For Companies, Data Analytics is a Pain; But Why?

1. Analytics is not a vaccine, but a routine workout
2. Insights are just the initiations, and don’t add immediate value to your business
3. Scalability
4. Descriptive analytics is a post-mortem, does it really help
5. Human intervention in analytics is a friend and a foe too
6. Opportunities cost is huge; stale answers make dents
7. Manually intensive
8. Numerical data is analyzed, but what about categorical values
9. Users without expertise
10. Increased lead time to value

An introduction to Support Vector Machines (SVM)

So you’re working on a text classification problem. You’re refining your training set, and maybe you’ve even tried stuff out using Naive Bayes. But now you’re feeling confident in your dataset, and want to take it one step further. Enter Support Vector Machines (SVM): a fast and dependable classification algorithm that performs very well with a limited amount of data. Perhaps you have dug a bit deeper, and ran into terms like linearly separable, kernel trick and kernel functions. But fear not! The idea behind the SVM algorithm is simple, and applying it to natural language classification doesn’t require most of the complicated stuff. Before continuing, we recommend reading our guide to Naive Bayes classifiers first, since a lot of the things regarding text processing that are said there are relevant here as well. Done? Great! Let’s move on.

Taxonomy of Methods for Deep Meta Learning

Let’s talk about Meta-Learning because this is one confusing topic. I wrote a previous post about Deconstructing Meta-Learning which explored “Learning to Learn”. I realized thought that there is another kind of Meta-Learning that practitioners are more familiar with. This kind of Meta-Learning can be understood as algorithms the search and select different DL architectures. Hyper-parameter optimization is an instance of this, however there are another more elaborate algorithms that follow the same prescription of searching for architectures.

Set Theory Arbitrary Union and Intersection Operations with R

Part 3 of 3 in the series Set Theory
• Introduction to Set Theory and Sets with R
• Set Operations Unions and Intersections in R
• Set Theory Arbitrary Union and Intersection Operations with R

Interactive R visuals in Power BI

Power BI has long had the capability to include custom R charts in dashboards and reports. But in sharp contrast to standard Power BI visuals, these R charts were static. While R charts would update when the report data was refreshed or filtered, it wasn’t possible to interact with an R chart on the screen (to display tool-tips, for example).

Face Recognition in R

OpenCV is an incredibly powerful tool to have in your toolbox. I have had a lot of success using it in Python but very little success in R. I haven’t done too much other than searching Google but it seems as if “imager” and “videoplayR” provide a lot of the functionality but not all of it. I have never actually called Python functions from R before. Initially, I tried the “rPython” library – that has a lot of advantages, but was completely unnecessary for me so system() worked absolutely fine. While this example is extremely simple, it should help to illustrate how easy it is to utilize the power of Python from within R. I need to give credit to Harrison Kinsley for all of his efforts and work at – I used a lot of his code and ideas for this post (especially the Python portion). Using videoplayR I created a function which would take a picture with my webcam and save it as “originalWebcamShot.png”

Data Wrangling: Reshaping

Data wrangling is a task of great importance in data analysis. Data wrangling, is the process of importing, cleaning and transforming raw data into actionable information for analysis. It is a time-consuming process which is estimated to take about 60-80% of analyst’s time. In this series we will go through this process. It will be a brief series with goal to craft the reader’s skills on the data wrangling task. This is the second part of this series and it aims to cover the reshaping of data used to turn them into a tidy form. By tidy form, we mean that each feature forms a column and each observation forms a row.