Overcoming the five most common data analytics challenges by Christopher Roberts via @Infomgmt

To optimize your business, you must accumulate and analyze the data and feedback you’ve been getting from all aspects of your business. Here are the solutions to top challenges.

A very useful article that I think deserves a read and bookmark.

What happened to Hadoop? by @derrickharris via @Medium

It was the next big thing...until it wasn’t. Derrick Harris explains, “Hadoop’s path to ubiquity intersected a host of other technology shifts that as a whole would prove to be more impactful in the long run, in part by peeling off the most valuable promises of big data and making them more consumable.”

Definitely, a question that needed to be answered!  Thank you Derrick for the great answer - to thank him give him applause and a follow.

What’s next for the popular programming language R? by Dan Kopf via @qz

Hadley Wickham discusses R and where it’s going, tidy evaluation, and the different cultures of R and Python users and how their viewpoints differ.

Any R user will find this really interesting. I have to agree with him that there is no "competition" between R and Python - it's more about what is best for what you are doing, what you are comfortable using or a combination of the two. I like R and I am better at R than Python (which I struggle with at times). That's just me, and I'm sure others are the other way around.

Top Deep Learning Frameworks of 2019 and How Do They Compare by Gaurav Belani via @AiThority

This post looks at five deep learning platforms and offers the pros and cons of each.

I really like that the pros and cons are given for the 5 on this list. I'm not sure I could choose between them although I think I would want to go nearer to Keras than TensorFlow but that is more of personal preference and not necessarily something you should take notice of.

9 top trends that are driving AI and software investments by Laurent Bride via @infomgmt

IT and data leaders are constantly challenged to keep up with new trends in emerging and disruptive technologies and to determine how each can best aid the organization.

Definitely, something to review your strategy and training plans against when preparing for the future.

WEBINAR: - How to Use Time Series Data to Forecast at Scale - 12 Sept 2019

Data Science Central Webinar Series Event

How to Use Time Series Data to Forecast at Scale Join us for this latest DSC Webinar on September 12th, 2019 The growing popularity of sensor networks and telemetry applications has lead to the collection of a vast amount of time-series data, which enables forecasting for a multitude of use cases from application performance optimization to workload anomaly detection. The challenge is to automate a historically manual process handcrafted for the analysis of a single data series of just tens of data points to large scale processing of thousands of time series and millions of data points. In this latest Data Science Central webinar, we will demonstrate how to leverage InfluxDB to implement some solutions to tackle on the issues of time series forecasting at scale, including continuous accuracy evaluation and algorithm hyperparameters optimization. As a real-world use case, we will discuss the storage forecasting implementation in Veritas Predictive Insights which is capable of training, evaluating and forecasting over 70,000 time series daily. Speaker: Marcello Tomasini, Sr. Data Scientist -- Veritas Technologies Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central Title:  How to Use Time Series Data to Forecast at Scale Date:  Thursday, September 12th, 2019 Time:  9:00 AM - 10:00 AM PDT Space is limited so please register early: Reserve your Webinar seat now

7 Reasons Your Data Science Resume is Suboptimal by/via @odsc

Seven reasons your resume could be failing you.

Something for us all to look into no matter our experience level.