WHITE OWL Education

The majority of the money that you spend on monthly payments for your home (in the first 5 years) goes to the bank.

As a person who is for the first time, here is how you "win" at this game.

You end up paying an interest rate on your mortgage that is determined in part from the interest rates set by the Federal Reserve). The monthly payment is designed to be consistent. The downside though is that early in the loan, most of your payment is interest to the bank.

So how do you as a homebuyer “win” at this game?

Buy now with a fixed rate loan

Even though housing prices have been going down over the last year (and are still projected to go down in the near future), housing prices typically go up in the long run. In fact, if you are planning on buying now, I put together a spreadsheet that helps you buy a home. Details are at: https://www.etsy.com/listing/1362419938

Also, your monthly payment is FIXED. Your earning potential is NOT fixed.

Over time, household income tends to rise. Part of this could be from getting a better job, and part of it could be from inflation. This means if you made $28 an hour, it would take you roughly 95 hours a month to make a $2000 house payment assuming a 25% overall tax rate. If 10 years from now you make $40 an hour, then you work 30 HOURS LESS to make the payment.

I would like to hear your feedback on this. What challenges are you facing when you are ?

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In the blog today, I discuss how data scientists can maintain a competitive edge in the marketplace by learning how to build simulations.

Why are simulations the future of data science? Data science will be defined by the 3D world and not just by Excel tables. Here I discuss a couple of simulation use cases and a way to build your own simulations.

How does BigQuery make you more productive than Excel when working with big data?

A quick post for people who want to get into or

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There is minimal time needed for initial configuration.

Simply do the following:
💠Upload your data to Google Cloud Storage
💠Configure the table
💠Start your analysis

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BigQuery can start off at the same size as Excel, and scale into a data warehouse

✅Scalable, serverless
✅Access management ensures different teams are not interfering with each other
✅Stream millions of rows per second for real-time analysis

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BigQuery can do what Excel can’t:

⏫Petabyte level analysis
📊Predictions and forecasts with AI
🌎Geospatial analysis

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When should you use BigQuery over Excel? Use BQ when Data Ingestion is critical.

⚫️Data ingestion via streaming (DataFlow)
⚫️Data ingestion directly from SQL
LOAD DATA to pull in CSV data
⚫️BigQuery Data Transfer Service
⭕️Google Ads
⭕️YouTube Channel Reports

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When should you use BigQuery over Excel?

Use it to access public data sets.

You can augment your own analysis with public data sets.

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Google Cloud BigQuery can be used on small datasets, and some companies run queries run queries on massive amounts of data.

* Twitter in 2018 ran at least 8,000 queries a month ( processing 100 PB) on BigQuery (https://blog.twitter.com/engineering/en_us/topics/infrastructure/2019/democratizing-data-analysis-with-google-bigquery)

* Home Depot has a data warehouse built on BigQuery of over 15 petabytes (https://cloud.google.com/customers/the-home-depot)

* I have personally analyzed a smaller data set of 8GB of election donation data using BigQuery (https://www.whiteowleducation.com/blog/2022/11/15/tutorial-bigquery/)

* Yahoo will ingest 200 TB daily which is used as part of the 1M monthly queries that are made. (https://cloud.google.com/blog/products/data-analytics/benchmarking-cloud-data-warehouse-bigquery-to-scale-fast)

I have always believed in having the right tool for the job. If you are working with large CSV files, BigQuery should be one of the first tools that you use in order to explore and understand the data.

I just put together a tutorial that uses publicly available election contribution data to help you learn BigQuery, and help you prepare for a career as a data analyst ( https://www.whiteowleducation.com/blog/2022/11/15/tutorial-bigquery/).

Data Analyst Tutorial – Learn Google BigQuery Increase your chances of getting a data analyst job by learning Google BigQuery!

In the United States, today is Election Day. With a rumored $12B raised, I thought it would be helpful to take some of my data science background and private equity background to figure out (for non-commercial reasons) who were some of the top donors.

Using Google Cloud Big Query, I looked at about 43 million lines of data, and it turns out that hedge funds were some of the top contributors. If it turns out that you are looking to put together charts or analysis similar to what I put together, send me a message and let me know how I can help. Also, if you are interested in Google Cloud in general, I have a couple of posts about ways to use Google Cloud on my website (https://lnkd.in/dUBYN5Xm).

One goal of White Owl education is to scale by having students "ask questions" to low cost AI when they get stuck .

With the Big Science Research Workshop (https://bigscience.huggingface.co) having a 176B parameter, language model available soon, that goal is becoming more realistic.

QUESTION OF THE DAY:

Hello Ralph, what are the best and easy to use tools for Data Cleaning and creating models.?

ANSWER ABOUT DATA CLEANING:

Data is information about the world. It is pictures, sound, text, and just about anything else that can be ingested into a computer. Once you have this information, a basic way of putting together models is called supervised learning. In supervised learning, you have data (a picture of a stop sign) and a label (an indicator that the picture is a stop sign). Data cleaning is all about making sure that your label matches up to the picture.

It gets more complicated though. What if you are looking at an Excel spreadsheet that has indicators for the time that the customer was in the store, amount spent, etc. and you are trying to predict if the customer is going to return. Here your challenge can occur if the data was entered by hand (which could be prone to error).

ANSWER ABOUT MODELS:

A model is a prediction of the world. It takes an input (information about the world) and it produces an output. If you do your job right, that output is somewhat accurate.

WHAT THIS MEANS (and this is important): You want to put together models where you have a fairly good shot at doing predictions. You have a better chance of predicting you are looking at a stop sign (based on text="stop" and color="red") than you do of predicting stock market returns based on previous day returns.

If you are using Google Cloud, you could use Cloud AutoML and get decent results. If you are trying to build from scratch, first look into scikit-learn for python and then look into fast.ai.

Also, if you are looking into image classification, it might be worthwhile to check out Google Cloud's Visual Inspection AI (https://www.youtube.com/watch?v=60Sk-mq3Cr8&t=20s).