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).
Data visualization involves the creation and study of the visual representation of data. A primary goal of data visualization is to communicate information clearly and efficiently via statistical graphics, plots and information graphics, but if you don't master data visualization, you will miss the opportunity to explore data. What if you could change that? My complete Data Visualization course will show you the exact techniques and strategies you need to read and write data with python, deliver & serve the data, cleaning and exploring data with Pandas and build a simple Tooltip. For less than a movie ticket, you will get over 4 hours of video lectures and the freedom to ask me any questions regarding the course as you go through it.
This course is taught by Ted Petrou, an expert at Python, data exploration and machine learning. Ted is the author of the highly rated text Pandas Cookbook. Ted has taught hundreds of students Python and data science during in-person classroom settings. He sees first hand exactly where students struggle and continually upgrades his material to minimize these struggles by providing simple and direct paths forward. Ted is one of the foremost authorities on using the pandas library to do data analysis.
A common challenge affecting many scientists, especially those working in the area of molecular biology, is the vast amount of data that is created by their experiments. With such a large volume of data to consider, software tools are required to interpret their data effectively. Until now, computer software designed for this purpose has focused on being able to handle increasingly vast amounts of data and to a large extent applying standard statistical methods presented to the user in a technical specialist oriented user interface. As a result, the possibility for the scientist/researcher to approach and interpret data has partly been set aside, and a lot of data analysis can only be performed by specialist bioinformaticians and biostatisticians. In most cases, however, this model has several drawbacks, since it is typically the scientist who knows the most about the specific area being studied.