Data science is a vast field that combines several related disciplines. These are programming, mathematics & statistics, business analytics, and machine learning. Specialists in the field of data science are data analysts, who work with large amounts of data by extracting useful information for decision making in business. The result provides answers to many questions: for example, how many units of goods should be purchased in the next quarter, and which component of the medicine will improve the patient’s well-being. To solve such problems, specialists develop algorithms that are able to generate a result without human intervention.
What you need to understand
The requirements for training and the level of professional skills depend on which company the specialist will work in. For instance, in large corporations, data analytics is important to understand mathematics and statistics. Whereas, marketplaces and media companies need experts in developing recommender systems.
Following are the skills and areas of knowledge that a successful specialist will need.
Programming
The most popular and common language in Data Science today is Python. Earlier, the most popular language was R, which is still used for data analysis, scientific statistical analysis, and also in sociology by some data analysts. Among other languages, Python can be rated as good because you can develop almost any library, to perform a variety of improved tasks. The base Python distribution is small, and easy to install & upgrade.
Each library has extensive documentation, so they are easy to understand. Around the most popular and popular communities are formed that support the library, that develop new modules and functions for it.
Analysis
The basis of data analytics is working with data. Including those that can be fed to the developed algorithm. Around a huge amount of data, modern mankind generates them with incredible speed every day. These data must be able to collect, store, bring in a form acceptable for analysis or machine learning – to understand, format, and determine what exactly can be learned from these data.
Data analysis is closely connected with the use of specialized libraries for Python and with an understanding of the mathematical and statistical foundations of data analysis. It is important to be able to find patterns in them and understand the goals and objectives of the business.
Mathematics and Statistics
One can do data science without deep knowledge in fundamental mathematics as modern libraries contain a huge number of ready-made solutions. With the help of these libraries, one can analyze data and train algorithms without going into mathematical details. But only until the first truly complex or atypical task.
What a specialist needs to understand:
· Linear algebra: The basics of working with vector and matrix data representation.
· Mathematical analysis: To optimize models and algorithms, understanding where you can twist the model so that it works better and faster.
· Probability theory and statistics for many machine learning algorithms, data analysis and adequate a / b tests. It helps to evaluate how much you can rely on the available data, how to work with outliers in the data, which can ruin the reliability of the information.
· Using NumPy and other Python libraries for computing and building models. For mathematical and statistical calculations and transformations, Python is widely used everywhere. Knowledge of these tools is also necessary for working with machine learning.
Machine Learning and Deep Learning
Neural network training is one of the subsections of machine learning, which, in turn, distinguishes deep learning. A complex system of terminology is associated with the fact that the region developed for decades before the next breakthroughs in methods made it truly popular. Now, when it comes to training neural networks, most often it means deep learning methods.
Though machine learning is a part of data science, it is a huge independent field. It can be developed almost indefinitely, and new methods appear every year. Previously, Deep Learning was a conditionally unified area of ‹ ‹knowledge, but today its subdomains; computer vision, working with natural language, reinforced learning, generative-competitive networks, and other methods stand out in independent areas of specialization. This direction is growing incredibly fast, forcing specialists to constantly maintain their level of knowledge in order to keep pace with the market.
Data Engineering
Data has a structure in the analytical, informational, and even physical sense. The data needs to be stored somewhere, maintain accessibility and stability, and organize the architecture. For instance, the New York Stock Exchange generates 1 terabyte of trading data daily in the last session.
Organizing work with data measured in hundreds and thousands of terabytes is not easy. To interact with data engineers, there are approaches, concepts and tools. Information engineers are involved in organizing the collection, storage, and access to data.
Data Science in Production
Eventually, all knowledge and tools are needed to apply data science in the interests of business. Two skills are important here:
· The ability to determine exactly what tools are needed to solve a specific problem: where to implement machine learning, and where to build a database and implement SQL queries.
· A grocery outlook on your work: the ability to communicate and establish contacts with colleagues, argue your point of view, and manage processes in your work and team.
To become a specialist in data science, you need to master a lot of skills in a variety of areas. Future data analysts need self-organization and determination to master such a body of knowledge. You will have to navigate the flow of information, not get lost in the order of study, find the most relevant methods and principles, and most importantly figure out why something is working wrong and find a suitable field for practice.
