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
Understanding regression models is essential in data science.
In 4 minutes, I'll demolish your confusion. Let's go:
1. The 6 Diagnostic Checks Every Data Scientist Should Run
Once you've built a regression model, your job isn't done. These 6 checks will tell you whether your model can actually be trusted.
2. Posterior Predictive Check
Ask yourself: do the model-predicted lines resemble the observed data line? If your model is a good fit, simulated data from it should look similar to your actual data. When they diverge wildly, your model is missing something important.
Understanding probability is essential in data science.
In 4 minutes, I'll demolish your confusion.
Let's go!
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.
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.
These 7 statistical analysis concepts have helped me as an AI Data Scientist.
Let's go: 🧵
Step 1: Learn These Descriptive Statistics
Mean, median, mode, variance, standard deviation. Used to summarize data and spot variability. These are key for any data scientist to understand what’s in front of them in their data sets.
2. Learn Probability
Know your distributions (Normal, Binomial) & Bayes’ Theorem. The backbone of modeling and reasoning under uncertainty. Central Limit Theorem is a must too.