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Data Science 101: Handling Missing Data (Revisited)

I recently received the following question on data science methods from an avid reader of insideBIGDATA who hails from Taiwan. I think the topics are very relevant to many folks in our audience so I decided to run it here in our Data Science 101 channel. The issue of missing data is one most data scientists see quite frequently.

How Computers Learn

This Vienna Gödel Lecture provides a fascinating talk by Peter Norvig, Research Director at Google Inc. in the field of intelligent computers. Norvig talks about his long experience in AI and Machine Learning. The talk explains how computers learn from examples and what are the promises and limitations of these techniques.

Online Education Gets Up Close and Personal as Big Data Improves Performance

In this special guest feature, Jerry Huang, Chief Operating Officer of iTutorGroup, discusses how the collection and analysis of Big Data will continue to dramatically change the landscape of learning and pave the way for optimized learning experiences, leading to even better student outcomes.

Quantifying Data Science

Our friends over at The Data Incubator just released a new series of data-driven ranking reports that showcase the quantitative methodologies the data science fellowship, hiring and training company uses to actively teach their fellows. The idea was to develop a more data-driven approach to what the company should be teaching in their data science corporate training and their free fellowship for masters and PhDs looking to enter data science careers in industry.

Making the Leap from Data Science Hopeful to Practitioner

It’s a familiar dilemma. You’ve done your research, read some books, taken some online classes – and at long last, you’re finally ready to get real-life work experience as a Data Scientist. In this contributed article, Dan Saber, Data Science Hiring Manager at Coursera, offers three important steps for successfully transitioning into a data science career.

Interview: Emily Glassberg Sands, Data Science Manager at Coursera

I recently caught up with Emily Glassberg Sands, Data Science Manager at Coursera, to talk about applying machine learning, neural networks, natural language processing, and big data analytics to the retail industry. Emily leads an awesome team of data scientists and data science managers working on growth, discovery, the learning experience, and a new enterprise offering. The team’s job is to help build a better Coursera through data-driven decisions and products.

Machine Learning: Why it Matters?

In this contributed article, Smita Adhikary, Managing Consultant at Big Data Analytics Hires, provides a whirlwind overview of machine learning technology and why it’s important to increasing the value of enterprise data assets.

Get Up to Speed with Data Science in 7 Easy Steps

In this article, we’ll make sense of data science for those unacquainted with the field and outline a series of 7 easy steps to get up to speed with the technology. In doing so, we’ll highlight the integral steps in the “data science process,” so you can get a good grasp of how data science works and how it is of value to enterprises seeking to maximize the value of their data assets.

How to Grow your Data Science Team

If you’re building or growing a data science team, the first reflex is to hire new talent. Before you do so, take a few moments to ask yourself the following questions.

Learn Data Science: Eight (Easy) Steps

Our friends over at DataCamp have produced the “Become a Data Scientist in 8 Steps” infographic providing a view of the eight steps that you need to to through to learn data science. Some of these eight steps will be easier for some than for others, depending on background and personal experience, among other factors.