Data scientist
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Data science involves collecting, organizing, and analyzing large amounts of data to discover patterns and draw conclusions. It has become an in-demand career field due to growth of data from technology and the internet. While data science uses tools like statistics and algorithms to interpret data, it also requires skills in computer science, math, communication, and creativity to extract meaningful insights and apply them effectively. However, data can also be presented in a misleading way if not handled responsibly.
Data scientist
- 1. DATA SCIENCE MORE THAN MINING “The sexiest job in the next 10 years will be statisticians.” — Hal Varian, Chief economist, Google While the concept of data science has been around for decades, the notion of a data scientist has become a sought-after and in-demand career leading to a rise of a new generation of data scientists. The phenomenon in technology development significantly exposes the staggering growth rates of “big data.” Technology innovation and the World Wide Web provide for the growth of new types of data — such as user-generated content — and tools that can be used to interpret it. Social media platforms such as Facebook (the largest social network and valued at $52 billion) depend on data science to create innovative, interactive features that encourage users to get interested and stay that way — all so that we know it's important. But what does the term ‘Data Science’ really mean? What is data science? Data science can be broken down into four essential parts. Mining data Statistics Collecting and formatting Information analysis the information Interpret Leverage A B C ? Representation or visualization in Implications of the data, the form of presentations, application of the data, interaction infographics, graphs or charts using the data and predictions formed from studying it Defining a data scientist A good data scientist understands the importance of: Scouring Organization Their eyes search for Their voice asks questions information on the web about what they hope to Vectorized operations accomplish at the end of the project, setting Algorithmic strategizing information goals. APIs Extraction Expansion & Takes information they want and Application organizing it using formulas. They organize the information in order to The appropriate data flows form educated, insightful conclusions out of the person in the form using statistical and these of keywords, Facebook “Likes” mathematical methods: and other statistics. Factor Analysis Regression Analysis Correlation Time Series Analysis Creating new theories and predictions based upon the data Ask questions to further expound pile-up and missed opportunities. upon the data beyond the reaches of For example, statistics regarding hard numbers or facts. holiday shopping trends are Apply the information in a useful, imperative around the holiday innovative manner to applications season. If the statistics are whose success depends on data processed and the conclusions are science. drawn too late, the season has passed and the information can no Immediately process terabytes of longer be utilized to its full potential. data that flow in to prevent Required skills for a data scientist A successful data scientist must have a combination of skills that opens up possibilities both for that individual and their team. Visualization processes are often disjointed since each person is typically assigned to a specific part of the project. The designer depends on the information architect. The information architect depends on stats from the statistician, and so on. A true data scientist should be skilled in multiple areas. Expertise in Hacking and Mathematics, Computer Statistics, Creativity Science Data Mining & Insight % Knowing how to take Pulling important Knowing what advantage of statistics and statistics are computers and the coherently organizing important and how internet to create them using to leverage them data-mining formulas mathematic prowess and computer formulas Dangers of data science Statistics can be displayed in a misleading manner Leading the pollee: What type of question are you more likely to answer “yes” to? 85% 70% No Yes Should Americans be taxed Should taxes support the so others can take advantage government’s aid to those of welfare and avoid working? who are unable to find work? Facts that are left out Including only the starting and ending points of data makes the change seem more drastic. A collage of carefully 9 of 10 selected information combined to induce a certain opinion Selection bias occurs when an unrepresentative population has been taken for a survey or study and then the results are advertised to the public consumers as if it represented the total population. An example is a toothpaste brand that shows the user how ‘studies’ can often be weighted in a company's favor. Ironically, facts and stats can be used to paint a very inaccurate — and damaging — picture of a business, organization or general topic. Facts about data science 1790 The first big data collection project in history was by the U.S. Census, which started in 1790. 5MB When hard drives were first invented, a 5 megabyte server took up roughly the space of a luxury refrigerator. Today, a 32 gigabyte micro-SD card measures around 5/8 x 3/8 inch and weighs about 0.5 grams. 32GB When collecting mass quantities of data, some human remedial input is needed, this gave birth to crowd sourcing, The best example is Amazon's mechanical turk. Modern collecting of big data is possible with cloud computing, or the spreading of the data across several physical resources that can be accessed remotely, rather than concentrated at one location. “The computing and processing of data is literally 100 to 1,000 times faster and cheaper than before.” — Scott Yara, Greenplum