Data Science


Data science is the field of study that combines programming skills, and knowledge of mathematics and statistics to extract value  from data. Data scientists apply machine learning algorithms to numbers, text, images, video, audio, and more to find business insights buried in data. Data science could be broken down in three main topics: preparing data for analysis and processing, performing advanced data analysis, and visualization. Presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.

Data scientists must be able to code in order to create models. The most popular programming languages among data scientists are Python, SQL, r and Julia. Open source languages that include  pre-built statistical, machine learning and graphics libraries.

Among all of these languages, Python and its data related libraries such us pandas, Scikit-learn, numpy and  matplotlib stands out as the most prominent tool.

Even business analysts, consultants, operations and IT staff need to know Python and some of the former libraries. An important amount of their tasks consists of putting data in a consistent format for analysis and preparing massive “.csv” or spreadsheets that are not manageable in a manual way. To minimize errors and automate repetitive tasks, Python is the tool of choice. In fact,  it is becoming a required skill for many positions where data management is a must.

To learn more about data science and how to gather, aggregate, clean, prepare and process big files, Practity has several data practice projects for aspiring data scientists and Python learners. All these challenges are real and developed by data scientists and senior developers.



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