Related Posts
Best YouTube channel to learn Salesforce?
Microsoft is hiring an account executive and I’m really interested in the role. I have B2B sales experience in a different industry, but my skills include lead generation, prospecting, account management, negotiating, etc (all within the C Suite and other decision makers).
Would appreciate knowing if this is something I have a shot at with no tech sales experience and what I could expect for promotional opportunities and compensation.
Thanks for the help!
More Posts
Conversation Starter
Guys what is paytm kit???
Enthusiast
Additional Posts in Data & Analytics Consultants
Mentor
Got messaged by a C3 . ai recruiter. Read that wlb is bad and that the interview process is absurdly long, but the Glassdoor reviews are 4.2 and can't find actual hours worked posted by anyone. How's the culture really? I'd be aiming for DS consulting, something more functional but with DS/ML concepts as my differentiator.
C3.ai, Inc.
New to Fishbowl?
unlock all discussions on Fishbowl.
For python you’ll want to learn data wrangling with Pandas — basically can you filter for rows of data based on give criteria like amount and time, create new columns on the dataframe to do additional calculations, do get aggregate metrics like average, sum, count, for whatever groupings of rows.
For SQL there are some conceptual overlaps. But the main operations to get familiar with are joins, filters, aggregate functions. Once you are more advanced you can get into stuff like window functions. There should be lots of articles on basic SQL skills, start there.
GitHub is probably unnecessary. Databases and ETL too, but can’t hurt if you wanna pick up some background knowledge.
I think one of the stronger moves you can make, apart from applying these skills at work, is putting together a basic data pipeline and model from a publicly available dataset, and get some insights. You’ll get a sense for what data processing is like and how models work. If you end up participating in Kaggle competitions it could be a good talking point for interviews too.
My response skews higher up in the programming skill chain. If you don’t want to get so deep into coding, there’s plentiful jobs in other niches as well.
Sink or swim. Two ways.
1. Prep for interviews seriously. Then talk to contract vendors. Then knock it out of the park.
2. Start doing the job in place. Get some data and analyze the bajezus out of it to the point whoever is the would-be stakeholder basically has to take your project. Eventually get a title change.
Ya “data analytics” doesn’t use Python and barely any SQL, that’s data science, and if you don’t have a PHD you shouldn’t really be doing data science in a professional setting bc you won’t actually understand anything you’re using the algorithms for. If you know powerbi and tableau (and can back it up) your a job application away from being a data analyst. Now if you add some basic schema architecture in there and some solid SQL fundamentals then you’re getting into BI Engineer territory. People often over inflate what data people do, it’s not that wild in 98% of situations
@SC1 considering that you were the one who originally pushed back on Python and SQL because it belongs to “data science”, the hashtag at the end is bit ironic.
Agree that it may be tough to break into DS roles without some kind of shift, but a PhD is absolutely not necessary for 99% of companies that build models for marketing analytics, demand forecasting, supply chain optimization, geofencing, etc etc. The required technical skills can be learned in a masters program for CS, DS, or analytics, and business knowledge can be as important as technical knowledge here. Again, the current generation of algorithms is highly performant and well documented for a variety of contexts. You do not need a stats PhD level of knowledge to build models, and the technical bar is only to get lower and lower as ML becomes more commodified, and more and more companies want people who can model. Of course that doesn’t mean that an entry level DS should be able to lead a whole project, regardless of whether or not they have a PhD.
If you’re talking about actually creating novel algorithms, that is very niche stuff which does required an advanced level of stats knowledge. But that’s largely irrelevant on the job market apart for research scientist roles at places like Google and Meta that do novel research. For industry applications a PhD is overkill, nobody expects that anymore. If someone want to spend 5 years of your life to feel like they’re doing “real data science”, go ahead. I say it’s a much better use of your time to go for a CS/DS masters at strong state school and target local companies if need be.
So the Consensus is continue Python master SQL learn some more stats and build data engineering skills to be even better. Then domain knowledge come later
Agree that it’s a good idea to leverage whatever domain knowledge you have, provided your industry has a lot of data jobs and pays them well
We're hiring for a Senior Business Data Analyst/ BI / Dashboard developer.
5+ years exp in data, analytics or other data heavy roles. Must be highly proficient in SQL, Qlik Sense, and Excel. Position is in Mountain View CA. Nice-to-have Skills: Workato experience, Procure to Pay, Sourcing and Supply Chain knowledge, Python Oracle (eBiz R12) & exp building custom APIs. We’re a big global FinTech company. Send me your LinkedIn link if you are interested!