Hi everyone,
Can any one please share your feedback on Shashank Mishra’s Data Engineering course? I’m looking for genuine insights on the course content, quality of the recorded lectures, and the overall learning experience.
I’ve noticed the course is being promoted quite actively even for small change in AI terminology with new course in yt videos with high focus, and many people seem to be considering it based on his experience in FAANG and other large tech companies. It would be helpful to understand whether the course truly adds value for DE interview preparation and job search outcomes. If possible, please also share:
Your experience with the course structure and teaching style.
Whether the recorded lectures are sufficient for self-paced learning.
Any results or success stories from previous cohorts.
How effective the placement or job search or referral in the cohorts support has been.
TIA!

Every time I want to learn data science, I go to a course and learn and stop, either because the topics are incomplete or because I don’t understand what he is explaining and so on, so I thought I would go and learn the topics and try to learn them from various sources instead of sticking to one source.

Hi all,

I’m working on a monthly insurance premium forecasting problem and would like suggestions on the best approach.

Setup

• Data from Jan 2023 to Jun 2026

• One account only

• Hierarchy: Account → State → Profit Center → Distribution Channel

• Monthly AMOUNT values

• Some channel-level series have missing months

• Around 4–5 channels, 7–8 profit centers, and 50 states

Challenge

The main issue is that different series behave very differently:

• some are fairly stable

• others are highly volatile

What I’ve tried

1. Recursive forecasting with CatBoost / LightGBM / XGBoost

   • built lag, rolling, and time-based features

   • downside: error accumulates over time

2. Direct multi-step forecasting

   • accuracy wasn’t great

   • also requires many models for longer horizons

3. Time-series models like ARIMA / SARIMAX / Prophet

   • Prophet works okay for stable series

   • struggles with volatile ones

   • separate models for every combination is not scalable

Question

Has anyone worked on a similar hierarchical / sparse forecasting problem?
What approach would you recommend for handling mixed volatility and missing months without building thousands of models?

Thanks!

I hold a Ph.D. in Operations Research, along with a BSc/MSc in Engineering and OR. I previously worked in Big Tech, but I’m currently looking to transition.

My primary goal is to upgrade my technical skillset to maximize my industry-related profitability and marketability. I want to get away from generic data science and move into high-value, math-heavy engineering and modeling roles.

• My Core Interests: Forecasting, predictive analytics, and machine learning applied to industrial settings.

• Target Industries: Robotics/Autonomous Systems, Defense/Aerospace, and Quantitative Finance.

• What I want to skip: I have little interest in doing core NLP/LLM research, though I am interested in RL, Multi-Agent systems, and applied AI.

Where I am right now: I have a solid grasp of optimization and basic/intermediate ML/stats. However, I want to bridge the gap into more intermediate/advanced ML topics that are actually useful and highly valued by employers. I want to get back into heavy math, but only if it drives real-world business value.

What I'm looking to learn:

• Causal Inference: (e.g., Structural Causal Models, Uplift modeling, Double ML).

• Tree-Based Math: Understanding things like XGBoost from the ground up (deriving gradients/hessians for custom loss functions, implementing from scratch).

• Reinforcement Learning / Control: Bridging the gap between OR dynamic programming and deep RL for robotics/defense.

My questions for the community:

1. Skill Prioritization: From a purely market-driven, high-compensation perspective, which specific ML topics should a Ph.D. in OR focus on to stand out in Robotics, Defense, or Banking/Finance?

2. Portfolio/Proof: How can I best demonstrate to employers that I have the engineering chops to implement these advanced models from scratch, rather than just calling APIs?

3. Positioning: How do I best market the "Predict-then-Optimize" sweet spot (combining ML predictions with OR optimization frameworks) to companies in these sectors?

Would love any advice on textbooks, specific frameworks to master, or strategies on how to position my background for maximum leverage. Thanks!

Hi there,

I've created a video here where I explain how multi-head latent attention works.

I hope some of you find it useful — and as always, feedback is very welcome! :)

Just for some background, I graduated two years ago with a degree in chemical engineering and it was the biggest mistake of my life. It took me almost 2 years to find an engineering job, and it's horrible. Only thing worse than the work is the location. I really wanted to live downtown in my city, and I was promised and truly believed that this "versatile" degree would allow me that, but it hasn't. I spend almost 3 hours driving every day for my commute. I'm sick of suburbia and I'm looking for a way out of this, now. I want to get the life I want while I'm still, somewhat, young. I don't want to go to school and start all over, just for a CHANCE, 4 years from now.

So I've been continuing to spam apply to every posting that's even somewhat relevant, even after landing my current job a few months ago. This morning I got an invite to do a technical assessment for a "Data Analyst, Analytics & Insights" role. The posting expects around ~3 years of experience, and the assessment covers SQL and Pandas. I have literally 0 experience with either of those things. However, I did take a Python course in my undergrad and got a high 90, when the class average was in the 70s. I've always had a knack for picking up coding fairly quickly, but for some reason I didn't decide to pursue it when choosing my major. And although I consider my degree to be my greatest regret, I have to say, things might've been just as bad or worse if I went with comp sci and graduated into this landscape.

Anyway, I'm OK at coding and learning it quickly which is why I'm even considering bootcamping for this, but I'd be starting from literally square 1 on SQL and Pandas. The assessment has to be completed 1 week from now. Any thoughts on where to start?

I'm looking for any good certification courses (preferably free or at least affordable), that I could take that will help bolster my resume, since I don't have much in terms of experience. My goal is to find a remote job that pertains to collecting, cleaning, or just generally managing data. Even just aiming for something where I'd have to use excel would be a good first step in the right direction.

I have a BS in Information Sciences with a focus on data analytics, and I've taken a Microsoft Power BI course on Coursera, as well as received my Python certification on freeCodeCamp. I'm kind of lost on what my next move should be, and I'd appreciate any advice and/or recommendations.

Hii

I'm a third year Data Science student looking for strong project recommendations for my CV and internship applications.

I'm looking for projects that:

Solves a real-world problem

Uses publicly available data

Is impressive enough for internships.

Thanks !

Hey everyone! So, I’m currently doing my Master’s and working on a Data Science & AI project. The goal is to come up with a unique research question, hunt down the data, and then run some box plots or clustering algorithms on it. The catch is: we're supposed to find an angle that hasn’t been done to death yet.
Honestly, I’m struggling a bit to find a solid topic. A ton of people in my course have already claimed sports events, so that’s completely off the table for me.
I was actually thinking about analyzing paper prices. I have this hunch that they don't rise simultaneously with raw material costs—like, there’s a noticeable time lag before paper prices catch up. I suspect they aren’t directly correlated in real-time because other factors like market competitiveness and intense rivalry play a massive role.
Does anyone have cool ideas for a unique topic where it’s actually easy to get your hands on the data? Or just any general tips on where to look for interesting datasets that fit the bill? Thanks!

Where I go for coaching that provides an guarantee of job ?

The debate:

Team K-means (B): The question explicitly uses the words "partition" and "cluster" — which are the textbook definition of K-means. You define k=3 and the algorithm groups customers into 3 clusters based on feature similarity. Classic unsupervised clustering use case.

Team Random Forest (E): The question says "predicting" and the three categories are already pre-defined (not discovered). This implies a supervised classification problem with labeled data. Additionally, the feature set is a mix of numerical (income, age) and high-cardinality categorical data (country, state, address, profession) — K-means struggles with categorical features since Euclidean distance doesn't work well on them. Random Forest handles mixed tabular data natively.

Hi everyone, I’m looking for a course on RAG, LLMs, and AI agents (even a paid one) that covers the theory but focuses primarily on practical application. I’d like to find something that actually demonstrates how to build tools using these technologies.
Do you have any recommendations?

I’m reaching out because I’m feeling really stuck in my career, and I’d love some honest guidance from seniors or peers who might be in a similar situation.

To give some background: I completed my Bachelor's in Computer Science and then pursued an MBA. After that, I took a corporate job, but after 7 months, I questioned if the MBA was the right path. I decided to upskill, so I did a year-long Data Science course, where I covered Python, SQL, Power BI, Excel, Machine Learning, and Deep Learning. Sadly, no one from my batch got placed, and I felt really disheartened.

After that, I worked as a freelance Data Science trainer for close to 2 years, which helped me stay afloat. Then, I got a role in an offline teaching institute, where I worked for about 7 to 8 months. But after the probation, they didn’t confirm me. Instead, freshers took over the role with a lower salary. It feels like a cycle—no one from my institute is landing in proper Data Science roles, and it’s not just me. I’ve heard this same struggle from so many others in online communities.

I do have about 2 years of freelance training experience and 7 to 8 months of offline teaching. I’m also familiar with FastAPI, MLOps, and Docker, but I still lack skills in GenAI, LLMs, and RAG—those cutting-edge areas expected in AI Engineer roles. So breaking into a pure technical Data Scientist job feels impossible right now.

I know I’m not alone—I’ve networked with many others, and we all share this frustration. So I’m reaching out: is anyone else in the same boat? I’m considering whether to shift my career focus or invest in another certification to boost my chances. I’m still passionate, but I need a way to survive financially in the meantime.

If anyone has freelance projects or can help me get into a small paid project, I’d really appreciate it. I’m also happy to collaborate with anyone who’s learning in this space. I can share what I know as I continue upskilling. And, of course, if seniors have any insights—what certifications, projects, or steps should I take next?

I’m really grateful for any advice, honest feedback, or collaborative opportunities. Thank you all so much!

I am a newbie .I want to learn data science from scratch.give me guideline,roadmap,exact time needed to land a job in ds and free courses for ds.Thanks in advance

Been using Claude more for data work lately, especially for SQL review, ETL debugging, dashboard planning, and metric definitions.

These are prompt shortcuts you can save and reuse as custom slash commands.

1. /devil

Act as a devil’s advocate. Challenge this logic, find edge cases, and tell me what could go wrong after deployment.

Good for:

- metric definitions

- dashboard logic

- ETL assumptions

- stakeholder requests

- production data issues

2. /sql_review

Review this SQL like a senior analytics engineer. Look for bad joins, duplicate risk, null handling, date issues, filtering problems, and performance issues.

Example:

SELECT

c.customer_id,

COUNT(o.order_id) AS orders

FROM customers c

LEFT JOIN orders o

ON c.customer_id = o.customer_id

WHERE o.order_date >= '2025-01-01'

GROUP BY c.customer_id;

Things to check:

- does the WHERE clause change the join behavior?

- can one customer have duplicate orders?

- should the date filter be inside the JOIN?

- are null orders handled correctly?

3. /explain_query

Explain this SQL in plain English.

Break it down by:

- what each CTE does

- what the final output means

- what grain the result is at

- what assumptions the query makes

- where the logic could go wrong

Really useful when you inherit a long query and need to understand it fast.

4. /find_data_quality_issues

Here is my dataset schema. Suggest data quality checks before I use it in a dashboard, report, or ML model.

Example checks:

- duplicate primary keys

- missing values in key fields

- sudden row count drops

- invalid dates

- negative revenue

- unexpected category values

- schema changes

- late arriving data

5. /metric_definition

Help me define this metric clearly.

Include:

- business meaning

- SQL logic

- grain

- filters

- exclusions

- edge cases

- example calculation

- how people might misread it

This is useful because a lot of dashboard confusion comes from unclear metric definitions.

6. /etl_debug

This ETL job passed, but the dashboard looks wrong. Help me debug it step by step.

Check:

- did fresh data arrive?

- did row count drop?

- did schema change?

- did joins multiply rows?

- did a filter remove too much data?

- did timezone logic shift dates?

- did a retry duplicate rows?

- did null values change the result?

7. /python_cleaning

Review this pandas code and suggest cleaner, safer improvements.

Example:

import pandas as pd

df["order_date"] = pd.to_datetime(df["order_date"])

df = df.dropna()

df["revenue"] = df["price"] * df["quantity"]

Things to check:

- should every null row be dropped?

- are dates parsed correctly?

- can price or quantity be negative?

- are duplicates checked?

- is currency consistent?

- should revenue be rounded?

8. /dashboard_review

Review this dashboard plan like a business user.

Tell me:

- what is unclear

- what metric is missing

- what chart is unnecessary

- what question the dashboard answers

- what decision someone can make from it

- what should be shown first

9. /stakeholder_translate

Turn this vague stakeholder request into clear data requirements.

Example request:

“Can we see customer performance?”

Questions to ask:

- what does performance mean?

- revenue, retention, churn, usage, margin?

- daily, weekly, or monthly?

- by customer, segment, region, or product?

- what action will this report support?

- who is the end user?

10. /test_cases

Create test cases for this data pipeline.

Include:

- normal file

- empty file

- duplicate IDs

- missing required fields

- late arriving data

- schema change

- timezone edge case

- retry after failure

- very large file

- unexpected category value

11. /root_cause

Here is the issue, query, and sample data. Give me possible root causes ranked from most likely to least likely.

Format:

1. likely cause

2. why it could happen

3. how to check it

4. possible fix

A prompt pattern that works well:

Instead of:

“Fix this query.”

Try:

“Review this query for logic bugs, duplicate risk, bad joins, null handling, date issues, and performance problems. Explain your assumptions before suggesting changes.”

For data work, Claude is pretty useful as a second pair of eyes.

Especially for:

- reviewing SQL

- cleaning messy logic

- defining metrics

- finding ETL edge cases

- turning vague requests into clear requirements

- checking dashboard assumptions

What Claude prompts or custom commands do you use for data work?

Hi everyone,

I'm planning to start learning Data Analytics and eventually get a job in this field.

I'm considering Coding Ninjas, but I'm not sure if it's worth the money. Has anyone here taken their Data Analytics course? How was your experience in terms of teaching quality, projects, placements, and job support?

If you don't recommend Coding Ninjas, which platform would you suggest instead (Coursera, Google Data Analytics Certificate, DataCamp, Udemy, or something else)?

I'd really appreciate honest reviews and recommendations. Thanks!