Start your self-learning journey into the world of data right now

When Aristotle and Plato were passionately debating whether the world is material or the ideal, they did not even guess about the power of data. Right now, Data rules the world and Data Science increasingly picking up traction accepting the challenges of time and offering new algorithmic solutions. No surprise, it’s becoming more attractive not only to observe all those movements but also be a part of them.

Well, generally speaking, Data Science is not a certain or one realm, it’s like a combination of various disciplines that are focusing on analyzing data and finding the best solutions based on them. Initially, those tasks were held by math or statistics specialists, but then data-experts began to use machine learning and artificial intelligence, which added optimization and computer science as a method for analyzing data. This new approach turned out to be much faster and effective, and so extremely popular.

So all-in-all, the popularity of Data Science lies in the fact it encompasses the collection of large arrays of structured and unstructured data and their conversion into human-readable format, including visualization, work with statistics and analytical methods - machine and deep learning, probability analysis and predictive models, neural networks and their application for solving actual problems.

Artificial Intelligence, Machine Learning, Deep Learning, and Data Science- undoubtedly, these major terms are the most popular today. And although they are somehow related, they are not the same. So, before jumping into any of those realms, it is mandatory to feel the difference.

Artificial Intelligence is the realm focusing on the creation of intelligent machines that work and react like humans. AI as a study dates back to 1936 when Alan Turing build first AI-powered machines. Despite quite a long history, today AI in most areas is not yet able to completely replace a human. And the competition of AI with humans in chess, and data encryption are two sides of the same coin.

Machine learning is a creating tool for extracting knowledge from data. In ML models can be trained on data independently or in stages: training with a teacher, that is, having human-prepared data or training without a teacher, working with spontaneous, noisy data.

Deep learning is the creation of multi-layer neural networks in areas where more advanced or fast analysis is needed, and traditional machine learning cannot cope. “Depth” provides more than one hidden layer of neurons in the network that conducts mathematical calculations.

Big Data -work with huge amounts of often unstructured data. The specifics of the sphere are tools and systems capable of withstanding high loads.

Data Science is the addition of meaning to arrays of data, visualization, collection of insights, and making decisions based on these data. The field specialists use some methods of machine learning and Big Data -cloud computing, tools for creating a virtual development environment and much more.

So, what does Data Scientist do? Here is all you need to know about it:

• detection of anomalies, for example, abnormal customer behavior, fraud;
• personalized marketing - personal e-mail newsletters, retargeting, recommendation systems;
• Metric forecasts -performance indicators, quality of advertising campaigns and other activities;
• scoring systems - process large amounts of data and help to make a decision, for example, on granting a loan;
• basic interaction with the client – standard answers in chat rooms, voice assistants, sorting letters into folders.

To do any of the above tasks you need to follow certain steps:

•  Collection Search for channels where you can collect data, and how to get it.
•  Check. Validation, pruning anomalies that do not affect the result and confuse with further analysis.
• Analysis. The study of data, confirmation of assumptions, conclusions.
• Visualization. Presentation in a form that will be simple and understandable for perception by a person - in graphs, diagram.
• Act. Making decisions based on the analyzed data, for example, about changing the marketing strategy, increasing the budget for any activity of the company.