Are You Learning The Right Data Science Skills?

Are You Learning The Right Data Science Skills? 

    Alt=”Data science skill set”

There is a wide range of specialized job opportunities cropping up today, especially in the field of data mining and data science. In fact, according to a report by the McKinsey Global Institute on Big Data, the United States alone would need to hire 140,000 to 190,000 individuals with knowledge on analyzing data by 2018 in order to meet the required capacity of this growing sector. The country would also need about 1.5 million analysts and managers with knowledge on evaluating big data in order to make strategic qualitative and quantitative executive decisions.[1]

The increase of job opportunities due to the rise of big data isn’t about to stop anytime soon. Right now, most, if not all businesses, are relying on data-driven technology to become more efficient and productive.[2] Even the United States recognizes the importance of understanding big data. The US hired its first US Chief Data Scientist in 2015 in its bid to welcome the innovative power of data science.[3]

It would therefore be a smart move for companies to invest in hiring more human resources with appropriate data science skill sets. However, most people fail to benefit from this increase in job opportunities due to their lack of knowledge and training in data science. A burgeoning opportunity for people, who are still unsure about their career prospects, or who want to climb the employment ladder, is to learn the skill set needed in data science. Data science is multidisciplinary, so focus on the skills, and not the job titles.

To help you gain a deeper understanding of the data science field, we have consolidated information that aim to teach you the basics of big data and data science. This article is divided into five sections: (1) What is Data Science? (2) What is Big Data and Where is it Derived From?, (3) Different Roles in Data Science, (4) Obtaining Technical Skillsets in Data Science, and (5) Introduction to Basic Data Science Software and Languages. After reading this article, you will have a clear idea of why data science is necessary, and why you should hop on the big data train.

What is Data Science?

You might not be aware of it, but data science is already deeply integrated into your everyday life. Every marketing campaign you see, your tailor-fitted recommendations in online stores like Amazon and eBay, and even your everyday commute with Uber or Lyft are just some of the innovations that are powered by the brilliance of data science.[4]

Data Science is a multi-disciplinary field where data inference, algorithm development, and technology are combined in order to resolve complex problems and predict customer behavior.[5] At the core of data science is big data, another aspect of data science, which will be explained thoroughly in the next section of this article.

Simply put, data science is a field that unlocks the real value of data by deriving and generating actionable analytics from the raw data. Data science involves gathering and organizing the information, analyzing and modeling the data, and finally engineering products or designing actions that aim to solve the problem faced by the business. To visualize the processing of big data in data science, here is a flowchart.

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An example of data science in action is the design of the friend finder feature on Facebook. Data scientists working at Facebook found that people would stay more active on the website if they had at least ten friends on the social media site. With this knowledge, they developed the friend finder machinery devoted to helping people find new friends on Facebook.[6]

Other examples of data science in the real world include Gmail’s inbox and spam filter, targeted digital advertisements, image recognition, and even self-driving cars. With the help of big data and data science, companies can generate products and make strategic executive decisions that help power growth and development.

What is Big Data and Where is it Derived From?

Right now, we are living in a world directed and driven by data. After all, data science would not be able to function without its key ingredient: big data. When you first hear the term “big data”, you might be wondering what this particular technical term really means. Is it just a really big collection of data, or is it something more intricate than that?

In its simplest definition, big data is a collection of traditional and digital data that is used as the base for analysis and interpretation by data scientists.[7] It is defined by 4 V’s: volume, velocity, variety, and veracity.[8] These 4 V’s are metrics that ensure that only relevant and valid data are integrated and interpreted by data scientists.

With the host of data-driven technology today, it is not hard to think of sources in which big data can be derived from. A study conducted by Baesens, Rapna, Marsden, Vanthienen, and Leon Zhao8 found that big data today comes from five major sources:

  • Large Scale Enterprise System

The Large Scale Enterprise System is made from the different already-existing systems that companies have been using for years. These include data from supply chain management, customer relationship management, enterprise resource planning, and other similar sources.

  • Mobile Devices

Around 5 billion mobile handsets are being used worldwide, serving as one of the main vehicles of information and communication between people from all around the world. With the use of mobile devices, companies can acquire and track a limited amount of information from individuals across the globe.

  • Online Social Graphs

Major social networks like Facebook, Twitter, and WeChat are dominating the sphere of interactions between individuals from all over the globe. These particular interactions leave digital trails, some of which can be tracked and analyzed.

  • Open and Public Data

There is a plethora of open and public data available on the web today, all thanks to the different research efforts of individuals, organizations, and academic institutions. Everything from traffic data, maps, housing, healthcare, and even weather data can all be analyzed in order to solve particular problem sets in one’s company.

  • Internet of Things

This involves the sensor-enabled ecosystem emerging in the market today. This technology facilitates human interaction with objects, and integrates them to make life more productive. Examples of this include smart and sensor-enabled homes, automobiles, and streetlights. Human interactions with these things generate data that can be analyzed and interpreted in order to make products and services more effective.

Steven Weber, a professor at UC Berkeley School of Information and Department of Political Science, defines big data as “data at a scale and scope that changes in some fundamental way the range of solutions that can be considered when people and organizations face a complex problem.”[9] This means that big data is not just a simple collection of data existing for its own sake. Big data is generally the key to solving the most intricate problems within organizations. According to Daniel Gillick, a senior research scientist at Google, big data represents a huge cultural shift where products and decisions are going to be made and determined not just by executives, but also by algorithms, logic, and immutable evidence.[10]

Different Roles in Data Science

As previously mentioned, data science is an inter-disciplinary field that organizes, analyzes, models, and engineers products and decisions from relevant and valuable data sets. This field is composed of several actors, each with specific roles that make the processes of data science more effective. These different roles include[11]:

  • Data Scientists

The role of data scientists is to tweak and adjust the statistical and mathematical models applied to the acquired data. These people translate formal business problems into workable data questions, and build models in order to predict or have an idea of the upcoming data that they will receive. A good data scientist will know how to effectively theorize, implement, and communicate their acquired data. As a data scientist, you will need to understand mathematics, statistics, algorithms, and programming languages such as R and Python.

  • Data Engineers

Data engineers are generalists who utilize software engineering and computer science to process large quantities of data sets. The main role of data engineers is to clean and organize data sets, write code, and carry out requests made by data scientists. Data engineers take predictive models made by data scientists, and transform them into code. Data engineers must have a broad understanding of programming languages like Python and Java, and frameworks like Hadoop and Spark. Data engineers must also have a deep knowledge on warehousing solutions and data storage (SQL and NoSQL).

  • Data Analysts

Think of the Data Analyst as a junior data scientist. The central role of data analysts is to scan the data and give explanations, reports, and visualizations that show insights derived from the data. Data analysts interpret queries with charts and know the business implications of the interpreted data. Generally, data analysts should have basic knowledge of statistics, an understanding of a querying language (SQL, Hive, Pig), a scripting language (Python, Matlab), a statistical language (SAS ,R, SPSS), and a Spreadsheet (Excel). But if you want to move on up and become the lead Data Scientist, a strong math background, and experience creating algorithms, is crucial.

To be a holistic actor in the field of data science, you should know how the different roles connect with each other, and what specific skill-sets are required by each role.

Alt=”Skills needed for a data scientist"

If you are just beginning to form a career in the field of data science, it is important to resist focusing exclusively on your job title. It is more important to have a firm understanding of the importance of data science, how all the various sub-fields of data analysis intersect, as well as having knowledge on the different programming languages that are used in the field.

Introduction to Basic Data Science Software and Languages

To help you have an idea of the technical data science skillsets that are required in order to be an effective data scientist, here are some of the most used programming languages and software:

  • SAS

SAS is an integrated system of software solutions that makes it possible for data scientists to: (1) enter, retrieve, and manage data, (2) use writing and graphics design, (3) use mathematical and statistical analysis, (4) make business forecasting support, and (5) do operations research and management, among other functions. This programming language is flexible and powerful. It lets its users analyze a large number of reports. It can also simplify programming for beginners with its built-in programs known as SAS procedures.[12]

  • R

R is a programming language and environment used for statistical graphics and computing. This language provides its user with a wide array of statistical and graphical techniques, like linear and nonlinear modelling, time-series analysis, clustering, classical statistical tests, and others. One of the best features of R is its high-quality and well-designed plots that can be easily used in publications. This plot can even include mathematical symbols and formulas if needed by the user. If you want to manipulate and calculate data, and then transform it into an effective graphical display, the R language is the right programming language for you.[13]

  • Microsoft Access

Yes. Microsoft Access. You might be surprised to see it on the list but it is an overlooked software that is used widely by the business community. Microsoft Access is a database engine from Microsoft, used for both large and small database deployments. This is a data management tool that lets its user store information for reference, analysis, and reporting. This database also allows users to create connections or relationships between different data, and store related information together in order to have a higher level of efficiency and productivity in the workplace.[14]

SAS, R, and Microsoft Access are just a few of the programming languages that will help you as a data scientist. Understanding how to operate these software and programming languages, together with acquiring necessary knowledge in mathematics and statistics, can help you become an even better data scientist in the workplace.

Gain Data Science Skillsets and Become an Asset to Your Company

In this data-driven economy, not having a background in data science is not an excuse to not learn it. Everybody has to start somewhere, and learning the basics about data science, big data, and programming languages is just the first step to gaining the necessary skills in this field. The demand for data scientists is growing, and there are plenty of opportunities for you to take advantage of this situation. Learn the technical skillsets in data science and become an asset to your company now!

References

The R Foundation. (n.d.). What is R? Retrieved from R: https://www.r-project.org/about.html

Analytics Vidhya Content Team. (2015, September 21). Amazing Applications and Uses of Data Science Today. Retrieved from Analytics Vidhya : https://www.analyticsvidhya.com/blog/2015/09/applications-data-science/

Arthur, L. (2013, August 15). What is Big Data? Retrieved from Forbes: https://www.forbes.com/sites/lisaarthur/2013/08/15/what-is-big-data/#2f4d6b025c85

Baesens, B., Bapna, R., Marsden, J., Vanthienen, J., & Leon Zhao, J. (2016). Transformational Issues of Big Data and Analytics in Networked Business. Management Information Systems (MIS) Quarterly, 807-817.

Dutcher, J. (2014, September 3). What is Big Data? Retrieved from Berkeley Univesity of California – Data Science: https://datascience.berkeley.edu/what-is-big-data/

Hempel, J. (2015, February 18). White House Names DJ Patil as the First US Chief Data Scientist. Retrieved from Wired: https://www.wired.com/2015/02/white-house-names-dj-patil-first-us-chief-data-scientist/

Huang, R. (2016, March 28). Data Science Career Paths: Introduction. Retrieved from Springboard: https://www.springboard.com/blog/data-science-career-paths-different-roles-industry/

Lo, F. (2016). What is Data Science? Retrieved from Data Jobs: https://datajobs.com/what-is-data-science

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Hung-Byers, A. (2011). Big data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute.

McAfee, A., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Busines Review.

OpenGate Software Inc. (2017). What is Microsoft Access Used For? Retrieved from OpenGate Software: http://www.opengatesw.net/ms-access-tutorials/What-Is-Microsoft-Access-Used-For.htm

Pandya, N. (2015, February 10). Understanding Data Science and Why It’s So Important. Retrieved from LinkedIn: https://www.linkedin.com/pulse/must-read-understanding-data-science-why-its-so-important-pandya

SAS Institute Inc. (2001). Step-by-Step Programming with Base SAS ® Software.

[1] (Manyika, et al., 2011)

[2] (McAfee & Brynjolfsson, 2012)

[3] (Hempel, 2015)

[4] (Analytics Vidhya Content Team, 2015)

[5] (Lo, 2016)

[6] (Pandya, 2015)

[7] (Arthur, 2013)

[8] (Baesens, Bapna, Marsden, Vanthienen, & Leon Zhao, 2016)

[9] (Dutcher, 2014)

[10] (Dutcher, 2014)

[11] (Huang, 2016)

[12] (SAS Institute Inc, 2001)

[13] ( The R Foundation, n.d.)

[14] (OpenGate Software Inc., 2017)

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