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πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
@mdancho84
May 10, 2023 β€’ 8 tweets β€’ 7 min read β€’ Read on X
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Learning data science on your own is tough...

...(ahem, it took me 6 years)

So here's some help.

5 Free Books to Cut Your Time In HALF.

Let's go! 🧡

#datascience #rstats #R Image
1. Mastering #Spark with #R

This book solves an important problem- what happens when your data gets too big?

For example, analyzing 100,000,000 time series.

You can do it in R with the tools covered in this book.

Website: therinspark.com Image
2. Geocomputation with #R

Interested in #Geospatial Analysis?

This book is my go-to resource for all things geospatial.

This book covers:
-Making Maps
-Working with Spatial Data
-Applications (Transportation, Geomarketing)

Website: r.geocompx.org Image
3. Tidy Finance with #R

What tools exist in R for #Finance?
And how do I use them?

Answers to these questions are covered in this book!

P.S.- This book uses my R package, #tidyquant

Website: tidy-finance.org Image
4. Text Mining with R

This is a fantastic introduction to text analysis and text mining with the #tidytext R package.

This book singlehandedly made me MORE CONFIDENT with text analysis.

Website: tidytextmining.com Image
5. #Forecasting Principles and Practice

This is the best β€œtheory” book on #timeseries analysis and forecasting.

Topics Covered:
- ARIMA,
- Exponential Smoothing,
- TimeSeries Decomposition
- A lot more!

Website: otexts.com/fpp3/ Image
1-Dollar Bonus Book:

This is a massive value- Gives you a complete plan for EVERYTHING you need to know about learning data science.

It's only a buck.

And it will cut 2-3 years off your journey.

Website: learn.business-science.io/if-i-had-to-le… Image
Want even more help becoming a 6-figure data scientist?

I have a free workshop that will help you become a $100K+ earner as a #DataScientist even in a Recession.

πŸ‘‰Register Here: us02web.zoom.us/webinar/regist… Image

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More from @mdancho84

πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Aug 5
Principal Component Analysis (PCA) is the gold standard in dimensionality reduction.

But almost every beginner struggles understanding how it works (and why to use it).

In 3 minutes, I'll demolish your confusion: Image
1. What is PCA?

PCA is a statistical technique used in data analysis, mainly for dimensionality reduction. It's beneficial when dealing with large datasets with many variables, and it helps simplify the data's complexity while retaining as much variability as possible. Image
2. How PCA Works:

PCA has 5 steps; Standardization, Covariance Matrix Computation, Eigen Vector Calculation, Choosing Principal Components, and Transforming the data.
Read 12 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Aug 4
🚨BREAKING: New Python library for Bayesian Marketing Mix Modeling and Customer Lifetime Value

It's called PyMC Marketing.

This is what you need to know: 🧡 Image
1. What is PyMC Marketing?

PyMC-Marketing is a state-of-the-art Bayesian modeling library that's designed for Marketing Mix Modeling (MMM) and Customer Lifetime Value (CLV) prediction.
2. Benefits

- Incorporate business logic into MMM and CLV models
- Model carry-over effects with adstock transformations
- Understand the diminishing returns
- Incorporate time series and decay
- Causal identification Image
Read 9 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Aug 2
Random forest was wild to me.

In 3 minutes, I'll share 3 weeks of research on Random Forest.

Let's go: Image
1. What is a Random Forest?

Random Forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Each tree in the random forest gives a prediction, and the most voted prediction is considered as the final result.
2. Bagging (Bootstrap Aggregations):

Each tree is trained on a random subset of the data (sampling of data points) instead of the entire training dataset. This technique is called "bootstrap aggregating" or "bagging".
Read 9 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Jul 30
RIP Data Scientists.

The Generative AI Data Scientist is NOW what companies want.

This is actually good news. Let me explain: Image
Companies are sitting on mountains of unstructured data.

PDF
Word docs
Meeting notes
Emails
Videos
Audio Transcripts

This is useful data. But it's unusable in its existing form. Image
The AI data scientist builds the systems to analyze information, gain business insights, and automates the process.

- Models the system
- Use AI to extract insights
- Drives predictive business insights

Want to become a Generative AI Data Scientist in 2025? Image
Read 6 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Jul 29
Bayes' Theorem is a fundamental concept in data science.

But it took me 2 years to understand its importance.

In 2 minutes, I'll share my best findings over the last 2 years exploring Bayesian Statistics. Let's go. Image
1. Background:

"An Essay towards solving a Problem in the Doctrine of Chances," was published in 1763, two years after Bayes' death. In this essay, Bayes addressed the problem of inverse probability, which is the basis of what is now known as Bayesian probability.
2. Bayes' Theorem:

Bayes' Theorem provides a mathematical formula to update the probability for a hypothesis as more evidence or information becomes available. It describes how to revise existing predictions or theories in light of new evidence, a process known as Bayesian inference.
Read 13 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Jul 28
Understanding P-Values is essential for improving regression models.

In 2 minutes, I'll crush your confusion.

Let's go: Image
1. The p-value:

A p-value in statistics is a measure used to assess the strength of the evidence against a null hypothesis. Image
2. Null Hypothesis (Hβ‚€):

The null hypothesis is the default position that there is no relationship between two measured phenomena or no association among groups. For example, under Hβ‚€, the regressor does not affect the outcome. Image
Read 15 tweets

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