First 88 seconds, solve all the major problems for most of the companies.

Very Insightful and well said 🔥 Glad I got your video ✨

funny enough descriptive analysis falls under data science as well and many data analyst jobs keep you in meeting and creating quick dashboards with little to no analysis. To do well, have all skills under your belt just in case

I couldn’t agree more !!!

The one thing I feel an overriding sense of with the data field in general is that it's increasingly hard to stay "on top" of it and still have a balanced, normal life. This is contrary motion, because AI has made a lot of the nuts and bolts of our job easier...but in my job we're just using that to accelerate our ML designs, which demands so much more study than hacking together Python and SQL ever did. Maybe it's just me, but I find I have to spend so much time learning how all these new methods work and dig into the theory (EXTREMELY so in ML modelling, where you seriously should know the maths behind what you're running) I don't know if I can realistically pursue it anymore. The gap between true programmer "lifers" and those who just want to do it in their 9-5 is really starting to show imo. Granted, a few of them are also simply gifted also!

As someone who has worked both data analyst and data scientist positions at small to large companies, I 100% agree! 90% of the problems are better and more quickly solved with analytics and data pipelines than with ML. It’s sad for the people who learn a lot of fancy algorithms and never get to use them.

@redwannabil8031

11 ай бұрын

somewhere i heard data structure algorithm is also needed to be learned if i want to get into data scienece field...how much true is it? Could you please share your thoughts?

@0713athena

11 ай бұрын

@@redwannabil8031 for the vast majority of data science positions, you will not need to understand anything about data structure algorithms. Only for very specialized positions. The reason is most of the core data structures we use now have already been implemented in open source libraries and we only work with abstractions.

The need to trains humans on the AI model is a great way to put it

Agree max! I think Data Science is WAYYY too overhyped whereas their business value is really isnt that much when compared to other roles. Data Scientists are usually caught in this weird grey area between Data Engineers and Data Analysts Its also a v vague title that could mean n number of things, dependant on the org anyway. I'm personally more inclined towards business intelligence rn as im starting my career cause most stakeholders really do not care what kind advanced analysis u run on their data, they just care about how accurate the insight is and what it is (at least in my experience) At the end of the day, these roles are mostly support roles that help a certain fucntion of a company i.e sales, marketing or product etc

@mustafaback1

3 ай бұрын

Well said, stakeholders don't focus on effort how much u putted to make a dashboard or eye appealing reports. They focus on accuracy, and whether the insights are making sense or not. So, to have accuracy and business sense, proper mixed of analytics, business and tech must be.

hey, i am an engineer and doing de & bi masters, now i have an analyst role and strong desire for de role, you know there are lots of challenges and things to learn that path, rather 20 years of pure sql and dashboarding this kind of mix up is makes sense or not? what would you do

@Bump2310

Жыл бұрын

No-one can tell you what data professionals will be doing in 20 years time, but I would suggest that if you focus on being someone that can enable analytics at a business level via DE work or an analyst that can help executives answer questions about their business and focus on that as your goal (rather than being good at SQL, being good at Spark, etc.) then your skills will be in demand. The tools will change over the next decades, but people needing to understand their business will not until the end of capitalism at least :D

@bwb9479

Жыл бұрын

@@Bump2310 thank you

My experience is that Data Science, ML, and Big Data are typically pretty useless for 95% of use cases. Unless you want to do dynamic pricing or have huge volumes of B2C data that need to be analyzed its objectively not very useful in improving business solutions.

@rameeziqbal8711

6 ай бұрын

What about Data Analytics?

sad for me as a data scientist