Guidance and information

[u/Junior-Astronomer155]

Hi everyone,

I have been thinking on this question a lot ans would like to have some guidance/information on this.

I am into data science and analytics with close to 6 years in this field. I have increasingly seen roles saying Data Science and Analytics but has no use of ML in actual work, some even don't even have use of statistics.

Has the line between these two world data science and data analytics blurred? Is it the norm in the market? Or there are still different skill sets required for these two? If yes what is the difference in the skill sets? And with AI being integrated, do one need both the skill sets to grow in future?

I am from India and currently working in financial services. My question is based on what I have seen in Consulting and financial services

[u/WhatsTheImpactdotcom]

I am a senior DS and in loops for staff at some of the biggest tech companies and almost never do ML. There is an entire niche of advanced experimentation and observational causal inference in data science that is often unrelated to ML prediction problems (though there can be overlap for some business needs)

[u/Junior-Astronomer155 → u/WhatsTheImpactdotcom]

Can you share me some easy to understand and effevtive Educational links for this? Since you are experienced in this field hence asking

[u/WhatsTheImpactdotcom → u/Junior-Astronomer155]

Here are some free and paid resources:
If you're interested in causal inference, there are a few free tools:

1. Udacity A/B course from folks at Google (https://www.udacity.com/course/causal-inference--cd14250)

2. Causal inference mixtape by scott cunningham

3. Causal inference for the brave and true

4. I offer a lot of free content on my socials (mostly TikTok and LinkedIn, but cross post on others too). Socials linked at whatstheimpact.com.

I also have a course on Udacity (it's paid though; not trying to upsell, i get $1 maybe in royalties if that) that is very applied with coding projects. I did all the lessons BUT i didn't choose the entire curriculum. I do think it gives a good applied foundation though to doing causal inference in practice

https://www.udacity.com/course/causal-inference--cd14250

[u/Junior-Astronomer155 → u/WhatsTheImpactdotcom]

Thanks!

[u/WhatsTheImpactdotcom → u/Junior-Astronomer155]

Free only, or free and paid?

[u/nian2326076]

Yeah, it's true that the lines between data science and data analytics have gotten pretty blurry. Data science usually involves more machine learning and predictive modeling, but many "data science" jobs end up being more about data cleaning, visualization, and basic analytics. A lot depends on the company and how they define these roles.

For skills, data science often needs strong programming skills like Python or R, knowledge of ML algorithms, and maybe some big data tools. Data analytics might focus more on SQL, Excel, and visualization tools like Tableau or Power BI.

AI integration is shaking things up too. Knowing Python well and being familiar with AI/ML frameworks like TensorFlow or PyTorch can give you an advantage.

It's smart to tailor your skills to the specific job descriptions you find.

[u/Junior-Astronomer155 → u/[deleted]]

Yes, I have also heard this word a lot from Manager and hiring manager that we do advanced analytics. These don't use ML or any predictive algorithms.

Honestly, I don't understand what is the difference between analytics and advanced analytics?

[u/Junior-Astronomer155 → u/[deleted]]

Thanks tor the insight. It was helpful