August 28th, 2015

An Introduction to Data Science: Meetup Summary Guest Post by Zen Kishimoto

RSS icon RSS Category: Uncategorized [EN]
Fallback Featured Image

Originally posted on Tek-Tips forums by Zen here
I went to two meetups at H2O, which provides an open source predictive analytics platform. The second meetup was full of participants because its theme was an introduction to data science.
Data science is a new buzzword, and I feel like everyone claims to be a data scientist or something relating to that these days. But other than real data scientists, very few people really understand what a data scientist is or does. Bits and pieces of information are available, but it takes a basic understanding of the subject to exploit such fragmented information. Once you are up to a certain altitude, a series of blogs by Ricky Ho are very informative.
But first things first. There were three speakers at that meetup, but I’ll only elaborate on the first one, who described data science for laymen. The speaker was Dr. Erin LeDell, whose presentation title was Data Science for Non-Data Scientists.


Dr. Erin LeDell

In the following summary of her points, I stay at a bare-bones level so that a total amateur can grasp what data science is all about. Once you get it, I think you can reference other materials for more details. Her presentations and others are available here. The presentation was also videorecorded and is available here.
At the beginning, she introduced three Stanford professors who work closely with H2O:

The first two professors publish many books, but LeDell mentioned
that two ebooks on very relevant subjects are available free of charge.
You can download the books below:

###Data science process
LeDell gave a high-level view of the data science process:


A simple explanation of each step is given here, in my words.

Problem formulation

  • A data scientist studies and researches the problem domain and identifies factors contributing to the analysis.

Collect & process data

  • Relevant data about the identified factors are collected and processed. Data processing includes cleansing data, which means getting rid of corrupt and/or incorrect values and normalizing values. Some data scientists say that 50-80% of their time is spent cleansing data. This was mentioned by   several data scientists.

Machine learning

Insights & action

  • The results are analyzed for appropriate action.

What background does a data scientist need?

This is a question asked by many non–data scientists. I have seen it many times, along with many answers. LeDell answered: mathematics and statistics, programming and database, communication and visualization, and domain knowledge and soft skills.
Drew Conway‘s answer is well known. Actually, the second speaker referred to it.
Data Scientist Skills

This diagram is available here.

Machine Learning

LeDell classified machine learning into three categories: regression, classification, and clustering.
These algorithms are well known and documented. In most cases, a data scientist uses an existing algorithm rather than developing one.

Deep and ensemble learning

LeDell introduced two more technologies: deep and ensemble learning.
Deep learning is described as:
“A branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures, composed of multiple non-linear transformations.” (Wikipedia, 2015)
In ensemble learning, multiple learning algorithms obtain better predictive performance with the penalty of computation time. More details are here.
Finally, LeDell gave the following information for more details on the subject.
I skipped some of her discussion here, but I hope this is a good start to understanding what data science is, and that you will dig further into it.

Zen Kishimoto

About Zen Kishimoto

Seasoned research and technology executive with various functional expertise, including roles in analyst, writer, CTO, VP Engineering, general management, sales, and marketing in diverse high-tech and cleantech industry segments, including software, mobile embedded systems, Web technologies, and networking. Current focus and expertise are in the area of the IT application to energy, such as smart grid, green IT, building/data center energy efficiency, and cloud computing.

Leave a Reply

+
Three Keys to Ethical Artificial Intelligence in Your Organization

There’s certainly been no shortage of examples of AI gone bad over the past few

September 23, 2022 - by H2O.ai Team
+
Using GraphQL, HTTPX, and asyncio in H2O Wave

Today, I would like to cover the most basic use case for H2O Wave, which is

September 21, 2022 - by Martin Turoci
+
머신러닝 자동화 솔루션 H2O Driveless AI를 이용한 뇌에서의 성차 예측

Predicting Gender Differences in the Brain Using Machine Learning Automation Solution H2O Driverless AI 아동기 뇌인지

August 29, 2022 - by H2O.ai Team
+
Make with H2O.ai Recap: Validation Scheme Best Practices

Data Scientist and Kaggle Grandmaster, Dmitry Gordeev, presented at the Make with H2O.ai session on

August 23, 2022 - by Blair Averett
+
Integrating VSCode editor into H2O Wave

Let’s have a look at how to provide our users with a truly amazing experience

August 18, 2022 - by Martin Turoci
+
5 Tips for Improving Your Wave Apps

Let’s quickly uncover a few simple tips that are quick to implement and have a

August 9, 2022 - by Martin Turoci

Start Your Free Trial