“The Best Public Datasets for Machine Learning and Data Science” by Stacy Stanford

https://datasimplifier.com/best-data-analyst-projects-for-freshers/

https://toolbox.google.com/datasetsearch

https://www.kaggle.com/datasets

http://mlr.cs.umass.edu/ml/

https://www.visualdata.io/

https://guides.library.cmu.edu/machine-learning/datasets

https://www.data.gov/

https://nces.ed.gov/

https://www.ukdataservice.ac.uk/

https://datausa.io/

https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html

https://www.kaggle.com/xiuchengwang/python-dataset-download

https://www.quandl.com/

https://data.worldbank.org/

https://www.imf.org/en/Data

https://markets.ft.com/data/

https://trends.google.com/trends/?q=google&ctab=0&geo=all&date=all&sort=0

https://www.aeaweb.org/resources/data/us-macro-regional

http://xviewdataset.org/#dataset

http://labelme.csail.mit.edu/Release3.0/browserTools/php/dataset.php

http://image-net.org/

http://cocodataset.org/

http://visualgenome.org/

https://ai.googleblog.com/2016/09/introducing-open-images-dataset.html?m=1

http://vis-www.cs.umass.edu/lfw/

http://vision.stanford.edu/aditya86/ImageNetDogs/

http://web.mit.edu/torralba/www/indoor.html

http://www.cs.jhu.edu/~mdredze/datasets/sentiment/

http://ai.stanford.edu/~amaas/data/sentiment/

http://nlp.stanford.edu/sentiment/code.html

http://help.sentiment140.com/for-students/

https://www.kaggle.com/crowdflower/twitter-airline-sentiment

https://hotpotqa.github.io/

https://www.cs.cmu.edu/~./enron/

https://snap.stanford.edu/data/web-Amazon.html

https://aws.amazon.com/datasets/google-books-ngrams/

http://u.cs.biu.ac.il/~koppel/BlogCorpus.htm

https://code.google.com/archive/p/wiki-links/downloads

http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/

https://www.yelp.com/dataset

https://news.1rj.ru/str/DataPortfolio/2

https://archive.ics.uci.edu/ml/datasets/Spambase

https://bdd-data.berkeley.edu/

http://apolloscape.auto/

https://archive.org/details/comma-dataset

https://www.cityscapes-dataset.com/

http://aplicaciones.cimat.mx/Personal/jbhayet/ccsad-dataset

http://www.vision.ee.ethz.ch/~timofter/traffic_signs/

http://cvrr.ucsd.edu/LISA/datasets.html

https://hci.iwr.uni-heidelberg.de/node/6132

http://www.lara.prd.fr/benchmarks/trafficlightsrecognition

http://computing.wpi.edu/dataset.html

https://mimic.physionet.org/

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💯 Top 100+ Google Data Science Interview Questions

🌟 Essential Prep Guide for Aspiring Candidates

Google is known for its rigorous data science interview process, which typically follows a hybrid format. Candidates are expected to demonstrate strong programming skills, solid knowledge in statistics and machine learning, and a keen ability to approach problems from a product-oriented perspective.

To succeed, one must be proficient in several critical areas: statistics and probability, SQL and Python programming, product sense, and case study-based analytics.

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Feature Scaling is one of the most useful and necessary transformations to perform on a training dataset, since with very few exceptions, ML algorithms do not fit well to datasets with attributes that have very different scales.

Let's talk about it 🧵

There are 2 very effective techniques to transform all the attributes of a dataset to the same scale, which are:
▪️ Normalization
▪️ Standardization

The 2 techniques perform the same task, but in different ways. Moreover, each one has its strengths and weaknesses.

Normalization (min-max scaling) is very simple: values are shifted and rescaled to be in the range of 0 and 1.

This is achieved by subtracting each value by the min value and dividing the result by the difference between the max and min value.

In contrast, Standardization first subtracts the mean value (so that the values always have zero mean) and then divides the result by the standard deviation (so that the resulting distribution has unit variance).

More about them:
▪️Standardization doesn't frame the data between the range 0-1, which is undesirable for some algorithms.
▪️Standardization is robust to outliers.
▪️Normalization is sensitive to outliers. A very large value may squash the other values in the range 0.0-0.2.

Both algorithms are implemented in the Scikit-learn Python library and are very easy to use. Check below Google Colab code with a toy example, where you can see how each technique works.

https://colab.research.google.com/drive/1DsvTezhnwfS7bPAeHHHHLHzcZTvjBzLc?usp=sharing

Check below spreadsheet, where you can see another example, step by step, of how to normalize and standardize your data.

https://docs.google.com/spreadsheets/d/14GsqJxrulv2CBW_XyNUGoA-f9l-6iKuZLJMcc2_5tZM/edit?usp=drivesdk

Well, the real benefit of feature scaling is when you want to train a model from a dataset with many features (e.g., m > 10) and these features have very different scales (different orders of magnitude). For NN this preprocessing is key.

Enable gradient descent to converge faster

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Key Concepts for Data Science Interviews

1. Data Cleaning and Preprocessing: Master techniques for cleaning, transforming, and preparing data for analysis, including handling missing data, outlier detection, data normalization, and feature engineering.

2. Statistics and Probability: Have a solid understanding of denoscriptive and inferential statistics, including distributions, hypothesis testing, p-values, confidence intervals, and Bayesian probability.

3. Linear Algebra and Calculus: Understand the mathematical foundations of data science, including matrix operations, eigenvalues, derivatives, and gradients, which are essential for algorithms like PCA and gradient descent.

4. Machine Learning Algorithms: Know the fundamentals of machine learning, including supervised and unsupervised learning. Be familiar with key algorithms like linear regression, logistic regression, decision trees, random forests, SVMs, and k-means clustering.

5. Model Evaluation and Validation: Learn how to evaluate model performance using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, and confusion matrices. Understand techniques like cross-validation and overfitting prevention.

6. Feature Engineering: Develop the ability to create meaningful features from raw data that improve model performance. This includes encoding categorical variables, scaling features, and creating interaction terms.

7. Deep Learning: Understand the basics of neural networks and deep learning. Familiarize yourself with architectures like CNNs, RNNs, and frameworks like TensorFlow and PyTorch.

8. Natural Language Processing (NLP): Learn key NLP techniques such as tokenization, stemming, lemmatization, and sentiment analysis. Understand the use of models like BERT, Word2Vec, and LSTM for text data.

9. Big Data Technologies: Gain knowledge of big data frameworks and tools like Hadoop, Spark, and NoSQL databases that are used to process large datasets efficiently.

10. Data Visualization and Storytelling: Develop the ability to create compelling visualizations using tools like Matplotlib, Seaborn, or Tableau. Practice conveying your data findings clearly to both technical and non-technical audiences through visual storytelling.

11. Python and R: Be proficient in Python and R for data manipulation, analysis, and model building. Familiarity with libraries like Pandas, NumPy, Scikit-learn, and tidyverse is essential.

12. Domain Knowledge: Develop a deep understanding of the specific industry or domain you're working in, as this context helps you make more informed decisions during the data analysis and modeling process.

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

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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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🔍 Real-World Data Analyst Tasks & How to Solve Them

As a Data Analyst, your job isn’t just about writing SQL queries or making dashboards—it’s about solving business problems using data. Let’s explore some common real-world tasks and how you can handle them like a pro!

📌 Task 1: Cleaning Messy Data

Before analyzing data, you need to remove duplicates, handle missing values, and standardize formats.

✅ Solution (Using Pandas in Python):

import pandas as pd  
df = pd.read_csv('sales_data.csv')  
df.drop_duplicates(inplace=True)  # Remove duplicate rows  
df.fillna(0, inplace=True)  # Fill missing values with 0  
print(df.head())

💡 Tip: Always check for inconsistent spellings and incorrect date formats!


📌 Task 2: Analyzing Sales Trends

A company wants to know which months have the highest sales.

✅ Solution (Using SQL):

SELECT MONTH(SaleDate) AS Month, SUM(Quantity * Price) AS Total_Revenue  
FROM Sales  
GROUP BY MONTH(SaleDate)  
ORDER BY Total_Revenue DESC;

💡 Tip: Try adding YEAR(SaleDate) to compare yearly trends!


📌 Task 3: Creating a Business Dashboard

Your manager asks you to create a dashboard showing revenue by region, top-selling products, and monthly growth.

✅ Solution (Using Power BI / Tableau):

👉 Add KPI Cards to show total sales & profit

👉 Use a Line Chart for monthly trends

👉 Create a Bar Chart for top-selling products

👉 Use Filters/Slicers for better interactivity

💡 Tip: Keep your dashboards clean, interactive, and easy to interpret!

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Essential skills for today’s tech-driven world✅️

Want to make a transition to a career in data?

Here is a 7-step plan for each data role

Data Scientist

Statistics and Math: Advanced statistics, linear algebra, calculus.
Machine Learning: Supervised and unsupervised learning algorithms.
xData Wrangling: Cleaning and transforming datasets.
Big Data: Hadoop, Spark, SQL/NoSQL databases.
Data Visualization: Matplotlib, Seaborn, D3.js.
Domain Knowledge: Industry-specific data science applications.

Data Analyst

Data Visualization: Tableau, Power BI, Excel for visualizations.
SQL: Querying and managing databases.
Statistics: Basic statistical analysis and probability.
Excel: Data manipulation and analysis.
Python/R: Programming for data analysis.
Data Cleaning: Techniques for data preprocessing.
Business Acumen: Understanding business context for insights.

Data Engineer

SQL/NoSQL Databases: MySQL, PostgreSQL, MongoDB, Cassandra.
ETL Tools: Apache NiFi, Talend, Informatica.
Big Data: Hadoop, Spark, Kafka.
Programming: Python, Java, Scala.
Data Warehousing: Redshift, BigQuery, Snowflake.
Cloud Platforms: AWS, GCP, Azure.
Data Modeling: Designing and implementing data models.

#data

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📌 Preparing for Python Interviews in 2025?🗣

If you’re aiming for roles in data analysis, backend development, or automation, Python is your key weapon—and so is preparing with the right questions.💻✨️

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