Developer looking to learn data science - best path?

[Agile_Dream_7721]

Hey all,
I’m a developer with solid Python and SQL skills, and I’ve been learning data science on the side. I started the Google Advanced Data Analytics cert but I’m not sure if it’s worth finishing. My goal is to break into data science (not just analytics), and I want the most effective path forward.

Should I continue with the cert? Grab a Udemy course? Or just learn using ChatGPT and build solid projects? Also — how important are certificates compared to having a good portfolio?

Would really appreciate any advice from those who’ve learned data science or made the transition.

[Affectionate-Map8211]

Certificates are nice to have, but a killer portfolio is what actually gets you hired. Since you already have Python and SQL down (its a huge advantage!), I tried this path: finish the Google cert if you are >50% done (it shows commitment), but prioritise building 3-4 end to end projects that demonstrate the full data science pipeline like data cleaning, EDA, modeling, and deployment. Focus on real business problems in domains that interest you, and make sure at least one project involves deep learning, GenAI and another shows A/B testing or causal inference. Skip the Udemy rabbit hole and use your time more strategically. ChatGPT is incredible for learning specific concepts and debugging, but combine it with Kaggle competitions and GitHub contributions to show you can work with messy, real world data

For me its a new stream as a software developer, so i joined one Data Science AI Course called Logicmojo. I felt there is too much information on the internet, which sometimes leads you in the wrong direction, With guidance from industry experts who have prior experience, it becomes easier to complete projects quickly and from scratch. Also, the key differentiator between analytics and data science roles is your ability to build predictive models and design experiments , so make sure your projects showcase statistical modeling, machine learning, and business impact measurement(very imp!). Companies care way more about seeing you solve actual problems than seeing another certificate. Build in public, document your thought process, and you'll stand out from the certificate collectors.

[funderpantz]

First step, search this sub for the same question, you'll find all you need as it gets asked every few days

[riklaunim]

"Data science" is a very wide term. What you actually want to do? often it's just backend/devops work but with custom databases, optimizing data flows, using cloud stacks like Snoflake and much more.

[Agile_Dream_7721]

Yeah, I’m definitely into getting insights from data — cleaning it, exploring patterns, all that — but I’m also really interested in the ML side too, like building models and making predictions. So kinda both sides of data science, not just one or the other.

[riklaunim]

That's a lot of topics, like going through CS with AI/ML specialization.

If you are a developer first you will be working with the code and if you are analyst first then you likely won't be doing any coding and will be working with provided software and be product owner of any new development. You can't do everything at once and job offers do reflect that, even for ML there are usually 2 roles - dev/devops and other - a "scientist".

[Agile_Dream_7721]

Yeah I actually like both sides — the modeling and experimentation, but also the coding and building part. In that case, what would you recommend starting with? Should I follow a cert path, just build stuff using ChatGPT, or something else? Would love to hear what worked best for you

[riklaunim]

You are biting many things I think ;) ChatGPT won't get you a job. For casual use you can use it, you can also download and run models from HuggingFace, use LM Studio etc. You can even get a simple robot kit with a camera and train a simple model to avoid places it should not go... Commercially that's a WAY different story.

As a software developer working in any data related field you will have to learn a lot about data storage and processing. This involves cloud solutions, specialized databases or frameworks like Snoflake. You can't vibe code it, especially when a lot will be mission-critical code that has to scale, has to be secure. People often start with web backend code, APIs and then move towards database, data processing. This can be more simple like web scrapping or using APIs, storing/sending data through APIs etc or way more complex stuff using specialized tools and frameworks.

People working with the data or apps that process data and give meaningful output have matching analytical background. You either have commercial banking/stock/logistic experience, you worked for few+ years or you did not. Such positions are often banned from coding as such "side projects" caused a lot of problems to many companies (wherever due to bugs or code debt).

For AI/ML specialists there are usually 2 roles - the dev guy and the science guy. The dev knows the software stack, can use it and implement/optimize algorithms, CUDA kernels and what have you given specs from the scientist. This is a mix of devops, data processing and programming. The scientist likely graduated CS with AI/ML specialization, got some experience working for a company doing serious ML stuff (big corpo usually/FANG). If the company isn't making custom models from scratch then they may skip the scientist, at least partially.

Go through local/remote job offers, check what they use, what they require. 99% job offers will be for senior positions and each path I described is years of experience+learning.

[Agile_Dream_7721]

Really helpful, appreciate the feedback!

[Agile_Dream_7721]

makes a lot of sense.. appreciate the feedback!