…or what good data scientists have in common.

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What I plan to write in this article is built around my experience of working with very good data scientists. I do not claim that I’m one of them as of today. However, I keep working and studying to become one.

Efficient, organized, and elegant.

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Real-life data is usually messy. It requires a lot of preprocessing to be ready for use. Pandas being one of the most-widely used data analysis and manipulation libraries offers several functions to preprocess the raw data.


Which took me a long time to realize.

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I would like to start with stating a ground truth just in case you have not realized by now: Data science is an extremely broad field.

Practical guide with examples

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Data structures are an essential part of any programming language. How you store and manage data is one of the key factors for creating efficient programs.

  • List
  • Set
  • Tuple
  • Dictionary

They all have different features in terms of storing and accessing data. These differences matter because what fits best for a particular task depends on them. How you can interact with or manipulate these data structures are also different.


List is a collection of objects, represented in square brackets.

mylist = [1, 2, "a", True]
  • Lists can be used for storing objects with any…

This is how you contribute to a project

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After a long period of hard work and dedication, you have landed your first job as a data scientist. The orientation and getting-familiar-with-the-environment period is over. You are now expected to work on real life projects.

A practical Pandas tutorial.

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Pandas provides plenty of functions for efficient data analysis and manipulation. In this article, we will focus on Pandas functions about a particular data manipulation operation.

Everything in data science starts from data

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There are several software tools and packages in the data science ecosystem. These tools accelerate the routine processes as well as helping us manage, explore, and analyze data.

Discover its full potential

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Pandas is a widely-used data analysis and manipulation library. It provides numerous functions and methods to perform typical operations simply and efficiently.

import numpy…

I mastered Python and SQL but am I ready?

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Data science has gained a tremendous popularity in recent years. The ever-increasing ability to collect, transfer, store, and process data is a significant factor in the prevalence of data science.

R makes it quite simple to perform data analysis tasks

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Python and R are the two dominant programming languages in the data science ecosystem. Both have many libraries that offer efficient and simple methods to perform data analysis tasks.


Soner Yıldırım

Writing about Data Science, AI, ML, DL, Python, SQL, Stats, Math | linkedin.com/in/soneryildirim/ | twitter.com/snr14

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