2016 Mar Tutorials, Overviews
All (114) | Courses, Education (9) | Meetings (11) | News, Features (18) | Opinions, Interviews, Reports (38) | Publications (2) | Software (9) | Top Tweets (4) | Tutorials, Overviews (18) | Webcasts (5)
- Avoiding Complexity of Machine Learning Problems - Mar 31, 2016.
Sometimes machine learning is the perfect tool for a task. Sometimes it is unnecessary overkill. Here are important lessons learned from the Quora engineering team.
- How to Compute the Statistical Significance of Two Classifiers Performance Difference - Mar 30, 2016.
To determine whether a result is statistically significant, a researcher would have to calculate a p-value, which is the probability of observing an effect given that the null hypothesis is true. Here we are demonstrating how you can compute difference between two models using it.
- How To Become A Machine Learning Expert In One Simple Step - Mar 29, 2016.
This post looks at perhaps the most important, and often overlooked, step in learning machine learning, an aspect which can make the biggest difference in one's skill set.
- Engineers Shouldn’t Write ETL: A Guide to Building a High Functioning Data Science Department - Mar 28, 2016.
An exploration of data science team building, with insight into why engineers should not write ETL, and other not-so-subtle pieces of advice.
- Top 10 Data Science Resources on Github - Mar 24, 2016.
The top 10 data science projects on Github are chiefly composed of a number of tutorials and educational resources for learning and doing data science. Have a look at the resources others are using and learning from.
- Training a Computer to Recognize Your Handwriting - Mar 24, 2016.
The remarkable system of neurons is the inspiration behind a widely used machine learning technique called Artificial Neural Networks (ANN), used for image recognition. Learn how you can use this to recognize handwriting.
- Doing Data Science: A Kaggle Walkthrough – Cleaning Data - Mar 23, 2016.
Gain insight into the process of cleaning data for a specific Kaggle competition, including a step by step overview.
- R Learning Path: From beginner to expert in R in 7 steps - Mar 23, 2016.
This learning path is mainly for novice R users that are just getting started but it will also cover some of the latest changes in the language that might appeal to more advanced R users.
- Lift Analysis – A Data Scientist’s Secret Weapon - Mar 22, 2016.
Gain insight into using lift analysis as a metric for doing data science. Understand how to use it for evaluating the performance and quality of a machine learning model.
- Must Know Tips for Deep Learning Neural Networks - Mar 22, 2016.
Deep learning is white hot research topic. Add some solid deep learning neural network tips and tricks from a PhD researcher.
- 3 Viable Ways to Extract Data from the Open Web - Mar 11, 2016.
We look at 3 main ways to handle data extraction from the open web, along with some tips on when each one makes the most sense as a solution.
- 4 Lessons for Brilliant Data Visualization - Mar 11, 2016.
Get some pointers on data visualization from a noted expert in the field, and gain some insight into creating your own brilliant visualizations by following these 4 lessons.
- How to Use Cohort Data to Analyze User Behavior - Mar 10, 2016.
In the world of data analysis, cohorts are often pushed aside due to their seemingly complex nature. Learn what this analysis can offer and how to do it.
- Deriving Better Insights from Time Series Data with Cycle Plots - Mar 9, 2016.
Visualization plays key role in analysis of time series data, to understand underlying trends. Here we are demonstrating the cycle plot which shows both the cycle or trend and the day-of-the-week or the month-of-the-year effect.
- R or Python? Consider learning both - Mar 8, 2016.
The key to become a data science professional is in understanding the underlying data science concepts and work towards expanding your programming toolbox as much as you can. Hence, one should understand when to use Python and when to pick R, rather mastering just one language.
- Introducing GraphFrames, a Graph Processing Library for Apache Spark - Mar 7, 2016.
An overview of Spark's new GraphFrames, a graph processing library based on DataFrames, built in a collaboration between Databricks, UC Berkeley's AMPLab, and MIT.
- The Data Science Process - Mar 4, 2016.
What does a day in the data science life look like? Here is a very helpful framework that is both a way to understand what data scientists do, and a cheat sheet to break down any data science problem.
- Top Big Data Processing Frameworks - Mar 3, 2016.
A discussion of 5 Big Data processing frameworks: Hadoop, Spark, Flink, Storm, and Samza. An overview of each is given and comparative insights are provided, along with links to external resources on particular related topics.