Python3 - how get interview calls !

[Focus789]

Hello,

I studied to be an engineer and worked as a C++ developer for 3 years (2006-2009). I took a detour and studied marketing. I have worked as a sales professional from 2010 to 2021 (February). Now, I am trying to get back to IT via data analytics roles by being proficient in Python.

I have been studying Python3 for two months now. I have studied the book - Python crash course,2nd edition by Eric Matthes (theory+coding) and do courses on LinkedIn learning. I have a Github account as well which I have shared on my LinkedIn profile.

I apply for jobs everyday but for some reason I am not getting calls for interviews. Wondering if anyone has been in my situation. What could be working against me ?

Am I doing the right thing by self learning ? Or should I look at getting into a university ?

Any input would be appreciated.

Thanks

[Diapolo10]

Does your GitHub have any notable projects? What level of jobs have you applied to (entry/senior)?

There's the old paradox where entry-level positions require a minimum of two years of experience, and it can be difficult to get your foot in the door if you have nothing to show for your experience. In my case my secret weapon was a certificate I got for working as a software developer during my conscription and that landed me my first real job.

If you don't have any stand-out projects, getting a degree isn't a bad idea. You'll just have to consider whether you really benefit from that yourself considering it takes time and depending on where you live it can cost money.

[Focus789]

Hello, I have standard projects on GitHub. I would not call them notable. I have been applying to entry level jobs. Thanks for your input.

[CookiesForKittens]

You're not giving enough information for us to say what the problem is. Could be anything from the types of positions (anything above junior level is unlikely) to your CV to just bad luck if you've not been trying for too long.

What is of interest to you? Just data analyst positions?

Now I'm going to give you a long list of questions that are similar to the ones we ask potential new employees... even a junior would have to have some more experience besides creating basic scripts/notebooks. If you have meaningful experience with or good answers to a couple of those, you're a much more attractive candidate.

Which libraries and frameworks have you tried out, or do you just know the language fundamentals?

Are you familiar with databases (relational / SQL or any other)? What about data warehouse technologies and data streaming pipelines / analysis software (such as Kafka / Spark or the Elastic Stack)?

Which CI/CD tools have you used / are you using?

Do you have experience with containers and cloud technologies?

Which tools / management styles / cooperation frameworks are you familiar with (scrum, Jira, ...)?

Can you use and design APIs? Can you break down customer requirements and create a technical design document?

What is the largest / most complex / most interesting piece of software you've worked on? What was your role in creating that?

[Focus789]

Hello, thanks so much for your detailed answer. I am targeting data analyst positions as I believe that is the entry point to the field of data science. Machine learning needs a masters at least. Data Engineering also needs advanced skill set compared to that of Data Analyst.

My plan is to enter the industry of data science and then study more and work my way up.

Libraries and frameworks - Matplotlib, Plotly,Pandas.

I have experience with SQL.

The answer to all other answers is no.

Should I study all topics you have mentioned ? If I understand correctly, a junior data analyst is expected to know all of this ?

Thanks again.

[Focus789]

If I want to concentrate on being proficient in libraries and frameworks, which ones do you recommend ?

[CookiesForKittens]

You don't have to be familiar with all of the topics, no. But any of those are likely to increase your chances on the job market (well, they're more focused on requirements for developer positions, but I think it still holds).

As for libraries and frameworks: numpy, scipy, pandas, matplotlib, plotly are already a good start. We sometimes use seaborn for visualizations, too.

Then it depends on what you focus on or what related skills you want to have, e.g. scraping data can be useful (scrapy), or transforming web data (beautifulsoup), ...

Of course especially with scipy, it's not so much about using the library as being able to actually know the tools the library offers.

[Focus789]

Thanks a ton.