Navigating Career Change within Data: Q&A with Juhi Singh | Coursera Community
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Navigating Career Change within Data: Q&A with Juhi Singh

Navigating Career Change within Data: Q&A with Juhi Singh
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Juhi Singh, Data Scientist at Coursera, is taking your questions about working in the Data Engineering domain in a rapidly growing company with different businesses, challenges in architecting scalable data models, etc.

This thread is now closed to new questions. Juhi will respond by the end of April.

About the Q&A Host
Juhi is a Data Scientist at Coursera and formerly worked as a Data Engineer for 2.5 years. As a Data Engineer, she worked to democratize data among internal and external stakeholders in service of Coursera's mission of transforming lives through learning. A 3-year Courserian, Juhi has built partner-facing data offerings ranging from the new Course, Specialization, and Admin Dashboards to DataHub. She holds a master's degree in finance from MIT, a master's degree in economics, and a bachelor's degree in engineering from the Birla Institute of Technology and Science. In her spare time, Juhi enjoys hiking and exploring new trails in California, reading, and dancing.

18 replies

Rapid change and intensified competence is prevalence in data science domain. Learning ancient philosophy of your culture help you building core values. It guide your direction when you are confusing.
do you have any advice for individuals currently working in oil & natural gas or an industry that isn't always a front runner in new technology/methods? data science will have a massive impact on our industry, but we're usually slow to adapt to & adopt new techniques & utilize new technologies or platforms. i don't necessarily plan to be come a full blown data scientist (personally), but would like to leverage my existing knowledge of the industry with the pending influx of data scientists & data heavy projects. thanks!
I'm curious to know if there are students data science courses in Coursera who are above 50 years old ? Is the digital tech community ready to accept and work with mid-lifers willing and able to re-skill themselves ? With wealth from the repository of their experience, what do you think we can draw from them ? thank you.
As you have been both on the data engineering and science sides, I have questions on both domains.
1. Data science: other than clustering algorithms do you see unsupervised and semi supervised techniques widely adopted by practioners from industry who either can't afford to label the data or don't have very large data to leverage on deep learning techniques?

2. Data Engineering : how often do you refractor the pipeline building code to meet the 99.99 uptime requirement? Or is it managed purely by tweaking the load balancers? How do you forecast your public cloud bills in times when the load is uncertain.
Hi Juhi - thank you for giving all us inquisitive people this opportunity to ask questions. I really appreciate your time.

i see you have a background in finance. I too have an education in financial engineering and currently work in the sell side quant industry. Could you please provide some insight into the path you recommend individuals like me can follow to make a successful transition from finance to data science? In particular, what technical skills do you see being used in the data science industry that are not necessarily front and center in finance? Thank you.
Thanks for having you...
My question is how is it possible to combine Data Science and Cyber Security.
Could you please advice on the pathways available ?
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I'm a fairly new data analyst. I work in a field - the utility industry - where data science is looked at as holding a lot of potential to manage ongoing issues (reliability, pricing, etc.). My biggest concern is that I am often asked to make calculations based on assumptions that are not quite correct or a misunderstanding of what that calculation actually means. My question is, what are the biggest pitfalls you see where data science can be or has been misapplied to a problem? Is that a tactic you suggest to communicate better what a formula represents to people who are more verbal vs. numeric in their thinking? Thank you!
I'd like to build a portfolio of applications that make use of data science. My background consists of machine learning, web development and cyber security.

How valuable will a such a portfolio be when I apply for jobs in data science, how to maximise its benefits in my job hunt 2 years from now, and what aspects of my career search and personal time management do I need to pay attention to while creating these apps? Thanks.
Hello Juhi, thank you for sharing your experience with us.
My questions are very simple and open:
would recommend a career in Data Analysis to beginners like us ?
I just took a course on Coursera about the basic of Data Analysis
What skills you used to grow in this field ? So what challenges you had to face ?
Thanks very much
thanks for helping us on Data Analysis
What background do you expect in a candidate profile ?
I mean I have a degree in Business Economics and then a course in data Analysis, would be a good start?
do you think creativity and passion are good elements in this field ?

(Moved to active Q&A event with Juhi Singh by @Laura)
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Hi there, Juhi!

In your personal experience.... Any advice you could give us in regarding: algorithmic trading and speed trading would be more than fine, involving stock exchanges and mercantile exchanges...

Any advice for people with a finance background looking to get into data science?
  • Are there a minimum set of skills that should be obtained/demonstrated in order to land a role?
  • Are there a subset of specific roles that one should target in order to land on the data science beachhead more easily?
  • How is the perception of self-learners vs people with degrees in data science?
  • With the quickly morphing landscape in data science, is it a domain that will offer stable employment for years to come or is it more vulnerable to automation than others?
  • What are the salary expectations for someone looking to switch careers into data science (i.e. mid -career - approx 40 years old)?
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Anybody ever progress into this career without a technical background or a degree? I’m very interested in becoming a data scientist so am self teaching math and statistics while also working through the Kaggle learn material before I start to do real projects. Ideally I’d like to make the transition into this career within 1-2 years. What is likelihood of being taken seriously?
I have just started learning Machine Learning and I want to be able to got professional in the shortest time possible. What are your suggestions for learning paths and methods that will help me achieve that. Currently I am working in the finance industry as a full stack developer. How viable is for me to change my current work and become a data scientist without a masters degree or a phd.
Hello Juhi Singh and Laura. Thank you for this post. 🙂 It will be very useful.

My questions are regarding one specific role in the Data Scientist area: Data Architect

I am at the Data Science world since 2 year ago. I started has an ETL trainee.
Meanwhile my boss told that with my personal and professional characteristics I will be an excellent Data Architect - A role that I never heard before he mention.

Then I saw this fantastic article from DataCamp:

My questions are these:

  • In your opinion what should be the Data Architect characteristics? - either in terms of technical knowledge or personal characteristics.
  • Which should be a good study path to be a Data Architect?
There are lots e-learning courses that focus on Data Analyst and Data Scientist career path (for example in DataCamp and Coursera) but it looks like that to be a Data Architect we must take individual courses in several MOOC/schools so we get knowledge about ETL with python, Data Warehouse, Big Data courses, etc.

  • And for last, regarding the previous question do you recommend any good online courses that I should take?
I know these weren't simple questions but the information about Data Architect is so confuse and different in several articles/blogs/forum that I am totally confuse with this role.

Thank you very much in advance :)
Best Regards
Thanks for helping us.
I have done done bachelors of business administration from India and would be doing my Masters in Management Information Systems this fall in US. I also have no work experience. I am currently learning Python and SQL from coursera, and I am planning to do the IBM certified data science specialization. My questions are as follows:
1) I understand that for a fresher a role like Data Analyst/Business Analyst is more accessible. Are their certain technologies/skills you would suggest someone like me to learn so that I can get an internship/full time job in data science domain.
2)Since you have done your masters from US, are their certain tips you would like to suggest to incoming grad students like me, in order to successfully land a job?
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This thread is now closed to new questions. @juhi.singh will be replying to your questions shortly!
freetest2019 wrote:

Rapid change and intensified competence is prevalence in data science domain. Learning ancient philosophy of your culture help you building core values. It guide your direction when you are confusing.

Hi freetest2019,

Thanks for that note - I agree with your sentiments. Irrespective of your function in your organization, I think it is imperative that we consciously decide what our core values are and how are we letting it shape our worth ethics. As data scientists, we provide our teams with the insights that shapes decisions and the company's growth. Topics like privacy concerns and algorithmic fairness are important to be kept in mind. If you are interested, I would recommend the Data Science Ethics course as a good place to understand and think about this topic.



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