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One of the reasons why Python dominates data science is the rich selection of libraries it offers to the users. The active Python community keeps maintaining and improving these libraries which helps Python to stay on top.

Some of the most commonly used Python libraries for data science are Pandas…


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Pandas and Numpy are the two most popular Python libraries used for data analysis and manipulation. Pandas is equipped with a lot of practical and handy functions.

Pandas also allows for using some Numpy functions which results in more functional and efficient operations with Pandas. …


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Data science is not something you can learn from listening to podcasts. However, what podcasts provide is something you cannot learn from other resources: real life experience.

We live in a time where it is extremely easy and cheap to access information. Same goes for data science as well. …


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Pandas is one of the most widely-used Python libraries. It provides numerous functions and methods to perform efficient data analysis and manipulation.

We tend to associate tabular data with numbers. However, a substantial amount of raw data comes in textual form. Thankfully, Pandas has several methods to manipulate textual data.


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Python and R are the dominating programming languages in the data science ecosystem. I started to learn data science with Python and I suggest you do so as well.

Since I started to work as a data scientist, I have been using both Python and R extensively. …


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Data science being one of the most popular jobs in recent years has attracted numerous people from a variety of professions. These people, including myself, spend a great deal of time to become a data scientist.

On the other side of the river, there are people who help or guide…


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Pandas is one of the most widely-used libraries in the data science ecosystem. It provides numerous functions and methods for efficient data analysis and manipulation.

Reading the entire documentation and trying to learn about all the functions and methods at once is not a smart way for mastering Pandas. …


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It has been almost three years since I took my first step into data science. When I first started, I thought I was too late. There were a lot of tools and concepts to learn, a ton of papers to read, countless certificates, and so on.

What concerned me the…


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Data visualization is an integral part of data science. It expedites many tasks such as exploring data, delivering results, storytelling, and so on. Thankfully, there are great data visualization libraries for Python.

Altair is a declarative statistical visualization library for Python. …


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I have been using Python since I took my first step into data science. Although I excessively focus on the third party libraries such as Pandas and Scikit-learn, it is of vital importance to learn base Python as well.

Python is a great language for various reasons. It is easy…

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Data Scientist at Invent Analytics | linkedin.com/in/soneryildirim/ | twitter.com/snr14

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