If you've been in data science for a while, you've probably run an A/B test. You split users randomly, measure an outcome, run a t-test. That's the foundation — and it's genuinely important to get right.

But as you move into senior and staff-level roles, especially at large tech companies, the problems get harder. You're no longer always handed a clean randomized experiment. You're asked questions like:

• A PM launched a feature to all users last Tuesday without telling anyone. Did it work?

• We had an outage in the Southeast region for 6 hours. What did that cost us?

• We want to measure the impact of a new lending policy, but we can't randomize who gets it due to regulatory constraints.

This is where causal inference comes in — a set of methods for estimating the effect of an intervention even when randomization isn't possible or didn't happen.

Note that this skill is often tested in the case study interview for product and marketing data science roles.

The spectrum from junior to senior experimentation:

At the junior end, you're running standard A/B tests — clean randomization, simple metrics, straightforward analysis.

At the senior/staff end, you're dealing with:

• Spillover effects — when treatment and control users interact, contaminating your experiment (common in marketplaces and social platforms)

• Sequential testing — running experiments where you need to make go/no-go decisions before fixed sample sizes are reached, while controlling false positive rates

• Synthetic control — constructing a counterfactual "what would have happened" using pre-treatment data from other units

• Difference-in-differences — comparing treated vs. untreated groups before and after an event

Where is this actually used?

This skillset is highly valued at mature tech companies — Netflix, Meta, Airbnb, Uber, Lyft, DoorDash — where the scale of decisions justifies rigorous measurement and the data infrastructure exists to support it. If you're at an early-stage startup, you likely don't have the data volume or the stakeholder demand for most of this yet, and that's fine.

If you're aiming for a senior DS role at a large tech company, causal inference fluency is increasingly a differentiator — both in interviews and on the job.

Whether you're just starting or already an expert, Excel has the power to boost your income.

Check out this video to learn how to create Fault Trees for Risk Management. Watch here → https://youtu.be/c4b5YW_lj_Q

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check this out if u wanna know more about Data Science

World is hit by recession, US federal reserves is increasing the rates to balance inflation, people are loosing job, structural readjustment, moon lighting, cost cutting are the words that you will hear by big tech firms as reason to do the lay off. But I believe this too shall pass, and that's what I have discussed in my latest podcast.

Listen to it and let me know what you feel about recession and job losses

Link of podcast : Recession and Layoffs

After coaching dozens of DS candidates into roles at Meta, Uber, Airbnb, Google, and Stripe, the most common mistake I see isn't getting the stats wrong — it's asking the interviewer to do your job for you.

It sounds like: "What metrics does the business care about?" Candidates think this shows humility or thoroughness, but interviewers hear it as an inability to think independently about a business problem.

Strong candidates propose metrics with reasoning instead. For a coupon campaign, that might sound like: "I'd focus on revenue per user rather than conversion rate — coupons typically lift conversions while hurting margin, so conversion rate alone isn't actionable." One sentence. Product intuition, statistical awareness, and business judgment all at once.

If you do want to ask a clarifying question, frame it around a proposal. Something like: "Uber prioritized user growth over revenue for years — if this team is in a similar growth phase, I'd focus on conversions or new user acquisition. If not, I'd prioritize revenue or profitability." That's a clarifying question that still demonstrates business judgment.

That instinct — working through a problem systematically rather than outsourcing it to the interviewer — is exactly what I teach 1:1 and in my interview prep course. If you're targeting roles at Meta, Netflix, or Uber, this can help you stand out among hundreds of qualified applicants and be the difference between an offer and a rejection.

Which class is best to learn it ? With placement assistance.

Hi everyone! I am a beginner in Data Analytics and I would like to start with the (very) basics.

Can someone guide me on:

• Which is the first tool beginners should know? Which is a first language?

• Any resource/tutorial on self-study?

I am here to seek some basic advice that will help me get off on the right foot.

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Hello All, I am learning Data Science from scratch with Julia, and I am blogging and vlogging about it here https://data-science-with-julia.gitlab.io/2021/07/26/k-nearest-neighbors-in-action.html

I hope you people feel it's useful.

Hello, and thankyou for opening my post.

I seem to hear from a lot of people who seem to make a lot of money from IT industry. Last few days talked to some of my school mates, who were below average in school, could not clear IIT JEE .Studied in tier 3 colleges entered into 15000 rupees job and now after 4 yoe they brag about their salaries as 14 lpa just by switching companies:/. This makes me feel where did I go wrong(I am a teacher).

Maybe I am in the wrong field where 1lpm salary is quite far away. But I know it's not just me, I have read in some places how IT people suffer in this industries, recent layoffs from service based industries etc.

Please tell me does everyone earns this much or it's just bragging and how much is in hand salary per month?

Also please mention the lifestyle and hours of work in a day and in a week. What are the working shifts?

Thankyou for reading till the end.❤️

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In today's fast-paced business environment, the jargon surrounding AI and Machine Learning can often blindfold business leaders. Many such believe that every piece of information—be it PDF files, images, or other data—is suitable for ML workflows.

Take, for example, a leading laboratory that has a wealth of test results. What they truly need to know is whether the results are positive or negative. 🤔

This brings to mind the age-old proverb: "Don't use a sword when a needle will do." 🪡 In situations where simple rules can effectively solve problems, there's no need to complicate matters with ML or DL classifiers.

Let's focus on leveraging the right tools for the right tasks! 💡

I am just starting with my data science degree and we are going to learn python and r. For what use cases do you prefer using r?

Are you interested in AI and healthcare? Attend the first annual California AI Summit of the Girls Computing League on September 25 with a prize pool of $2400.

To compete for prizes, you can submit a 3-5 minute pitch describing an AI and Healthcare product you could see as useful to your community. During the event, winners will be announced, multiple professors and researchers in AI and healthcare will conduct workshops, and attendees will play interactive games.

Please note that you do not need to submit a project to attend and we encourage anyone interested in learning more about AI and healthcare to join us. Additionally, this event is open internationally but based in California.

For more information and signing up, check out the link below. Hope to see you there!

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Breaking the LLM Hype Cycle: A Practical Guide to Real-World Adoption

LLMs are the most disruptive technology in decades, but adoption is proving much harder than anyone expected.

Why? For the first time, we’re facing a major tech shift with almost no system-level methodology from the creators themselves.

Think back to the rise of C++ or OOP: robust frameworks, books, and community standards made adoption smooth and gave teams confidence. With LLMs, it’s mostly hype, scattered “how-to” recipes, and a lack of real playbooks or shared engineering patterns.

But there’s a deeper reason why adoption is so tough: LLMs introduce uncertainty not as a risk to be engineered away, but as a core feature of the paradigm. Most teams still treat unpredictability as a bug, not a fundamental property that should be managed and even leveraged. I believe this is the #1 reason so many PoCs stall at the scaling phase.

That’s why I wrote this article - not as a silver bullet, but as a practical playbook to help cut through the noise and give every role a starting point:

• CTOs & tech leads: Frameworks to assess readiness, avoid common architectural traps, and plan LLM projects realistically

• Architects & senior engineers: Checklists and patterns for building systems that thrive under uncertainty and can evolve as the technology shifts

• Delivery/PMO: Tools to rethink governance, risk, and process - because classic SDLC rules don’t fit this new world

• Young engineers: A big-picture view to see beyond just code - why understanding and managing ambiguity is now a first-class engineering skill

I’d love to hear from anyone navigating this shift:

• What’s the biggest challenge you’ve faced with LLM adoption (technical, process, or team)?

• Have you found any system-level practices that actually worked, or failed, in real deployments?

• What would you add or change in a playbook like this?

Full article:
Medium https://medium.com/p/504695a82567
LinkedIn https://www.linkedin.com/pulse/architecting-uncertainty-modern-guide-llm-based-vitalii-oborskyi-0qecf/

Let’s break the “AI hype → PoC → slow disappointment” cycle together.
If the article resonates or helps, please share it further - there’s just too much noise out there for quality frameworks to be found without your help.

P.S. I’m not selling anything - just want to accelerate adoption, gather feedback, and help the community build better, together. All practical feedback and real-world stories (including what didn’t work) are especially appreciated!

I have been living and breathing AI tools, not just writing about them but using them every day in my work as a data scientist. They have completely changed how I get things done, helping me write cleaner code, improve my writing, speed up data analysis, and deliver projects much faster.

Here are the 7 AI tools:

1. Grammarly AI

2. You.com

3. Cursor

4. Deepnote

5. Claude Code

6. ChatGPT

7. llama.cpp

Read more here: https://www.kdnuggets.com/7-ai-tools-i-cant-live-without-as-a-professional-data-scientist

A Data Scientist is responsible for extracting, manipulating, pre-processing and generating predictions out of data. In order to do so, he requires various statistical tools and programming languages. In this article, we will share some of the Data Science Tools used by Data Scientists to carry out their data operations. We will understand the key features of the tools, benefits they provide and comparison of various data science tools.

You must check – Top skills to boost Data Science Career

Introduction to Data Science

Data Science has emerged out as one of the most popular fields of 21st Century. Companies employ Data Scientists to help them gain insights about the market and to better their products. Data Scientists work as decision makers and are largely responsible for analyzing and handling a large amount of unstructured and structured data. In order to do so, he requires various tools and programming languages for Data Science to mend the day in the way he wants. We will go through some of these data science tools utilizes to analyze and generate predictions.

Top Data Science Tools

Here is the list of 14 best data science tools that most of the data scientists used.

1. SAS

It is one of those data science tools which are specifically designed for statistical operations. SAS is a closed source proprietary software that is used by large organizations to analyze data. SAS uses base SAS programming language which for performing statistical modeling. It is widely used by professionals and companies working on reliable commercial software. SAS offers numerous statistical libraries and tools that you as a Data Scientist can use for modeling and organizing their data. Read More

Hi everyone, hope everyone is doing great and finding work where they can, we know it’s difficult to get into a job that can be consistent, so we are here to help find the right career for you.   Data Analyst positions are increasing by the day, with over 1100% of an increase in the past two years in the UK alone. Positions like this tend to be flexible as they can sometimes be remote, so in some rarer cases working for a company in another country is possible whilst working remotely.   There is a massive market for data and it’s slowly becoming one of the most needed IT jobs for companies around the world, with there being so few people trained in this industry and there being so many vacancies, it opens up salaries to a vast amount, ranging from 30k upwards a year. which allows you to start off small and work your way up.    We are helping the data sector by training up groups and getting them ready to work in a data environment we offer this service free to the UK to get started sooner rather than later, drop me a message and I can assist you further.

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i just want to know where to learn statistics for data science

all i have found are telling the very dump things every one know and this is boring really

whatched a video and it's really good from a playlist from "the organic chemistry turtor" youtube channel but it's too long(100+) video so should i continue in it? for real?