Statistical Learning (Stanford University)Courses / Self-paced online course
“This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical). This … Continue Reading
Intro to Machine LearningCourses / Self-paced online course
From Udacity: “Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions. Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts … Continue Reading
Python for Data Science and Machine Learning BootcampCourses / Self-paced online course
From Udemy: “Learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more! What Will I Learn? Use Python for Data Science and Machine Learning Use Spark for Big Data Analysis Implement Machine Learning Algorithms Learn to use NumPy for Numerical Data Learn to use Pandas for Data Analysis Learn to use Matplotlib for Python Plotting Learn to use Seaborn for statistical plots Use Plotly for interactive … Continue Reading
Data Science and Machine Learning with Python – Hands On!Courses / Self-paced online course
From Udemy: “Become a data scientists in the tech industry! Comprehensive data mining and machine learning course with Python & Spark. What Will I Learn? Develop using iPython notebooks Understand statistical measures such as standard deviation Visualize data distributions, probability mass functions, and probability density functions Visualize data with matplotlib Use covariance and correlation metrics Apply conditional probability for finding correlated features Use Bayes’ Theorem to identify false positives Make predictions … Continue Reading
R Reference Card for Data MiningCheat Sheets / References, Learning Guides, Etc.
This cheat sheet covers the following aspects in data mining in R: Association Rules & Frequent Itemsets Sequential Patterns Classification & Prediction Regression Clustering Outlier Detection Time Series Analysis Text Mining Social Network Analysis and Graph Mining Spatial Data Analysis Statistics Graphics Data Manipulation Data Access Big Data Parallel Computing Generating Reports Interface to Weka Editors/GUIs Other R Reference Cards RDataMining Website, Package, Twitter & Groups
SAS Learning path and resources – Business Analyst in SASCurricula / References, Learning Guides, Etc.
This is a curriculum compiled by Analytics Vidhya as a guide to learning SAS for business analytics. The curriculum is composed of the following steps: Step 0: Why learn SAS Step 1: Downloading and Installing SAS Step 2: Base SAS on sas.com Step 3: SQL Step 4: Descriptive Statistics with the Udacity Intro to Descriptive Statistics course Step 5: Inferential Statistics with the Udacity Intro to Inferential Statistics course Step … Continue Reading
Learn Data ScienceCurricula / References, Learning Guides, Etc.
Learn Data Science is “a collection of Data Science Learning materials in the form of iPython Notebooks” and the “associated data sets.” The following topics are covered, with at least three notebooks including an Overview – an exposition of the technique for the math-wary”, Data Exploration – “the nuts and bolts of real world data wrangling”, and Analysis – “using the technique to get results”. Topics: Linear Regression Logistic Regression … Continue Reading
DataCamp – Unsupervised Learning in PythonCourses / Interactive tutorial style course
From DataCamp: “Say you have a collection of customers with a variety of characteristics such as age, location, and financial history, and you wish to discover patterns and sort them into clusters. Or perhaps you have a set of texts, such as wikipedia pages, and you wish to segment them into categories based on their content. This is the world of unsupervised learning, called as such because you are not guiding, … Continue Reading
DataCamp – Unsupervised Learning in RCourses / Interactive tutorial style course
From DataCamp: “Many times in machine learning, the goal is to find patterns in data without trying to make predictions. This is called unsupervised learning. One common use case of unsupervised learning is grouping consumers based on demographics and purchasing history to deploy targeted marketing campaigns. Another example is wanting to describe the unmeasured factors that most influence crime differences between cities. This course provides a basic introduction to clustering and … Continue Reading
csv,confConferences / Gatherings and Organizations
From csv,conf,v3: “csv,conf is a non-profit community conference run by some folks who really love data and sharing knowledge. If you are as passionate about data and the application it has to society as us then you should join us in Portland! This isn’t just a conference about spreadsheets. We are curating content about advancing the art of data collaboration, from putting your data on GitHub to producing meaningful insight … Continue Reading