Python is a popular high-level object-oriented programming language which is used widely by a huge number of software developers. Guido van Rossum designed this in 1991, and Python software foundation has further developed it. But the question is, with dozens of programming languages based on OOP concepts already available, why this new one? So, the main purpose to develop this language is to emphasize code readability and scientific and mathematical computing (e.g. Python's syntax is very clean and short in length.
Most aspiring data scientists begin to learn Python by taking programming courses meant for developers. They also start solving Python programming riddles on websites like LeetCode with an assumption that they have to get good at programming concepts before starting to analyzing data using Python. This is a huge mistake because data scientists use Python for retrieving, cleaning, visualizing and building models; and not for developing software applications. Therefore, you have to focus most of your time in learning the modules and libraries in Python to perform these tasks. Follow this incremental steps to learn Python for data science.
Many developers (including myself) have included learning machine learning in their new year resolutions for 2018. Even after blocking an hour everyday in the calendar, I am hardly able to make progress. The key reason for this is the confusion on where to start and how to get started. It is overwhelming for an average developer to get started with machine learning.
Data science and machine learning are the most in-demand technologies of the era, and this demand has pushed everyone to learn the different libraries and packages to implement them. This blog post will focus on the Python libraries for Data Science and Machine Learning. These are the libraries you should know to master the two most hyped skills in the market. Here's a list of topics that will be covered in this blog: When I started my research on data science and machine learning, there was always this question that bothered me the most. What led to the buzz around these two topics?
The best trained soldiers can't fulfill their mission empty-handed. Data scientists have their own weapons -- machine learning (ML) software. There is already a cornucopia of articles listing reliable machine learning tools with in-depth descriptions of their functionality. Our goal, however, was to get the feedback of industry experts. And that's why we interviewed data science practitioners -- gurus, really --regarding the useful tools they choose for their projects. The specialists we contacted have various fields of expertise and are working in such companies as Facebook and Samsung. Some of them represent AI startups (Objection Co, NEAR.AI, and Respeecher); some teach at universities (Kharkiv National University of Radioelectronics).