Since the internet went public in 1995, there’s been an explosion in data creation. In 2013, there were 4.4 zettabytes of data in the world and this is expected to increase tenfold by 2020. To put this into perspective, a zetta equals 1021, a number so large it defies human imagination. And this figure is only growing exponentially, fueling a global demand for people with strong skills in data analytics.
Of course there’s a fine line between having too much and too little data. Too few hard numbers may require an organization to make guesses or assumptions. There are occasions when a “go with the gut” leap of faith can be useful and rewarding, especially if part of a company’s culture and vision is to be innovative and daring. But in other instances, trying to answer a question such as “What do our customers really want?” without having solid, reliable information can be troublesome, especially if the only evidence you have are a handful of personal anecdotes or online comments.
Too much data can cause opposite problem. Besides making Powerpoint presentations infinitely drier, too many numbers and statistics can be overpowering, almost to the point of paralysis.
Ultimately, a quantitative overabundance can become too big to manage, bogging down the decision-making process.
So where is that sweet spot that can easily provide just the right amounts and types of data to help objectively guide a company in its decision-making process? People who can answer this question may become superstars in the workplace, building databases, rebuilding legacies, and providing insights on demand, including projections for a company’s future budget, operations, and sales targets by identifying appropriate associations, trends, and patterns.
Certainly, companies have had access to different sources of information, such as sales trends and various government reports about domestic production or payroll, but until recently, the typical company didn’t always have time or resources to mine too deeply. Today’s world of Big Data is different, with fast and easy access to shared databases in the cloud; database software with better searchability; site tools, extensions, and plugins that gather the tidbits of info; and more courses to teach people useful skills and processes.
It’s not surprising that the Big Data profession—a group which includes various analysts, scientists, engineers, and administrators—is expected to see 700,000 fresh openings by 2020.
Furthermore, the Bureau of Labor Statistics (May 2016) reported that the mean salary for database administrators was $87,130.
Even for non-quants, learning how to interpret and manage figures can prove very beneficial across industries. Learn why skills in data analytics are expected to be valuable and how to develop knowledge in this field.
Mastering data analytic skills can open doors into a variety of industries and help professionals remain valuable to current employers. Virtually all industries can benefit from trained data experts, including engineering, architecture, pharmacology, manufacturing, and healthcare, among others. But, according to IBM, the heaviest demand—59 percent of all jobs in this field—is going to be in finance, insurance, professional services, and IT companies.
The highest regions of demand for skills in data analytics are financial, high-tech, and political metropolitan communities with large populations, including New York (eight times higher demand than average); San Francisco/San Jose (22 percent higher demand than average); and Washington, D.C. (14 percent higher). Los Angeles, Dallas, Boston, Atlanta, and Philadelphia are also considered to be rapidly growing for this sector as well. Plus, due to lucrative salaries offered, it might be easier to survive in some of these communities with higher costs of living.
People with database knowledge combined with other skills can be seen as even more assets to an organization, including software engineering; programming (e.g., SQL, Python, Apache Hadoop, SAS, Java); communication; business ethics; and even machine learning, where people can teach computers how to follow instructions and recognize patterns. This type of automation can save time and produce useful results.
Here is a featured selection of academic programs for aspiring experts in Big Data and analytics.
This 120-credit online program provides an overview of where and how to find data and how to organize and present it. Skills like statistical analysis, simulation, and optimization are emphasized, along with non-technical skills such as communication and decision-making. The program also includes access to professionals across various industries who can provide real-world examples of where their data analysis skills have been useful.
The 16-credit program provides strategies on proper mining methods for acquiring data, as well as overviews of statistical topics such as applied probability, visualization techniques, and discovering patterns. Instruction emphasizes how to present visual information effectively and make educated decisions. The program is offered online, on campus, or in a hybrid setting. Students can also continue at the school for a master’s degree in either applied business analytics or computer science with data analytics concentration. Please note that BU also has an exceptional graduate certificate program in web application development.
This 11-week online program available through the ECornell online program teaches the strategies of researching data in order to make effective business decisions. Students learn how to create frameworks to test business hypotheses and reach firm conclusions. They also receive training in statistical systems and concepts, as well as how to use regression models to predict outcomes and reduce uncertainty.
The 27-credit MIDS program through the School of Information has an interdisciplinary focus on data from a wide array of sources, including sites, sensors, and mobile devices. The program discusses how to collate and analyze this info into something useful, make data-driven decisions, and properly communicate findings. The program also discusses ethical informational issues such as privacy and security. It includes both online and on-campus portions.
The goal of this program in the School of Management is to teach students how to effectively work with data science to solve business challenges. Students can potentially receive their bachelor’s and master’s degrees plus a certificate and a PhD in similar analytics from the same faculty. This multidisciplinary program covers a range of topics, including machine learning, probability, optimization methods, and social media.
Along with the accredited, traditional degree programs profiled above, people seeking ways to boost their knowledge of data analytics can also find a variety of free or inexpensive online resources.
This online program partners with various universities to offer more than 200 data-related courses in the form of mobile-friendly video lectures, online quizzes, student forums, and peer-reviewed projects. Basic courses are divided into weekly units, and each course various from one to 12 units, priced from specialty programs to master’s degrees. Completion comes with an electronic certificate that can be placed on sites or LinkedIn profiles. Niche programs include those in natural language processing, statistics, applied data science, probability and data, and data structures and algorithms, among others. It costs $29-$99 for courses; $39-$79 per month for specialties; and $15-$25,000 for online degrees.
These self-paced courses from various universities offer an overview of different data topics, everything from database systems and design to statistical concepts. Some courses provide a general foundation, while others can help students prepare for various certifications and exams in different industries or trade associations. Some teach how to use certain programs, and others feature practical applications like genomic data analysis or compiling baseball statistics. Types of courses include MicroMasters (i.e., master’s-level credit classes); Professional Certificates, which focus on certain skills in certain fields; and XSeries certificates, which come from completing a group of courses. Prices are between $50-$300 per course for credit and a certificate, or free to audit.
Students interested in everything from computer languages like Python and SQL to database management methods may enjoy these online courses that range in price from $15 to $200. There’s also a focus on neural networks, artificial intelligence, and game development.
This aggregate service, short for Massive Open Online Courses, offers information about many other free or low-cost online courses in a variety of data-related subjects from different providers, including edX and Coursera. Some are self-paced and others follow a schedule. The courses focus on a broad range of subfields, including developing data products, data mining, customer analytics, statistical inference, and more.