Analytics careers are rapidly progressing into new frontiers. According to The Quant Crunch: How the Demand for Data Science Skills is Disrupting the Job Market, the number of jobs for all data professionals in the United States will increase by 364,000 openings to 2.7 million by 2020. McKinsey estimates there will be a shortage of 140,000 to 190,000 data scientists by 2018. Due to the vastness of the projected data science talent gap, data-savvy pros are beginning to make the leap into higher-paying citizen data science roles to fulfill the unmet demand.
Give Yourself a Raise
According to current Indeed.com statistics, which are based on 30,869 salaries submitted anonymously to Indeed by employees and users and collected from job advertisements posted over the past 36 months, the average salary for a Data Analyst is $70,021 per year in the United States. The average salary for a Data Scientist is 85.8% higher at $130,098. Citizen data scientist salaries likely range in between these two salary averages.
Although data science talent shortages will continue to dominate headlines, most new hires will work in analytics or citizen data science roles. This next generation of talent will be pervasive throughout Industry 4.0 organizations, which constitutes a huge opportunity for analysts to advance their careers. But how can you transition your career from data analyst to citizen data scientist? Consider starting with automated machine learning to quickly reveal hidden knowledge.
If you already use tools like Excel, Tableau, Qlik, Power BI, or TIBCO Spotfire and have a deep understanding of your business and its data, then you have what it takes to make the move into citizen data science with automated machine learning. This technology is the ideal innovation for data analysts and business intelligence pros who want to take their careers to the next level. With automated machine learning, you don’t need statistics or programming skills to uncover predictive insights to solve significant business challenges, allowing you to help fill in the data science skills gap that is already plaguing industries across the economy.
Please join me in an upcoming webinar on Thursday, April 26, 2018 at 1:00 PM ET to see how you can use automated machine learning to further your career.
What is Machine Learning?
Machine learning is a subset of artificial intelligence (AI) that allows computers to learn to perform tasks and make predictions without being explicitly programmed. Machine learning is closely related to AI, data science, computational statistics, and mathematical optimization. The basic idea of machine learning is to take information from historical observed data and run it through algorithms that uncover patterns that generalize well to previously unseen situations. To put it simply, the algorithm learns by example. We then apply those self-learning algorithms to similar data and make predictions about future trends, basically like our own data-driven crystal ball – that’s why machine learning is often referenced in relation to the field of predictive analytics.
The best thing about machine learning is that it has practical implications across numerous industries. When implemented effectively, machine learning allows you to discover optimal solutions to practical business problems.
Why is Machine Learning Important?
While most statistical analysis relies on rule-based decision-making, machine learning excels at tasks that are hard to define with exact step-by-step business rules. We can apply machine learning to numerous business scenarios in which an outcome depends on hundreds of factors that are difficult or impossible for a human to keep track of. As a result, businesses deploy machine learning for predicting loan defaults, churn, fraud, insurance claims, hospital readmission, service outage, and many other common cases.
As you can probably surmise, companies that can effectively implement machine learning and other AI technologies gain a massive competitive advantage. In another recent report by McKinsey & Company, they estimated AI technologies will create $50 trillion of value by the year 2025. Companies that fail to adopt AI and machine learning technologies will be unable to compete with early adopters.
AI technologies will create $50 trillion of value by the year 2025
Machine learning allows us to start looking forward and optimize future outcomes.
Machine Learning + DataRobot
Machine learning used to be a tedious process that required heavy, complex manual coding. Building a high-quality machine learning model often required elaborate feature engineering, deep knowledge of statistics, and extensive software engineering experience. Without teams of PhD-level data scientists available, most companies were limited in the number of machine learning models they could develop and test in a timely manner.