Introduction

Tableau is a powerful Data Visualization software, and much of its popularity is due to its interactive visualizations & user-friendly interface.

With the Covid-19 Pandemic raging throughout the world, the availability of Vaccinations in recent months has been a sigh of relief. In today’s data-driven world, this also means the creation of new data, and in turn, a need for new insights.

Combining Tableau’s visualization prowess with the constantly growing Vaccination Data, in the form of a Dashboard, will help us get a bird’s-eye view of the current vaccination scenario.

Here’s a step-by-step guide on how you can build a…

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Python is one of the most popular languages for Data Science, and having a strong understanding of its Data Structures is a must for beginners as well as experienced professionals. Even though there are numerous data structures available, only four of them are used approximately 80% of the time.

Lists, Tuples, Dictionaries, and Sets are the Four Horsemen of Data Structures in Python.

Let’s go through their basic characteristics, operations, use cases and understand how they compare to each other.

Lists

Lists are Ordered, Mutable collections of Data, enclosed by square brackets [ ], and use commas to separate their values…

Using IBM Db2 for Cloud

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SQL is an indispensable tool for Data Scientists and analysts as most of the real-world data is stored in databases. It’s not only the standard language for Relational Database operations, but also an incredibly powerful tool for analyzing data and drawing useful insights from it.

Let’s use IBM’s Db2 for Cloud, which is a fully managed SQL Database provided as a service. Economies of scale, Disaster Recovery and Accessibility are few of the many benefits of Cloud Databases.

For this analysis, I’ll be using the “All Space Missions from 1957” dataset from Kaggle, which I’ve loaded on to the Db2…

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“You can have data without information, but you cannot have information without data.”

- Daniel Keys Moran

The very existence of Data Science is due to the need for understanding data. Data Science is multi-disciplinary, and arguably the strongest pillar of data science is Statistics. Remove the programming and technical components from a Data Scientist, and you have a Statistician.

A statistical mindset is key to making sense of the data. Before diving into Machine learning algorithms, it’s paramount to know what you’re dealing with!

Statistics not only enables us to fathom the data we have but also gives better…

Leveraging the Foursquare API, Folium & Data Science for intelligent investment decision making

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Real Estate Investment involves purchasing, managing, renting and selling real or physical property, for a profit. Such an investment is characterized by a large amount of capital, and involves careful planning and decision making, for it to be a successful one.

Toronto is the largest and most populous city in Canada, known for being diverse, multicultural and home to world class-amenities, making it a great place to invest in a house!

When investing in a property, an overall market analysis will provide a good idea of the trends, but will not suffice. It is imperative to buy property in the…

Asmath Ruhi

Data Science Enthusiast | For the love of Data | MBA https://www.linkedin.com/in/ruhii

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