to use R, SAS, and SQL, which are important programs used for statistical computing, graphics, and data modeling.

Data analysts make an average of about $80,000/year working for companies like Amazon or Google. Over the next 10 years, the demand for data analysts is projected to grow by about 22%, which is faster than average job growth rate.


Careers in Data Science: Figuring Out How to Manipulate Data

Unlike data analysts who work with existing data, data scientists are the pioneers of data exploration. Using their knowledge, they build algorithms that set the parameters for data collection and organization. Through experiments that they design, they can help businesses gain valuable insights to help them achieve and maintain sustainable growth.

The main goal of a data scientist is to ask questions in order to locate potential avenues of study. They take analysis one step further and use data to develop new processes for data modeling and production, using tools like algorithms and machine learning to achieve those goals.

A data scientist will typically be well-versed in machine learning, software development, and object-oriented programming. They will be able to use programs like Hadoop and be able to code in Java or Python.

Data scientists make an average of around $120,000/year, working for companies like IBM, Microsoft, and Meta, to name a few. The demand for data scientists will continue to remain high over the next 10 years in various industries such as banking, finance, insurance, and tech.



A young woman with glasses working on her laptop | Big Data Jobs - ALX Global
Photo by Bruce Mars via Freerange Stock

Big data is only getting bigger. This means that jobs in data science and data analytics aren’t going anywhere anytime soon. As organizations grow their data collection scope and sophistication, they will inevitably need scientists to help build the infrastructure and analysts to help them make sense of the data. To seize the numerous opportunities in this rapidly expanding field, it is crucial to start learning the skills necessary to tap into this market, land a secure job, and increase your salary.

At ALX Global, we offer online courses in Data Analytics and Data Science that are designed for the future. You can harness the global tech transformation with AI training and career-ready skills by enrolling in one of our courses.

Sign up today to join our August cohort and learn the skills you need to use big data to advance your career into the future.



What is big data?

Big data refers to really large and complex datasets that come from new data sources. It is called “big data” because the datasets are so big that traditional data processing software can’t manage them. By unlocking the hidden insights in this data, businesses can increase their efficiency and revenue.

How do I start my career in data analytics or data science?

The first step to starting a career in data analytics or data science is to study! There are online courses, like the ones offered at ALX Global, that can help you get started in these ever-important fields.

By studying online, you can learn on your own time and enhance your skillset to prepare yourself for the future.

What is the difference between data analytics and data science?

In simple terms, data scientists build algorithms that help model data, while data analysts examine datasets to identify trends. Both help businesses make strategic decisions using collected evidence.