What is data science? How Data Scientists Work?
The problem with Big Data is that it is not just about large volumes of data. The analyzes covered by this
appellation require complicated and above all complementary skills. First understand what is data science and how data scientists work.
With the recognition of data as an essential raw material, the profession of the Data Scientist is being developed. The famous “data scientists,” which everyone seeks to recruit, combine strong mathematical and statistical skills combined with a good knowledge of computing, enabling them to code.
They also have a knowledge of the professions in order to exchange with the directions for which they will develop applications. They differentiate themselves from data analysts or data miners in that they master the technologies of data collection from very heterogeneous sources such as social networks,weblogs, business tools, and languages such as Python, R or Spark.
The most important IT jobs of tomorrow
For years, application developers, network administrators, and consultants, especially SAP systems, have been among the most sought-after specialists in the IT environment. But what does it look like in the near future?
According to the IDG study “IT Jobs 2020”, software engineering specialists and consultants will still be in high demand in three years. The demand for security professionals will increase most by 2020. Even today, specialization in this environment is worthwhile, but IT security experts among the IT specialists without personnel responsibility deserve the best. An equally high demand prompts respondent to the cloud architect, who must orchestrate the many different cloud solutions in the company.
A rare commodity
The significant schools have set up specialized training courses in Big Data over the last two years, but it is too early to assess the quality of these courses and the promotions are very small. Everyone wants to recruit specialists: service companies and consulting firms, start-ups and key accounts. As a result, data scientists are very courted. Young people have several offers before they graduate. As for the data scientists in the post, they are very solicited by headhunters and on the social networks. As a result, remuneration has a 20–30% surcharge on the usual salaries.
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As more and more companies are collecting and processing data, processing, and storing data, data jobs are among the top-notch IT jobs in 2020: from the data artist, who visualizes the data the data scientist, who works with large amounts of data and generates insights using analysis methods based on big data technologies such as Hadoop, to Data Architect.
Profile of data scientists
Data Engineers and Enterprise architects are to bring order and structure into the data flow. Data Scientists are concerned with the process of researching the data per se. The explorer is more important than the algorithm.
1. Data Scientists must form a team with Data Engineers and Business Representatives of the business lead data scientists develop hypotheses and assumptions. Data Engineers support this process; the keyword is DevOps. The artificial word from development and operations is intended to close the gap between the exploratory process and the structural one.
2. Data Scientists like to take raw data Corporate architects are only good at presenting data in a clean, orderly and structured way. But Data Scientists need access to raw data because they work experimentally.
3. The process itself can achieve success Success monitoring is challenging with Predictive Analytics. Financial benefits are difficult to measure. Companies need to understand that Data Scientists may be randomly coming up with important connections or interesting results. In any case, they gain insight into internal processes.
4. Speed is important Frameworks like Hadoop support Data Scientists. Because their work is iterative and generates more and more data. The faster the IT data can manage, the better.
what is data science and who are data scientists? Many companies are already looking for Data Scientists or are about to start their search. The skills required a range from Large-Scale Predictive Modeling, Data Mining, Advanced Analytics, and Big Data.
1. The tasks of the Business Developer Business Developers are deeply involved in the business processes and can link corporate goals with data analysis. They develop a first questionnaire or reveal a problem that should be solved using the collected data. As an interface between the business world and technology, he can best assess the benefits of the analysis results and therefore works closely with the data analyst.
2. The tasks of the Data Analyst The Data Analyst has the profound knowledge of data-driven analytical methods, data mining techniques and data visualization techniques. They can automatically be used to classify records or to group them with respect to their similarity. In this way, the data analyst can evaluate the meaningfulness of the data and recognize relevant patterns and abnormalities in the data streams.
3. The tasks of the Data Manager The Data Manager ensures that the quality of the data is optimized and that metadata adequately describes it. This includes the fact that the Data Manager provides an overview of the usage rights and knows the sensitive data for which they can be used.
4. The tasks of the Application Developer The application developer implements the platform on which the data is integrated, and the applications are developed and installed. He has mastered various tools for parallelization and real-time processing so that the statistical models of the data analyst can also be used in large amounts of data.
5. The tasks of the Security Manager The Security Manager ensures that the aggregation, enrichment, and analysis of data does not allow conclusions to be drawn on individuals and thus violate the personality rights. The security manager must, therefore, implement the data protection organizationally and technically.
How to Become a Data Scientist
In the course of Big Data, the analysis and visualization of data have become a key to company success. It is, therefore, an advantage not to shy away from dealing with data, even without previous training as a Data Scientist. In this course you will understand what is data science and the role of data scientist.
According to LinkedIn 2017, data analysis and visualization are among the top ten most popular skills for employers. According to a recent study by US-based consultant Winter Wyman, with some 620 IT positions, Big Data Engineers have more than the US $ 15,000 a year as the specialist for the user interface and up to $ 27,000 more than software engineers.
But in only one year to become a data specialist — is this at all? The problem with many companies: They know they need data peers, but it is not necessarily clear to them what exactly the skills should be and how to find them. After all, there are currently a number of basic answers to these pressing questions. The needs of the companies are obviously based on the fact that the job profile is linked to something unreal. You cross a sheep, a chicken, and a cow, and you always have wool, eggs, and milk. But, it doesn’t not stop here.
1. Do not be afraid of large amounts of data Whether Big Data or Small Data — to evaluate data, you do not have to be a statistician or data scientist. The handling of data is already the order of the day in many companies. Accordingly, sophisticated business intelligence tools are designed to help users analyze their data as well as professionally and visually. In the case of external training, the safe handling of appropriate software can be trained. For the beginning, there is also free help on the net with training videos and blogs.
2. Train with software and brain Just as important as the safe handling of technical tools are personal competences. Through the unfolding of critical thinking and analytical curiosity, not only the handling of data is improved, but the soft skills are also improved.
3. Ask, ask, ask Only those who ask questions about data can draw insights from them. With interactive data dashboards, questions can be asked to the data live and share in the team and answered in real-time. Frequently, this not only answers the original questions, but also provides new insights and perspectives. The goal is to respond to each response with a new “Why” until the root of the problem is reached and it can be fixed.
4. Ignore the abdominal feeling The constant handling of information is practiced, while at the same time the appearance becomes more sovereign and more competent.
5. Trying to go about studying The Internet is the most significant data source in the world with numerous freely available data. On Bigdataguys, users make their data and visualizations available with small classes that are primarily hands-on taught by senior data scientists and senior data engineers with extensive years of in-service experience. In addition to the pool of data, there is also daily the best visualizations for inspiration. With data from publicly accessible sources, the analysis and visualization skills can be practiced.
6. Fun is trump For a start to the data profit, it does not have to equal the business figures from the last quarter. Why not even analyze the films of the favorite director or something about football? For the first successes, simple datasets and fun on the topic are crucial.
7. Learning from the community Feedback is the key to continuous learning. The Internet is the best place to network and exchange with other newcomers or professionals. Numerous blogs help in initial mastering hurdles. Big Data Scientists act independently Data Scientists are neither scientists with white coats nor specially trained experts working in software companies. The key is the confrontation with large amounts of data and the ability to quickly evaluate them for companies or business areas.
Whether in marketing, controlling or even in Facility Management — Big Data already affects all companies and areas of expertise. An individual support by the IT department or by colleagues with special mathematical knowledge is not possible across the board. This is why Big Data Scientists, who are able to act independently, are particularly in demand in specialist departments.
It is the colleagues, who, equipped with appropriate software, ensure that the available data becomes crucial information. Entirely different competences occupy corresponding positions. The overarching qualification is to build a bridge between professionalism and easy-to- use Big Data Analytics.
Modern evaluation tools: The prerequisite is that Data Scientists can use modern evaluation tools and understand the requirements of the subject area. Blurred department boundaries: Added to this is the fact that traditional departmental boundaries are becoming more and more blurred.
One example of this is the growing closeness between CIOs and Chief Marketing Officers (CMOs).
Many innovations in the big-data context are responses to questions that can no longer be answered with classical business intelligence. Business Intelligence is, in a simplified way, the statistical view into the reflection, which involves questions such as: What have we done? What effects have we achieved? What can we learn from it retrospectively? The in-depth analysis of Big Data, on the other hand, focuses on the future. Big Data, therefore, requires more complex mathematics in the software — we are talking about Analytics — but it is also the key to much more valuable information. So you should not hide your experiences. Also, because your new goals as a logical next step are absolutely comprehensible for every potential employer.
The role of a data scientist
You may now know what is data science. Now understand the role of data scientist. A long-term consultant and project manager in the field of data warehousing and business intelligence are interested in big-data topics from the IT perspective — for example, data architectures, memory structures for structured and unstructured data, NoSQL, Hadoop or MapReduce. In two to three years he wants to change into the big data area and expand his competences in the direction of Big Data Architect. What should he consider in his new and reorientation?
The analytical skills of a scientist and the creativity of an artist ideally combine ideal placement with IT know-how. Bigdataguys offers appropriate training courses and boot camp programs, and the potential users of Big Data need to know where they really want to go with data analysis. with complete understanding of what is data science, read this for the roles.
- The data scientist is responsible for the CTF-derived methodology of big-data analysis in the company.
- Data scientist works as a “Greater Data Science (GDS),” which includes data exploration and data preparation, data presentation and transformation, implementation of the necessary computing operations and application of the corresponding algorithms, data modeling (in the context of the Data Scientist projects) and data visualization.
- The Data Scientist communicates the results of the project in simple, clearly comprehensible language, especially with the help of anecdotes, and provides merely understandable and intelligible facts, which allow fact-based company decisions.
- Data Scientists have an interface with the Data Protection Supervisor to ensure compliance with legal requirements for data collection and analysis.
Besides, here are five points which must be observed:
Statistical Understanding: A Data Scientist must filter out the useful information from a wealth of data and be so versed in the fact that trends are recognized at an early stage. A university degree in mathematics would be desirable. However, it was to be assumed that most of the candidates had more practical training courses — computer science or engineering sciences as a rule.
Curiosity is essential: In order to fulfill the task, database queries must not only be implemented. The data specialist should think about it, design the right questions, which nobody else in the company would come up with an open the profit chances.
Knowledge about Databases: A good Data Scientist knows the design and implementation of databases — even if that does not fit into the superficial view of Big Data. Even if this term includes unstructured data, a basic understanding of both relational and columnar databases will help.
Big Data may be sexy, but many useful information and trends could be distilled from traditional databases. Knowledge in this area is also helpful for the creation of new, more sophisticated systems. In addition, many developers of Big Data software are deliberately using SQL-like languages. Classic administrators are not to be frightened if they do not want to go into MapReduce. Traditional SQL knowledge will continue to throw dividends.
Basic skills in scripting languages: The best applicants master the Python script language, which has been promoted in the Big Data environment. Python is an open source language that is easy to understand and practical to use. It should not be too high a hurdle. It is also possible to test how applicants deal with pseudo-codes or whether they can explain algorithms and queries in normal language.
No wage dumping: Startups reward Data Scientists and enable them to work on exciting products. If it comes to what is data science, Data Science is not for impatient people; a data scientist needs a long breath and a lot of sense for connections.
The perfect candidate is a figure-genius and a scholar in company policy who deals with statistical computer languages. But it is difficult to translate this idea into a practical job description and the matching search criteria. The expert recommended a close collaboration between the IT department and the personnel department for recruitment.
Specialization is always a possible career path since IT topics are subject to a high degree of dynamics, so you are absolutely right to build up basic competencies in the analytical field.
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Originally published at www.bigdataguys.com on October 16, 2017.