How can you Build a Career in Data Science? 

Today’s economy is leaning more toward analytics—companies have been collecting data for many years. According to LinkedIn, there is a massive demand for people who can mine and interpret data. These are the data scientists.

Who is a Data Scientist?

Data scientists are a mix of mathematicians, trend-spotters, and computer scientists. The data scientist’s role is to decipher large volumes of data and carry out further analysis to find trends in the data and gain a deeper insight into what it all means. Data scientists operate between the business and IT worlds and drive industries by analyzing complex datasets to tease out insights that companies can leverage into actions.

What are Data Science Roles Out There?

To name a few, some of the most common job titles for data scientists include:

1.   Business Intelligence Analyst

A BI analyst uses data to help figure out market and business trends by analyzing data to develop a clearer picture of where the company stands.

2.   Data Mining Engineer

The data mining engineer examines not only the data for their own business but also that of third parties. In addition to analyzing data, a data mining engineer will create sophisticated algorithms to help analyze the data further.

3.   Data Architect

Data architects work closely with users, system designers, and developers to create blueprints that data management systems use to centralize, integrate, maintain, and protect data sources.

4.   Data Scientist

Data scientists begin by translating a business case into an analytics agenda, developing hypotheses, and understanding data—as well as exploring patterns to measure what impact they will have on businesses. They also find and choose algorithms to help further analyze data. They use business analytics to not only explain what effect the data is going to have on a company in the future but can also help devise solutions that will help the company to move forward.

5.   Senior Data Scientist

A senior data scientist can anticipate what a business’s future needs will be. Apart from gathering data, they also analyze it thoroughly to resolve highly complex business problems efficiently. Through their experience, they can not only design but drive forward the creation of new standards, as well as create ways to use statistical data, and also develop tools to help further analyze the data.

  Courtesy to  Prof. Avantika Monnappa

Data Science Careers Shape Our Future

INDUSTRY ADVICE ANALYTICS COMPUTING AND IT

For three years in a row, data scientist has been named the number one job in the U.S. by Glassdoor. What’s more, the U.S. Bureau of Labor Statistics reports that the rise of data science needs will create 11.5 million job openings by 2026. Not only is there a huge demand, but there is also a noticeable shortage of qualified data scientists.

Daniel Gutierrez, managing editor of insideBIGDATA, told Forbes, “The word on the street is there’s definitely a shortage of people who can do data science.” If you have a passion for computers, Math, and discovering answers through data analysis, then earning an advanced degree in data science might be your next step.

What is Data Science?

Dr. Martin Schedlbauer, data science professor at Northeastern University, says that data science is used by “computing professionals who have the skills for collecting, shaping, storing, managing, and analyzing data [as an] important resource for organizations to allow for data-driven decision making.” Almost every interaction with technology includes data—your Amazon purchases, Facebook feed, Netflix recommendations, and even the facial recognition required to sign in to your phone.

Amazon is a prime example of just how helpful data collection can be for the average shopper. Amazon’s data sets remember what you’ve purchased, what you’ve paid, and what you’ve searched. This allows Amazon to customize its subsequent homepage views to fit your needs. For example, if you search camping gear, baby items, and groceries, Amazon will not spam you with ads or product recommendations for geriatric vitamins. Instead, you are going to see items that may actually benefit you, such as a compact camping high chair for infants.

Similarly, data science can be useful for reminding you of habitual purchases. If you order diapers every month, for example, you might see a strategically placed coupon or deal around the same time each month. This use of data is meant to act as a trigger, prompting you to think, “I just remembered I need to buy diapers, and I should buy them now because they are on sale.”

Data science benefits both companies and consumers alike. McKinsey Global Institute found that big data can increase a retailer’s profit margin by 60 percent, and “services enabled by personal-location data can allow consumers to capture $600 billion in economic surplus,” meaning they are able to purchase a good or service for less than they were expecting. For example, if you budgeted $7,500 to purchase a jacuzzi and then found the exact model you wanted for $6,000, your economic surplus would be $1,500. Data science can simultaneously increase retailer profitability and save consumers money, which is a win-win for a healthy economy.

Data Science is Helping the Future

Data science enables retailers to influence our purchasing habits, but the importance of gathering data extends much further.

Data science can improve public health through wearable trackers that motivate individuals to adopt healthier habits and can alert people to potentially critical health issues. Data can also improve diagnostic accuracy, accelerate finding cures for specific diseases, or even stop the spread of a virus. When the Ebola virus outbreakhit West Africa in 2014, scientists were able to track the spread of the disease and predict the areas most vulnerable to the illness. This data helped health officials get in front of the outbreak and prevent it from becoming a worldwide epidemic.

Data science has critical applications across most industries. For example, data is used by farmers for efficient food growth and delivery, by food suppliers to cut down on food waste, and by nonprofit organizations to boost fundraising efforts and predict funding needs.

In a 2015 speech, Economist and Freakonomics author Steven Levitt said that CEOs know they are missing out on the importance of Big Data, but they do not have the right teams in place to perform the skills. He says, “I really do believe still that the combination of collaborations with firms’ big data and randomization […] is absolutely going to be at the center of what economics is and what other social sciences are going forward.”

Pursuing a career in data science is a smart move, not just because it is trendy and pays well, but because data very well may be the pivot point on which the entire economy turns.

In-Demand Data Science Careers

Data science experts are needed in virtually every job sector—not just in technology. In fact, the five biggest tech companies—Google, Amazon, Apple, Microsoft, and Facebook—only employ one half of one percent of U.S. employees. However—in order to break into these high-paying, in-demand roles—an advanced education is generally required.

“Data scientists are highly educated–88 percent have at least a master’s degree and 46 percent have PhDs–and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist,” reports KDnuggets, a leading site on Big Data.

Here are some of the leading data science careers you can break into with an advanced degree.

Business Intelligence (BI) Developer

Average Salary: $89,333

Typical Job Requirements: BI developers design and develop strategies to assist business users in quickly finding the information they need to make better business decisions. Extremely data-savvy, they use BI tools or develop custom BI analytic applications to facilitate the end-users’ understanding of their systems

Notable Companies: DollarShave Club, Discover, and Liberty Mutual

Data Architect

Average Salary: $137,630

Typical Job Requirements: Ensure data solutions are built for performance and design analytics applications for multiple platforms.

Notable Companies: IBM, eBay, AAA Club Alliance, T-Mobile

Applications Architect

Average Salary: $134,520

Typical Job Requirements: Track the behavior of applications used within a business and how they interact with each other and with users.

Notable Companies: UPS, Humana, Dow Jones, Oracle

Infrastructure Architect

Average Salary: $126,353

Typical Job Requirements: Oversee that all business systems are working at optimally and can support the development of new technologies and system requirements. A similar job title is Cloud Infrastructure Architect, which oversees a company’s cloud computing strategy.

Notable Companies: Abbott Labs, Hewlett-Packard, Dell, Ford Motor Company

Enterprise Architect

Average Salary: $161,272

Typical Job Requirements: According to Techopedia, an enterprise architect, “Works closely with stakeholders, including management and subject matter experts (SME), to develop a view of an organization’s strategy, information, processes and IT assets.”

Notable Companies: Cisco, Boeing, Lockheed Martin, Microsoft

Data Scientist

Average Salary: $139,840

Typical Job Requirements: Find, clean, and organize data for companies. Data scientists will need to be able to analyze large amounts of complex raw and processed information to find patterns that will benefit an organization and help drive strategic business decisions. Compared to data analysts, data scientists are much more technical.

Notable Companies: Facebook, Capital One, Airbnb, Twitter

Data Analyst

Average Salary: $83,878

Typical Job Requirements: Transform and manipulate large data sets to suit the desired analysis for companies. For many companies, this role can also include tracking web analytics and analyzing A/B testing.

Notable Companies: Walmart, Gap, Bank of America, Kohler

Data Engineer

Average Salary: $151,307

Typical Job Requirements: Perform batch processing or real-time processing on gathered and stored data. Make data readable for data scientists.

Notable Companies: Spotify, Verizon, General Motors, Shutterfly

Machine Learning Scientist

Average Salary: $139,840

Typical Job Requirements: Research new data approaches and algorithms.

Notable Companies: Apple, The Johns Hopkins Hospital, Expedia, Tinder

Machine Learning Engineer

Average Salary: $114,826

Typical Job Requirements: Create data funnels and deliver software solutions.

Notable Companies: Nike, Dropbox, LinkedIn, Uber

Statistician

Average Salary: $93,589

Typical Job Requirements: Interpret, analyze, and report statistical information, such as formulas and data for business purposes.

Notable Companies: U.S. Census Bureau, Google, PayPal, U.S. Department of Agriculture

Data Scientists are in Constant Demand

Dr. Schedlbauer concludes that while some data science work will likely be automated within the next 10 years, “there is a clear need for professionals who understand a business need, can devise a data-oriented solution, and then implement that solution.”

Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. Data science careers are in high demand and this trend will not be slowing down any time soon, if ever.

Courtesy to Prof. Ashley Eneriz