Datascience is exploding in popularity due to how it's tethered to the future of technology, supply-demand for high paying jobs and being on the bleeding edge of corporate culture, startups and innovation!

Students from South and East Asia especially can fast…

Metaprogramming is a collection of programming techniques which focus on ability of programs to introspect themselves, understand their own code and modify themselves. Such approach to programming gives programmers a lot of power and flexibility. Without metaprogramming techniques, we probably wouldn’t have modern programming frameworks, or those…

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Data Science Trends of the Future 2022

Data…

We have created a language that is faster than python in every way, works with the entire Java ecosystem (such as the Spring framework, Eclipse and many more) and can be deployed into embedded devices seamlessly, allowing you to collect and process data from pretty much any device you want even without internet.

Our…

In this article, we will explore the Liskov’s substitution principle, one of the SOLID principles and how to implement it in a Pythonic way. The SOLID principles entail a series of good practices to achieve better-quality software. In case some of you aren’t aware of what SOLID stands for, here it is:

• S: Single…

Tensor Flow permits the subsequent:

• Tensor Flow helps you to deploy computation to as a minimum one or extra CPUs or GPUs in a computing tool, server, or mobile device in a completely easy manner. This way the matters may be completed very speedy.
• Tensor Flow lets you specific your computation as a statistics…

To all Data Analysts big and small:

Many Corporates typically have Self service BI and DWH solutions ( I am asking only about those

who did NOT build an inhouse solution) : 

-When is programming needed in most leading Self Service configurations?

-When do analysts and Business executives require coding and programming when the Self…

In the past, slow evaluation of mathematical functions and large memory footprint were the most significant drawbacks of Java compared to C++/C for numeric computations and scientific data analysis. However, recent enhancements in the Java Virtual Machine (JVM) enabled faster and better numerical computing due to several enhancements in evaluating trigonometric functions.

In this article we will use the DataMelt (https://datamelt.org) for our…

By

• Priya Sharma – Sr. Data Scientist -IoT Analytics, SAS Institute Inc.
• Saurabh Mishra – Product Management, IoT, SAS Institute Inc.

June 12, 2020

Description: Majority of AI approaches are based on the construct of training against historical data and then inferencing new data. While this is a sound and proven approach, a lot of IoT assets coming online don’t have historical data and we don’t necessarily have the time to wait.

Modern Machine…

Python is one of the powerful, high-level, easy to learn programming language that provides a huge number of applications. Some of its features, such as being object-oriented and open source, having numerous IDE’s, etc. make it one of the most in-demand programming languages of the…

Quite often, non-technical executives have difficulties understanding what programming, on a very fundamental level, is all about. Because of that knowledge-gap, they tend to hire and overburden experienced data professionals with tasks which they are hopelessly overqualified for. Such as, for example, doing ad-hoc SQL queries on CRM data: "You're the go-to-guy for all things data, and we need the results for the board meeting tomorrow." That's a quite humbling and frustrating…

Over the last few years, Excel has been redesigned from the ground up. Currently, Microsoft is making the new Excel core-features available to every user, regardless of your Office 365 license. Thanks to the Microsoft naming conventions, it is easy to confuse the new features with existing ones. That being said, Power Query and Power Pivot are not the same things as Pivot Tables, which you have likely been using for years.…

Here is a short blog I was asked to make about making a personal Wiki from Wikipedia. It shows the basic steps in text processing so I hope it will be useful for data scientists. It also requires some knowledge of MediaWiki setup on a web server, and some (not very advanced) knowledge of the Python programming language. It takes only several days to create this Wiki with Wikipedia articles if you know…

Summary: Python’s open-source and high-level nature, as well as its comprehensive libraries, make it the perfect fit to solve the numerous real-life ML challenges.

The increasing popularity and accessibility of Artificial Intelligence solutions is rapidly reshaping many industries, from healthcare through finance to aviation. Although the application of the latest technologies has always been an essential consideration for companies striving to get…

Why would a data scientist use Kafka Jupyter Python KSQL TensorFlow all together in a single notebook?

There is an impedance mismatch between model development using Python and its Machine Learning tool stack and a scalable, reliable data platform. The former is what you need for quick and easy prototyping to build analytic models. The latter is what you need to use for data ingestion, preprocessing, model deployment and monitoring at scale. It…

October is historically the most volatile month for stocks, but is this a persistent signal or just noise in the data?

Stocks, Significance Testing & p-Hacking. Follow me on Twitter (twitter.com/pdquant) for more. Over the past 32 years, October has been the most volatile month on average for the S&P500 and December the least, in this article we will use simulation to assess the…

Electronic spreadsheets have been around for nearly 40 years now. They were invented by Bob Frankston and Dan Bricklin, founders of VisiCalc, and I had a chance to chat with both gentlemen a couple of months ago. I highly recommend watching this TED talk with Dan Bricklin:

It's important to understand for…

My writing engagement at Data Science Central came up unexpectedly. Back in August 2018, I stumbled upon an excellent write-up on Data Science Central. The author, Bill Vorhies, shared his thoughts on career transitioning toward data science. I wrote him an email, complimenting him on his blog post, and I dropped a few lines about my own transition. Here's his response:

"Congratulations on your remarkable journey. Perhaps you’d like to write one or more articles…