![\"\"](\"https:\/\/www.ebool.com\/hub\/wp-content\/uploads\/2019\/11\/Data-Science.jpg\")
<\/figure><\/div>\n\n\n\nSimilarly, in Data Science, first, one must\nprocess the raw data into usable data by using processes like data cleansing,\ndata munging, ETL (extract, transform and load), etc. Then the data is sorted\ninto various categories to be processed in appropriate ways. Following this,\nthe data is worked upon by algorithms, and various sources and features are\nadded to help the turn the data in usable results. Finally, these results are\npresented in the form of easy to understand graphics and charts to be utilized\nfor various purposes.<\/p>\n\n\n\n
Now that we understand the basis of what\nData Science is let’s take a deeper dive into various facts and statistics\nassociated with it.<\/p>\n\n\n\n
Basic Facts about Data Science <\/h3>\n\n\n\n
Data science is a vast field and can seem daunting to get into for uninitiated. So first, let’s start by learning some basic facts about this industry. <\/p>\n\n\n\n
1. The term Data Science is a lie: <\/strong>This is to say data science isn\u2019t classified as a scientific field\nof study. Data scientists don\u2019t work in academia, and their work doesn\u2019t revolve\naround research and publishing papers. \nThe term was coined in 1974 by Peter Naur, in his book Concise Surveys\nof Computer Methods. However, the term\u2019s current definition originated in 1996,\nduring the second Japanese-French Statistics Symposium, to refer to the methods\nand the people who utilize them to analyze various data.<\/p>\n\n\n\n (Source: Perceptual Edge)<\/p>\n\n\n\n 2. Users generate nearly 900 exabytes of\nraw data: <\/strong>Raw data refers to all the data that is\ncollected from a certain source using a basic set of parameters. For example,\nthe user data for everyone that bought a Toyota Car. Users are responsible for\ngenerating 900 exabytes (1 exabyte is equivalent to 1 billion gigabytes!) of\ndata, of which various enterprises store 80%.<\/p>\n\n\n\n (Source: Towards Data Science)<\/p>\n\n\n\n 3. Only 0.5% of the data we create is\never processed: <\/strong>The human race is constantly\ncreating gigantic amounts of data. Our lives have become increasingly digital,\nand that gives rise to a massive amount of data on a moment to moment basis.\nHowever, only a minuscule amount of this data is processed and organized.<\/p>\n\n\n\n (Source: Analytics Training)<\/p>\n\n\n\n 4. Nearly 80% of a scientist\u2019s job is\njust cleaning!<\/strong> Data cleansing refers to the process\nof removing or correcting corrupted or unimportant data from raw data. It is\nthe first step involved in any data science application. The expected output of\nthis procedure is to produce data that is similar to other data in its set. Cleaning\ndata takes up a majority of the time in a project as having a clean set of data\nimproves the efficiency of all the further steps by entire magnitudes.<\/p>\n\n\n\n (Source: Signify)<\/p>\n\n\n\n 5. Artificial Intelligence does most of\nthe work: <\/strong>When working with large amounts of data\n(large companies often need hundreds of terabytes of data to be processed), it\nis impractical to create models and algorithms to process it all manually. So,\ndata scientists train artificial intelligence to do their jobs easier. Machine\nlearning is a way to teach a computer to react dynamically to different types\nof data and in turn, process it accordingly. Machine learning algorithms take\nmuch longer to create than simple algorithms, but they are very effective at\ntheir jobs and do not leave any room for human error.<\/p>\n\n\n\n (Source: Machine Learning Mastery)<\/p>\n\n\n\n 6. Graphic Designers are data scientists\ntoo: <\/strong>Once the respective algorithms have generated the\nrequired results, they need to be collated and organized into a form that is\neasy to present and forward to the respective destinations. Here is where graphic\ndesigners come in. These are usually data analysts with a graphic designing\nbackground, and they convert numbers and statistics into easy to understand\ncharts and graphs. These are then forwarded to the various analysts that can\nuse these results to benefit the organizations for which the scientist works.<\/p>\n\n\n\n (Source: Medium)<\/p>\n\n\n\n Now that we understand the basics of how\ndata science works and what the various terms associated with it are, let’s\ntake a brief look into just how huge the Data Science industry is.<\/p>\n\n\n\n 7. 90% of all data was created in the\npast three years: <\/strong>Due to the rapid rise of IoT and\ninterest in Big Data, the amount of data we create has massively shot up in the\npast few years, with some estimates suggesting that nearly 90% of all data was\ncreated in the recent past.<\/p>\n\n\n\n (Source: Base Line)<\/p>\n\n\n\n 8. We create nearly 2.5 quintillion\nbytes of data every day: <\/strong>Each of us now has access\nto at least two devices connected to the internet. This makes it so that we\nkeep creating more data about ourselves every single second. All this data\nresides in the digital universe until someone makes use of it.<\/p>\n\n\n\n (Source: Data Never Sleeps)<\/p>\n\n\n\n 9. The internet traffic is growing at a\nrate of more than 50,000 Gb\/s: <\/strong>According to\nestimates by IBM, internet traffic in 2018 reached 50,000 Gb\/s. Some things\nthat happen on the internet every second are: 72 hours of footage is uploaded\nto YouTube, 216,000 posts are made on Instagram, 204 million emails are sent,\nand 500,000 cat\/dog images are viewed (very ruff estimate).<\/p>\n\n\n\n (Source: IBM Big Data & Analytics Hub)<\/p>\n\n\n\n 10. It is projected to be a 40.6\nbillion-dollar industry soon: <\/strong>Data analytics is a\nMulti-Billion dollar, rapidly expanding industry, and is projected to become worth\n40.6-Billion-dollar globally by 2023, showing a combined annual growth rate of\n29.7% from 2017. In India alone, this industry is worth nearly 2.7 billion dollars\nand is expected to be worth almost 3.30 billion-dollars by 2021.<\/p>\n\n\n\n (Source: Inside Big Data)<\/p>\n\n\n\n 11. The Big Data job market is going to\ngrow by 12%: <\/strong>In a time when most major industries\nare bearing witness to large scale lay-offs, an increasing number of companies\nare looking to hire data analysts. The job market is expected to grow by at\nleast 12% by 2024, with the median pay expected to be around 110,000 $\nannually.<\/p>\n\n\n\n (Source: Forbes)<\/p>\n\n\n\n 12. Federal Investments of up to 200\nmillion dollars are being made into data science: <\/strong>In\n2012, the Obama administration invested 200 million dollars into developing the\nbig data analytics industry. This investment was directly responsible for the\nrapid rise of data science in the US and led to an increasing series of\ninvestments being made each following year, totaling more than 125 million\ndollars into various federal agencies utilizing data science.<\/p>\n\n\n\n (Source: Datafloq) <\/strong><\/p>\n\n\n\n 13. A mere 10% increase in data\naccessibility increases revenue by 65 million dollars: <\/strong>Data quality and accessibility are the two highest priorities for\nlarge companies. Higher quality data can produce far better results than\nunreliable data. These results can then be utilized to design campaigns,\nproducts, services, etc to improve the market share of the company undertaking\nthis effort. According to Forbes, for a Fortune 1000 company, increase can be\nup to 65 million dollars for just a 10% increase in quality and quantity of\ndata.<\/p>\n\n\n\n (Source: Forbes)<\/p>\n\n\n\n 14. Bad\/Corrupted data causes major\nlosses to industries: <\/strong>Given how integral data\nanalytics has become to industries, it is no surprise that bad data can cause\nhorrific losses to companies. Each year nearly 3.1 trillion dollars are lost in\nthe US alone due to bad data. This number is estimated to be almost 21 trillion\ndollars worldwide.<\/p>\n\n\n\n (Source: IBM Big Data & Analytics Hub)<\/p>\n\n\n\n Having taken a look at the current state of\nthe data science industry and understood the massive impact it has on the\nmodern world, it is fascinating to note that this industry only came into being\nin the early 2000s. Once advancements in compression and storage had been made,\nit became cheaper and cheaper to store and process data. The easy availability\nof large quantities of storage and powerful processing capabilities led to the\nrise of Big Data. Let\u2019s briefly explore the various milestones and upheavals\nthis industry went through from its inception to reach the state it is at now.<\/p>\n\n\n\n 15. A college student was responsible\nfor the onus of Data Science: <\/strong>As we have seen, data\nscience is heavily dependent on having large amounts of data with which to work.\nThe mass storage of data is only possible due to a compression algorithm that\nwas created by David Huffman in 1951 when he was a student at MIT. David\nHuffman created the Huffman Encoding scheme as a final paper for an IT course.\nThis encoding technique forms the basis of all modern compression algorithms\nand is the reason large companies can store and process millions of terabytes\nof data without much trouble.<\/p>\n\n\n\n (Source: Ethw)<\/p>\n\n\n\n 16. The first commercially viable\ncomputer became available in 1981: <\/strong>IBM created their\nIBM PC and released it to the public in 1981, marking the first-time common\nusers could generate and interact with data in a meaningful way. The home\ncomputer market was further helped along by Apple\u2019s first PC in 1983 and then\nthe meteoric rise of Windows following their first PC launch in 1985. These\nadvancements led to the public starting to generate data that could be\nfruitfully utilized.<\/p>\n\n\n\n (Source: Britannica)<\/p>\n\n\n\n 17. Data Science was recognized as a field\nin 1996: <\/strong>In 1996, the members of the International\nFederation of Classification Societies recognized and classified Data Science\nas a field of emerging importance. This classification helped Data science get\nthe attention it needed to bring itself into the eyes of the mainstream public\nand kick-start the interest in this field by various organizations.<\/p>\n\n\n\n (Source: Data Science, Classification, and Related Methods)<\/p>\n\n\n\n 18. Google used Data Science for the\nfirst time to beat its rival in 2003: <\/strong>Both\nAltaVista and Google started to be used more and more by users as the internet\nbecame a common thing. By 2000 both these search engines had started to use\nrudimentary data science methods to improve their search efficiency. Recognizing\nthe potential of data science, Google established a dedicated team to use data\nanalysis techniques for the first time in history. They designed the\nproprietary PageRank algorithm which directly led to Google becoming the most\nused search engine by 2003, the position it still holds.<\/p>\n\n\n\n (Source: WhoIsHostingThis)<\/p>\n\n\n\n 19. In 2015 there were more than five\ntimes as many devices as people on Earth: <\/strong>In this\npast decade, the amount of people owning and operating a device connected to\nthe internet has increased many folds, with the number of devices far exceeding\nthe population of the Earth. This, in turn, has enabled Data Science as an\nindustry to flourish since now there are tons of ways to get useful data about\nan individual, from their smartphones to their wearable electronics. <\/p>\n\n\n\n (Source: Cisco)<\/p>\n\n\n\n We all know that the answer to everything in the Universe is the number 42. However, arriving at this conclusion takes a lot of computing power and data. After all, it was the most powerful Artificial Intelligence ever, working with the largest data set in the universe, that came up with the number 42 in the first place. <\/p>\n\n\n\n While modern data science hasn\u2019t yet\nreached the capability to answer every question in the universe, it is slowly\napproaching that state. Let\u2019s take a look into the various ways data science is\nbeing applied by organizations all around us to answer any and all questions\nthey might have.<\/p>\n\n\n\n 20 Most banks now use data science to\napprove loans: <\/strong>One of the most widespread uses of\ndata science is in the banking and finance sector. Most major banking companies\nrely on something known as a \u201cCredit Score,\u201d which is number assigned to every\nindividual. <\/p>\n\n\n\n This score is assigned to them by an\nalgorithm that takes all the available finance information for the individual,\nfrom their banking statements to their salary, housing status, family assets, etc.\nand generates a single tangible score for them to rank them against every other\napplicant that has been given a credit score. <\/p>\n\n\n\n This score is then taken into consideration\nby banks to assign the appropriate scheme to the applicant. <\/p>\n\n\n\n (Source: Dzone)<\/p>\n\n\n\n 21. Stockbrokers are getting their jobs\ndone for them: <\/strong>As if people needed any more reason\nto dislike stockbrokers! Most modern stockbroking and trading companies rely on\nproprietary algorithms to track the stock market. These algorithms take into\naccount a vast array of parameters related to the company\u2019s performance. After\ncollating these parameters, the algorithm is able to predict market growth\naccurately. This information then put to use by the stockbrokers.<\/p>\n\n\n\n (Source: Imarticus)<\/p>\n\n\n\n 22. Machine learning algorithms are a\nvery powerful weapon in the fight against Cancer: <\/strong>Until\nrecently, doctors had to rely on visual analysis of MRI and CT scans of\npatients to identify the presence of cancerous cells. While medical\nprofessionals are among the most skilled and careful people, human error is\nunavoidable. Hence to prevent misdiagnosis, extensive research has yielded many\nresources to help doctors process the scans through a machine learning\nalgorithm which can accurately diagnose the patients, removing human error\nentirely.<\/p>\n\n\n\n (Source: Nature)<\/p>\n\n\n\n 23. Machines monitor your every move to\nbring you the product\/service you desire: <\/strong>Ever\nwonder how Amazon or Flipkart know exactly what you want to buy and when? Has\nit happened to you that you started getting ads for flour or sugar right when\nyou ran out of them? Big corporations achieve such marketing by using data\nscience to track your purchases and searches online and bring you products and\nservices targeted exclusively at you. <\/p>\n\n\n\n (Source: Cognetik)<\/p>\n\n\n\n 24. Tagging people on Facebook has never\nbeen easier!: <\/strong>As soon as you upload a photo to\nFacebook, you instantly get an option to tag all your friends in the photo.\nThis is due to a facial recognition algorithm working behind the scenes. This\ntype of algorithm is a subset of a type of algorithm known as image processing.\nThese algorithms are designed to extract usable information from an image and\nto use it for a particular purpose. In this case, Facebook extracts information\nabout a person\u2019s face from their images and uses it to suggest people to tag.<\/p>\n\n\n\n (Source: Data-Flair)<\/p>\n\n\n\n 25. Blame Artificial Intelligence the\nnext time you can\u2019t find an appropriate flight: <\/strong>Aviation\nas an industry is struggling heavily, with most major companies operating at a\nloss. To help them recoup some of these losses more and more of them are\nturning to data science to help them make management decisions. They use\nmachine learning to predict delays, survey users to figure out what services to\noffer, build loyalty programs, figure out connections between flights, etc.<\/p>\n\n\n\n (Source: Analytics India Magazine)<\/p>\n\n\n\n 26. Your game\nis playing you!: <\/strong>Even the gaming industry has\nrecognized the potential of data science and is actively using it to make games\nmore appealing to its players. For example, games like Fortnight and World of\nWarcraft study their players extensively to find out their gaming habits and\nhow much they are expected to play and for what reason. Using this information,\nthey are able to make their game seems more enticing to player, incentivizing\nthem to play for longer periods of time.<\/p>\n\n\n\n (Source: ActiveWizards)<\/p>\n\n\n\n 27. Are you\nnot entertained?: <\/strong>Entertainment companies,\nespecially the ones that own and operate a streaming service, use a deep neural\nnetwork to track your likes and dislikes on their service. These are then put\nto use crafting the perfect library of entertainment for you, be it Netflix\nsuggesting movies, or Spotify suggesting a song, or even Steam suggesting\ngames. All of this is done to ensure you have the best experience you can on\ntheir service and keep paying them money!<\/p>\n\n\n\n (Source: KDnuggets)<\/p>\n\n\n\n 28. Self-Driving\ncars are closer than you think, all due to data science: <\/strong>Self-driving cars have always been a staple of fiction, but they are\nquickly turning to fact. Utilizing the road data from all sources such as\ntraffic cameras, GPS systems, voluntary data collectors and more, many\ncompanies now have been able to create working prototypes of self-driving cars.\nIn particular, Tesla cars are constantly learning on the road, and improve over\ntime, adapting your particular city and driving preferences. <\/p>\n\n\n\n (Source: Forbes)<\/p>\n\n\n\n Data refers to a collection of facts and statistics collected together for analysis. Our entire lives, in one way or another, are run entirely…<\/p>\n","protected":false},"author":1,"featured_media":285,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-283","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-general"],"yoast_head":"\nThe Massive Data Science Industry.<\/h3>\n\n\n\n
A Brief History of Data Science<\/h3>\n\n\n\n
Applying Data Science to Solve Everything<\/h3>\n\n\n\n
![\"\"](\"https:\/\/www.ebool.com\/hub\/wp-content\/uploads\/2019\/11\/Data-Tech.jpg\")
<\/figure><\/div>\n\n\n\nBibliography & Data Sources:<\/h3>\n\n\n\n