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(This article was first published on <strong><a href="http://ekonometrics.blogspot.com/2016/08/five-questions-about-data-science.html"> eKonometrics</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<div>From Safari Books Online (<a target="_blank" href="https://www.safaribooksonline.com/blog/2016/02/10/data-science-qa/">https://www.safaribooksonline.com/blog/2016/02/10/data-science-qa/</a>) </div>
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<p>Recently, we were able to ask five questions of<b><i> <a target="_blank" href="https://twitter.com/regionomics">Murtaza Haider</a>,</i></b> about the new book from <a target="_blank" href="http://www.redbooks.ibm.com/ibmpress/">IBM Press</a> called <a target="_blank" href="https://www.safaribooksonline.com/library/view/getting-started-with/9780133991246/">“Getting Started with Data Science: Making Sense of Data with Analytics.”</a> Below, the author talks about the benefits of data science in today’s professional world.
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<div><a target="_blank" href="https://www.safaribooksonline.com/library/view/getting-started-with/9780133991246/"><img alt="Getting Started with Data Science" class="wp-image-22033" src="https://i1.wp.com/www.safaribooksonline.com/blog/wp-content/uploads/2016/02/download.jpg?resize=102%2C152&ssl=1" data-recalc-dims="1"></a><span></span></div>
<h3>1. What are some examples of data science altering or impacting traditional professional roles already?</h3>
<div>Only a few years ago there did not exist a job with the title Chief data scientist. But that was then. Small and large corporations, and increasingly government agencies are putting together teams of data scientists and analysts under the leadership of Chief data scientists. Even White House has a Chief data scientist position, currently held by Dr. DJ Patel.
<p>The traditional role for those who analyzed data was that of a computer programmer or a statistician. In the past, firms collected large amounts of data to archive rather than to subject it to analytics to assist with smart decision-making. Companies did not see value in turning data into insights and instead relied on the gut feeling of managers and anecdotal evidence to make decisions.</p>
<p>Big data and analytics have alerted businesses and governments to the latent potential of turning bits and bytes into profits. To enable this transformation, hundreds of thousands of data scientists and analysts are needed. Recent reports suggest that the shortage of such professionals will be in millions. No wonder we see hundreds of postings for data scientists on LinkedIn.</p>
<p>As businesses increasingly depend upon analytics-driven decision making, data scientists and analysts are simultaneously becoming front-office superstars, which is quite a change from them being the back office workers in the past.</p></div>
<h3>2. What steps can a professional take today to learn how and why to implement data science into their current role?</h3>
<p>Sooner than later, workers will find their managers asking them to assume additional responsibilities that would involve dealing with data, and either generating or consuming analytics. Smart professionals, who are uninitiated in data science, would therefore proactively address this shortcoming in their portfolio by acquiring skills in data science and analytics. Fortunately, in the world awash with data, the opportunities to acquire analytic skills are also ubiquitous.</p>
<p>For starters, professionals should consider enrolling in open online courses offered by the likes of Coursera and BigDataUniversity.com. These platforms offer a wide variety of training opportunities for beginners and advanced users of data and analytics. At the same time, most of these offerings are free.</p>
<p>For those professionals who would like to pursue a more structured approach, I suggest that they consider continuing education programs offered by the local universities focusing on data and analytics. While working full-time, the professionals can take part-time courses in data science to fill the gap in their learning and be ready to embrace impending change in their roles.
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<h3>3. Do you need programming experience to get started in data? What kind of methods and techniques can you utilize in a program more commonly used, such as Excel?</h3>
<p>Computer programming skills are a definite plus for data scientists, but they are certainly not a limiting factor that would prevent those trained in other disciplines from joining the world of data scientists. In my book, Getting Started with Data Science, I mentioned examples of individuals who took short courses in data science and programming after graduating from non-empirical disciplines, and subsequently were hired in data scientist roles that paid lucrative salaries.</p>
<p>The choice of analytics tools depends largely on the discipline and the type of organization you are currently working for or intend to work for in the future. If you intend to work for corporations that generate real big data, such as telecom and Internet-based establishments, you need to be proficient in big data tools, such as Spark and Hadoop. If you would like to be employed in the industry that tracks social media, you would require skills in natural language programming and proficiency in languages such as Python. If you happen to be interested in a traditional market research firm, you need proficiency in analytics software, such as SPSS and R.</p>
<p>If your focus is on small and medium size enterprises, proficiency in Excel could be a great asset, which would allow you to deploy its analytics capabilities, such as Pivot Tables, to work with small sized data.</p>
<p>A successful data scientist is one who knows some programming, basic understanding of statistical principles, possesses a curious mind, and is capable of telling great stories. I argue that without the storytelling capabilities, a data scientist will be limited in his or her abilities to become a leader in the field.
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<h3>4. How do you see data science affecting education and training moving forward? What benefits will it bring to learning at all levels?</h3>
<p>Schools, colleges, universities and others involved in education and learning are putting big data and analytics to good use. Universities are crunching large amounts of data to determine what gaps in learning at the high school level act as impediments to success in the future. Schools are improving not just curriculum, but also other strategies to improve learning outcomes. For instance, research in India using large amounts of data showed that when children in low-income communities were offered free meals at school, their dropout rates declined and their academic achievements improved.</p>
<p>Big data and analytics provide instructors and administrators the opportunity to test their hypothesis about what works and what doesn’t in learning, and replace anecdotes with hard evidence to improve pedagogy and learning. Learning has taken a new shape and form with open online courses in all disciplines. These transformative changes in learning have been enabled by advances in information and communication technologies, and the ability to store massive amounts of data.
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<h3>5. Do you think that modern governments and societies are prepared for what changes that big data and data science might bring to the world?</h3>
<p>Change is inevitable. Despite what modern governments and societies like, they would have to embrace change. Fortunately, smart governments and societies have already embraced data-driven decision-making and evidence-based planning. Governments in developing countries are already using data and analytics to devise effective poverty-reducing strategies. Municipal governments in developed economies are using data and advanced analytics to find solutions to traffic congestion. Research in health and well-being is leveraging big data to discover new medicines and cures for illnesses that challenge us all.</p>
<p>As societies embrace data and analytics as tools to engineer prosperity and well-being, our collective abilities to achieve a better tomorrow will be further enhanced.</p></div>
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