However, breaking into data science can be a challenge.
First of all, you need some serious technical skills, and the sooner you start learning those skills, the sooner you can start down the path of actually being a data scientist.
Secondly, you need to convince someone to take a chance on you as a brand new data scientist. We all know that getting a job with years of experience can take some doing, but getting a job in a field that you have little experience in? That can be extremely difficult.
Thirdly, you need to know the industry. That comes from time, experience, interaction with data scientists and digging into actual data science problems.
However, there are ways you can become a data scientist in a very short amount of time, and you might not even have to quit your existing company to do it.
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Here is some real advice from real data scientists on how to get started with a career in data science.
1. Brush up on hard skills
A firm grounding in statistics is essential before jumping headlong into machine learning algorithms so that you know which algorithm is the correct one to apply to a data set.
Learn to write production ready code. Data Structure & Algorithms level coding practice in python will prove to be invaluable, and you will need to be able to write your own code.
SQL and experience in working with databases is essential. After all, a data scientist does work with vast quantities of data all day.
Learn skills that will work well together. No skills are standalone, and you will need to use them in combination to solve multiple problems at once.
An easy way to get started is through this 9 month Data Analytics certificate through CalTech. If you’ve already got a BA, you can jump right in to a MSc in Data Science through Liverpool John Moores University. No coding experience required.
2. Develop core soft skills
Part of working with data also requires the ability to communicate the results of your data to external stakeholders who will not possess technical or statistical knowledge. This requires translating your findings and using layman’s terms so that it can be easily understood by anyone.
Structured thinking is essential to have a firm understanding of the actual business problem. Learn to identify the true problem so that you can focus on creating the correct framework or application to find a solution for the maximum number of issues.
Structure problems so that you can approach them logically. Plan it out step by step so that you can reach a solution. This will mean that large problems are broken down into smaller chunks and errors located easier.
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3. Networking is critical
Data Science is fairly collaborative in that you often work in teams to deliver large projects. Even though the responsibility for individual components lies with one data scientist, solutions are often found collaboratively.
Even before getting into data science, networking can prove useful because it can help guide your thinking in terms of career path and strengths.
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Top tips for success:
- If you’re looking to switch careers, search for a data role within your current organisation. Identify a problem and collaborate with Data Science/Analytics or build and showcase a solution, building the case for a lateral move.
- If you’re interested in research, consider further education with a master’s or a PhD. Look for academics working on problems that you’re interested in and apply to their courses to study under them.
- If you’re still a student, consider working on problems and showcasing them on GitHub or Linked in to build your portfolio. Also consider taking short courses in coding, SQL, analytics and other related areas. You can explore free courses at upGrad before taking the plunge.
- Find a mentor in the field, preferably an experienced professional in a position you’d like to aspire to.
Contributions from Sameer, Shardool, Antan, Ashish, Data Scientists at upGrad