Abstract

Implementing some of the pillars of an automated machine learning pipeline such as (i) Automated data preparation, (ii) Feature engineering, (iii) Model building in classification context that includes techniques such as (a) Regularised regression [1], (b) Logistic regression [2], (c) Random Forest [3], (d) Decision tree [4] and (e) Extreme Gradient Boosting (xgboost) [5], and finally, (iv) Model explanation (using…

As an academic discipline, the rate of maturation for data science should be measured in light years. Although it's really only about 10 years old as a field of study – with the first Ph.D. program in the country emerging just four years ago – most, major universities across the world have integrated data science into their portfolio of degree options. Universities…

PREFACE

This article has 4 sections:

1. Introduction: Introduction to the gambler's ruin problem
2. Methodology: How the simulation will be carried out
3. Pseudocode: Summary of the Python Code
4. Theory: A summary of the theory

The full code and theory can be found here: …

This article is about Intuitive explanation of Degrees of Freedom and How Degrees of Freedom affects Sudoku.

A lot of aspiring Data Scientists take courses on statistics and get befuddled with the concept of Degrees of Freedom. Some memorize it by rote as ‘n-1'.

But there is a intuitive reason why it is ‘n-1’.…

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…

Imagine you are a company selling a fast-moving consumer good in the market.

Let’s assume that the customer would follow the given journey to make the final purchase:               These are the states at which the customer would be at any point in the purchase journey.

Now, how to find out in which state the customers would be after 6…

Imagine that one day, you see people are queuing up in front of Bank A; so you ask the staff at the counter, you are told that they are offering anyone (regardless of their credit history) a loan of $100,000 at a fixed annual rate at 2%. You then look around, the Bank B next door offers 1-year term deposit with a fixed annual rate at 3% for the same amount ($100,000). After 5 minutes' waiting, you sign for the loan from Bank…

As we all know in today’s world of quick results and insights nobody wants to spend time in understanding the core concepts of certain statistical terms while performing analytical routine. One statistical term that is talked a lot but known very less in terms of its mechanics is R Squared statistics a.k.a. coefficient of determination. This statistics helps to measure the closeness of the data to the fitted line of regression.

It is also worth mentioning that by squaring the…

Statistical Analysis is a way of collecting, presenting and exploring large amounts of data in order to discover underlying patterns and trends. It can be especially useful in banking, manufacturing or retail where knowing the future patterns might greatly benefit the businesses. Not without reason, it resembles and can cooperate with blockchain - a new tech…

What a weird question. That’s what you would have thought after reading the headline. Perhaps you thought the word “NOT” was accidental.

Hmm, for past few years many of us have come across articles like

• “Top 10 Machine Learning Algorithms every Data Scientist …

Many a blogs and articles are written on how to become a Data Scientist. The list normally goes like this

• Study descriptive statistics, hypothesis testing, probability
• Learn types of Machine learning algorithms – Supervised, Unsupervised
• Learn Python, R, SAS, SQL
• Apply machine learning techniques using Python, R, SAS
• Learn Data Visualization

While there is nothing wrong in the path illustrated above, it is not the…

Remember the …

It sounds almost absurd, but that could be one factor behind the so-called “reproducibility crisis”

It sounds almost heretical to ask the question: Is there too much scientific research?…

A data scientist needs to be Critical and always on a lookout for something that misses others. So here is some advice that one can include in the day to day data science work to be better at their work:

1. Beware of the Clean Data Syndrome

You need to ask yourself questions even before you start working on the data. **Does this data make sense?** Falsely assuming that the data is clean could lead you towards wrong Hypotheses. Apart from that, you can discern a…

Essentially good hypotheses lead decision-makers like you to new and better ways to achieve your business goals. When you need to make decisions such as how much you should spend on advertising or what effect a price increase will have your customer base,…

Ever wonder how you can create meaningful insights with your data? Tired of being asked what's your big data strategy, are you doing predictive analytics, or how you are making use of the latest technologies? There is a way for you to get what you need with Data Science. The key to new knowledge is to make sure you use Data Science in your organization.…

It is not only about understanding about statistics, it is also about implementing the correct statistical approach or method. In this brief article I will showcase some common statistical blunders that we generally make and how to avoid them.

To make this information simple and consumable I have divided these errors into two parts:

1. Data Visualization Errors (Erroneous Graphs)
2. Statistical Blunders Galore (pun intended)

Data…

In the wake of ZMOT (Zero Moment of Truth) it becomes pivotal for any product company to choose the most appropriate advertisement channel for the promotion of their products. This not only helps the organizations to maximize their chances of creating the best first impression but will also help them to be discovered by today’s tech savvy consumers.

Today we will talk about a very…

At Grakn Labs we love technology. Here is our December 15th edition by Filipe Pinto Teixeira, where he looked back at Predictive Analytics.