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📝 Ghibli Dataset

❓Original to #Ghibli art image generator and classifier dataset.

The Ghibli Art Image Dataset is a prototype designed for generating Ghibli-style images using machine learning. It includes three subsets—training, testing, and validation—each containing directories with two PNG images: one original (o.png) and one generated in Ghibli style (g.png). This dataset supports tasks like image classification and Ghibli-style image generation. As a small-scale version of a larger dataset, it provides essential resources like model code and a pre-trained Generator.pth model. The images were collected from platforms such as Meta, Google, and Instagram for research and experimentation purposes.

Ghibli Dataset

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Top 5 Case Studies for Data Analytics: You Must Know Before Attending an Interview

1. Retail: Target's Predictive Analytics for Customer Behavior
Company: Target
Challenge: Target wanted to identify customers who were expecting a baby to send them personalized promotions.
Solution:
Target used predictive analytics to analyze customers' purchase history and identify patterns that indicated pregnancy.
They tracked purchases of items like unscented lotion, vitamins, and cotton balls.
Outcome:
The algorithm successfully identified pregnant customers, enabling Target to send them relevant promotions.
This personalized marketing strategy increased sales and customer loyalty.

2. Healthcare: IBM Watson's Oncology Treatment Recommendations
Company: IBM Watson
Challenge: Oncologists needed support in identifying the best treatment options for cancer patients.
Solution:
IBM Watson analyzed vast amounts of medical data, including patient records, clinical trials, and medical literature.
It provided oncologists with evidencebased treatment recommendations tailored to individual patients.
Outcome:
Improved treatment accuracy and personalized care for cancer patients.
Reduced time for doctors to develop treatment plans, allowing them to focus more on patient care.

3. Finance: JP Morgan Chase's Fraud Detection System
Company: JP Morgan Chase
Challenge: The bank needed to detect and prevent fraudulent transactions in realtime.
Solution:
Implemented advanced machine learning algorithms to analyze transaction patterns and detect anomalies.
The system flagged suspicious transactions for further investigation.
Outcome:
Significantly reduced fraudulent activities.
Enhanced customer trust and satisfaction due to improved security measures.

4. Sports: Oakland Athletics' Use of Sabermetrics
Team: Oakland Athletics (Moneyball)
Challenge: Compete with larger teams with higher budgets by optimizing player performance and team strategy.
Solution:
Used sabermetrics, a form of advanced statistical analysis, to evaluate player performance and potential.
Focused on undervalued players with high onbase percentages and other key metrics.
Outcome:
Achieved remarkable success with a limited budget.
Revolutionized the approach to team building and player evaluation in baseball and other sports.

5. Ecommerce: Amazon's Recommendation Engine
Company: Amazon
Challenge: Enhance customer shopping experience and increase sales through personalized recommendations.
Solution:
Implemented a recommendation engine using collaborative filtering, which analyzes user behavior and purchase history.
The system suggests products based on what similar users have bought.
Outcome:
Increased average order value and customer retention.
Significantly contributed to Amazon's revenue growth through crossselling and upselling.

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Preparing for a data science interview can be challenging, but with the right approach, you can increase your chances of success. Here are some tips to help you prepare for your next data science interview:

👉 1. Review the Fundamentals: Make sure you have a thorough understanding of the fundamentals of statistics, probability, and linear algebra. You should also be familiar with data structures, algorithms, and programming languages like Python, R, and SQL.

👉 2. Brush up on Machine Learning: Machine learning is a key aspect of data science. Make sure you have a solid understanding of different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.

👉 3. Practice Coding: Practice coding questions related to data structures, algorithms, and data science problems. You can use online resources like HackerRank, LeetCode, and Kaggle to practice.

👉 4. Build a Portfolio: Create a portfolio of projects that demonstrate your data science skills. This can include data cleaning, data wrangling, exploratory data analysis, and machine learning projects.

👉 5. Practice Communication: Data scientists are expected to effectively communicate complex technical concepts to non-technical stakeholders. Practice explaining your projects and technical concepts in simple terms.

👉 6. Research the Company: Research the company you are interviewing with and their industry. Understand how they use data and what data science problems they are trying to solve.

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MUST ADD these 5 POWER Bl projects to your resume to get hired

Here are 5 mini projects that not only help you to gain experience but also it will help you to build your resume stronger

📌Customer Churn Analysis
🔗 https://www.kaggle.com/code/fabiendaniel/customer-segmentation/input

📌Credit Card Fraud
🔗 https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud

📌Movie Sales Analysis
🔗https://www.kaggle.com/datasets/PromptCloudHQ/imdb-data

📌Airline Sector
🔗https://www.kaggle.com/datasets/yuanyuwendymu/airline-

📌Financial Data Analysis
🔗https://www.kaggle.com/datasets/qks1%7Cver/financial-data-

Simple guide

1. Data Utilization:
- Initiate the process by using the provided datasets for a comprehensive analysis.

2. Domain Research:
- Conduct thorough research within the domain to identify crucial metrics and KPIs for analysis.

3. Dashboard Blueprint:
- Outline the structure and aesthetics of your dashboard, drawing inspiration from existing online dashboards for enhanced design and functionality.

4. Data Handling:
- Import data meticulously, ensuring accuracy. Proceed with cleaning, modeling, and the creation of essential measures and calculations.

5. Question Formulation:
- Brainstorm a list of insightful questions your dashboard aims to answer, covering trends, comparisons, aggregations, and correlations within the data.

6. Platform Integration:
- Utilize Novypro.com as the hosting platform for your dashboard, ensuring seamless integration and accessibility.

7. LinkedIn Visibility:
- Share your dashboard on LinkedIn with a concise post providing context. Include a link to your Novypro-hosted dashboard to foster engagement and professional connections.

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🚀 Want to Build Stunning Dashboards? Try This!

Creating an interactive and insightful dashboard is a key skill for any Data Analyst. Here’s a simple Power BI / Tableau dashboard idea to practice!

📝 Project Idea: Sales Performance Dashboard

📌 Dataset: Use free datasets from Kaggle or Sample Superstore (Tableau)

📌 Key Visuals to Include:
✅ Total Sales, Profit, and Orders (KPI Cards)
✅ Sales Trend Over Time (Line Chart)
✅ Top 5 Best-Selling Products (Bar Chart)
✅ Sales by Region & Category (Map & Pie Chart)
✅ Customer Segmentation (Filters & Slicers)

💡 Pro Tips:
🔹 Use conditional formatting to highlight trends 📊
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🔹 Keep colors consistent for better readability 🎨

📌 Bonus Challenge: Can you create a drill-through feature to view details by region?

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Datasets Guide 📚

A practical and beginner-friendly guide that walks you through everything you need to know about datasets in machine learning and deep learning. This guide explains how to load, preprocess, and use datasets effectively for training models. It's an essential resource for anyone working with LLMs or custom training workflows, especially with tools like Unsloth.

Importance:
Understanding how to properly handle datasets is a critical step in building accurate and efficient AI models. This guide simplifies the process, helping you avoid common pitfalls and optimize your data pipeline for better performance.

Link: https://docs.unsloth.ai/basics/datasets-guide

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The Data Science skill no one talks about...

Every aspiring data scientist I talk to thinks their job starts when someone else gives them:
    1. a dataset, and
    2. a clearly defined metric to optimize for, e.g. accuracy

But it doesn’t.

It starts with a business problem you need to understand, frame, and solve. This is the key data science skill that separates senior from junior professionals.

Let’s go through an example.

Example

Imagine you are a data scientist at Uber. And your product lead tells you:

    👩‍💼: “We want to decrease user churn by 5% this quarter”

We say that a user churns when she decides to stop using Uber.

But why?

There are different reasons why a user would stop using Uber. For example:

   1.  “Lyft is offering better prices for that geo” (pricing problem)
   2. “Car waiting times are too long” (supply problem)
   3. “The Android version of the app is very slow” (client-app performance problem)

You build this list ↑ by asking the right questions to the rest of the team. You need to understand the user’s experience using the app, from HER point of view.

Typically there is no single reason behind churn, but a combination of a few of these. The question is: which one should you focus on?

This is when you pull out your great data science skills and EXPLORE THE DATA 🔎.

You explore the data to understand how plausible each of the above explanations is. The output from this analysis is a single hypothesis you should consider further. Depending on the hypothesis, you will solve the data science problem differently.

For example…

Scenario 1: “Lyft Is Offering Better Prices” (Pricing Problem)

One solution would be to detect/predict the segment of users who are likely to churn (possibly using an ML Model) and send personalized discounts via push notifications. To test your solution works, you will need to run an A/B test, so you will split a percentage of Uber users into 2 groups:

    The A group. No user in this group will receive any discount.

    The B group. Users from this group that the model thinks are likely to churn, will receive a price discount in their next trip.

You could add more groups (e.g. C, D, E…) to test different pricing points.

In a nutshell

    1. Translating business problems into data science problems is the key data science skill that separates a senior from a junior data scientist.
2. Ask the right questions, list possible solutions, and explore the data to narrow down the list to one.
3. Solve this one data science problem

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Sharing 20+ Diverse Datasets📊 for Data Science and Analytics practice!


1. How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview

2. Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand

3. Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction

4. Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data

5. Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction

6. Iris Dataset: https://archive.ics.uci.edu/ml/datasets/iris

7. Titanic Dataset: https://www.kaggle.com/c/titanic

8. Wine Quality Dataset: https://archive.ics.uci.edu/ml/datasets/Wine+Quality

9. Heart Disease Dataset: https://archive.ics.uci.edu/ml/datasets/Heart+Disease

10. Bengaluru House Price Dataset: https://www.kaggle.com/amitabhajoy/bengaluru-house-price-data

11. Breast Cancer Dataset: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29

12. Credit Card Fraud Detection: https://www.kaggle.com/mlg-ulb/creditcardfraud

13. Netflix Movies and TV Shows: https://www.kaggle.com/shivamb/netflix-shows

14. Trending YouTube Video Statistics: https://www.kaggle.com/datasnaek/youtube-new

15. Walmart Store Sales Forecasting: https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting

16. FIFA 19 Complete Player Dataset: https://www.kaggle.com/karangadiya/fifa19

17. World Happiness Report: https://www.kaggle.com/unsdsn/world-happiness

18. TMDB 5000 Movie Dataset: https://www.kaggle.com/tmdb/tmdb-movie-metadata

19. Students Performance in Exams: https://www.kaggle.com/spscientist/students-performance-in-exams

20. Twitter Sentiment Analysis Dataset: https://www.kaggle.com/kazanova/sentiment140

21. Digit Recognizer: https://www.kaggle.com/c/digit-recognizer


💻🔍 Don't miss out on these valuable resources for advancing your data science journey!

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