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
🚨BREAKING: New Python library for agentic data processing and ETL with AI
Introducing DocETL.
Here's what you need to know:
1. What is DocETL?
It's a tool for creating and executing data processing pipelines, especially suited for complex document processing tasks.
It offers:
- An interactive UI playground
- A Python package for running production pipelines
2. DocWrangler
DocWrangler helps you iteratively develop your pipeline:
- Experiment with different prompts and see results in real-time
- Build your pipeline step by step
- Export your finalized pipeline configuration for production use
A Python Library for Time Series using Hidden Markov Models.
Let me introduce you to hmmlearn.
1. Hidden Markov Models
A Hidden Markov Model (HMM) is a statistical model that describes a sequence of observable events where the underlying process generating those events is not directly visible, meaning there are "hidden states" that influence the observed data, but you can only see the results of those states, not the states themselves
2. HMM for Time Series with hmmlearn
hmmlearn implements the Hidden Markov Models (HMMs).
We can use HMM for time series. Example: Using HMM to understand earthquakes.
❌Move over PowerBI. There's a new AI analyst in town.
💡Introducing ThoughtSpot.
1. AI Analyst
ThoughtSpot’s Spotter is an AI analyst that uses generative AI to answer complex business questions in natural language, delivering visualizations and insights instantly.
It supports iterative querying (e.g., “What’s next?”) without predefined dashboards.
2. Self-Service Analytics
Unlike Tableau and Power BI, which rely on structured dashboards, ThoughtSpot emphasizes self-service analytics with a search-based interface, making it accessible to non-technical users.
Its AI-driven approach feels like “ChatGPT for data.”
Top 7 most important statistical analysis concepts that have helped me as a Data Scientist.
This is a complete 7-step beginner ROADMAP for learning stats for data science. 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.