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🔥 Matt Dancho (Business Science) 🔥 Profile picture
@mdancho84
Aug 21, 2022 • 9 tweets • 3 min read • Read on X
What could go wrong?

LOL. 😂

Plus the 3 #datascience books that helped me learn #stats the most. 🧵

#rstats Image
I’m not saying you need to be an expert in advanced calculus to do machine learning…

BUT, there is a big difference between someone that does vs someone that does NOT have a good foundation in stats when it comes to getting & explaining business results.
My thought process back in the day was to obtain a great foundation in stats and machine learning at the same time.

So here’s what helped me. I read a ton of books.

Here are the 3 books that helped me learn data science the most...
1. R for Data Science (Wickham & Grolemund) r4ds.had.co.nz
2. Introduction to Statistical Learning (James, Witten, Hastie, & Tibshirani) statlearning.com
3. Applied Predictive Modeling (Kuhn & Johnson) appliedpredictivemodeling.com
Keep in mind that I’ve read 300+ books on stats, ML, time series, …

But these were the 3 best. Ones I got a ton of applied value out of.
Now you’re probably thinking reading these 3 books will take a long time, and still might not get you the whole way to data scientist.

That’s why I want to help you speed up the process.

So it doesn’t take you 5 years to learn data science (like it did me).
I compiled the top 10 most important skills that helped me learn and get results from data science.

And I put these top 10 data science skills into a FREE 40-minute webinar.

Enjoy!

learn.business-science.io/free-rtrack-ma… Image

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More from @mdancho84

🔥 Matt Dancho (Business Science) 🔥 Profile picture
Mar 23
Correlation is the skill that has singlehandedly benefitted me the most in my career.

In 3 minutes I'll demolish your confusion (and share strengths and weaknesses you might be missing).

Let's go: Image
1. Correlation:

Correlation is a statistical measure that describes the extent to which two variables change together. It can indicate whether and how strongly pairs of variables are related. Image
2. Types of correlation:

Several types of correlation are used in statistics to measure the strength and direction of the relationship between variables. The three most common types are Pearson, Spearman Rank, and Kendall's Tau. We'll focus on Pearson since that is what I use 95% of the time.Image
Read 11 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Mar 22
Data Science for Business.

The book that helped me connect the dots. Let's dive in: Image
1. CRISP Data Mining Process

The foundation for applying data science to business is the CRISP method.

This is a helpful framework for integrating data science with the business understanding. Image
2. Machine Learning Predictions as Probabilities

One of the most important part of machine learning is probability.

We can estimate the probability from machine learning predictions.

Once you get this, the next framework opens up: Image
Read 7 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Mar 21
90% of data scientists can improve their SQL for business intelligence.

In 3 minutes, learn the 20% of SQL gets 80% of results: Image
🔍 SELECT Basics:

Start with SELECT * FROM table to retrieve all rows & columns.

Remember, SQL isn’t case-sensitive—but capitalizing keywords (SELECT, FROM) makes your queries easier to read. Image
📝 Choosing Specific Fields:

Instead of the star (*), list only the fields you need (e.g., field1, field2) and rename them using AS for clarity.

Clear queries lead to clear insights. Image
Read 10 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Mar 20
Understanding probability is essential in data science.

In 4 minutes, I'll demolish your confusion.

Let's go! Image
1. Statistical Distributions:

There are 100s of distributions to choose from when modeling data. Choices seem endless. Use this as a guide to simplify the choice. Image
2. Discrete Distributions:

Discrete distributions are used when the data can take on only specific, distinct values. These values are often integers, like the number of sales calls made or the number of customers that converted.
Read 12 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Mar 17
6 statistical methods that can be used for A/B Testing (and when to use them). Image
A/B Testing is a staple of data science and data analyst interviews.

And it's the Number 1 technique that companies benefit from in improving customer revenue.

So here are 6 of the most common stat methods used in A/B testing.
1. Z-Test (Standard Score Test):

Ideal for large sample sizes (typically over 30) and when the population variance is known.

Compares the mean of two groups to see if they are different from each other.

Often used in conversion rate optimization, click-through rates. Image
Read 11 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Mar 16
R-squared is one of the most commonly used metrics to measure performance.

But it took me 2 years to figure out mistakes that were killing my regression models.

In 2 minutes, I'll share how I fixed 2 years of mistakes (and made 50% more accurate models than my peers). Let's go:Image
1. R-squared (R2):

R2 is a statistical measure used in regression models that provides a measure of how well the observed outcomes are replicated by the model, based on the proportion of total variation of outcomes explained by the model. Image
2. Range (0 to 1):

R2 ranges from 0 to 1. A higher R2 value indicates a better fit between the prediction and the actual data. For example, an R2 value of 0.70 suggests that 70% of the variance in the dependent variable is predictable from the independent variable(s). Image
Read 11 tweets

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