Our increasingly digitized world is generating huge amounts of structured and unstructured data. Businesses of all sizes are looking for ways to harness the power of this data to make better decisions, improve their operations, and gain a competitive edge. This has created a boom in the field of data science and big data analytics.

But what exactly are these two fields, and what are the main differences between them? In the following guide, we’ll walk you through each of these fields, explain the differences, and tell you how you can get a job as a data scientist. 

What Is Data Science and Big Data Analytics?

Data Science

Data science is a relatively new field that relies on scientific methods to extract insights from large amounts of structured and unstructured data. Data scientists use a variety of methods, including machine learning and statistical analysis, to uncover patterns and trends in data.

The goal of data science is to turn data from a broad range of domains into actionable insights that can be used to improve decision-making. Data science is often used in fields such as marketing, finance, and operations.

Data science has come a long way in recent years. Thanks to advances in computing power and data storage, it is now possible to work with extremely large data sets. This has opened up new research possibilities and allowed data scientists to tackle problems that were previously considered too challenging.

However, working with big data sets also comes with its own set of challenges. First of all, it can be difficult to obtain accurate and reliable data. Second, even when you have the data, it can be hard to make sense of it all. And finally, once you've made sense of the data, you need to find a way to use it effectively.

Big Data

Big data, by definition, is high-velocity and/or high-variety information assets that require advanced processing to enable enhanced decision making, insight discovery, and process optimization.

There are a number of reasons why businesses want to harness the power of big data. Some of the main purposes include:

  • To improve decision making
  • To gain a competitive edge
  • To improve operations
  • To uncover patterns and trends in data

Big Data Analytics

Big data analytics, as the name implies, is focused on analyzing large sets of structured, semi-structured, and unstructured data to identify correlations, patterns, and other insights. Big data analytics can be used by businesses for a variety of purposes, such as customer segmentation, fraud detection, and product recommendations.

Differences Between Big Data Analytics and Data Science

Both concepts are currently hot trends in the world of business and technology. But how exactly do these differ from one other?

Big data is a term used to describe massive amounts of complex digital data that are generated every day. Data analytics is the process of extracting meaningful information and making sense of large datasets. Data science takes things a step further and into a multidisciplinary field that relies on different approaches to extract broader insights and find solutions to problems.

Big data analytics is the process of using algorithms and tools to mine large datasets for patterns and trends. Data science, on the other hand, encompasses a broader range of activities, including statistical analysis, machine learning, and data visualization.

Big data and data science are inseparable, as big data analytics is a subset of data science. Data scientists use big data analytics to help organizations make better decisions by extracting valuable information from large datasets.

Working With Data

Data science is a huge and growing field, encompassing everything from statistics and machine learning to data mining and predictive analytics. As such, there are a wide variety of tools and techniques that data scientists use. Some of the most popular methods include:

  • Statistical analysis: This involves the use of calculations to understand and draw conclusions from data.
  • Machine learning: This is a branch of artificial intelligence that deals with training computers to learn from data without being explicitly programmed.
  • Data mining: This refers to the process of pinpointing valuable information within large data sets.
  • Predictive analytics: This is a type of statistical analysis that is used to make predictions about future events, trends, and behaviors.

All of these methods can be applied to your business or organization in order to help you make better decisions, improve your operations, and gain a competitive edge.

Big data uses a variety of tools and techniques to organize, process, and analyze large volumes of data. Some of the most popular big data tools and techniques include:

  • Data mining: By extracting relevant patterns from data, this process can be used to identify trends and assess likely outcomes.
  • Text analytics: This is a process of analyzing large volumes of text data to extract useful information. It can be used to understand customer sentiment, identify topics of interest, and track the spread of information.
  • Machine learning: Machine learning is a subset of artificial intelligence that allows computers to learn from data. It can be used to automatically detect patterns, classify data, and make predictions.
  • Visualization: This is the part when the data takes on the form of graphs and charts to highlight key findings. It can be used to understand complex data sets, identify trends, and spot relationships.

A Career in Data Analytics

The demand for data scientists and data analysts is growing at an unprecedented rate. According to recent statistics, the global big data market for software and related services will be worth $103 billion by 2027. Furthermore, it’s estimated that the number of data analyst job postings has increased substantially since 2015.

If you have the skills and experience, a career in data science or big data analytics can be very rewarding. These skills are in high demand, and there are many opportunities for growth. So if you're looking for a challenging and exciting career, consider a career in data science or big data analytics.

So, how does one get into this career? Here are a few tips to help you get started:

  1. Start by taking some courses in statistics and machine learning. These will give you the foundation you need in these fields.
  2. Next, consider getting a degree or certification in one data science. This will help you stand out from the crowd when you're applying for jobs.
  3. Finally, make sure to keep up with the latest trends in these fields. The world of data is constantly changing, and you need to be prepared to change with it.

The number of jobs requiring data science skills is certain to continue growing in the coming years. So if you're interested in such a career, now is the time to get started.

Resources for Learning Data Science

There are a number of resources available to help you get started in this field. Below are some of the best ones:

  • Dataquest: This is an online platform that offers interactive courses on data science and analytics.
  • Kaggle: This is a website that hosts data science competitions. It is also a great place to find datasets for practice, engage in discussions, and take courses.
  • Udacity: Udacity offers Nanodegree programs, which are online courses taught by industry leaders. They offer a Nanodegree in data science.
  • Codecademy: This platform offers free online courses in a variety of programming languages, including Python and R.
  • edX: This is a nonprofit MOOC provider that offers online courses from a variety of universities, including Harvard and MIT.
  • Data science bootcamps: There are a number of bootcamps that offer intensive training from the concept of data science to practical use. These programs typically last for a few weeks or months and can be quite expensive.

Starting a Career in Big Data Analytics

Big data is revolutionizing the business world, and those with the skills to analyze large data sets are in high demand. If you're thinking about starting a career in big data, here are a few things you should know.

First, it's important to have strong math and analytical skills. You should be comfortable working with numbers and able to identify patterns and trends.

Second, you need to be able to use data structure and data visualization tools to create charts and graphs that will help communicate your findings.

And finally, you should be able to write clear and concise reports that explain your analysis in plain language. If you have these skills, then a career in big data analytics could be a great fit for you.

Resources for Learning Big Data Analytics

There is no shortage of resources for those interested in becoming a big data professional. Books, online courses, and even university programs are all excellent ways to gain the skills necessary for a data-focused career. However, with so many options available, it can be difficult to know where to start. 

Here are a few of the best resources for learning big data analytics:

  • Books: There are many great books on this subject, such as Data Science for Business by Foster Provost and Tom Fawcett as well as Big Data, Big Analytics by Ambiga Dhiraj, Michael Minelli, and Michele Chambers. These books provide meaningful insights into the field and can help you develop essential skills.
  • Online courses: Coursera offers a wide variety of online courses on big data analytics, including one that is taught by UC Berkeley faculty. These courses are a great way to learn the basics and get started with your career.
  • University programs: There are also a number of university programs that offer degrees in different data science categories. These programs can provide you with the skills and knowledge for data preparation and processing big data and teach you about market trends.

As you can see, there are many resources for paving the way to a career in big data analytics. These will help you acquire the right skills and training for this exciting field.