In 2020, the world is expected to generate 50 times more data than in 2018 and Data Scientist has been voted as the "Sexiest Job" by Harvard Business Review, there is a significant growth in demand for data-savvy professionals in businesses, public enterprises, and several nonprofit organizations.
According to a recent study conducted by the McKinsey Global Institute, a scarcity of the talent necessary to derive the most of Big Data is a critical challenge for the Big Data as well as Data Science industry. The report forecasts that there will be about five million jobs in the U.S. in 2018 that would require data analysis skills, and most of these positions are predicted to be filled only through training or retraining. There has been much debate among scholars as well as Big Data practitioners about what Data Science is, and what it isn't. Does it deal only with Big Data? And several such queries. Therefore, let's throw some light on the concept of Data Science before we proceed further.
What is Data Science?
Data Science is an evolutionary step in the field of business analysis that combines the methodologies and practices of computer science, modeling, statistics, analytics, and mathematics to drive business growth. Data Science involves leveraging automated methods to analyze a vast amount of data in order to extract insights from them.
It is the study that involves researching where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business strategies.
Who is a Data Scientist?
Data scientists are a new generation of analytical data experts who has the traits of a technical expert and the curiosity of a scientist. They have the technical skills to solve complex problems besides possessing a research mindset to explore what problems need to be solved.
Data Scientists are a sign of the times. They are the driver of all trades in the data analysis world. They are partly mathematicians, partly computer scientist and partly explorers. Since they can straddle both the business and technology worlds, they are highly sought-after and well-paid. Fortunately or unfortunately, the shortage of Data Scientists is a serious challenge faced by most of the business sectors. This makes Data Scientists even more valuable and the most headhunted professionals.
What are the basic skill-sets required?
Many Data Scientists have served as statisticians or data analysts early in their career. But as big data began to evolve and flourish, the roles surrounding data analysis evolved as well. The primary skills you need to become a Data Scientist is to be good at statistics and possess an analytical aptitude besides having the ability to think business, rather economics.
As we have mentioned earlier, the majors from which data science borrows are statistics, mathematics, economics, operations research, and computer science. Therefore, if you wish to become an adept Data Scientist, you should have studied some or one of these subjects and you should be able to design experiments and test hypotheses.
Steps To Become a Data Scientist:
Preparing yourself for a career in Data Science is, of course, a smart decision. However, positioning for the "sexiest job of the 21st century" isn't a cake walk. You may not break your bones, but, you do need to awaken a few more of your gray cells.
Data Science offers plenty of rewarding job opportunities in the technology field that have room for experimentation and creativity. So, let's plan a strategy.
1. Brush up your skills in applied mathematics and statistics:
While it is expected that most data scientists will have backgrounds as data analysts or statisticians, many come from fields such as business or economics. If you are from a non-technical field, learn applied mathematics and develop a solid understanding of statistics before you dig your hands on Data Science. If you are from already an analyst or statistician, just brush up your skills.
2. Grasp Machine Learning:
Machine learning is a critical component of Data Science. It refers to a broad array of methods that deal with data modeling. Machine learning is used to make predictions and discover patterns in data by using algorithms. Becoming a Data Scientist mandates familiarity with Machine Learning tools and techniques like k-nearest neighbors, random forests, ensemble methods, etc.
3.Learn to Code:
No matter what type of business you are working for or what organization you are interviewing for, as a Data Scientist you are expected to know a statistical programming language, like R or Python or SAS, and a querying language like SQL.
4.Understand Distributed Databases:
As a professional Data Scientist, you will almost always be working with databases to store data. A solid understanding of databases such as MySQL, Postgres, MongoDB, Cassandra, etc. is a necessity to shine in your career as a Data Scientist.
5.Master Multi-variable Calculus and Linear Algebra:
You may be wondering why a data scientist would need to understand multi-variable Calculus and linear Algebra when ski-learn or R can be used for out of the box implementations. Well, these form the basis of a lot of machine learning techniques that are used in Data Science. In interviews, you may be asked some basic multi-variable calculus or linear algebra questions as they help the interviewer judge your aptitude for Data Science.
6.Learn Data Mugging:
Data mugging is the process of manually cleaning up messy data sets to a convenient form prior to data analysis. Data gathered in businesses are often messy and are difficult to work with. Therefore, a Data Scientist, especially in small companies, is often required to clean the data before they can use it to draw insights.
7.Data Visualization and Reporting:
Visualizing and reporting data comprise an incredibly important part of the role of a Data Scientist as it helps others, especially the decision makers to take data-driven decisions to drive business growth. Familiarity with data visualization tools like d3.js, Tableau, chart.js, Raw, etc. are extremely helpful for Data Scientists. However, Data Scientists should not just be familiar with data visualization tools, but also with the principles and practices behind visually encoding data and communicating information.
8. Skill Up with Big Data:
Knowledge of Big Data technologies like Hadoop, MapReduce, Apache Spark, Hive, and Pig is a big plus to the career of a Data Scientist. Most Data Scientists work with large data sets that cannot be run on a single machine and require distributed data processing.
9. Get Hands-On Experience:
The best way to hone your skills as a Data Scientist is to get industry exposure. Start an internship or join a boot camp or if you already have experience as an Analyst, get started with a job.
10. Think Data:
They day you decide to become a Data Scientist you should start thinking like one. Companies seek data-driven problem solvers. During your interview process, at some point, you may probably be given a test situation, where you will need to take data-driven decision to make a profit. Basically, a Data Scientist is an extremely powerful and rare combination of a varied range of traits. He or she is an amalgamation of an analyst, communicator, data hacker, and a knowledgeable adviser.
Getting a job is a two-dimensional process. It is as much about finding an organization that needs your skills as it is about developing those skills which are required by a company. So, if you find it difficult to learn these skills on your own, take an online course or enroll for a live virtual classroom (LVC) training.