The Google’s Places API, in combination with the standard Python package provides an easy way to find places using names, address, phone number, keywords, or categories (restaurants, bars, stores, etc.)

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The role of geographic data has become an essential component of political as well as business decision-making for understanding and solving specific problems. For example, I am using it for my research in sustainability, such as identifying clusters of different industries, demographics, etc., around a particular location, where firms are involved in sustainable business practices.

One of the main challenges of GIS-based research is difficulties in obtaining data. Though many…

A simple and elegant algorithm outperforms many common ways of derive h-index

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Originally proposed by J.E. Hirsch, the h-index of an author is believed to be a reliable indicator of their scholarly achievement. In a common scholarly parlance, the h-index refers to the highest number ‘h’ such that the author has h publications with at least h citations. The members of Medium community can interpret it this way: it is the maximum number ‘h’ such that a writer has written at least h blogs that have received at least h claps.

Let us understand the concept with an example. An author has published five blogs (or research articles) and received the following…

A combination of web scraping and a document database in Python offers a convenient solution to collecting and storing unstructured information from websites.

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A large quantities of data are available on the internet for almost free and can potentially be used to generate valuable insights in various domains. However, such data are often available in unstructured format, and downloading and storing is remains a challenging task. …

Despite rising levels of automation through big-data, much of the data-mining and machine learning process still relies on human intervention, introducing different biases.

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The amount of structured and unstructured data generated has grown exponentially over the last few decades and will continue to do so for years to come. The ‘big data’ analytics could potentially overcome numerous challenges that corporations and governments have faced for centuries while making decisions: the lack of adequate data for formulating policies (e.g., targeting policies for a particular social group) or examining market or consumer expectations (e.g., recommendation system). …

An example of retrieving metadata from Scopus database

Applying average programming and analytics can help us resolving numerous problems in our daily lives and remains an underrated skill.

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The knowledge of a programming language has become an essential requirement for many data science related tasks for several reasons that we all are familiar with. However, I find that many people continue to perceive the coding/programming/data-analytics as an arcane field, which is best left to the experts. While we need experts in developing complicated algorithms for artificial intelligence, it is equally important that we must focus on applying the discovered knowledge to address the individual or social issues.


Empirical simulations can enhance the understanding of the statistical concepts among aspiring data-science enthusiasts.

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Bias-variance trade-off is an essential learning requirement for machine learning beginners to ensure that the most appropriate models are selected based on the sample data. The most common graph available online on the topic is illustrated in Figure 1, which shows three types of errors: bias, variance, and total and the relationship among them. as the model complexity increases, the bias decreases while the variance increases, with a parabolic shape of total error. Though many experts have rigorously written about it, these explanations can be complemented with certain empirical experiments to help learners gain a satisfactory intuitive understanding of the…

Sourabh Jain

Aspiring data scientist who is interested in applying math modeling and Python skills in renewable energy sector and circular economy.

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