As a working professional in Data Analytics, I have always been fascinated by new and upcoming ways of extracting data to create meaningful insights. There is a myriad of options for a data analyst to explore. However, some fit better than the others in certain contexts. In light of this possibility, I decided to explore the world of R.

With the looming May 25th deadline, the GDPR (General Data Protection Regulation) will be front of mind for all forms of marketers, from digital leads to field marketers. Content marketers will also be affected by the requirements of this new law, which seeks to protect the personal data of European consumers.

Plenty of companies have plenty of data and plenty of analytics tools, but they fall short when it comes to converting analytics results into action.

Check for element wise equality between two TensorFlow Tensors by using the TensorFlow equal operator to do the comparison.

Financial markets have been one of the earliest adopters of machine learning (ML). People have been using ML to spot patterns in the markets since the 1980s. Even though ML has had enormous successes in predicting the market outcomes in the past, the recent advances in deep learning haven’t helped financial market predictions much. While deep learning and other ML techniques have finally made it possible for Alexa, Google Assistant, and Google Photos to work, there hasn’t been much progress when it comes to stock markets.

I love Javascript. I think it is a great programming language. It is a versatile, constantly evolving, ever growing language. It is practically the only language that runs both on the client and server. NPM, node’s packaging system, is great. For many, Javascript is their first (and sometimes only) programming language. But when it comes to data science, it is all about Python (or R). While Python is a great programming language, I don’t see any reason Python should be more suitable for data science than Javascript. They are both interpreted, non-typed programming languages. They both can wrap around C libraries for computationally intensive code. Yet, today, data science is done in Python.

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The strides in ecommerce represent an entire paradigm shift in retail. Despite only making up around 10% of all retail purchases, ecommerce accounts for more than $2 trillion dollars in sales. Mapping the interactions between the offline and online world seems like an arduous task, but when we focus on each customer and their purchasing paths, it becomes something that can be broken up into a few different paths. We’re going to take a look at a few surprising ways that data science can increase your sales, both offline and online. Do you know who you are selling to? You have quite a few different systems for gathering information about your client. They scattered about and do not take them into consideration. You have loyalty information from in-store purchases because your front line is methodical asking but your online purchase history does not take this information into account. More precisely, groceries and big-box stores optimize separately for online and offline. We lose value of our marketing endeavors if we don’t take a wider look at our data and search for insights.

I think this is the R Tip that is going to be the most controversial yet. Its potential pitfalls include: it is a style prescription (which makes it different than and less immediately useful than something of the nature of R Tip: Force Named Arguments), and it is heterodox (this is not how magrittr/dplyr is taught by the original authors, and not how it is commonly used). However, I have not been at all good at anticipating which tips get which sort of reception (and this valuable feedback, public and private, is part of what I get of this series).

Fashion industry is an extremely competitive and dynamic market. Trends and styles change with the blink of an eye. Data Science can be used here on historical data to predict the trends which will be “Hot” hence potentially saving a lot of time and money.

The conversation around the Right to Explanation reminded me of the Mandela Effect. Just as Mandela’s death is believed by many to have happened before his real time of death, Right to Explanation is falsely attributed to GDPR’s collection of laws. An offshoot from early GDPR conversations, the rule has now developed its own literature on the internet. Posts suggesting that the law threatens Artificial Intelligence have flooded Google (examples here, here, and here), while uncertainty-fueled paranoia has taken over LinkedIn. Is it misinformation spread on the internet in its finest or is there more to the discussion? I suggest we review what a Right to Explanation is and why an absent law is causing so much stir on the world wide web.