STEPS TO BECOMING A DATA SCIENTIST
Studies show that by the year 2020, the world will be producing 50 times as much data as we were in 2011. As the need for data is increasing at an exponential pace, so is the necessity for data scientists to maneuver and handle this data. In recent years, the number of aspiring Data Scientists has been growing as well, especially bolstered by the fact that Harvard Business Review, in 2017, voted Data Scientist as the “Sexiest Job”.
However, no matter how much the popularity of becoming a Data Scientists is growing, the demand is even greater. The market has as of yet not been saturated, so it’s the perfect time for you to embark upon a career in Data Science and carve a name for yourself. According to research conducted by the McKinsey Global Institute, there will be a need for 5 million Data Scientist jobs in the U.S. in 2018. However, there’s a scarcity of data scientists to fill these roles, so they’ll have to be filled through training and retraining.
In this article, we’ll show you all the necessary steps to becoming a data scientist. However, before that, let’s explore what Data Science is and what a Data Scientist job entails.
What is Data Science?
Data Science is a field that makes use of computer science, statistics, and analytics to enhance business growth. It includes the use of automated and intelligent methods and computing power to analyze and go through vast amounts of raw data in order to find patterns and gain insight. As such, the information gathered can then be used to structure business strategies.
What does being a Data Scientist entail?
A Data Scientist is a relatively new field that combines aspects of an analytical data expert, technical expert, and a scientist. As such, they can use their technical knowledge to design methodologies to compute information, analyze data, and then use their research skills to structure solutions. Data Scientist is a highly sought-after position because of their current scarcity and their ability to combine the technical computer world with their business acumen.
Steps to Becoming a Data Scientist
Seeking a career as a Data Scientist is a smart move in this day and age. However, it’s also extremely difficult to break into and you need a lot of raw capabilities and skillsets related to statistics, analysis, mathematics, economics, operations research, and computer science. If you believe you have it in you, the following steps will show you how to go about it.
Applied Mathematics and Statistics
Most data scientists already have a background in analysis or statistics. However, many data scientists come from the opposing world of business and economics as well. As such, if you’re coming into this position from a non-technical field, you need to brush up on your applied mathematics and foster your skills as a statistician before you even consider data science. These will be crucial skills for you to have.
Machine Learning
Machine Learning is quite possibly the most crucial aspect of data science. To put it simply, machine learning is the process whereby algorithms are used to allow a machine to compute data, make predictions, and discover patterns. It’s a form of data modeling that’s crucial to the world of data science. As such, if you want to become a data scientist, you need to understand machine learning tools such as k-nearest neighbors, random forests, ensemble methods, and various others.
Coding
Some of the most common statistical programming languages are R, Python, and SAS. You need to be fluent in at least one of these statistical programming languages, and have a working knowledge of the others as well. Furthermore, you also need to be fluent in a querying language like SQL. This forms the cornerstone of data science and is crucial regardless of the position or industry you’re applying for.
Distributed Databases
You’ll be dealing with vast amounts of data and storing them in various databases. As such, you need to develop a sharp and thorough practical understanding of databases like MySQL, Postgres, MongoDB, Cassandra, and others. Quite simply, if you can’t manage data, you can’t be a data scientist.
Multivariable Calculus and Linear Algebra
It’s true that sklearn or R can be used effectively for out of the box implementations. However, it’s still necessary for you to have a working knowledge of multivariable Calculus and linear Algebra as they form the very basis of machine learning techniques. Furthermore, when you apply for jobs, it’s quite possible that you’ll be asked questions related to basic multivariable calculus or linear algebra based on which of your aptitude skills will be tested.
Data Munging
When you’re a data scientist you’ll be dealing with a lot of messy and haphazard data that will be impossible to make sense of. As such, before you do anything, you’ll have to clean up the data sets using a process called Data Munging. This will be especially useful if you’re working in small businesses.
Data Visualization and Reporting
In order for the decision makers of the company to benefit from the data enough to make relevant decisions and formulate strategies, they’ll have to understand the data. Which means that you’ll need to present it to them through visualizations and comprehensive reporting. As such, you need to be well-versed in using data visualization tools such as d3.js, Tableau, Power BI, Qlik, chart.js, and many others. However, it’s not just enough to technically understand data visualization, you need to also know how to best present it so it can be grasped by others.
Big Data Technologies
As a data scientist, you might be working with large sets of data that can’t be run and analyzed on a single system, especially if you’re working for big corporations. As such, you’ll need distributed data processing skills, for which you’ll need to know how to handle Big Data technologies like Hadoop, MapReduce, Apache Spark, etc.
Industry Exposure
Once you have mastered all of the technical skills, abilities, and tools mentioned above, you’ll need to start garnering industry experience. Before you can find a job, you’ll have to either land an internship or join a bootcamp. Once that is done, you can get a job as an Analyst.
Correct Mind Frame
In order to be a successful data scientist, you must always think like one. Your job depends on you taking data and coming up with creative solutions to turn a profit. During your interview process, you may very well be given a problem that you have to solve. As such, it’s important to start thinking data in all walks of life so that your responses and reflexes in terms of data analysis are sharpened.
To put it simply, a data scientist is a highly coveted job. However, because of the level of skills and specialization necessary, it’s also a scarcity. Most companies hire data scientists as individual consultants or keep them as retainers for large sums of money. If you play your cards right, this could be the perfect avenue for you.
