Data science is a trending topic right now because companies are scrambling to find candidates to fit this position.
Interest in working in this industry is growing, but not at a fast enough rate. A McKinsey study predicts that by 2018, there will be a need for 490,000 data science jobs in the United States but only 200,000 qualified candidates to fit this position.
The skills gap in data science has many marketers, business people, and computer scientists considering going into the field. And many of those people wonder: Where do I start? So for novices who want to get into data science, where there are jobs to be had, here is how to break into the field.
How to Become a Data Scientist
Go Back to School
This isn’t what most people want to hear, but the best way to start learning data science is reviewing courses that you took in high school and college. Depending on your math background, this could be intensive or a simple review. You will want to be familiar with the following courses:
- Linear Algebra
- Introduction to Computer Science
- Algorithms and Data Structures
If you are interested in data science to begin with, most likely you took many of these courses throughout the course of your studies. If you want a refresher, Coursera has classes from top universities across the country.
Master Coding Skills
Depending on your career, you most likely have knowledge of some code, at the very least HTML and CSS (if not, try taking Codecademy’s courses for free). To become a data scientist, it’s important to learn one or more of the following coding languages:
Many data scientists recommend learning Python, and Google has a free course to take on this. If you have mastered these languages, try learning one of these:
- Java Script
Mastering more than one programming language will make you a strong data scientist and a desirable candidate for companies.
Learn More about Data
If you’re going into this industry, you will have to really love data. If you enjoy analyzing data trends and using it to predict behaviors, then this is a field for you. To become a proficient data scientist, it’s important to learn certain data specialities, such as:
- Data Analysis in Python and in R
- Data Mining
- Machine Learning
- Data Munging
There are fantastic books for data novices to read to learn more about this topic. Here are seven books for beginners who want to learn more about what data can do.
Follow to the Data Science Community
This field is constantly changing as new technologies emerge. There are industry experts who blog about data science, giving advice, tips, and trend forecasts. Follow blogs such as:
Depending on the specialization, there are plenty of blogs and resources to learn more about data science and many of the authors are open to talking to newbies.
Boot Camps vs. Self-Study
More and more data science boot camps are popping up due to the interest and skills gap in the industry. Since data science changes rapidly, many universities have a tough time keeping up, so even students are deciding between self-study or going to a comprehensive boot camp to learn data science.
The clear benefits of boot camps is that, if you have the time and resources, you learn everything all at once with a multitude of industry experts and influencers. Upon graduation, members typically have a strong community to rely on when it comes to the job hunt.
However, boot camps are not free, so those happy at their current positions may choose self-study to master the field.
Data Science is Growing
No matter what the learning method, the fact is that data science is growing quickly. The number of data scientists has doubled over the last four years, as businesses are realizing the value of data.
The amount of data is more than we even know what to do with, as more data has been created in the last two years than in the entire history of the human race.
Therefore, the demand for individuals who can actually do something with this information is huge. Now more than ever, there are many resources and courses to help novice data scientists master the field.
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