We live in a data-driven world – every digital action we make translates into data of some sort.
Get this – by 2020, the world had generated around 64.2 zettabytes of data. To put this statistic into perspective, the amount of data that humankind generated right from the beginning of human history until about the mid-2000s was estimated to be around 5 exabytes of data. Do you know how many exabytes make up one zettabyte? One thousand. One exabyte is made up of a thousand petabytes and one petabyte is made up of a thousand terabytes and … it keeps stacking up. And that’s not the end of it.
We have been generating so much data that it is now estimated that we will hit a staggering 180 zettabytes by the end of 2025. But what do we do with all this data? How can we make use of it?
The answer is data science. What exactly does it do?
Here’s a look
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In 2013, the world had about 2 billion active Internet users. That number grew to 4.6 billion in 2021. This phenomenal growth in the number of users has been attributed to that one device we just cannot do without – smartphones. This simply means more data. Most of the data that we create is in the form of streaming, text messages, pictures, and calls. But not all the data that we create is stored. It has been identified that only two percent of the data that we created in 2020 was saved and carried over to 2021.
Let’s take a look at some of the interesting statistics related to data.
- One of the most impressive Google feats is that it processes around 3.5 billion queries on average per day in 2021. (While this article was being written, Google had already processed 7.5 billion queries a day)
- 5 billion active users on the Internet at the moment.
- Facebook has around 2.9 billion monthly active users.
- E-commerce websites saw over 22 billion visits in June 2021
- Users stream around 3.25 billion hours’ worth of content on YouTube a month
- 85 million photos are shared by different users every single day
Now that we have clearly established that there is a lot of data being generated, it’s time to understand where data science fits into.
Data science is the field that makes use of different scientific tools in order to form meaningful insights from raw data. An interdisciplinary field, data science brings together the best of computer science, mathematics, and statistics. Data science not only creates insights that help in analyzing information, but it also takes care of preparing the data or analysis, cleaning the data, processing it, and then producing readable patterns to help the various stakeholders to understand what it all means. Data science technically has five steps in its life cycle
- Data acquisition
- Data cleaning
- Data preprocessing
- Data analysis
- Prediction (producing results)
A data scientist is a person who has expertise in the field of data science. They are well-rounded individuals who are well-versed in mathematics, statistics, and computer programming so they can make use of these skills to develop software and algorithms to help an organization in developing predictive models that aid them in their services. A data scientist’s job is regarded as the “sexiest job of the 21st century”. Artificial intelligence and data science have taken over the world and have been radicalizing technological growth in the world. Areas like computer vision, machine learning, deep learning, and natural language processing are quickly becoming fields that need a lot more experts and have seen a lot of requirements opening up.
The demand for data science is huge. But why is there a demand? In order to understand this, we need to look at the different applications.
Data science has come to be used in almost every other application:
- Auto-fill – The most overlooked application of data science is “autocorrect”. Every individual’s way of speaking or communicating is different. The use of words is unique to every Internet user. Data science keeps a tab on the way individuals communicate and try to predict what a user will say next in a conversation.
- Medical – Data science can be implemented in classifying various diseases. It can also help in producing diagnoses that can act preventively, that is before an illness becomes serious. Many robots have already been deployed for performing surgeries and to act as assistants to surgeons that perform surgical procedures. The underlying framework of such robots is based on data science and it provides the necessary edge that these machines need in order to act intelligently and efficiently. It is also helpful in drug discovery, drug manufacturing, predictive models, and image processing in the medical sector.
- Chatbots – It has become increasingly difficult to differentiate between a chatbot and a human. Such is the development in the field and it is the effect of the rise of data science. Data science is used in conjunction with machine learning to produce chatbots that can generate human-like responses.
- Robotics – The advancements of robotics can be attributed to the rise of artificial intelligence and data science. In order to analyze data and produce the best results which can be turned into actions in real-time to achieve human-level performance, data science is important. Not only does this play an important role in the way robots interact with the environment, but it also plays an undeniable role in the manufacturing, development, and deployment of robots.
- Crime detection and prevention– IBM has developed a tool for police departments to help them fight crime. Through extensive analysis and predictions, this tool helps the police officers to pour their efforts into areas that require most of their attention.
Some of the other important applications of data science are in the fields of finance, banking, robot process automation, and an application that helps to provide loans, among others.
Artificial intelligence and data science are closely related fields. In 2020, the demand for AI specialists rose by around 74% in the US. The US Bureau of Labour Statistics has shared that the number of jobs in the field of data science is set to see a rise of around 28% by 2026. Do you want to pursue a career in Data Science? Some of the best roles in the field of data science are listed here.
- Data Scientist
- Data Engineer
- Machine Learning Engineer
- Data Analyst
- Applications Architect
- Data Architect
- Marketing Analyst
- Business Intelligence Developer
- Statistician
In order to land any of these roles, one needs to possess the necessary skills, educational qualifications, and the right amount of experience.
Data science requires those interested in working in the field to know various top courses in data science including programming languages and tools,
- R
- Apache Spark
- Tableau
- GitHub
- SQL
- MATLAB
- Hadoop
- Python
Other skills required to become a data scientist are good communication skills, the ability to work with teams, a thirst for learning new technologies and keeping oneself updated, the ability to read data precisely, and the mindset to ask the right kind of questions.
Of course, there are thousands of data science courses in the world that cater specifically to students looking to study data science. Some of the best courses from the top global universities are listed below.
- Carnegie Mellon University offers a graduate program – Masters of Computational Data Science, for those looking to improve their data science skills and get into the field of statistics and data analysis. They also offer an MS in Data Analytics for Science. The University also has a Department of Statistics and Data Science that offers undergraduate courses such as B.S Statistics, B.S Statistics and Machine Learning, and B.S in Economics and Statistics among others.
- Stanford University’s Department of Statistics Data Science offers an M.S in Statistics: Data Science. The Department of Statistics also offers a
- Massachusetts Institute of Technology offers interdisciplinary programs including a Bachelor’s program in Computer Science, Economics, and Data Science for undergraduates. MIT also offers minors and micromasters in Statistics and Data Science.
- University of California, Berkeley offers various courses for those interested in taking up data science. They also offer majors and minors in data science.
- New York University offers a Master’s program in Data Science. They also offer interdisciplinary majors and minors in Data Science.
- The Graduate School of Arts and Sciences at Harvard University offers a Master’s degree in Data Science. It is an interdisciplinary program led by the Computer Science and Statistics faculties. Harvard also offers an undergraduate program in statistics which can provide the required base for a data scientist.
This is by no means, an exhaustive list. There are hundreds of universities and institutions across the world that offer the best courses in data science along with thousands of online courses to choose from, depending on your requirements.
The world is moving towards a future where all of our operations are enclosed within a single word – data. In the coming decades, the applications that data science can cater to are exciting.
Improving technology, large-scale data creation, and the need for analysing markets before making decisions have made it clear that the use of data science is only going to increase. Self-driving cars, an example of how powerful data science has come to be, have already become popular thanks to various companies that are focusing on rolling out autonomous vehicles as quickly as possible.
The amount of data that can be generated in the next few years is unfathomable and such a huge amount of data can only be processed using the help of something as powerful as data science. Due to the technological advancements of today, there will not be a dearth of data but only a surplus. All of this means one thing – data is knowledge. To manage all of this and to know how to navigate data, the world will need a lot of data scientists. The future truly belongs to those who can manipulate and understand how data works.
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