Data-Science_preview

Extended Abstract, Intetics

10001 Tamiami Tr N, Ste 114, Naples, FL 34108, United States

intetics@intetics.com

Keywords: Big Data, Data Analysis, Data Mining, Knowledge Discovery, Machine Learning

90% of all historical data was produced in the last 10 years alone – and the amount of data churned out by humans on a daily basis is astounding. Data science leverages this mass of data to drive innovation in various industries – healthcare, transport, e-commerce, and more. This White Paper will examine components of data science, how it can be applied to various industries, the current market scope, and much more. A full overview of the information presented in the White Paper is included below.

I. Data Science Introduction

Learn how data science is relevant to modern society. You will get a brief introduction to the topic, a roadmap of areas that will be addressed further on in the whitepaper, and an interview of industries that can benefit from data science. 

II. How Data Science Helps Your Business Challenges

Get insight on business challenges that can be helped by data science, including lists of high-level problems and general problems. Then, the White Paper goes a step further and breaks challenges down by industry: manufacturing, retail, healthcare, finance, art, and utilities.

After a thorough presentation of challenges, learn how data science can help each one – and get real examples.

III. Data Science Overview

Become familiar with the skills a data practitioner must have, the lifecycle of data science, and definitions of key terms that will be used throughout the White Paper. Learn about various components of data science, such as acquisition, preparation, modeling, evaluation n, deployment, and optimization.

IV. Brief History

You will be presented with a history of data science that ranges from 1962 until 2013. Learn about big names who helped transform the industry, such as John Tukey, Peter Naur, and William S. Cleveland. 

V. Total Market Volume

Examine the scope of the data science market – the past, the present, and the future. Learn about major players (Google, Microsoft, Amazon, etc.), YoY revenue growth, and growth rate by geography. The White Paper compiles figures and projections from Grandview Research, Mordor Intelligence, and Market Watch. 

VI. The Technical Side of Data Science

Discover data science’s main components – from domain expertise and data engineering to software development and machine learning. Then, dive into three machine learning algorithms used in data science: linear regression, decision trees, and K-means clustering.

VII. Main Tech Architectures, Tools, Stacks Used

Learn about various areas that must be considered when an organization builds a data science tech stack: a data warehouse, ETL tool, business intelligence, and visualization tools, ML frameworks, and a deployment stack. Get visual representations of all-in-one data science stacks.

VIII. Case Studies

Read Case Studies of real-life data study projects:: building footprint detection from aerial photos, using satellite imagery for parking detection, and using sensor data for predictive bus maintenance. The White Paper breaks each case study down by objectives, technologies, solutions, and benefits.

IX. Standards in Use

Learn about the data standards in use by the Initiative for Analytics and Data Science Standards, the National Institute of Standards and Technology, and the Data Science Association.

X. Data Science Professional Communities to Join

If you are interested in joining a professional data science community, be sure to check out this section. You will get information on four reputable communities: Data Science Association, Harvard Data Science Review, Data Science Council of America, and IBM Data Science Community.

XI. Data Science Authorities to Follow

Get inspired by leaders of the data science space: the White Paper presents authoritative people and platforms, as well as information and links for each one. 

XII. Available Certifications for Practitioners

As data science is one of the most in-demand sectors for IT jobs, available certifications are highly relevant to this topic. Learn about several of the best data science certifications, including their costs and lengths of validity.

XIII. HealthCheck

Is your organization ready to implement data science? Is hiring a data scientist right for you, or will that just use up precious resources? With this checklist, you’ll learn about all the steps you need to take before hiring a data scientist.

XIV. Further Reading

Check out this section for further reading that will give you a deeper understanding of the covered topics. 

XV. Interesting to Know

Delve into miscellaneous info on data science, including quotes from thought leaders, the best data visualizations of 2020, and even a Harry Potter chapter generated with natural language processing.

XVI. Closing Thoughts

Wrapping up, the White Paper presents final thoughts on the scope of data science, showing how its scope grows each year and is applicable to a huge number of industries.

References

2016 Data Science Report. Crowdflower. http://www2.cs.uh.edu/~ceick/UDM/CFDS16.pdf.

Data science platform Market Analysis: Industry Growth 2021 to 2026 – Mordor intelligence. Data Science Platform Market Analysis| Industry Growth 2021 to 2026 – Mordor Intelligence. (n.d.). https://www.mordorintelligence.com/industry-reports/data-science-platform-market

Data science platform market size & Share REPORT, 2020-2027. Data Science Platform Market Size & Share Report, 2020-2027. (n.d.). https://www.grandviewresearch.com/industry-analysis/data-science-platform-market#:~:text=Report%20Overview,technological%20advances%20are%20occurring%20rapidly.  

Gandhi Centre for INCLUSIVE INNOVATION. Imperial College Business School. (2021, June 6). https://www.imperial.ac.uk/business-school/faculty-research/research-centres/gandhi-centre-inclusive-innovation/

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