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

Feb 5
Residuals are the key to improving model performance.

But it took me 5 years to figure this out.

In 5 minutes, I'll share what took me 5 years to figure out. Let's go. 🧵 Image
1. What are residuals?

In statistics and machine learning, "residuals" refer to the differences between observed values and the values predicted by a model. These are your model errors
2. Residual Analysis:

The key to understanding if your model is any good is residual analysis. What I'm looking for is: Linearity, Homoskedasticity (constant variance), and lack of pattern.
Read 11 tweets
Feb 4
Logistic Regression is how my simple lead scoring model grew revenue to $15,000,000.

In 3 minutes, here's what took me 3 months to figure out (business case included).

Let's dive in. 🧵 Image
1. Binary Classification:

Logistic regression is a statistical method used for analyzing a dataset in which one or more independent variables determine a binary outcome (in which there are only two possible outcomes). This is commonly called a binary classification problem. 0 = customer didn't buy, 1 = customer bought!
2. Linear Regression vs Logistic Regression (Why I made the switch):

In 2015 I was still in the early stage of my data science journey. And when I first modeled leads, I made a rookie mistake: using linear regression. While it actually worked (sort of) for lead scoring, I had a big problem. Linear regression didn't provide a probability.
Read 12 tweets
Feb 1
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
Jan 30
Data Scientist vs. AI Engineer (Generative AI Edition)

I've been studying AI for 18 months. This is what I discovered about the rise of this new role:
1) Context: The Rise of AI Engineering

- Data scientists have been called the “sexiest job of the 21st century.”
- But generative AI breakthroughs have led to a new role: AI engineers.
- Think of data scientists as data driven decisioneers vs. AI engineers as AI system builders.
2) Use Cases

Data Scientists:

- Focus on descriptive & predictive analytics (e.g., EDA, clustering, regression, classification).
- Turn messy data into actionable insights.
Read 12 tweets
Jan 24
The cost of the Python AI / ML stack:

Langchain $0
Langgraph $0
Scikit Learn $0
H2O $0
Torch $0
Pandas $0
Numpy $0
Plotly $0
Statsmodels $0
Ollama $0
OpenAI (<$1.00 per month)

Becoming a Generative AI Data Scientist cost me $12: 🧵 Image
1. Environment:

- VSCode
- Conda
- Jupyter VSCode Integration

Start here: code.visualstudio.com/docs/datascien…Image
2. Data Analysis and Visualization:

- Pandas
- Plotly
- Numpy
- Statsmodels
- Scipy

Start here: pandas.pydata.org/docs/getting_s…Image
Read 10 tweets
Jan 20
Can AI do Time Series Forecasting?

This is what I found out. Image
Over the past 2 years, I've been studying AI. Why?

Because there are 1,000s of ways we can combine AI with Data Science.

Time series is one of them. Image
Most people have bad results with AI. And Time Series is no different.

Time Series is niche.
It's nuanced.
Practitioners have a set of steps that they follow.
These steps can differ from person to person. Image
Read 7 tweets

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