5 Free Data Science eBooks For Your Summer Reading List

Going somewhere nice for your summer holidays? Somewhere with a nice beach perhaps - Goa, Grand Cayman or Grimsby? Or a bustling city break? Wherever you're going there's sure to be long periods where you'll sit for hours on end with little to do but read, so I thought I'd throw together a few free eBooks for your Kindle to while away the long hours in the airport, in a traffic jam or on the beach.

Source: DSC

What Getting A Job In Data Science Might Look Like

I've read a number of articles stating how hard it was to get into Analytics and Data Science. This hasn't been my experience, so I wanted to share. We'll look at interviewing, the tools I currently use, what parts of industry I wasn't prepared for in school, and what my career trajectory has looked like. But not in that particular order.

Source: TDS

5 Challenges Your Chief Data Officer is Likely to Face

With data now easier to collect and analyze than ever before, companies are recognizing the business value of such information - and the need to tap into it quickly. Enter the chief data officer (CDO), the newest role in the C-suite that is responsible for defining and putting into place a data management strategy.

Source: DataScience

Becoming a Self-Driving Car & Machine Learning Engineer

How I left my full-time job, studied at Udacity, and landed a job at BMW. The past year has been quite a journey for me. A year ago I left my full-time job in computer chip design, to go "back to school" for a career change into machine learning. However, I didn't go back to school in a traditional senseâ??-â??I decided to pursue online education via Udacity.

Source: Medium

8 ways to perform simple linear regression and measure their speed using Python

We discuss 8 ways to perform simple linear regression in Python ecosystem. We gloss over their pros and cons, and show their relative computational complexity measure.

Source: Freecodemap

Deep Learning - Past, Present, and Future

There is a lot of buzz around deep learning technology. First developed in the 1940s, deep learning was meant to simulate neural networks found in brains, but in the last decade 3 key developments have unleashed its potential.

Source: Kdnugget

4 Things Universities Need to Know to Teach Data Science

A new report aims to help universities shape data analytics education. Technologies that collect data for analysis exist in pretty much every aspect of life - from smart thermometers in homes to wireless sensor networks.

Source: EdTech

Microsoft Aiming to fill skill gaps in AI, Microsoft makes training courses available to the public

As a software engineer at Microsoft, Elena Voyloshnikova's job is to make informed recommendations about how to improve the performance of software engineering tools.

Source: Microsoft

L'Oreal: Major step towards pioneering at AI in the beauty industry

What makes a person beautiful? For centuries, scholars debated what comprises of beauty and how to measure beauty in a standardized, reproducible way. Some argue that beauty is not real, that it's a myth, that the perception of beauty is learned, not developed.

Source: HOB

Data scientists get most salary hikes in startups

Data scientists and front-end developers were the roles that received the maximum compensation increases in startups in 2017. Both received an average salary increase of about 25% last year, compared to the year before, according to data shared by recruitment firm Belong.

Source: TOI

How Can Data Scientist Become IoT Experts?

Gartner predicts that the number of IoT devices will surpass 11.2 billion this year, the majority of which are in the consumer sector. The same report forecasts that the endpoint spending will exceed $2 trillion, in hardware and software combined.

Source: iotevolutionworld

What is ML and how to learn with Python?

All have probably heard the buzz, the age of Machine Learning has arrived.

Source: HOB

Deep Learning vs Classical Machine Learning

Over the past several years, deep learning has become the go-to technique for most AI type problems, overshadowing classical machine learning. The clear reason for this is that deep learning has repeatedly demonstrated its superior performance on a wide variety of tasks including speech, natural language, vision, and playing games.

Source: TDS

1 year doing data science in the real world

The most important lessons I've learned so far... Ok, so it's actually 14 months but it didn't have the same ring to it as "1 year". I've spent that 14 months at one company, News UK. I'm now about to embark on a new journey at Deliveroo as a consumer and growth algorithms data scientist, so I thought now would be a good time to reflect on the things that I've learned during my time at News UK.

Source: TDS

The 10 Deep Learning Methods AI Practitioners Need to Apply

Deep learning emerged from that decade's explosive computational growth as a serious contender in the field, winning many important machine learning competitions. The interest has not cooled as of 2017; today, we see deep learning mentioned in every corner of machine learning.

Source: Kdnugget

Top 20 Deep Learning Papers, 2018 Edition

Deep Learning is constantly evolving at a fast pace. New techniques, tools and implementations are changing the field of Machine Learning and bringing excellent results.

Source: Kdnugget

How to write a production-level code in Data Science?

Ability to write a production-level code is one of the sought-after skills for a data scientist role- either posted explicitly or not. For a software engineer turned data scientist this may not sound like a challenging task as they might have already perfected their skill at developing production level codes and deployed into production several times.

Source: TDS

Why Are Data Science Leaders Running for the Exit?

I've had several conversations recently with people I know in the data science space that always start out about business and then drift to the state of data science as a whole.

Source: DataScience