Data Scientist – One of the Most Important Jobs of the Future
Data Science (DS) has become an extremely trendy and in-demand area in IT in recent years. And this is no coincidence, because data science is closely interconnected with such promising technologies as Big Data, Machine Learning, neural networks, etc. Along with it, the Data Scientist profession has gained increased popularity, which we will also tell you about in our new article.
Content:
1 . What is Data Science? The history of the emergence and development of this direction
2 . Scopes of Data Science
3 . Tools and workflow steps in Data Science
4 . Who is a data scientist and what does he do?
5 . What does it take to master this profession?
6 . Conclusion
From the text you will learn about what Data Science is, when it appeared and how it developed, what methods and tools it has, for what tasks and in what areas it is used. In addition, you will get to know who data scientists are, what they do and what it takes to master this profession.
What is Data Science? The history of the emergence and development of this direction
Data Science is a field of computer science that specializes in the analysis and processing of Big Data (massive amounts of information in an unstructured form). It uses a range of relevant technologies, methods and tools, including mathematical statistics, artificial intelligence (AI), machine learning (ML) and deep learning (DL), as well as database design and development. And the specialist should be familiar with different processes, and data labeling methods.
The use of statistical methods and software algorithms allows DS professionals to find relationships and patterns in unstructured data arrays, and then use them to make estimates and forecasts for the future.
Data Science is located at the intersection of several exact sciences: mathematics, computer science and system analysis. The term was first coined by the Danish programmer Peter Naur, who used it in his book A Brief Review of Computer Methods, which was published in 1974. He called it the science that studies the life cycle of digital data, and also coined another alternative term - datalogy.
More than one decade has passed from the moment of the first mention to the general recognition of the term Data Science. Data science was recognized as a separate academic discipline only in the early 2000s. In many ways, this was facilitated by an article by statistics professor William Cleveland on the technical aspects of statistical research. Also in 2002-2003, scientific journals on the subject began to appear, including CODATA Data Science Journal and The Journal of Data Science, published by Columbia University.
Even greater public interest in DS arose in the 2010s against the backdrop of the massive spread of Big Data technologies. Since 2011, the American publishing company has hosted the Strata data science conference series, and EMC has hosted an annual data science trending summit.
In 2012, the data scientist profession was recognized as one of the most promising, attractive and in demand in the modern world. Since 2013, a number of the world's leading universities have launched master's programs in Data Science, and some of them have received multi-million dollar grants to advance data science.
Scopes of Data Science
Data science is in high demand in many industries and areas of activity - and especially where you need to assess risks and make forecasts.
Specific areas include:
- Banks and other financial organizations. For example, DS technologies help to assess the degree of solvency of customers and use this information to develop algorithms for automatic loan approval. In addition, in the field of finance, there are many other types of Big Data that are of interest to a data scientist.
- E-commerce and business in general. The use of statistics and machine learning methods allows you to identify more or less popular products and product categories among a huge range of large online stores. Data Science also makes it possible to automatically create collections of goods or services based on purchases made by customers or items viewed. More broadly, DS can be used to forecast demand for new products in any business area.
- IT. Data scientists bring a lot of value in the process of creating search algorithms, designing, developing and implementing machine learning (ML) and artificial intelligence (AI) models, developing bots, etc.
- Transport and logistics. Data Science helps transport companies plan the best route for transportation, taking into account various factors (weather conditions, etc.). Thanks to it, a business can minimize its costs and expenses, avoid downtime and force majeure situations.
- Medicine and Science. DS-technologies allow you to create "smart" algorithms for automatic diagnosis of diseases based on the data provided. In addition, this area is extremely in demand in modern genetic research - it helps to build genetic maps, etc. Data Science is also actively used in physics (detection of elementary particles and their traces), sociology (for automatic processing of collected data), meteorology (forecasting weather and climate change), and in many other areas of science.
- Production. Data science helps to optimize production processes and predict many important aspects. For example, ranging from the likelihood of equipment failures or product defects, ending with the simulation of common workplace injuries of workers.
- Insurance and Risk Assessment. Data Science technologies are successfully used in various areas of insurance business. They help assess vehicle damage, predict health insurance claims, predict bankruptcies, manage market risk, predict claims waivers, detect anomalies, and more.
- Agriculture. Using DS-algorithms, specialists are able to predict the dynamics of agricultural prices, conduct yield analysis, plan land use taking into account ecosystem restoration, segment fields, identify plant pests and diseases, predict groundwater depth and perform irrigation analysis.
Tools and workflow steps in Data Science
Data science is an interdisciplinary field that uses a range of systems, methods, processes, and algorithms.
Among the main areas worth noting are the following.
Big Data
Big data is rightfully considered the main field of activity and working tool of data science. Specialists in this field most often interact with the BD-system for storing and processing information. Specific examples include NoSQL databases, the Apache Hadoop stack, and so on. Big Data technologies allow you to efficiently collect, store and process huge arrays of structured and unstructured data of various types, as well as use them to achieve specific goals. Analyzing big data, a data scientist develops a predictive model - a software algorithm designed to solve a given problem.
Machine Learning
Creating new machine learning models and modifying existing ones is an essential part of being a data scientist. ML models allow you to automate (and, therefore, simplify and speed up) the processing of large amounts of Big Data to obtain more accurate and efficient forecasts. Machine Learning technology makes it possible to create self-learning networks that can independently build predictive models based on the unstructured or structured information processed by them.
Data Mining
Next on the list of the main DS tools is Data Mining - extracting patterns from data using special algorithms. During this process, experts collect the data they need, and then perform its intellectual analysis, for which they also use machine learning algorithms. Self-learning ML models are able to extract potentially valuable patterns from datasets and use them in further preparation of forecasts. As working tools of Data Science, statistical methods of analysis (factorial, dispersion, component, relationship analysis, etc.), mathematical statistics and probability theory are used.
Deep Learning
Deep Learning is a process based on deep layered neural networks (DNN) technology. This is one of the classes of machine learning algorithms that is used to solve more complex problems than conventional ML models.
- Automate the work of an online store or landing
- Empower through integration
- Don't spend money on programmers and integrators
- Save time by automating routine tasks
As for the life cycle of data science, it consists of the following stages:
- Planning. At the first stage of the data analysis cycle, data scientists prepare a list of tasks that can be solved using Data Science methods, as well as predict the expected results of the project.
- Capture. Upon completion of the preliminary stage, they move on to data capture activities, including data collection, input, retrieval, etc.
- Modeling. The next step is to build an ML data model. For this purpose, not only free access to a certain data array is required, but also sufficient computing power and the right set of tools. These include database tools, visualization and profiling tools, libraries, and so on.
- Model evaluation. The researchers then evaluate the data model they have built using a rich set of metrics and visualizations. With their help, it is possible to determine the accuracy of the work of models with specific information, evaluate their performance and expected behavior, rank them by time, etc. After checking these and other factors, data scientists confirm the high degree of accuracy of their models, which is necessary for their successful application.
- Explanation of the model. Once the assessment is complete, DS specialists provide a detailed explanation of the predictions that the ML models they have created generate. This stage is becoming more relevant and important as the popularity of Data Science and Big Data technologies grows. Explanations allow third-party specialists to understand how the significance factors and relative weights used in the preparation of the forecast are determined, as well as other nuances of the results of the ML-models.
- Model deployment. One of the key stages of the Data Science cycle is the deployment of trained machine learning models. This complex and time-consuming process can be optimized by running models as scalable APIs or by using special ML models for databases.
- Monitoring and analysis of results. Continuous monitoring of ML models allows you to verify their proper operation, which makes it a mandatory step following deployment. Finally, the final stage of the cycle is the analysis of the results of the project and their comparison with the planned tasks and goals. In addition, the resulting forecasts or estimates are often used in the preparation of business intelligence materials.
Who is a data scientist and what does he do?
A data scientist works with Big Data - those very large arrays of structured or unstructured data. In particular, he collects and organizes databases, analyzes them, and also looks for certain connections and patterns in them. Based on the processed and received information, he creates a machine learning (ML) model that allows you to make a forecast or predict the result in the future.
As for the job responsibilities of a data scientist, they include the following operations:
- search for relationships and patterns in big data sets;
- preparation of data for the development of an ML model: sampling, cleaning, feature generation, integration and formatting;
- modeling and visualization;
- development of hypotheses for optimizing business indicators using machine learning models, as well as their further testing.
At first glance, it may seem that the work of a data scientist is in many ways similar to that of a business analyst, but there are significant differences between these specialties. While a business analyst interacts with commercial data (website traffic, sales, conversion, etc.) and makes a forecast on his own, a data scientist organizes large arrays of any data and develops a software algorithm for their automated processing.
What does it take to master this profession?
The profession of a data scientist is becoming more and more in demand every year, and data scientists themselves are successfully employed in companies of various sizes and directions - from promising start-ups to large transnational corporations.
However, for the successful development of this specialty, a rather deep and comprehensive preparation is required. First of all, Data Science relies on the knowledge of a number of mathematical disciplines, including mathematical statistics, mathematical analysis, linear algebra and probability theory.
In addition, a data scientist must know programming and be able to write code, as he needs to develop software algorithms (ML models) for making predictions, analyzing and evaluating data. Among the specific tools in this area, knowledge of Java, Hive, Python, C ++, R, and also SQL databases is useful.
Finally, among other necessary skills of a specialist in the field of Data Science, one can note Data Science Courses, Machine Learning, Deep Learning, English and, of course, the specifics of the industry from which he needs to process data.
Conclusion
Data Science is becoming an increasingly important and in-demand IT profession every year. It focuses on the analysis and processing of big data arrays, as well as the compilation of machine learning models based on them. The main advantage of this direction is its versatility, DS is actively used in many areas: in online commerce and business in general, manufacturing, IT, finance, insurance, agriculture, medicine, etc. Data science tools include a range of the latest emerging technologies, including Big Data, Machine Learning, Deep Learning, Data Mining, and more.
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