Becoming a Data Scientist: With or without a degree
Nowadays, a pervasive question that resounds in every sector is, if one can become a professional without having a degree in a particular field. This question is ‘frequently-asked’ in the Data Science domain. A lot of DS enthusiasts always have one question on top of their minds- if they can be a part of this field without a relevant degree.
Let’s put your qualms to rest!
DJ Patil, the Chief Data Scientist of the US (2015), who built the first data science team at LinkedIn, tweeted the fact that a degree or course major does not affect the chances of learning data science and becoming a data scientist. He also mentioned that the management must make judgments while hiring individuals based on their work rather than their degree.
Becoming a data scientist without a degree
We have a piece of good news. There is a probability of becoming a data scientist without a degree. However, if you aspire to become a data analyst, you will require a graduate degree in science, engineering, technology, or mathematics. Also, you must possess experience in computer modelling, predictive analytics, or programming.
You can improve your chances of becoming a data scientist without a degree by following the steps mentioned below:
Look for learning resources everywhere:
Get a sense of which learning style out of all different learning resources and communities suites you. Get acquainted with the fact of how you want to learn and establish your learning routine based on it. Also, get a sense of the skills required to be a data scientist and the amount of progress required to achieve the goal. All you need is the right resource to give wings to your dreams of becoming a data scientist.
Learn a programming language:
Learn at least one programming language to start working with data at scale as a foundation. To begin with, embrace either Python or R. Both languages support the data science workflow, though Python tends to be more prevalent in the industry, and R tends to be more prevalent in the academic community.
Instead of learning both languages, you should focus on learning one language to get started. If you chose Python, you might need to install the Anaconda distribution as it simplifies the process of package installation and management on Windows, OSX, and Linux. Focus on mastering data types, data structures, conditional statements, comparisons, imports, functions, loops, and comprehensions, rather than focusing on becoming a language expert.
Learn the basics of statistics:
As a data scientist, you will be required to analyse and interpret data using statistical methods. The fundamental law of data science is to infer insights from smaller data sets onto larger populations using statistics. Be familiar with the statistical methods and thinking probabilities to b a data scientist.
Learn the importance of data in a particular industry:
The best data scientists understand the ins and outs of the business, along with having the ability to work with large and complex data sets. To start sighting the insights of the business concerned, start combing your acquainted data science methods with the domain knowledge.
Build real-world projects:
Start building your portfolio and acquire exciting data science projects with your knowledge. Build analysis based on different angles and questions and learn to communicate it to others. Use sites like WordPress and Github to apply your skills in theory and create an exciting portfolio for yourself.
Network and get to know the data science community:
Make a network and create connections with the people in the data science community. To understand the kind of opportunities there are for you in data science, and it is necessary to start networking. Also, start finding people you can collaborate with and learn from, and start building relationships with hiring managers or the people who are seeking data scientists for their respective firms. Even if you are not willing for a full-time job in data science, you can opt for freelancing roles and can still build projects on a professional level.
Prepare for the data science interview process:
Start reaching out for job opportunities after growing your network and building an impressive portfolio. Focus on mastering your data science skills to ace your job interview.
Skills required to become a Data Scientist:
The following skills will be required to become a data scientist with or without a degree:
Acquire Data Science skills with SRM University AP and LEARNXT.
The focus of the world is on being a professional data scientist, but SRM University AP focuses on training students to be quality data scientists. You will gain in-depth knowledge of data science and will have your hands-on experiential learning. Bringing the best of international collaborations at your doorsteps, SRM train students to handle large data sets comfortably and efficiently. You can enrol in the various full-time & part-time courses offered by SRM University AP in collaboration with LEARNXT to realise your ‘Data Science’ dreams.
Stay tuned to LEARNXT to know how you can become Data Scientist with or without a degree.