Most of us look for inspiration. Inspiration helps a lot in breaking the self-imposed barriers. Inspiring stories not only help in improving self-confidence but is also a great source of motivation. It helps a lot of people to reach great heights.
Many people reach me for guidance and support in learning Data Science. Many of them even have a lot of self-doubts they ask me to validate if they have the skills to succeed in data science. I often try to point them to someone from whom they can draw inspiration. I have read and listened to the successful transition of many people into data science. I will be sharing some of the inspiring stories of people who found their way to data science.
Before getting into those inspiring stories. Let me explain the valuable lessons I learned from these inspiring stories. There were more than few traits common across these people’s journeys. Let me put it in a way easy to understand and could help you to kick start your journey.
Willingness to learn to code
Programming is an important aspect of a career in data science. Almost every activity that a data scientist would perform involves coding. Right from data collection, exploratory analysis, and model building.
While looking for inspiring journeys I focus on people coming from a non-traditional background. People coming from non-technology backgrounds. People having zero coding experience. I guess this makes their story inspiring. All those who found their success in data science were willing to learn to code. They were not intimidated by the Kaggle notebooks that they were not able to understand initially. They all understood that it takes time to gain knowledge and pursued till they acquired all the required knowledge.
Programming is one of the biggest show stoppers. It is this particular skill that makes many frustrated. It even makes them give up their passion for a career in data science. Programming is not exactly a hard thing to learn. Yet, approaching without a proper plan can go bad. If you are looking forward to the right way to learn to code for data science, read the below article.
Can’t Code? Here is the Best Way To Learn To Code For Data Science
Signing up for courses
Signing up for courses helps a lot. It provides a structured approach to learn something new. There isn’t a single most popular course. In fact, there wasn’t much overlap in the courses they have undertaken.
Some of them enrolled themselves in a self-paced online course. There were many who have opted for a course that needs to be completed in a fixed time. I guess the fixed time makes people push their limits.
Parkinson’s Law holds very much true, "Work expands to fill the time available".
It is also common for people doing self-paced courses to not get enough time to focus on their learning. Trust me, learning is an important aspect of a career in data science. Even after finding your way into data science, it is important to continue learning to stay relevant.
Though the courses undertaken by the individual were different from one another. The path they all took to learn data science was more or less similar. Their learning journey was mostly looking like,
- Learning to code (Python or R)
- Learning to perform data analysis
- Learning other topics like SQL, Statistics, and Math
- Learning algorithms
The key benefits of signing up for a course are, the structured approach and the tangible benefits offered. The structured approach help in better managing and tracking the learning. The certifications help many in their job search and it also helps many in getting a pay rise. If you are looking forward to a plan to learn data science, check below video
Below are some popular resources to get started with learning data science. There is no good or bad option. It’s all about which one works best for you.
- Python/ R – Codecademy, HackerRank, and datacamp
- SQL – Mode and LearnSQL
- Stats – Khan Academy
- Math – Mathematics for Data Science Specialization by HSE University
- Data analysis – Data Analysis with Python
- Machine Learning – Andrew NG’s ML Course, Applied Data Science By University of Michigan, and IBM Data Science
Having a growth mindset
Everyone from my inspiring people’s list had a personality that embraces a growth mindset. Having a growth mindset is more about believing in yourself and your ability. People having a fixed mindset tend to believe certain skills are inborn. They believe that those skills can’t be acquired. Having a growth mindset is the exact opposite. It is about believing in one’s self that anything can be learned.
Having a growth mindset plays a major role in data science. There are many topics to learn and it can be overwhelming. Instead of saying, I can’t learn math, I can’t be a good programmer, I can never understand statistics. People with a growth mindset tend to stay positive and keep trying. Some might learn quickly, some might need more time. It is OK to be a slow learner. Everyone who shows resilience and persistence will eventually reach their goal.
Many of these people who finally made it in data science faced failures in the beginning. Many of them had unsuccessful job interviews. Many of them ran into issues while learning data science. It would have been easier to give up but none of them gave up. They took learnings from their failures and continued to move forward.
Networking
Networking plays a very important role in finding the right job. Most of these people were open to networking. Most of them were looking for opportunities to expand their professional network. Few things that helped in expanding their professional network were,
- Attending the local meetups
- Attending data science events
- Sending out messages and connection requests to strangers on LinkedIn
- Volunteering with organizations like DataKind
Getting the first job in data science can be very difficult. There are many people out there trying for a breakthrough in data science. There are 5 million registered users on Kaggle. There are more than 5 million people enrolled on Andrew NG’s machine learning course. It takes effort to stand out from the rest. Having a good professional network helps a lot.
Another convincing reason to work on building your professional network is it helps in your job search. Many organizations prefer recruiting people through employee referral programs. Having a good professional network will expose you to more job opportunities.
5 people’s inspiring journey to data science
Below is the journey of five people into data science. I found them to be very inspiring. If you are looking for some inspiration to get started in data science. Below are stories of people from non-programming backgrounds who found their way into data science. Hope it gives you the motivation to kick start your journey into data science.
Kate’s journey into data science after a Ph.D. in Neuroscience
- Kate’s Ph.D. in Neuro Science provided exposure to some technical aspects
- She lacked practical exposure to data science
- She had zero programming skills when she started
- She first started to learn to code using CodeAcademy
- She continued to learn advanced topics from online courses
- It took her 4 different courses to learn the concepts and to find a job
- Meeting people from a similar background who made their way to data science helped her in better understanding her strengths and weaknesses.
- Here is Kate’s full story
How I went from zero coding skills to data scientist in 6 months
Kelly’s journey to her dream data science job
- Kelly has a bachelor’s degree in economics. She moved to the US for a master’s in business administration.
- After an unsatisfying job stint, she applied for Galvanize data science immersive program, a competitive course for data science.
- After getting rejected four times at Galvanize data science program she finally got selected in her 5th time
- It took her 475 job applications and 6 months to get her dream job. There is definitely a lot to learn from this journey.
- Based on her experience she suggests introspecting after every interview to understand things that are working and those that need improvement.
- She says that following tech blogs helped her in interviews.
- Here is Kelly’s Full story,
How to land a Data Scientist job at your dream company – My journey to Airbnb
Sarita’s journey from chemical engineering to data scientist
- Sarita has a Ph.D. in chemical engineering and later moved into data science
- Her passion for data science made her restart her career and begin from the scratch.
- On completing her 12-week data science course she started working as an intern and later got into a permanent role at the same company
- Sarita says her SQL and Python skill helps her a lot at work
- Here is the full story of Sarita:
How Sarita launched her data science career in 12-weeks | Institute of Data
LinkedIn influencer Nick Ryan’s journey into data science
- From wanting to play basketball to stats, this is indeed a motivating journey many can relate to.
- Nick utilized his 2 hours of travel time to learn programming, stats, and machine learning
- Nick suggests everyone learning data science need to focus on the basics first before getting into the algorithms
- Some of the interesting tips were the importance of staying motivated to learn data science and how networking help in getting a job in data science
- Nick also talks about the advantages of building a professional network
- Here is the complete journey below
Rutger Ruizendaal’s journey to data science while doing a non data science master’s program
- The search for his master’s thesis actually led him to data science
- Rutger’s thesis was on social media strategy for the music industry it led him to Twitter sentiment analysis using R
- Rutger decided to use data science for his thesis that helped him to learn data science and complete his thesis as well
- It then led him to further courses on Python, SQL, Tableau, and Machine Learning
- One thing is very clear from Rutger’s journey, he wasn’t rushing to learn about the algorithms. He instead focused on the basics first.
- Then got a job in data science just after graduating from his masters in communications
- Rutger in his learning journey completed about 20+ data science-related courses
- Link to the full story below
Tips to embark on your journey into data science
- It is important to have a goal and set a plan to achieve your goal. Your passion for data science can only take you to a certain level. It needs proper planning and tracking to reach your goal
- Take time to chose the right resource to learn data science. There are many resources out there on the internet to learn data science. But, you need to choose the right ones that match your need.
- It will be great to meet people in data science with a background like yours. Talking to them helps in understanding the actual areas which need proper focus. For example, people from research backgrounds might need minimal support in learning statistics. But, they would need more support to learn to code and to understand the application side of data science.
- For people with experience in non data science. Try to look for job opportunities where you can leverage your current skills. For example, people coming from a science background can look for jobs in health care. Data Science is industry or domain agnostic.
- Do not apply for your dream jobs first. Many companies have a policy of having a cool-off period before shortlisting a candidate again for the interview. Give a few interviews and once you are confident then go for the bigger ones.
- Subscribe to popular tech blogs, that help a lot in improving your knowledge. It also helps in better understanding the application of data science.
- Have a mentor or a person to whom you can seek advice and get inputs. A mentor would be helpful in developing your professional network. Having a mentor for support and guidance helps a lot in making the right decision. Small right decisions throughout your career will have a huge compounding impact.
- Don’t rely on the courses you sign-up for. Practical exposure is equally important in data science. A good way to increase practical exposure is learning by doing projects. Here is a blog about learning data by doing projects.
A Step-by-Step Guide to Completely Learn Data Science by Doing Projects
- Have learning buddies. It helps in solving complex problems and having accountability. Having a deep technical discussion with your buddies helps in better understanding the concepts.
- Learn to write and promote your work. It is important to work on your eminence in data science. It will help a lot in bringing better opportunities.
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