According to a Business Insider report, data scientists are the most sought-after tech professionals in the US with a median salary of $110,000. Besides the high earning potential, data scientists also have one of the highest career satisfaction rate and they can work in any industry. From finance to entertainment to retail, data scientists are helping organizations with data analysis and risk analysis projects. Governments and businesses depend on this information to make critical decisions that shape the future of society. So data professionals will continue to a play vital role in the coming years.
Options for Big Data career
Big Data careers can lead to data scientist, data engineer or data analyst positions. All of these require expertise in math, statistics, software engineering, and data communication. But the skill emphasis will depend on the job duties.
Data scientists work on modeling data. They handle the cutting-edge research in data analysis and risk analysis. People with advanced degrees in mathematics, physics, computer science and engineering are well-suited for these positions.
Data engineers focus more on the handling of large datasets. They use their technical skills to help solve data logistics issues. Computer science and engineering majors are best suited for these jobs.
Data analysts concentrate on deriving meaning from the data. They work on reports and data visualization to help organizations make better decisions. Business, economics, and statistics majors are the most suited for these positions.
Big Data and risk modeling learning path
Every organization creates its own technology stack for Big Data. The stack will depend on the particular challenges that organization faces. It's impossible to master all the skills without knowing the technology stack. Beginners looking into Big Data should concentrate on broad areas to pick up the necessary skills and fine-tune them as they gain more expertise. Here are a few categories to keep in mind:
Mathematics and statistics are fundamental to Big Data. Potential candidates don’t have to major in mathematics or statistics but having a background in any STEM-related (Science, Technology, Engineering or Mathematics) major can improve the chances of performing better at data-related jobs.
Big Data requires a lot of data processing. It's cumbersome to run these tasks manually, so programming skills are essential for implementing automation and improving efficiency. As most data processing takes place on the Linux platform, skills in Linux and bash scripting should be an essential part of a data professionals repertoire. Besides bash scripting, programming languages like Python, Java, R, and Scala are also popular in the Big Data space, so data scientists, engineers, and analysts should at least master a programming language.
Businesses, large or small, are moving to the cloud because it's more scalable and cost-effective, so data professionals need to be adept at cloud computing. Organizations have the option to choose from AWS, Azure, Google Cloud, Digital Ocean and more. There are also open source cloud technologies like OpenStack. Data scientists and analysts might be able to perform their duties with basic cloud knowledge, but data engineers will need a higher level of competency.
Distributed and real-time systems
Distributed file system (DFS) like Hadoop is an important tool in Big Data. Knowledge about databases is also crucial. Depending on the career path, data professionals will have to also look into tools like Kafka, MapReduce, Apache Spark, Apache Storm, Apache Kinesis and more.
Risk analysis and risk modeling
Quantitative risk analysis and modeling skills help data scientists and analysts figure out exposure to risks. For that reason, they need to learn the basics of risk measurement and management and how to create predictive models that can help organizations mitigate the problems.
Risk modeling today is nothing like it was a decade ago, owing to the advancements in computing power. Big Data can be harnessed to help businesses guide and predict their financial actions. With powerful hardware accounting for countless factors in predicting probability of events is crucial for institutions like banks that require accurate forecasting of market and stock exchange. Neural networks and deep learning technologies have been the catalyst of a revolution in risk modeling, allowing exponential databases to be interpreted by algorithms more efficiently and effectively than ever before, allowing the financial forecasts to be of previously unmatched accuracy.
Training and education
Big Data is an amalgamation of complex disciplines and it requires mastery of various skill sets. However, there are a number of resources available that can help people who are interested in learning.
University education is the best option to gain knowledge and expertise. Today most schools have specific programs for data science, but students don't need to specialize in these programs. They can choose a STEM major which will provide them with the necessary knowledge base for a future Big Data career. Choosing a university with a good reputation in the industry will also help open up better job prospects after graduation.
Over the last few years, massive open online courses (MOOCs) have opened up a new pathway to affordable education. Most prominent universities have an online presence. There are also outlets like Udemy, Udacity, Lynda, Coursera and more that have offerings on Big Data and programming. One of the benefits of MOOCs is that students can take them at their own pace. If they don't understand a topic, they can revisit the lessons. There are online communities that provide support for MOOCs, however, the lack of direct contact with teachers means students need to be self-motivated to complete these courses.
Meetups and hackathons
Meetups and hackathons are great ways to stay motivated and create social connections. Meetup in major cities has Big Data support groups and meetings. These groups and meetings are great places to learn and network with fellow data professionals. Another great option are hackathons like Lion’s Den. Generally, hackathons are held during the weekends. Participants can spend 24-48 hours building an application with other members. For people who are interested in building a data analysis or risk analysis tool, a hackathon is a great place. Hackathons provide work experience that can help with real-world problem-solving.
Volunteering and learning from work experience
Students can also learn through volunteering for real projects. Often non-profit organizations that have limited budgets might be looking for extra help. Volunteering provides opportunities to help out with real-world problems. Additionally, freelancing is a good way to learn while earning cash, students can start with easier tasks and then attempt more challenging projects as they learn more.
Big Data can provide a satisfying and lucrative career. There are many resources available to learn and improve. Whether students use universities, online courses or hackathons, they have lots of options. They can use these resources to become experts in Big Data which can lead to long and prosperous careers. Gaining knowledge on risk modeling is crucial for those pursuing analytic-related positions in finance businesses. This skill is now more accessible than it was a decade ago owing to neural networks, deep learning, and immense advancements in computing power. Educating oneself in this direction can be achieved at universities or other facilities, but self-tutoring and challenges like data and risk-oriented hackathons might prove equally successful.