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Pinned Threads
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4
shweta
Alex
janhavi
pangaeaX
- 1 month ago
Resources to Learn Data Science
A collection of the best courses, books, and tools to learn data science.
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5
- 9 months ago
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4
- 7 months ago
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3
- 9 months ago
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3
- 9 months ago
Weekly Posting Rituals
What are the key benefits of staff outsourcing
Staff outsourcing can offer numerous advantages to small businesses, particularly in terms of efficiency and cost-effectiveness. Here are some key benefits: Cost Savings: Staff Outsourcing allows businesses to avoid the…
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2
kashaf
- 2 months ago
What benefits come with employing a developer for Power BI?
Hire Power BI developer with cross-functional knowledge has several benefits, one of which is a deeper comprehension of various business processes and their interconnections. These developers are able to produce…
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4
KnowledgeKingX
- 1 year ago
How do data visualization consulting companies ensure the scalability of solutions?
Data visualization consulting company ensures scalability by designing solutions that can grow with the business. They implement data models and structures that can handle increasing data volumes without compromising performance.…
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4
- 9 months ago
Bounty Questions
🏅 Bounty: 200 pts
How often you update feature engineering after deployment to handle data drift in ML ?
In your machine learning projects, once a model is deployed, how often do you revisit and adjust the feature engineering process to address issues caused by data drift?What indicators or…
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4
- 5 months ago
🏅 Bounty: 200 pts
How much statistics you need to know as a data analyst?
I am planning to learn data analytics and i got overwhelmed by all the information at the internet so I am asking here how much statistics do you need and…
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2
- 5 months ago
How do you prevent LLM vendor lock-in at scale?
As OpenAI models become deeply embedded in enterprise workflows, a key architectural concern is vendor concentration risk. How should organizations design AI systems that: Maintain interoperability across multiple model providers Avoid lock-in at the API, fine-tuning, and orchestration layers Preserve…
What makes a data report truly actionable rather than just informative?
Many dashboards and reports present large volumes of data, but not all of them drive decisions. In your experience, what differentiates a report that influences action from one that simply displays metrics? Is it clarity of KPIs, storytelling, stakeholder alignment,…
Why do I get a NameError when sorting a pandas DataFrame column?
I am working with a pandas DataFrame and trying to sort a numeric column (trip_distance) in descending order. However, I receive a NameError: name 'trip_distance' is not defined when using: trip = sorted(trip_distance) print(trip) The column exists inside my DataFrame…
How do you distinguish additive, semi-additive, and non-additive measures in practice?
While working with data warehouses and BI dashboards, I often see confusion around additive, semi-additive, and non-additive measures. Conceptually, additive measures can be summed across all dimensions, semi-additive across some dimensions, and non-additive across none. But in practical implementations, especially…
How to update dictionary values while iterating in Python?
I’m working with a Python dictionary and need to replace all None values with an empty string "". For example: mydict = { "name": "Alice", "age": None, "city": "New York", "email": None } I started with: for k, v…
Why do NLP models perform well in validation but struggle in production?
We often see strong validation accuracy during training, yet performance drops once the model faces real-world inputs. For example: from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments import numpy as np # Split…
What does data-driven transformation look like in practice, beyond dashboards and tools?
Many organisations claim to be data-driven, yet decisions, incentives, and workflows often remain unchanged. Data investments frequently stop at reporting rather than influencing how work actually happens. How should teams think about data-driven transformation as a shift in decision-making, ownership,…
How should teams structure analytics so insights lead to decisions?
Many teams collect strong data and build detailed reports, but decision-makers still struggle to act on them. Structure, framing, and clarity often matter more than the volume of metrics. At a high level, how do experienced teams think about organizing…
How should teams approach building real-world applications using OpenAI models in 2026?
I’m exploring how organizations can practically adopt OpenAI models for production use cases such as analytics, automation, customer support, and decision-making. With rapid changes in model capabilities, costs, governance, and integration patterns, what are the recommended best practices for: Choosing…
How should AI outputs be positioned within human decision-making workflows?
I’m working on an AI project where the model performance itself isn’t the main challenge. Accuracy and validation are reasonable, and the outputs are fairly consistent. A simplified version of the logic looks like this: risk_score = model.predict_proba(X)[0][1] if…
Business Intelligence or Artificial intelligence or both?
I am a new graduate and I am thinking whether to get into business intelligence profile or Artificial intelligence? I did read up on google. Is business intelligence stepping stone to world of data?
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6
MJ
Wrapo Biz
NA_analytics
- 3 years ago
Is MS Excel still in the game of reporting?
I am an experienced data analyst using MS Excel for years with VBA expertise. Do you think I should continue creating dashboards for it or learn one of these fancy tools of today? If yes, what should I choose?
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6
LookUpVisualizer
Razrap
- 3 years ago
Model is failing in spite of continuous training.
How and what can I do to train my model. My sample population doesn’t seem to work. My inputs don’t change that often.
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3
- 3 years ago
What is the best way to collect the data?
I have tried my best to collect data from surveys, questionnaire, interviews and group discussions. What else can be my choice? I follow the above model. Please suggest a better framework to better represent the collected data.
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3
- 3 years ago
Curious to know, what are the best tools on visualization in the market?
Have you used tools like Domo, Looker or Birst? Are these worth it according to you?
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3
Rajesh_Vincent
- 3 years ago
How to optimize steps in pentaho?
The current transformation does not run fast enough. The work is done but it takes longer than expected causing delays in the report generation. Any tips will help.
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1
- 3 years ago
Difference between analytics and analysis?
I am not from data background I am curious to know what really is the difference from the professionals, not the bookish definition of it.
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3
Sameer Barari
- 3 years ago
Analyst role in the companies
I have heard of data analysts who handle different set of things for different companies. A data analyst could be a data engineer or a data scientists or just into data analysis. What do you think is the actual role…
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2
- 3 years ago
R Web scraping issue
I am trying to enter keywords in a search field on a web page through R. Firstly I access link in R, use selector gadget to select the search field, extract keyword from list in R, ‘paste’ in search field…
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2
- 3 years ago
SQL – Recommendation needed
Hey guys, I’m currently studying Data Analytics and I’ve finally started writing SQL queries! Any recommendations for structured SQL learning?
Business Intelligence or Artificial intelligence or both?
I am a new graduate and I am thinking whether to get into business intelligence profile or Artificial intelligence? I did read up on google. Is business intelligence stepping stone to world of data?
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6
- 3 years ago
Is MS Excel still in the game of reporting?
I am an experienced data analyst using MS Excel for years with VBA expertise. Do you think I should continue creating dashboards for it or learn one of these fancy tools of today? If yes, what should I choose?
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6
- 3 years ago
Resources to Learn Data Science
A collection of the best courses, books, and tools to learn data science.
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5
- 3 years ago
Seeking Power BI Consultants/Freelancers for Partner Opportunities
Hi Folks- I recently launched a data platform designed for non-technical users. It’s a simple data hub for structuring, sharing, and collecting data. Other nice features: better data governance through reporting, data catalog, and sharing approval workflows, row level access…
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5
Kavya Iyer
Shweta Dubey
- 3 years ago
Is data mining redundant now? Or are there still data miners out there?
Data mining is often described as the process of discovering patterns, correlations, and trends within large datasets to generate actionable insights. But in today’s context—where data is abundant and growing exponentially—how do we ensure that the patterns we uncover are…
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4
- 3 years ago
Tools You Can’t Live Without
Share the tools that make your data workflow more productive.
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4
- 3 years ago
How often you update feature engineering after deployment to handle data drift in ML ?
In your machine learning projects, once a model is deployed, how often do you revisit and adjust the feature engineering process to address issues caused by data drift?What indicators or monitoring strategies help you decide when updates are needed?
-
4
- 3 years ago
What benefits come with employing a developer for Power BI?
Hire Power BI developer with cross-functional knowledge has several benefits, one of which is a deeper comprehension of various business processes and their interconnections. These developers are able to produce dashboards that serve several departments, guaranteeing that different stakeholders receive…
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4
- 3 years ago
Do you prefer heavy data transformations during early ETL or later in modelling? Why?
I’m exploring best practices in designing data pipelines and want to understand how different teams handle computationally intensive transformations. Some advocate for doing it early during ETL to keep models clean and fast, while others prefer flexibility and defer transformations…
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4
Sameena
Arindam
- 3 years ago
How do data visualization consulting companies ensure the scalability of solutions?
Data visualization consulting company ensures scalability by designing solutions that can grow with the business. They implement data models and structures that can handle increasing data volumes without compromising performance. Consultants also choose visualization tools that are flexible and capable…
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4
- 3 years ago
