In this article, we will establish the recursive relation for evaluating the Euler Integral:

for *n = 0, 1, 2, 3, . . . *, and *a > 0*. Later, we show that other integrals in statistics and physics can also be evaluated using differentiation.

To derive the base case, we know that

Using this, we can compute the base case (*n = 0*), that is

Using the substitution *u = a x*, we obtain

Hence, our base case is therefore given by

Starting from the base case and taking the derivative of the left and right sides of the equation with respect to *a*, we…

In the past decade, the demand for individuals with data skills has skyrocketed. A recent study using data collected from LinkedIn shows that most of the top tech jobs in the United States and worldwide are related to data, as shown in the figures below:

With automation becoming increasingly popular in the field of machine learning, one may wonder if the role of humans in machine learning will become non-essential at some point.

When building a machine learning model, it’s important to remember that the model must produce meaningful and interpretable results in real-life situations. This is where the human experience comes in. A human (qualified data science professional) has to examine the results produced by algorithms and computers to ensure that the results are consistent with real-world situations before recommending a model for deployment. …

Two years ago, I had a screening interview with a financial company that uses data science and analytics to predict the credit worthiness of it’s customers to determine how likely they are capable of repaying a loan in full. As part of the interview process, I was assigned a take-home challenge problem. Please see below for the project description and instructions.

The dataset for this problem can be downloaded from this GitHub repository.

The dataset here is complex (has 50,000 rows and 2 columns, and lots of missing values), and the problem is not very straightforward. You have to examine the dataset critically and then decide what model to use. This problem was to be solved in a week. …

Data is now considered to be one of the fastest-growing, multibillion-dollar industries. As a result, corporations and organizations are trying to make the most out of the data they already have and determine what data they still need to capture and store. In addition, there continues to be an incredible need for data scientists to make sense of the numbers and uncover hidden solutions to messy business problems. A **recent study** using the LinkedIn job search tool shows that a majority of top tech jobs in the year 2020 are jobs that require skills in data science.

With all the exciting opportunities in data science, educating yourself about data science is a great way to gain the skills and experience needed to stand out in this competitive field and give your employer an edge over the competition. Before jumping into the field of data science, it is important to examine the following questions to evaluate if data science is really for you. …

In data science or machine learning, we use data for descriptive analytics to draw out meaningful conclusions from the data, or we can use data for predictive purposes to build models that can make predictions on unseen data.

As a novice or seasoned Data Scientist, your work depends on the data, which is rarely perfect. Properly handling the typical issues with data quality and completeness is crucial, and we review how to avoid six of these common scenarios.

In this section, we discuss six common mistakes that can severely impact the quality of a data science model. …

Every beginner trying to learn the fundamentals of data science is often faced with the following questions:

*What data science courses should I take and in what order?**What platform should I take data science courses from, edX, Coursera, Udemy, DataCamp, etc?**What are the best data science massive open online course (MOOC) specializations?*

I started learning about data science about three years ago. It was quite challenging from the beginning as I had these same questions in my mind. After taking several data science MOOCs from a wide variety of platforms, I found three important specializations that I consider to be among the best MOOC specializations in data science. …

Data science projects vary in scope and complexity. Sometimes, the project could be as simple as producing summary statistics, charts, and visualizations. It could also involve building a regression model, classification model, or forecasting using a time-dependent dataset. The project could also be very complex and difficult, with no clear guidance as to the specific type of model to use. In this case, it is the task of the data science aspirant or professional to come up with a model that best suitable for addressing project goals and objectives.

I would argue that the solution to a data science project is not unique. Even when clear guidance is provided about the type of model to use, the implementation could vary from individuals to individuals based on their level of experience working on data science projects. …

For anyone interested in jumping into the field of data science, one of the most important questions to ask is: *How long does it take to gain competency in data science?*

This article will discuss the typical timeline for data science competency. The time required to gain competency in data science depends on the level of competency. In **Section II**, we will discuss the three levels of data science. In **Section III**, we discuss the time required for gaining data science competency based on the level of interest. A short summary completes the article.

** The views provided here are my views and are based on my own journey to data science.** …

Linear Algebra is a branch of mathematics that is extremely useful in data science and machine learning. Most machine learning models can be expressed in matrix form. Because data science deals with real-world problems, matrices in data science must be real and symmetric. There are some exceptions to this. In advanced data science models such as image processing, Fourier analysis is heavily used. Hence one could easily encounter matrices that are defined over the space of complex numbers. Other than that, for most basic data science and machine learning problems, the matrices encountered are always real and symmetric.

In this article, we will consider three examples of real and symmetric matrix models that we often encounter in data science and machine learning, namely, the regression matrix (**R**); the covariance matrix, and the linear discriminant analysis matrix (**L**). …

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