For anyone who is actively involved in the tech world, the term “Data Science” shouldsurely sound quite familiar. Data Science technology has positively impacted various aspects of our day to day life in many ways, such as finding the most affordable gifts or items while shopping online or while booking a movie ticket by comparing prices and reviews across different websites and apps – just to name a few.
As more and more data is being generated every day, it is creating new challenges as well as opportunities for tech enthusiasts.
Do you know how much data is generated every day on this planet?
Before getting to that, let us first check the number of active Internet users we have in this world. As per the report provided by Internet live stats, in 2014, around 2.4 billion people accessed the Internet and that number kept growing continuously. In the year 2016, we had around 3.4 billion users. Reports suggest that as of 2019, the number of active Internet users has reached 4.4 billion.
According to a report, from the last 5 years, on average, 640,000 people per day went online for the first time. This was about 27,000 every hour. Incredible, isn’t it? With all these stats, we can understand why so much data is getting generated every day.
As per the available data from weforum.org, around 3.5 billion searches are made on Google alone and about 281 billion emails are being sent and received each day. Nearly 5787 tweets are made every second, which adds up to 500 million tweets per day. In YouTube, nearly 5 billion videos are watched every day and up to 300 hours of videos are uploaded every minute. An estimate says that around 2.5 quintillion bytes of data is being generated every day.
Now we hope that you got the answer to the above question. These are quite big numbers, aren’t they?
All this data is a goldmine of information and intelligence hidden within it. The challenge, however, is how to meaningfully make sense of the data at this scale and volume. This is where Data Science has its role to play. Data Science can help us to make sense of the data at this scale and make smarter predictions/decisions with the kind of precision which was never heard of earlier. As the saying goes:
Data is the new oil for all the industries and data science is the electricity that powers the industry!
This makes Data Science a very important field, which places its professionals in high demand. Harvard Business Review has even called it “The Sexiest Job of the 21st Century”.
Now let us define Data Science:
Data Science is a multi-disciplinary field that applies scientific methodologies to extract meaningful information from billions and trillions of bytes of structured and unstructured data. It employs concepts and methods drawn from the fields of mathematics, statistics, computer science and information science.
Data Science has become a buzzword, it is interchangeably used with Data Mining and Big Data.
In simple terms, Data Science can be understood as the study of data. It involves developing various methods of recording, storing and analyzing data to effectively extract useful information.
Evolution of Data Science
Data Science emerged with the use of statistics: an idea to use data better and to find better solutions for problems.
In earlier days, say in the 1800’s, simple statistical methods were employed to collect, analyze and manage the data. These statistical models were used to recognize patterns, model and visualize uncertainty and predict the future of data.
Later, with the emergence of industries across different segments, these statistical methods eventually evolved into Operations Research: a discipline that applies scientific methods and modern technologies to organize data efficiently, enabling larger enterprises to make better decisions for their business enhancement.
With further advancements in technologies, different algorithms and techniques, such as Data Analysis, started to evolve, and in a way led to the growth of Data Science.
In the 1970’s, the world gradually entered into the Digital Age. During this period, IBM introduced personal computers to the mainstream public, which led to the advancement of the digital world, resulting in the availability of a large amount of data from different sources. Thus, new technologies increased in order to collect such data, store it systematically and manipulate it properly. As a result of these developments, statistics become computerized, leading to the emergence of new concepts such as Data Mining and Data Analytics.
In the later years, several mathematicians and Data Scientists published several journals and research papers on Data Science, in a way contributing to the growth of this wonderful field of technology.
We have tracked a few notable works in below discussion.
In the year 1974, Peter published Concise Survey of Computer Methods, where he used the term Data Science in its survey of contemporary data processing methods: a data processing method used in most of the applications.
In the year 1977, The International Association for Statistical Computing (IASC) was established as a section of ISI with a mission to apply modern technologies to convert data into information and knowledge.
In September 1994, Business Week published a cover story on “Database Marketing” in which it discussed how companies were collecting information about their consumers and analyzing what kind of product they buy, which helped them to enhance their business growth.
In the year 2008, the term was Data Science was popularized by D. J. Patil and Jeff Hammerbacher, the pioneer leads of data and analytics efforts at LinkedIn and Facebook.
As the Internet started becoming the main source of gathering data, it also emphasized the importance of Data Science. Today, Data Science has evolved into an interdisciplinary field that can be used to analyze the large amount of data and extract valuable insights from it and help businesses across many industries such as manufacturing, banking and finance, and retail, etc., to leverage their business.
This was a brief history of the evolution of Data Science. You can also read an article, “A Brief History of Data Science” to find some more information about Data Science history.
Working with Data Science:
Now that we know how this Data Science technology slowly evolved and became a mainstream technology, let us look into some common steps involved in most of the Data Science projects. As the steps vary with each project, we discuss some of them that are most commonly executed in every project. These steps are as follows:
- Understanding the problem
- Getting the relevant Data
- Data Cleaning
- Data Visualization
- Data Selection
Now let us understand each of these steps individually.
- Understanding the problem:
The first phase of any Data Science project is to understand the actual problem that you need to solve. Once you know what the issue is and what type of solution you need to provide, only then you can search the relevant data for it. So, understanding the problem is very important.
In order to understand the business problems, you need to work with your customer and stakeholders. Prepare a list of questions that define business goals which can be solved using Data Science.
- Getting the relevant data:
After understanding the problem, the next step would be to start gathering the required Data much needed to able to find a solution for the problem. You can get this Data from different sources such as emails, applications, databases, servers, and many other services. Finding the right Data takes both time and effort.
- Data Cleaning:
Data Cleaning is a very important step. As per Data Scientists, they spend much of their time during the project in executing this step alone. This step involves cleaning and pre-processing the entire Data before feeding it to model. In some big projects, massive amount of Data has been obtained, which often involve terabytes of Data to work with.
Different types of Data requires different types of cleaning. Some steps involved in cleaning data are removing unwanted observations i.e., duplicate and irrelevant observation, correct structural errors that arise during measurements, data transfer, and then handle missing data, etc.
- Data Visualization:
Once the Data is cleaned and pre-processed, then it is important to visualize the data to find out the right features or columns to use for our model. Here you need to explore the Data and identify different types of Data like numerical data, categorical data, etc. Here, you can take help of simple charts such as bar charts or line charts that help you to understand the importance of Data.
This can be through the help of Data Visualization tools such as Tableau, Infogram, ChartBlocks. These tools help you to visualize all your data in very less time. They provide a pictorial representation of your Data that can viewers to easily understand the Data.
- Model Selection:
This phase involves selecting the right model. Here, you will develop data sets for training and testing purpose. You need to check if the existing tools are enough to meet the requirements or you need some other better tools. This is necessary as every model cannot fit in perfectly for every data set.
Finally, now you need to evaluate if you were able to achieve your goal as per the plan. It is important to communicate the findings, in simple terms to the businessman, and to the stakeholders, as they are not expected to understand the technical know-how of Data Science.
The above discussed are a few common steps that every Data Scientist follows while working on Data Science projects. There are many additional helpful resources readers can access to understand about how Data Science projects work.
Real world applications of Data Science
Now that you have learned what is Data Science, but still you might have questions as how it is able to become the mainstream technology of the present IT industry.
To help you to find answer to this question, we present here some real world applications that are provided by Data Science. We will discuss few scenarios as how Data Science is able to bring in the change.
Reading the following scenarios will help you understand the importance of learning Data Science:
- Data Science helped Netflix to double its income
Netflix is one of the largest Internet-television networks in the world is a known fact for most of us. But have you ever thought about how it grabs new customers to its business and how it remains one of the most profit-making companies in the world? Yes, it is all happening because of Data Science. Founded in 1997 as a mail-order DVD company, now it has more than 53 million members in around 50 countries.
If you ever watch a movie on the Netflix platform, then it is more likely that it shows the list of other movies that you wish to watch later. It is possible because of the recommendation feature provided by Data Science.
The recommendation feature provides users with various contents, based on their preferences and liking. It uses various Machine Learning algorithms to analyze the user’s data and then manipulate it to extract some useful information from it.
The company’s most-watched shows are generated from recommendations it provides to its viewers using insights provided to it from Data Science.
So, Netflix is constantly working on recommendation engines.
- Data Science for Education
Data Science is playing a very important role in making education better for society. Data Science allows instructors to collect feedback from their students and analyze that data and use it to improve their teaching methodologies.
Data Science helps the teachers to understand their student requirements. Teachers can analyze the meaningful insights provided by Data Science about students and understand their requirements and make necessary changes.
Nowadays, it has become a challenge for the universities to keep themselves updated with the latest industry demands in order to provide appropriate courses for their students. To overcome this challenge, universities are using Data Science systems to analyze the latest trends in the market. Using various statistical measures and monitoring techniques, Data Science can determine the present trends in the industries and help the universities to come up with new courses that can benefit their students.
- Data Science has helped e-commerce platform such as Flipkart.
Flipkart is one of the leading e-commerce platforms of India. It has registered a customer base of over 100 million. Flipkart has around 80 million products across 80+ categories. It has over 100,000 registered sellers and revolutionized the way many brands and the MSME’s do business online.
Data Science has played a very important role in the success of Flipkart business. Some of the notable features such as recommendation system and product quality have helped the Flipkart to stay ahead in the market.
Recommendation system: The recommendation system has been a very important factor in increasing the business of the Flipkart. This feature provides various recommendations to the customers by studying their purchase history.
For example, when a customer visits Flipkart website and searches for various products in catalogues page, the search patterns are recorded and analyzed systematically, so that whenever the customer returns to the website, recommendations will be shown to them about the relevant products in a way that helps them to purchase items easily.
Product quality: Customers doesn’t purchase just by looking at the recommendations provided to them. They often look for a high-quality product. Hence Data Science makes sure that that the website shows the high-quality products at the top of its recommended list.
Apart from the above scenarios, we would like to share few applications provided by Data Science here. They are as follows:
1) Internet Search:
Do you wonder how your search engine provides you all the useful results for every query you make on it? Do you know how it shows all the related recommendations along with the results it provides? If your is guess is ‘Data Science’ responsible for all this, then you are absolutely right.
All the search engines such as Google, Yahoo, Bing, etc. are now utilizing the Data Science algorithms to provide the best outcome for the query you make. So, Internet Search is considered as one of the major application of Data Science.
2. Targeted Advertising:
Data Science algorithms are also used for finding the right customers to one’s business through digital advertising. Though Internet searching has been a major application of Data Science, along with that its capability to provide a CTR through digital advertising is also gaining momentum. Data Science helps the business to post their adds on relevant websites so that they can reach to more number of targeted customer in less time.
Overall, by looking at these above scenarios, we can say that Data Science has not just allowed the companies to increase their business, but has also made it possible for them to manage and increase their performance.
Let us look into some companies that are providing Data Science as a service (DSaaS)
Every time you shop online and click a company website, you are unknowingly generating data. This results in the rise of Internet business because of the increase in the collection of digital information. This is why the Big Data is introduced. It can reveal the customer’s behavior, show the trends, and in a way provide some decision making insights. But combining all such data is not an easy task to do. That’s where Data Science comes into the picture.
Data Science comprises of highly knowledgeable experts, capable of using various data analytics tools and scientific methodologies to extract meaningful insights from the data that helps the companies to make various business decisions.
These Data Science professionals are in huge demand. An article by KDnuggets says many of the job portal websites show an increase in the demand for such professionals. Looking at this shortage of demand and supply, many prominent companies such as Numerator, Musigma, Cloudera, Mixpanel, and Sisense are offering Data Science as a Service to their clients.
These companies are providing Data Science services across various industries such as e-commerce, retail, telecom, manufacturing, healthcare and many more.
Future potential of Data Science:
Opportunities in Data Science are boundless. The work that is being done manually takes a lot of time and resources, so many big companies are looking at ways of implementing a new technology such as Data Science that can automate these tasks and save money.
“When we have all data online it will be great for humanity. It is prerequisite to solving many problems that humankind faces” says Robert Cailliau, a Belgium informatics engineer and computer scientist, who, together with Tim Berners-Lee, developed the World Wide Web (WWW).
With its new features, Data Science has revolutionized many sectors and has resulted in increase in a huge number of job openings for the professionals working in this field.
Overall, what we can say is that for anyone willing to become a Data Science professional, it is right time to learn Data Science skills through online platform such as Simpliv and build their career in this wonderful technology.
In the end, Data Science has definitely became an existing field and has the immense potential to revolutionize the way the businesses make their decisions and we strongly believe that speaking the language of Data Science is a necessary skill in today’s workplace, irrespective of the what field you belong to.
Fueled by AI and Big Data, data science skills are in huge demand. However, supply of quality professionals who are equipped with all required skills is slower compared to the demand. Hence, there cannot be a better time to jump in and start a career in this field. Let us know if you are planning to build your skills set and stay competitive.
Now, if you are looking at where you can learn this technology in detail, then we recommend that you quickly sign up for a Data Science course which contains all the valuable insights regarding Data Science.
If you think this blog has enabled you to understand Data Science technology and a few related concepts, then we request you share it among your circles, so that it can reach to someone who wants to learn this technology.
We will soon come up with a new blog here discussing about the various job opportunities and career prospects attached to this technology.