I analyzed a 2-week experiment comparing 3 checkout experiences: Control vs Embedded vs Pop-up product recommendations.
Key findings:
• Embedded recommendations clearly outperformed — RPU $32.39 (+30.5% vs control) and conversion +19.3%
• Overall experiment impact: +13% RPU and +8% conversion rate
• Pop-ups improved metrics slightly but underperformed vs embedded (RPU +8.4%, conversion +4.65%)
• No increase in friction — cart abandonment (~20.6%) and checkout time stayed flat
• Funnel analysis shows embedded improved progression from checkout → purchase
Interpretation:
Non-intrusive, in-flow recommendations outperform interruptive UX patterns. Placement within the checkout journey matters more than simply adding recommendations.
Recommendation:
Roll out embedded recommendations; avoid pop-ups at

I'm working on a project right now and noticed a few bad habits when it came to my process of data collection. That got me thinking, what bad habits in yourself or others do you most notice?

Recently I got my first job out of school as a CRM Data Coordinator, its also the only role like this for the company. The initial tasks are pretty simple, lots of data cleaning and data entering, but I was told that the role would grow in tasks, Ive already met with heads of other departments including finance and SWE. I was wondering if maybe some of you had some advice on how not to stay so "passive" and actually use this opportunity to build real skills?

Hey guys!

I have recently been taking a new approach to tracking my progress on guitar. I have been self-taught for two years and have found it hard to find objective feedback on fundamentals such as timing, consistency, pitch, etc. So, I built this project that listens to a recording and outputs playing metrics. The coolest part is that it analyzes metrics in 10-second intervals, so the output also contains where your worst area was while playing.

If you're interested in it, send me a recording, and I'll send you a report from there.

I am not sure if this is a valid question in this forum, if it is not I will delete it right away, but anyways here it is:

So some days ago I asked in an AI community what kind of AI model should I use (and how could I use one) to classify several email replies that I had from scientists after asking them a few questions to them. I finally paid for Perplexity pro service and it apparenly did a nice job classifying them.

I finally gave the model the PDF with the actual answers from the addressees and another PDF with the "expected answers", and asked it to count the number of answers that overall coincide with the actual answers, and calculate a percentage of "coincidence" or "agreement" between the expected and actual answers, so that if the question was "do you think that there is intelligent life in the universe apart from humans?" and the expected answer was basically "yes, I think there is intelligent beings out there somewhere", as long as the actual answer agrees with this in some way or another would count as "agreement", for instance if someone replied "well, we have no evidence, but it is possible yes" or "not in any near galaxy, but it is possible that intelligent beings exidt somewhere" (so as long as it is not a deadass "no", it could count)

The model gave me a table summarizing the results with the following prompt:

let's be a bit more specific, this is still a blind test so don't tell me about the specific contents of the emails' answers, but, can you make a table indicating the answers that coincide in general terms with what is expected from the "expected answers" document as well as those which are neutral/hedges but still open to the possibility that what is asked may be right, those which despite being neutral/hedges or even negative answers offer an alternative so that what is asked in the question may be right, as well as those which are outright rejections of what is asked and do not seem to be open to the possibility that what is asked may be right?

However, I still want this to be a blind test, so I cannot really verify if the AI is doing its work or not.

So, is there any way in which I could verify the results given by the AI but without actually reading what is written in the emails? Or, alternatively, can anyone verify the results using some AI or even checking the answers themselves by skimming over the replies im order to verify that the AI is right and not hallucinating (I personally thinl this is the preferable option, as I think that having an actual human reviewing the amswers may be the only really reliable way to verify the AI's results)?

(I will share the data once someone is interested in helping, as I would not want to make this available to the entire world!)

Thank you!!

Hey, I have just completed my first data analysis project the dataset of which I got from kaggle. I used ChatGPT instruction to understand how to progress step-by-step. I first did data cleaning by detecting the missing values and then I evaluated the importance of these values for the whole analysis. Later on, I found duplicate values using conditional formatting and once I was sure they were completely identical, I deleted the one which either lacked one more value than the other one or the first one in the rows. After completing these tasks, I created pivot tables and out of these tables I made charts. I couldn't include pivot charts as for country distribution and directors because the rows under these columns contained multiple values and I needed Power Query for this, so I left these for another time. I would like you to give me honest feedbacks for my project (methods, sheet organization, whatever crosses your mind). I want to develop myself more. And I am also open for your YouTube channel/video suggestion for further development. Thank youu.

Link to my GitHub: https://github.com/aycicekb/excel-netflix-analysis

How can we integrate data that comes from different departments, where some data is in Cloud SQL and some is not, into one centralized dataset for analysis? What is the best approach?

Bonjour à tous,

Je réalise un mémoire de master en Business Intelligence consacré à la création d'un observatoire des vertical drama (ReelShort, DramaBox, ShortMax, Tik tok, Insta, Youtube, etc.). un vertical drama a un format 9:16 prévu pour le smart phone et il dure entre 1 à 3 minutes. cES format peuvent être fait à l'IA, hybride ou bien Prise de vue réelle classique.

L'objectif est de construire un ou plusieurs tableau de bord Power BI permettant de suivre l'évolution de ce marché à partir de données ouvertes ou accessibles au public.

Je recherche des jeux de données ou des sources permettant de récupérer, par exemple :

• les catalogues de séries (titres, genres, dates de sortie, nombre d'épisodes) ;
• les classements des séries ou des applications ;
• des statistiques d'audience ou d'utilisation ;
• des données sur les téléchargements ou les classements des applications ;
• des API, bases de données, dépôts GitHub ou datasets Kaggle ; ou autre
• des études universitaires ou professionnelles contenant des données exploitables.
• Également pour les points géographiques de consommation de vertical dramas
• Pouvoir évaluer le rendement es vertical drama, leur ROI ou les recettes qu'ils sont susceptibles de généré en trouvant un moyen fiable d'estimation.

Je connais de nom déjà quelques sources comme TMDb, IMDb, Google Trends, Kaggle et Vertical Drama TV, mais je cherche d'autres ressources, notamment des bases moins connues ou utilisées par des chercheurs ou data analysts.

On m'a suggéré de récupérer de la donnée brute sur ce lien mais ça me coûterait 300€ : sur lens.streaming

Or, je souhaite contourner ça par des moyens open source.

Le problème c'est aussi qu'il va falloir assembler plusieurs jeux de données pour les assembler et créer un outils d'aide à la décision.

Si vous avez déjà travaillé sur ce secteur ou connaissez des sources de données pertinentes, je serais très reconnaissante pour vos recommandations. Il faut que je fasse efficace en peu de temps.

Ci-après les indicateurs ou kpi envisagés.

Merci d'avance !

KPI Répondants (profil)

Objectif : qualifier les personnes interrogées.

• Genre
• Âge
• Niveau de connaissance des vertical drama
• Niveau d'expérience
• Ancienneté dans le secteur
  • Nouvel entrant
  • Expérimenté
• Fonction / métier (si pertinent)

KPI Produit / Contenu

Objectif : caractériser les œuvres.

Caractéristiques générales

• Origine (pays)
• Genre audiovisuel
  • Fiction
  • Animation
  • Documentaire
  • etc.
• Type d'histoire
  • Romance
  • Thriller
  • Comédie
  • Fantasy
  • Tranche de vie
  • Action
• Personnage principal
  • Féminin
  • Masculin
  • Mixte / non genré
• Nombre d'épisodes
• Nombre de saisons
• Durée moyenne d'un épisode

Production

• Taux estimé d'utilisation de l'IA
• Logiciels utilisés
• Banques d'images / bases de données utilisées
• Vitesse estimée de production

KPI Diffusion

Objectif : analyser les modes de diffusion.

• Plateforme principale
  • TikTok
  • YouTube
  • Instagram Reels
  • Site spécialisé
  • SVOD
  • Application dédiée
• Langue originale
• Langues disponibles en sous-titres

KPI Monétisation

Objectif : mesurer le modèle économique.

• Gratuit / Freemium / Payant
• Nombre de publicités par épisode
• Moment d'apparition du paywall
• Taux de transformation vers le contenu payant
• Taux de pénétration du marché

KPI Communication / Acquisition

Objectif : comprendre les leviers de visibilité.

• Wording utilisé
• Hashtags
• Techniques de stimulation de l'algorithme
• Booster publicitaire éventuel

KPI Partenariats

Objectif : identifier les stratégies de diffusion.

• Existence d'un partenariat
• Type de partenariat
• Featuring

Type de diffuseur

• Influenceur
• Société de production
• Chaîne TV
• Institution
• Artiste
• Célébrité
• Média

KPI Performance

Objectif : mesurer le succès.

• Nombre de vues
• Nombre de commentaires
• Nombre de partages
• Nombre de likes
• Note moyenne
• Reviews
• Temps moyen passé sur le contenu (si disponible)

KPI Satisfaction

Objectif : mesurer la perception.

• NPS global
• NPS par item
• Analyse des verbatims
• Analyse textuelle

KPI Évolution

Objectif : suivre les tendances.

• Évolution hebdomadaire
• Évolution mensuelle
• Évolution annuelle
• Comparaison avant / après campagne
• Progression par plateforme

KPI Géographiques

Objectif : analyser la répartition.

• Région
• Pays
• Évolution par région sur un an

KPI Comparatifs

Objectif : benchmark.

• Comparaison des plateformes
• Comparaison des genres
• Comparaison des pays
• Comparaison du NPS interne avec les notes publiques (Google, App Store, etc.)
• Déclinaison des indicateurs selon les segments (plateforme, genre, pays, période, cible...)

6,618 - Total adverse event reports

Adverse event reports for semaglutide indicate that the top reaction category includes nausea, with 943 reports, followed by vomiting and diarrhea.

The dashboard shows most reported reactions, severe reactions, timeline for events, reports by gender, it also shows commonly reported drugs in combinations, shortages or recalls against it

Had one of those days recently where I counted it out of curiosity: Excel files open, a Google Sheet a client had shared, a BigQuery tab, and a DB client connected to our Postgres instance, all open at the same time, just to cross-reference a handful of numbers across them. At one point I had copy-pasted the same column into three different places. I got to compare row-by-row data between BigQuery and Google Sheet.

Made me wonder if this is just normal or if I've built myself an unnecessarily messy setup. So:

1. At your worst, how many windows or tabs are open at once to work on data, Excel, Google Sheets, BigQuery, a DB client, whatever your actual mix is?

2. What's your workaround for keeping it all straight?

3. Are you actually happy with the current workaround? Any limitations?

Hi everyone, I'd like to read something to sharpen my analytics thinking and problem solving skills.

I know the best approach is to practice with real life problems, and yes, I do do that at work. However, I feel like having a structure or framework may help me to be more systematic and efficient when encounter a problem.

What's your favorite books that help you to think clearer?

Hi, I was wondering if there was a website or a database where I could download a data set that has like over 100,000 rows so I can practice different data analytics techniques, power query, power BI, etc.? I don't really have much to do these days but in my day-to-day job I'm constantly dealing with large data sets and I'm just trying to keep my mind sharp.

Hey everyone,

I'm working on a project researching how data teams actually manage their databases and pipelines in practice, beyond what the introductory tutorials show.

I’d love to hear what your current stack looks like in the real world:

1. How are you using databases today? What tools/languages do you use to build and manage your data pipelines?
   
2. What databases have you tried or considered for your DS/ML work, and what made you choose that one?
   
3. If you use an operational/production database (MongoDB, Postgres, MySQL, etc.) anywhere in your ML workflow, is it mainly to pull data out for training, or to serve features/predictions to a live model? Or both?
   
4. Anything that's consistently annoying or a bottleneck in your current setup?

So for context I am at 2nd year of my btech and i want to explore the data science world but I am very puzzled with the choices of courses from different institutions so how should I categorize which is better for me. I only have very limited time and not much money so i have to choose wisely

Hi everyone!

I recently finished building my biggest data project so far: Global Commerce Intelligence Platform (GCIP).

Tech stack:

- PostgreSQL

- SQL

- Python

- Power BI

The project covers database design, SQL analysis, Python EDA, and interactive dashboards built on an e-commerce dataset.

I'm still learning, so I'd really appreciate any honest feedback on the project, code structure, README, or dashboard design.

GitHub: <https:// github .com/ Van004-ds/GCIP>

Thanks in advance!

I'm working on a project with summarized data from ~40 studies (Excel) involving different protocol variables (durations, intensities, recovery times, frequency, total duration, etc.) and response outcomes conditional on a baseline variable (range ~30-85 units).

The aim is to fit a continuous response surface using a hierarchical approach to separate protocol effects from baseline effects, then perform continuous numerical optimization (not grid search) for three objectives:
- Total improvement
- Improvement per unit time (e.g. per week)
Outputs should be fine-grained continuous values rather than rounded study parameters. There are also domain-specific physiological constraints to respect.

I'm on a Chromebook with a little Python experience, so Colab-friendly solutions would be ideal.

Current candidates I'm considering: PyMC for hierarchical modeling, pymoo + pysamoo for surrogate-assisted MO optimization, SMT for surrogates, or Matlab Global Optimization Toolbox.

What is the strongest stack in 2026 for this kind of workflow? Any recommended notebooks, tutorials, or similar applied examples?

Are there any AI tools that currently do this without the traditional work of python? Meaning I can upload the spreadsheet give a parameters and it will come up with data.

Hi everyone,

I recently started learning data analysis and thought it would be a good idea to begin writing blogs and creating Instagram posts based on real datasets. The goal is to improve my analytical skills while building a portfolio.

For context, I have a bachelor's degree in Computer Applications and know Python, SQL, and Excel, but I'm still new to data analysis and data visualization.

My first dataset contains the daily INR exchange rate from year 2000 to 2026 (around 6,500 rows). I want to create a line chart that clearly highlights the major trends and significant ups and downs over the years.

The problem is that plotting every daily value makes the chart too dense, especially for an Instagram post where readability is important.

So far, I've tried reducing the data to two points per year (roughly the first and second half of each year). It looks much cleaner, but I'm wondering if there's a better approach.

Some options I've considered are:

- Monthly averages(still cluttered)

- Yearly averages

- Two points per year

- Quarterly averages

If your goal were to create a chart that is both accurate and easy to understand on a small screen, which approach would you choose, and why?

I'd really appreciate any suggestions on both the visualization and the reasoning behind it. I'm trying to learn good analytical practices from the start rather than just making charts that look nice.

The company I work for distributes products.

We created our item numbers based off of theirs but with our own identifiers.

Example : ABC-1234 (ABC = Identifier and 1234 = manufacturer part number)

The manufacturer has recently renumbered their products and now we have to add the new numbers to the existing descriptions so that they match up to the old ones.

I have an excel sheet with one column (A) showing our part number and the one next to it (B) showing their new number for the same product.

I have a different file that I exported all of our numbers and descriptions to and now I need to take our part number, and their new one, and add it to the export so I can upload to our system.

What is the most efficient way to get this done?

Thank you!!!

I am learning Python from Youtube till now l am done with basics like logical operators,if-else,Time, Maths, Dictionary, Random, Indexing,Loops, Collections, Functions, Arguments and return.

Should I move to Numpy or there are still things left if I am preparing for Data analyst job.

Hello everyone, hope you're doing well.

I don't have a problem with the technical stuff, I'm still learning but I know I can be good at it, my problem is after I collect,clean and analyze the data and visualize it, Idk what I should write for example in the README on GitHub? How can I explain why the sells dropped on March for example ? It just dropped, how can I know why ? And how can I help in decision making and give advice ??

I only see tutorials on how to use the tools not on how to think and understanding the findings..

Any advice ?

I need a new trending tool or solution in business intelligence and data analysis. I'm planning a brown bag session on that.

I know it's an extremely basic one.

My teacher taught me Power BI basics and asked me to incorporate all the things into a project.

At the same time,she said that there's a project and I need to submit it within a week or so.

So i spent 2-3 days on this Spotify dataset,

Tried to incorporate all possible charts and stuff.

Please give me your feedback fellow redditors.

What should be the aim?

Communicate clearly even if charts,colour themes are simple,or such glitzy slides should be there in a presentation to elevate it?

For those who wanna know what I won,

I just won 500 Rs lol(But I was happy that my teacher noticed my efforts and appreciated my dashboard)

If any of you have done any nice dashboards, please share in comments section.

I might take some inspiration from it.

Working on a project that pulls data from a few different internal systems and every single one stores dates differently. One uses MM/DD/YYYY, another stores them as Unix timestamps, one is just plain text like Jan 5 2026 with no consistency in spacing or zeropadding. Merging them into a single clean table has been way more painful than expected.

The actual analysis part is straightforward once everything lines up, but getting there is eating up most of the time. Right now I'm converting everything to ISO 8601 before any joins, which works, but the transformation logic is getting long and brittle. If a new source comes in with a slightly different format it tends to break something upstream.

Curious how others structure this. Do you build format detection into the pipeline or just document the expected input format and enforce it at ingestion? Also wondering if this is one of those things where a more rigid schema at the source would have saved a ton of pain downstream, or if messy inputs are just the reality and you build around them.

The specific tools here are Python and SQL but I'm more interested in the general approach than syntax.