You are not a data scientist
- Transfer
The IT industry is undergoing rebranding: engineers are turning into architects, and in-depth training can compete in popularity with cats. It's time for data geeks to turn into data scientists.
Who are “data scientists” and what are they really doing - will tell Grigory Bakunov at our career meeting “What Data Scientist Lives” .
Bet you have colleagues or friends who proudly call themselves Data Scientists? Do not take it to heart, but most of them are not related to Data Science at all. Calling yourself a scientist means truly engaging in science, practicing scientific methods. You put forward a hypothesis, reinforce it with the results of experiments, and after proving / refuting it, move on or conduct new iterations.
Data science is an applied science. And the task of any applied scientist is to create models, methods, algorithms that are of practical value.
These things are very important, as they can predict future results from relatively small input data. In some cases, your models are nothing more than black boxes: you cannot explain where the forecast came from, but you have already proven the accuracy of this data.
Thus, in order to maintain the purity of the concept of “Data Science”, here are a few statements that will help you understand that you are not a Data Scientist:
- You have rich expertise in the field of business analytics. You spent a lot of time forecasting the past by analyzing the time series of historical data. This is not data science — you rarely experiment, your ability to predict is deceiving.
- Experience in programming in Hadoop, R, Python, Octave, Mathematica and Matlib - Data Scientist tools. The ability to use tools does not yet give you scientific influence.
- A degree in mathematics, statistics, econometrics does not give you the right to call yourself a scientist according to data. We hope that you have learned to apply descriptive and prognostic methods while maintaining an understanding of the basic theory. But data science is an applied discipline that focuses on a specific data domain, so most likely you will not get enough real experience in pursuing a bachelor's degree.
- Advocating the role of large, medium, small, and any data as the future of intellectual entrepreneurship looks appropriate in your resume, it can be a way to start a conversation with a geek or entertain friends at a party. You do not become a scientist from this.
- An eight-week course at Cousera or a visit to a scientific camp for Data Scientists makes you the same scientist as yesterday, a professional athlete makes me a golf lesson. The theory "live a century - learn a century" and the thirst for continuous self-improvement are simply self-deception.
- The subject expert and master of Excel, you create incredible charts, graphs and crazy tables. Again, these indispensable skills do not make you a scientist.
- You recently acquired a Data Science platform from SAS, IBM or Microsoft, and without proper experience, after reading the instructions, watching 10 introductory videos and taking a five-day course, you believed that you were ready to create predictive / explanatory models of subject data by simply moving the algorithm widgets to the canvas and pressing the button "Learn". You are not a Data Scientist at all, but simply a dangerous subject.
Who are “data scientists” and what are they really doing - will tell Grigory Bakunov at our career meeting “What Data Scientist Lives” .
Bet you have colleagues or friends who proudly call themselves Data Scientists? Do not take it to heart, but most of them are not related to Data Science at all. Calling yourself a scientist means truly engaging in science, practicing scientific methods. You put forward a hypothesis, reinforce it with the results of experiments, and after proving / refuting it, move on or conduct new iterations.
Data science is an applied science. And the task of any applied scientist is to create models, methods, algorithms that are of practical value.
These things are very important, as they can predict future results from relatively small input data. In some cases, your models are nothing more than black boxes: you cannot explain where the forecast came from, but you have already proven the accuracy of this data.
Thus, in order to maintain the purity of the concept of “Data Science”, here are a few statements that will help you understand that you are not a Data Scientist:
- You have rich expertise in the field of business analytics. You spent a lot of time forecasting the past by analyzing the time series of historical data. This is not data science — you rarely experiment, your ability to predict is deceiving.
- Experience in programming in Hadoop, R, Python, Octave, Mathematica and Matlib - Data Scientist tools. The ability to use tools does not yet give you scientific influence.
- A degree in mathematics, statistics, econometrics does not give you the right to call yourself a scientist according to data. We hope that you have learned to apply descriptive and prognostic methods while maintaining an understanding of the basic theory. But data science is an applied discipline that focuses on a specific data domain, so most likely you will not get enough real experience in pursuing a bachelor's degree.
- Advocating the role of large, medium, small, and any data as the future of intellectual entrepreneurship looks appropriate in your resume, it can be a way to start a conversation with a geek or entertain friends at a party. You do not become a scientist from this.
- An eight-week course at Cousera or a visit to a scientific camp for Data Scientists makes you the same scientist as yesterday, a professional athlete makes me a golf lesson. The theory "live a century - learn a century" and the thirst for continuous self-improvement are simply self-deception.
- The subject expert and master of Excel, you create incredible charts, graphs and crazy tables. Again, these indispensable skills do not make you a scientist.
- You recently acquired a Data Science platform from SAS, IBM or Microsoft, and without proper experience, after reading the instructions, watching 10 introductory videos and taking a five-day course, you believed that you were ready to create predictive / explanatory models of subject data by simply moving the algorithm widgets to the canvas and pressing the button "Learn". You are not a Data Scientist at all, but simply a dangerous subject.