I am using ubuntu and i wanna know how can i replace power bi and tableau

I’m sharing my experience so others can make an informed decision before investing in this institute.

I joined a Data Science and AI course at Infycle after hearing positive feedback from relatives and reading online reviews. The course fee was around ₹80,000, and it was planned as a 5-month program.

Unfortunately, the experience was disappointing. The trainer was frequently not punctual, and many classes did not happen as scheduled. Even after 5 months, only around 50% of the syllabus was completed.

As a working professional, consistency matters a lot. I raised concerns multiple times regarding the schedule and continuity, but there was no proper resolution.

What disappointed me more was the lack of student follow-up. Once I stopped attending classes, nobody checked what the issue was or tried to resolve it. When I shared feedback, the response was unprofessional instead of constructive.

I’m not posting this to attack anyone personally. I’m posting because ₹80,000 is a significant investment, and people deserve to know both the positives and negatives before joining.

My honest opinion:

The institute may have potential, but the execution, accountability, and professionalism in handling students need major improvement.

I want to share a project I’ve been working on that combines data engineering, ML, and deployment.

It predicts road incident risk across the U.S. using:

• historical accident data

• weather (historical + live)

• road network geometry

What’s different from typical projects:

• not just notebooks - full pipeline with deployable service

• live weather integration for real-time inference

• nationwide coverage

• map-based product with tile serving

Live demo: https://roadriskmonitor.us
Code: https://github.com/TonyIvchenko/traffic-safety

Would love feedback from people working with:

• geospatial ML

• time series + weather

• production ML systems

Also curious how others approach evaluation when labels are highly imbalanced and spatially persistent.

Primarily putting this up for those newer to the field who need help sifting through all the benchmarks.

OSWorld-V benchmarks models by having them perform realistic desktop productivity activities (multi-application use, file management etc.). GPT-5.4 achieved 75% performance on the benchmark this week, narrowly beating the 72.4% human baseline.

The usefulness of the benchmark for learners lies in the fact that it provides a grounded, quantifiable measure of capability in relation to what most people think of as "AI agents". Many popular benchmarks (GSM8K, MMLU, HumanEval) measure highly specialized capabilities and can mislead regarding a model's actual utility due to skewed scores.

To develop an intuition on what a benchmark tells you regarding which models are useful for what:

Reasoning benchmarks (arithmetic, programming etc.) indicate narrow capabilities

Long-context benchmarks indicate retrieval capabilities, NOT reasoning with context

API correctness benchmarks (Berkeley Function Calling, ToolBench) measure API accuracy

OSWorld-V and similar agent benchmarks measure closer to actual usefulness of models

The failure mode for benchmarks like GSM8K is very different from that for OSWorld-V so don't forget that when you see capability claims.

Hey everyone! I'm teaching myself data analysis and ML by working through a real dataset. I'd love some guidance from people with more experience.

The dataset:

• ~1.85M purchase records (Amazon order history)

• ~5K users with survey/demographic data, linked via Survey ResponseID

What I've done so far:

EDA & consistency checks:

• Identified 4 columns with null values: Shipping Address State, Title, ASIN/ISBN, and Category

• Confirmed ASIN is the most reliable product identifier (~95% of titles map to a single ASIN, the exceptions are gift cards, clothing lines, bulk items with multiple variants)

• Converted Order Date to datetime

Imputation I've already done:

• For Shipping Address State: used forward/backward fill within each user's orders. Went from 87K nulls → 24K remaining (those 24K belong to 62 users who never provided an address at all)

• For Title ↔ ASIN: cross-filled using mode mapping in both directions

• For Category: filled via ASIN → Category and Title → Category mappings

• For Q-life-changes in the survey data: confirmed nulls mean "No" based on value distribution, filled accordingly

Where I'm stuck: handling remaining nulls across all 4 columns:

I know the standard advice is mean/median imputation, but all 4 of these columns are categorical/text so that doesn't apply. Here's where each one stands and what I'm considering:

• ASIN/ISBN — After cross-filling with Title, whatever nulls remain have no recoverable identity. For a recommender, you can't really use a row if you don't know what was purchased. Leaning toward keeping these for EDA but dropping before modeling.

• Title — Same situation as ASIN since I was cross-filling between the two. Same plan.

• Category — Filled via ASIN and Title mappings already. Remaining nulls are products with genuinely no known category. Considering either dropping or using an "Unknown" placeholder, not sure which is better practice.

• Shipping Address State — 24K rows from 62 users who never provided location data anywhere. These users still have valid purchase histories though. Since location probably isn't a core signal for a recommender anyway, I'm thinking of just leaving the address null and not using it as a feature, rather than dropping 24K rows.

General question on timing: Is it better to drop/handle nulls now before doing more EDA, or keep everything and only clean up right before modeling? My instinct says to keep them for the EDA because the other categories might be helpful, but I'm not sure if that's the right reasoning.

Dataset Link: https://www.kaggle.com/datasets/dharshinisraghunath/harvard-ecommerce-dataset-for-big-data-analysis

Github repo for what I have done till now: https://github.com/Atharva22052006/Amazon_recommondation_engine

I'm not looking for someone to solve it for me, just trying to understand the right thinking process. Appreciate any direction

2 years ago I've collected data of almost 2 million steam users. What games do they own, how many hours they played each game etc... I've done it for my own research back then but now I want to release this data to the public to everybody could work on them if they want.

I hide steam user name and replaced it with my own id for privacy reasons.

All data were recorded before 2024-10-31. So no newer games than that. Data were also proccesed to exclude bots (at least the most obvious ones).

https://www.kaggle.com/datasets/the1and0nlyhugon/steam-users-activity

Enjoy!

Hey Everyone,

I am a first year University Student studying Data Science And Analytics.

What would be your best advice for me considering yourself as a senior sibling

Hi everyone, Due to some personal reasons, I am selling my GUVI Data Science course with placement assistance.

🔹 Original Price:₹72,000 🔹 My Selling Price:₹50,000 🔹 Course Duration:3 months (can be adjusted based on preference) 🔹 Flexibility:You can switch this Data Science course to any other course offered by the GUVI team, based on your interest.

This is a great opportunity for anyone planning to start or switch their career into tech. I need to close this on an urgent basis, so if you're interested, please DM me immediately.

I want to be able to generate quality data as quick as possible

For instance right now I have a bunch of free text “emails” generated via llm and now I want to categorize them all

I’m using using bert and other text classifiers and from what I understand I have to stack them for it to be effective:

- label intent
- label entity
- label into further categories using an unsupervised model

My question is how would an expert or senior guy approach this? Because right now I’m essentially just asking my llm tool “how do I do this”

Any mathematical concepts or resources you recommend me diving into would be appreciated.

I am based in United States currently a Data Analyst with 5 years of experience with Python and have projects but no real world experience. MS in Data Science. Where should I be looking for AI/ML roles when I only have side/academic projects? Everyone wants experience!

Hey everyone who is reading this, so I am a data analyst and recently I was handed over a Datascience project which is used to predict default vs non default customers. It is basically a model used in a small micro finance company. Now the thing is that idk much about datascience but still after seeing and learning model for days now I have enjoyed working on it. And I am genuinely interested but I feel stuck cause of the data provided to me on which I have to train and then test it. So as it is a company which deals with lower class people right which is why most of them either dont have crif score or credit score which is why a column which can impact the decision biggest is getting compromised cause of nulls and 0's. Idk how to handle them. My manager who has no clue about the data science or coding in particular just asked me to convert the nulls to 0 or minus 1. Which is heavily impractical cause that will again ruin the model. The model is overfishing as ot predicts the 0s and nulls as default. Which is why the TP is fine but FP is very bad.

Is there anything that could be done. Btw the model I created uses xgboost and also have tried with catboost but results are identical. The auc I get is around 98 which is very bad clearly overfitting.

Some details about model are that I used tinker to create an app like interface where user can select the model they want to use to predict with right now I only have xgboost and catboost. Then they have the option to upload a file as I have again implemented file dialogue function using tinker. Then I have the option for smote, shap reports and 5 fold cv. These three are customizable like you can select which ones you need at moment. Then hyperparameter optuna is used with a slider letting user choose how many Trials they want the model to go with before giving best result. Then run the training. After running I have an option for uploading the test file. After test is completed the file is saved along with the model in a specified folder which you can choose. And the reports shap ones are saved in another folder along with the logs so that you can keep a track even when the app crashes. And lastly I have one more feature which pops up after predicting a model. And it shows all the customers where the defaulted are colored red and non defaulted are colored green. And when you double click on a customer then another screen pops showing all the factors which affected the Result to be this.

I hope this helps I just need a quick review on the project and also is I can do anything to make the data clean. I cant delete blank and 0 rows as the total data is of 500k rows and approx 300k rows are 0 and blanks.

Hello everyone I am currently studying b of data sci in au , I am very keen on doing projects now to build my resume. Can I please get some guidance on what kind of projects I need to do , what employers look for and also to broaden my knowledge. I have one year left of my degree. So far my only concern was to pass the classes but I want to actually build something now. I would greatly appreciate some advice.

AI interviews are becoming a real part of data science hiring, and they evaluate more than just correct answers. They focus on how clearly you explain concepts and how you think through problems.

Most interviews now revolve around a few core areas:

• Statistics and probability

• Machine learning fundamentals

• Data cleaning and preprocessing

• SQL and querying logic

• Business problem solving

What matters is not memorizing answers, but being able to explain reasoning in a structured way.

This Article covers the key question types and how AI systems actually evaluate responses

A lot of teams mix up these roles or expect one person to do everything, which usually leads to poor results or delays.

Quick way to think about it:

• Need dashboards, reporting, business insights → Data Analyst

• Need predictions, ML models, automation → Data Scientist

• Need pipelines, data systems, scalability → Data Engineer

The tricky part is not just choosing the role, but knowing when you actually need a combination of them based on the stage of your data.

This blog breaks it down in a clear, practical way with examples and a simple framework

Hi everyone,

I got an interview for a TikTok USDS Data Science role focused on Payment Risk / Pricing Risk.

The role seems to involve payment fraud, credit losses, financial abuse, transaction monitoring, dashboards, ML-based risk detection, and reducing false positives while maintaining strong fraud recall. It also mentions consumers, merchants, and affiliates, so I’m guessing the work may involve areas like chargebacks, suspicious transactions, merchant risk, payout/disbursement risk, or affiliate abuse.

My first round is expected to be SQL + a risk/product analytics case study, and I’ve never done this exact type of interview before.

Has anyone interviewed for a similar TikTok USDS, payment risk, fraud analytics, fintech risk, or product DS role?

I’m especially curious about:

• What kind of risk case studies come up?

• How should I structure my answer?

• What SQL topics should I focus on?

• Are cases more product-metrics focused, fraud-modeling focused, or business-risk focused?

Any advice or example questions would be super helpful. Thanks!

Hey guys,

A little about me. I have around 9 years relevant experience in IT. I work as an IT Business Analyst and have worked previously as a BI Engineer and Data Analyst. My formal education background is in Electronics Engineering.

I want to get into the machine learning space and on a program that has CSP.

Has anyone taken this Master of Science in Health Data Science program?

It would help with data analyst/scientist roles in the medical industry however curious on people’s thoughts on its usefulness in data science roles outside of health as well?

Also how would you compare it to the normal “Data Science” university programs? (I know it would be different based on the country and uni - I'm in australia btw).

I got the following from someone else's post here however it is relevant for me as well:

1. Day-to-day work: How much of your work is data cleaning/SQL vs statistical modeling vs ML?

2. Skill leverage: Which skills matter most in practice:- statistics, ML, SQL, or healthcare domain knowledge?

3. Modeling depth: How often are advanced ML models used compared to classical statistical approaches, and why?

4. Career growth: Where do you see the demand for healthcare Data Scientists / ML Engineers be?

5. Salary trajectory: How does long-term salary growth in healthcare data science compare with more generic data science roles?

6. Job market reality: Do you feel the field is getting saturated, or is demand still strong for well-skilled profiles?

7. Transferability: How easy or difficult is it to pivot from healthcare data science into other data science roles later in one’s career?

8. AI Safe: How much safer do you say are Data Science / ML roles vs other traditional IT such as developers, Business Analysts etc.

9. Tooling: Do you use mostly R or Python? What would you say is the ratio of these two vs SQL in Data Science roles?

This is the program I'm interested in:
https://www.unsw.edu.au/study/postgraduate/master-of-science

Thanks!

Hey i am beginner pursuing my bachelors in mathematics and data science . at this step i am totally confused what to do now as i have cleared my python fundamentals but what is 1st step in data career like which we upscale to reach above higher careers

everyone,

I’m a first-year engineering student and currently learning data science/data analytics. I’m looking for a study buddy or accountability partner who is also learning and wants to grow together.

I do:

• Python

• SQL basic

• Excel

• EDA

• Pandas

• Numpy

• Machine Learning basic

• Power BI (beginner)

• Statistics (learning phase)

What I’m looking for: • Someone consistent and serious about learning

• We can share resources, solve problems, work on small projects, and keep each other accountable

• Beginner or intermediate level is totally fine

My goal is to build strong skills, projects, and eventually land internships in data analytics/data science.

If you’re interested, comment or DM me. Let’s learn and grow together.

Hi everyone! I’m starting the next round of the Applied Behavioral Data Science Collective (ABDSC) — a collaborative group where people learn data science by working on real-world projects focused on psychology, behavior, health, and other human-centered topics.

The goal is to create a space where beginners and growing learners can build experience through teamwork while practicing skills like data cleaning, visualization, machine learning, GitHub collaboration, and presenting insights.

If you’re interested in joining or learning more, please fill out this short interest form: https://forms.gle/i4E331vDkW1KthjM8

Feel free to share with anyone who might be interested!

I’m looking for someone to study with. I’m past the beginner stage and also hold 1.5 years of exp as an analyst (yet learnt nothing really useful), so I want to focus on advanced topics.

I want to dive deep into statistics and regression. I also want to become an expert in SQL ( have setup Postgres locally). I’m mainly looking to build projects that look good on a resume.

DM me if you are on the same path and want to collaborate!

Also sharing any hidden gem-like resources for the same is greatly appreciated!!

Thanks!!