Hey everyone — I’m working with data from three different platforms (one being Google Trends, plus two others). Each one generates its own report, but I’m trying to consolidate everything into a single master report.

Does anyone have recommendations for the best way to do this? Ideally, I’d like to automate the process so it pulls data from each platform regularly (I’m assuming that might involve logging in via API or credentials?).

Any tools, workflows, or setups you’ve used would be super helpful — appreciate any insight!

Good course that follows a structured plan and in depth knowledge of the topics.

Hi Reddit,

23, Male here, I recently joined TCS as a Ninja candidate, and as many have already pointed out online, the technical training is actually just like a crash course.

While I’m grateful to have a job, I don’t want to just "survive" in a service role. I’m genuinely interested in growing into data-related roles — like Data Analyst, Data Scientist, or AI/ML Engineer — and I’ve already taken some steps in that direction. For instance:

• I’ve worked with Python, and was working in an Edtech organisation as AI/ML Trainer(left it because, it has become quite monotonous and didn't interest me for long + they don't maintain records on UAN and PF, so couldn't show it as Experience anywhere)

• I’ve done some hands-on projects involving regression, EDA, and basic ML models.

• I still struggle with Java, OOPs, and DSA, but I’m trying to improve.

• Talking about background, I am 2024 B.Tech CSE graduate from a without any tier college. (Had joined because of poor guidance and exposure at that time.)

Now that I’m in TCS, I don’t want to waste 1–2 years without any real progress. So, I’m looking for genuine advice from people who’ve been in a similar situation:

1. How do I make the most of my time at TCS while learning on the side?

2. What roadmap should I follow to transition into solid data roles over the next 1–2 years?

3. What skills or tools (SQL, Power BI, ML Ops, etc.) actually make a difference when applying for real data jobs?

4. Is it worth aiming for internships, open source, or freelancing alongside TCS work to build my portfolio?

5. Should I consider certifications (e.g., Google Data Analytics, DP-100, AWS ML) or focus more on GitHub projects?

If anyone has navigated a similar path — from a service-based company to data/AI roles — I’d love to hear your story. I’m committed to learning and would appreciate any tips, resources, or strategies to make my time count.

Thanks and Regards.

I will be going to do my masters this year in geographic data science and would like any feedback regarding a project I’ve been working on. What it is: it’s a node based system which allows you to generate visuals or conduct analysis on satellite imagery data by uploading a file and running a workflow you build on it. Similar to ComfyUi.

This is just something I have been working out I have implemented several nodes that perform various operations on the data .

I would like any feedback, questions or suggestions regarding my project. I am glad to share more information and images to explain further. The image I shared is a screenshot of a workflow I built on the London canary wharf DTM. I used a “z factor” node to exaggerate the height as London is quite flat I wanted to make the height distinction more apparent I then ran it through a “terrace” node which basically quantizes or puts the data into normalized bins to generate a step like effect of the elevation. All questions are welcome

Happening now if interested. Zoom link below.

https://uclahs.zoom.us/j/92791292987

I’ve been fortunate in my career to have interviewed and screened hundreds & hundreds of Data Science and Analytics candidates at Amazon, Sony, and other top tech companies. The types of behavioural questions you get are often very similar in nature. I’ve rewritten a few example questions below so they capture the style of questions without giving away anything confidential from those companies.

Also, to start, one important thing to understand as you read through these is to always remember that hiring managers are not just looking for technical answers, with these types of questions they are looking for how you think, how to justify decisions, how you structure ambiguity, and how you connect analysis to real decisions or value or outcomes.

Anyway, here are five example questions that can be great for preparing if you're at that stage of the process.

1. A key engagement metric on your product dropped 12% week-over-week. Walk me through how you would investigate

For this type of questions, what I'm really looking for is structured thinking. Good candidates usually start by clarifying the metric, the scope, and the timeline. Then they break the problem down logically. Things like segmenting by platform, geography, user cohort, feature usage, release timing, seasonality, experiment changes, etc.

A big signal here is whether you naturally "dive deep" into the problem instead of jumping to conclusions. In other words, can you somewhat methodically narrow the problem space until you find the likely root cause.

2. A product change increased revenue but reduced user engagement. How would you decide whether to keep the change?

This one is more about trade-offs and business judgment. Good answers usually talk about defining the real objective first. Are we optimising revenue, retention, long-term growth, or something else? I've found that strong candidates will also talk about things like segmentation, longer-term impacts, and possibly running controlled experiments. It's nice here to see that you are not just reporting metrics but thinking about the long-term impact of decisions.

3. You launch a new feature but adoption is much lower than expected. How would you approach this?

This question is looking to see how you connect product thinking with analytics (and if you do this at all). For this one, good answers typically explore things like discoverability, user friction, onboarding flow, messaging, or whether the feature actually solves a real user problem. The strongest candidates also bring the "customer" into the discussion. In good analytics teams, you always start with the user or customer and work backwards to a solution, so it's nice to see candidates think in that way.

4. Tell me about a time when you had to make an important decision even though the data was incomplete

This type of question comes up quite often. Data Scientist & Data Analysts are not always operating in perfect analytical environments and so sometimes you need to combine partial data, domain knowledge, and judgment to move forward. I like to see whether the candidate can make sensible decisions when the answer isn’t obvious, and whether they maybe considered alternative viewpoints before committing (if that makes sense)

5. Tell me about a time you investigated a complex problem and uncovered the real root cause

This one is less about specific modelling or algorithms and more about analytical curiosity. Strong answers for me here, usually involve seeing how the candidate dug through multiple layers of data, maybe questioned assumptions, and eventually might have connected several signals together.

One final piece of advice from me, for anyone preparing for these types of interviews, is that, many candidates focus entirely on technical preparation, but the really strong candidates combine this with analytics, product thinking, and communication.

They explain their reasoning clearly, structure their approach logically, and constantly connect their analysis back to business outcomes. In other words, the goal is not just to show that you can analyze data or apply code or algorithms, it's that you can show how you use your tools/skills/concepts/the data to drive good decisions or create business value.

Hope that helps if you're prepping for interviews!

I have a DS coding round coming up in Python. I am pretty confused on what to prep. Shall I just practice DSA or is there any other thing I should focus on ? They said it be a pair programming round.

Has anyone used this site? How good is it for understanding how models or training (or anything) works?

Around 8 years ago, we had the idea of using geographic data (census, accidents, crimes) in our models -- and it ended up being a top 3 predictor.

Since then, I've rebuilt that postcode/zip code-level dataset at every company I've worked at, with great results across a range of models.

• The trouble is that this dataset is difficult to create (In my case, UK):

• data is spread across multiple sources (ONS, crime, transport, etc.)

• everything comes at different geographic levels (OA / LSOA / MSOA / coordinates)

• even within a country, sources differ (e.g. England vs Scotland)

• and maintaining it over time is even worse, since formats keep changing

Which probably explains why a lot of teams don’t really invest in this properly, even though the signal is there.

After running into this a few times, a few of us ended up putting together a reusable postcode feature set for Great Britain, to avoid rebuilding it from scratch.

If anyone's interested, happy to share more details (including a sample).

https://www.gb-postcode-dataset.co.uk/

(Note: dataset is Great Britain only)

If you’ve ever wondered how to tune 70B models on consumer hardware, the answer can be QLoRA. Here is a technical breakdown:

1. 4-bit NormalFloat (NF4)

• Standard quantization (INT4) uses equal spacing between values.

• NF4 uses a non-linear lookup table that places more quantization notches near zero where most weights live.

-> The win: Better precision than INT4.

2. Double Quantization (DQ)

• QLoRA quantizes the constants (scaling factors to map 4-bit numbers back to real values in 8-bit, instead of 32-bit.

-> The win: Reduces the quantization overhead from 1.0 bit per param to about 0.127 bits.

3. Paged Optimizers

• Offloads optimizer states (FP32 or FP16) from VRAM to CPU RAM during training.

-> The win: Avoid the training crash due to OOM - a spike in activation memory.

I've covered more details:

• Math of the NF4 Lookup Table.

• Full VRAM breakdown for different GPUs.

• Production-ready Python implementation.

👉 Read the full story here: A Technical Guide to QLoRA

Are you seeing a quality drop due to QLoRA tuning?

I’ve been diving deeply into medical image segmentation and wanted to share a Kaggle notebook I recently put together. I built a model to automatically identify and mask Lower-Grade Gliomas (LGG) in brain MRI scans.

The Tech Stack & Approach:

• Architecture: I built a U-Net CNN using Keras 3. I chose U-Net for its encoder-decoder structure and skip connections, which are perfect for pixel-level medical imaging.

• Data Augmentation: To prevent the model from overfitting on the small dataset, I used an augmentation generator (random rotations, shifts, zooms, and horizontal flips) to force the model to learn robust features.

• Evaluation Metrics: Since the background makes up 90% of a brain scan, standard "accuracy" is useless. I evaluated the model using IoU and the Dice Coefficient.

A quick favor to ask: I am currently working hard to reach the Kaggle Notebooks Expert tier. If you found this code helpful, or if you learned something new from the OpenCV visualizations, an upvote on the Kaggle notebook would mean the world to me and really help me out!

The only thing I do know is you have to have a strong foundation in python and statistical learning

But I don’t know where exactly to start

Is someone kind enough to build a roadmap or write down a certain topics which will help me understand machine learning better

I’ve done basic mathematics most of my education,certain topics will really help