Data analytics entails extended training and learning of theoretical knowledge, technology, and software to master. Those with a fascination to unravel how things work and deconstruct complicated tasks through set teachings and theory rather than common sense and human intuition will be drawn to data analytics.
Those with backgrounds in statistics, computer programming, mathematics, and technology systems are also going to take to the topic with ease.
However, there’s nothing stopping you from mastering data analytics even if you don’t have a background in related fields. As long as you have the willpower and enthusiasm to learn programming, statistics and data software management you should be able to eventually earn a 6-figure income.
Career opportunities in data analytics are both expanding and becoming more lucrative at the same time. Due to current shortages of qualified professionals and the escalating demand for experts to manage and analyze data the outlook is bright for future data professionals.
To work in data analytics you will need both a strong passion and the dedication to gain education on the various facets of data analytics. Identifying a university degree, an online degree program or open online course are common entry points.
In regards to specific careers in data science, popular job titles include Data Scientist, Business Intelligence Architect, Business Analytics Specialist, and Machine Learning Specialist.
Data Scientist
National U.S Average Salary: $63,632 - $138,782
The role of a data scientist is one of the hottest jobs on the planet, according to the Harvard Business Review and Business Insider. In 2014 the Harvard Business Review published the article: Data Scientists: The Sexiest Job of the 21st Century. Then in 2016, Business Insider broke the news that data science was the number one profession in the U.S as rated by Glassdoor.
The Glassdoor report itself shows that four of the top five jobs in the United States directly deal with data. Those four jobs (in descending order) were data scientist, DevOps engineer, data engineer, and analytics manager.
So what exactly is a data scientist? Data science is a broad term and a data scientist is an equally general job title. As a generalist, the key role of a data scientist is to collect as much relevant data as possible to conduct analysis on past performance in an attempt to predict the future.
On a day-to-day basis, a data scientist will spend time overseeing how data is collected and optimizing the data acquisition process. It is important that data scientists then have the right strategic mindset and business acumen to decide which questions to ask from the data and how to analyze the data. Curiosity and the vision to ask the right questions are overlooked but important traits behind becoming a successful data scientist.
Next, a data scientist needs to be able to step back onto the technical side to manage data infrastructure, and apply various algorithms and statistical equations through programming to extract value from the data.
Finally, when results and new insights have been yielded, the data scientist must call on their communication skills and knowledge of data visualization to communicate the results with their peers and relevant decision-makers.
Communication skills are vital for a data scientist because unless they can persuasively present todecision-makerss, their findings will fall on deaf ears and cannot be actioned. Versatility is key to the role. Being technically brilliant at programming and data science is one thing, but an ideal candidate will also have strong interpersonal and communication skills.
Compared to other more specialized jobs in data science, there are fewer entry requirements to finding employment as a data scientist. Training in computer science or statistics should be sufficient to find an entry-level work position. A postgraduate degree, such as a Master's degree in Data Science or Machine Learning, would be of an advantage but is not always strictly required.
Finally, data scientists are often cited with having promising potential to grow into company leadership positions given their knowledge of the company’s performance metrics, in addition to their broad skillset and strong communication skills.
Business Intelligence Architect
National U.S Average Salary: $78,556 - $140,165
Bonus: $1,994 - $19,928
Profit Sharing: $0.50 - $22,510
Total Pay: $80,303 - $152,210
A Business Intelligence Architect or ‘BI’ is responsible for collecting, managing and processing corporate data, as well as communicating and providing actionable information to decision-leaders within an organization.
Business Intelligence Architect positions are generally offered to experienced data science professionals and not as an entry-level position. A Business Intelligence Architect will most often work above a technical team or as a senior member of the data analytics team. Their main responsibility is to plan and execute a system to maximize the full value of their company's data assets. The architectural aspect of this role—hence the name—is to design a system that can pool together relevant data from multiple stand-alone data collection points.
Machine Learning Engineer/Scientist
National U.S Average Salary: $65,436 - $163,091
Machine Learning Engineers (or Machine Learning Scientists) are responsible for programming computers to learn on their own. Given the inherent complexities of programming a computer how to think, this job title entails higher requirements and higher salary.
To work as a Machine Learning Engineer it is important that you are not only creative, organized and have a high attention to detail, but that you lso well trained. Technical requirements include expertise in programming languages such as Python, C++ or R, as well as expertise with machine learning libraries and tools, including Pandas, Scikit-learn and Tensorflow.
As Machine Learning Scientists are often working on cloud-based infrastructure they also need to be familiar with this technology, including Hadoop and GPU instances. Sound training in statistics, probability and math skills are other essential credentials.
Business Analytics Specialist
National U.S Average Salary: $65,115 - $128,800
A Business Analytics Specialist straddles both the business and technical sides of machine learning to implement a strategy set by the company’s BI Architect. If a company does not have the resources to hire a BI Architect and implement a customized architecture, then a Business Analytics Specialist will typically rely on third-party software products to integrate business analytics capabilities into the company.