About Manuel Amunategui

Data scientist with over 20-years experience in the tech industry, MAs in Predictive Analytics and International Administration, co-author of Monetizing Machine Learning and VP of Data Science at SpringML.

From consulting in machine learning, healthcare modeling, 6 years on Wall Street in the financial industry, and 4 years at Microsoft, I feel like I’ve seen it all. And this has opened my eyes to the huge gap in educational material on applied data science. Like I say:

It just ain’t real 'til it reaches your customer’s plate

I am a startup advisor and available for speaking engagements with companies and schools on topics around building and motivating data science teams, and all things applied machine learning.

Reach me at amunategui@gmail.com

Data Exploration & Machine Learning, Hands-on

Recommended free walkthrough, check it out and boost your career:

Convolutional Neural Networks And Unconventional Data - Predicting The Stock Market Using Images

Practical walkthroughs on machine learning, data exploration and finding insight.

Support the Site and Check Out my Udemy Classes

Practical walkthroughs on machine learning, data exploration and finding insight.

Hi again, thanks for your continued interest and support! Here are all my classes on Udemy. Check them out and give me a good review if you like them :-)

Automating Data Exploration with R

Course on 'Automating Data Exploration with R' available at Udemy.com: - click here for $19 coupon!

In this class you will learn:

Build a pipeline to automate the processing of raw data for discovery and modeling
Know the main steps to prepare data for modeling
Know how to handle the different data types in R
Understand data imputation
Treat categorical data properly with binarization (making dummy columns)
Apply feature engineering to dates, integers and real numbers
Apply variable selection, correlation and significance tests
Model and measure prepared data using both supervised and unsupervised modeling

Reducing High Dimensional Data in R

11 lectures on 'Reducing High Dimensional Data in R' at Udemy.com - click here for $19 coupon!

In this R course, we'll see how PCA can reduce a 5000+ variable data set down to 10 variables and barely lose accuracy! We'll look at different ways of measuring PCA's effectiveness and other ways of reducing wide data sets (those with lots of features/variables). We'll also look at the advantages and disadvantages with different ways of reducing data.

You will learn:

Understand various ways of reducing wide data sets
Understand Principal Component Analysis (PCA)
Control, tune and measure the effects of PCA
Use GBM modeling to measure the effectiveness of PCA
Reducing dimensionality with classic GBM & GLMNET variable selection
Use ensembling techniques to find the most stable variables

Supercharge R with SparkR - Apply your R chops to Big Data!

21 lectures on 'SparkR, modeling, S3 and HDFS' at Udemy.com - click here for $25 coupon!

In this class you will learn:

How to use R in a distributed environment
Create Spark clusters on Amazon's AWS
Perform distributed modeling using GLM
Measure distributed regression and classification predictions
Access data from csv's, json, hdfs, and S3

Analyzing Stock Market Data with R

18 lectures on 'Quantmod and R' available at Udemy.com - click here for $25 coupon!

In this class, we explore various technical and quantitative analysis techniques using the R programming language. All the code is included in PDFs attached to each lecture.

What is covered:

Easily access free, stock-market data using R and the quantmod package
Build great looking stock charts with quantmod
Use R to manipulate time-series data
Create a moving average from scratch
Access technical indicators with the TTR package
Create a simple trading systems by shifting time series using the binhf package
A look at trend-following trading systems using moving averages
A look at counter-trend trading systems using moving averages
Using more sophisticated indicators (ROC, RSI, CCI, VWAP, Chaikin Volatility)
Grouping stocks by theme to better understand them
Finding coupling and decoupling stocks within an index