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Data science Success stories

by | Jan 7, 2022 | Uncategorized | 0 comments

Willingness to learn

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.

Steps to go through

  • Learning to code (Python or R)
  • Learning to perform data analysis
  • Learning other topics like SQL, Statistics, and Math

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.

Initiatives

Some initiatives we can take to boost data science friends circle

  • Attending the local meetups
  • Attending data science events
  • Sending out messages and connection requests to strangers on LinkedIn

To be hired to the first job in data science is most difficult. Then things would be more easy

Some case studies to the data science journey

1.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.

To read Kellys story in detail please click here

How I went from zero coding skills to data scientist in 6 months

2.

Sarita from chemical engineering to data scientist career

  • 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:

To read Sarita story in detail please click below

How Sarita launched her data science career in 12-weeks | Institute of Data

TYips to be followed in the journey to 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.

 

 

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