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This is the fifth part of my Pandas & Python TricksPhoto by Andrew Neel on UnsplashA couple of days ago, I shared some Python and Pandas tricks to help Data Analysts and Data Scientists quickly learn new valuable concepts that they might not be aware of. This is also part of the collection of tricks I share daily on LinkedIn.Combine SQL statements and PandasMy gut feeling is telling me that more than 80% of the Data Scientists use Pandas in their daily Data Science activities.And, I believe that this is because of the…

A complete overview revealing a diverse range of strengths and weaknesses for each data versioning toolPhoto by Alina Grubnyak on UnsplashWith business needs changing constantly and the growing size and structure of datasets, it becomes challenging to efficiently keep track of the changes made to the data, which leads to unfortunate scenarios such as inconsistencies and errors in data.To help data practitioners, this blog will cover eight of the top data versioning tools in the market. It will provide a clear explanation…

This is the fourth part of my Pandas & Python TricksPhoto by Andrew Neel on UnsplashA couple of days ago, I shared some Python and Pandas tricks to help Data Analysts and Data Scientists quickly learn new valuable concepts that they might not be aware of. This is also part of the collection of tricks I share daily on LinkedIn.Change columns data typeWrong data format is a common challenge when dealing with real-world 🌏 data.For instance, you might have a numerical value that is stored as a string such as “34” instead…

Learn about classification in machine learning, looking at what it is, how it’s used, and some examples of classification algorithmsPhoto by Markus Winkler on UnsplashNowadays, many industries have been dealing with very large data sets of different types. Manually processing all that information can be time-consuming and might not even add value in the long term. Many strategies, from simple automation to machine learning techniques, are being applied for a better return on investment. This conceptual blog will cover one…

This is the third part of my Pandas & Python TricksPhoto by Andrew Neel on UnsplashA couple of days ago, I shared some Python and Pandas tricks to help Data Analysts and Data Scientists quickly learn new valuable concepts that they might not be aware of. This is also part of the collection of tricks I share daily on LinkedIn.Replace values from a dataframe based on conditionsIf you want to replace values from a dataframe based on conditions✅ You can use the built-in 𝗺𝗮𝘀𝗸() function from Pandas.Below is an illustration…

This article is a comprehensive overview of the fundamentals of statistics for Data Scientists and Data AnalystsImage by Clay Banks on UnsplashBuilding Machine Learning models are cool for making predictions. However, they are not suitable when it comes to having a better understanding of your business problem, which requires the most time in statistical modeling.This article will first try to build your understanding of the fundamentals of statistics that can be beneficial for Data Scientists and Data Analysts'…

Understand Transformers and harness their power to solve real-life problems in PythonImage by Aditya Vyas on UnsplashIn the field of Natural Language Processing (NLP), researchers have made significant contributions over the past decades, resulting in innovative advancements in various domains. Some examples of NLP in practice are provided below:Siri, a personal assistant developed by Apple, can assist users with tasks like setting alarms, sending texts, and answering questions.In the medical field, NLP is being utilized…

We explore a comprehensive overview of Data Blending using Tableau for Data Scientists and Data AnalystsImage by Lukas Blazek on UnsplashNowadays, companies use data from different sources to solve their business problems. Being able to efficiently collect and combine those data has become an essential skill for all Data Scientists and Data Engineers in order to assist organizations with smart decision-making.In this tutorial, we will first build your understanding of a powerful data combination approach called Data…

This is the second part of my Pandas & Python TricksPhoto by Andrew Neel on UnsplashA couple of days ago, I shared some Python and Pandas tricks to help Data Analysts and Data Scientists quickly learn new valuable concepts that they might not be aware of. This is also part of the collection of tricks I share daily on LinkedIn.remove duplicates from a listWhen trying to remove duplicates from a list, you might attempt to use the 𝗳𝗼𝗿 loop approach.This works but is inefficient ❌ when dealing with very large…

A complete overview of the KMeans clustering and implementation with PythonImage by Guillermo Ferla on UnsplashImagine that you are a Data Scientist working for a retail company and your boss requests for the customers’ segmentation into the following groups: low, average, medium, or platinum customers based on spending behavior for targeted marketing purposes and product recommendations.Knowing that there is no such historical label associated with those customers, how is it possible to categorize them?This is where…