What is the difference between data scientists and machine learning engineers? - kdnuggets

In today's digital age, the world revolves around thousands of data . Powerful devices for processing these data have become necessary . Now? , These machines should be automated , Or should these systems be designed in such a way ： These devices should be able to automatically and successfully process the data . therefore , To build these systems , We need professionals like machine learning engineers and data scientists . Now? , That's the importance of data science and machine learning .

There is a lot of confusion between data science and machine learning, and between the roles and responsibilities of data scientists and machine learning Engineers , Because these two terms are relatively new in the technology industry .

Data scientists and their significance

Data science is usually defined as the description of structured and unstructured data 、 Prediction and operation . This process helps business companies and organizations make business related decisions for the benefit of the company . Some might describe it as the origin of data 、 And what it represents and how to turn it into valuable resources , And to do that , Data science and technology is used to mine large amounts of data to find patterns, which will help enterprises to compete better than others , Understand new opportunities in the market , Increase of efficiency , And bring many of these benefits . 

In defining data scientists , A lot of definitions are used , But if we have to sum up in a few words , Data scientists are only professionals in the field of data science . The responsibilities of data scientists include using their scientific expertise to solve complex problems and scenarios . The roles and responsibilities of data scientists also include special areas where skills are needed , For example, speech analysis , Text , Image and video processing, etc . Each of these roles and responsibilities of data scientists is very limited in number , therefore , Their positions are very valuable , So the market is in great demand . In short , Whenever a business needs to answer or solve a problem , 

Machine learning engineer and its significance

Machine learning is a branch of artificial intelligence , It deals with a class of data-driven algorithms , These algorithms enable software or systems to accurately predict the results of operations , Without human intervention or pre programming the system . There are many similarities between predictive modeling and data mining . This is because both methods and processes involve identifying patterns in the data , And adjust and modify the program accordingly . 

Machine learning engineers are often referred to as senior programmers , They can develop machines in some way , Make them understand and apply knowledge without any particular direction . Artificial intelligence is the goal of machine learning engineers , But the focus of these computer programmers is not just to design specific programs for specific tasks . 

Now that we know about the two areas of data science and machine learning , So it's important to understand the difference between data science and machine learning and get better ideas .

Machine learning engineers compare with data scientists

  In recent years , There have been a number of data science work , And flooded the market . In the business of Data Science , Data scientists and machine learning engineers are relatively new trajectories . Between data science and machine learning difference when , Many parameters can be considered . 

 1. Requirements for data scientists ：

The job of data scientists requires them to be highly educated . To qualify as a data scientist , A master's or doctorate in data science is required . According to recent research , Finding data scientists in Computer Science , engineering , mathematics , Advanced degree in statistics and information technology related topics . therefore , Let's briefly introduce the required skills .

1. Data scientists should at least have computer science , engineering , A master's or doctor's degree in mathematics or statistics , To apply for the position of data scientist . in addition , Individuals should learn something like R,Python,SQL And many of these new technologies and Trends , In order to learn data science , To get data science work . Now? , All of these programming languages can be learned in today's very common data scientist course . 
2. One should be proficient in mathematics , Or have very strong mathematical skills and the technical and analytical ability to become a data scientist . 
3. Data mining and statistical techniques are areas where experience should be gained . Such as data enhancement , Generalized linear model or regression , Data mining technologies such as network analysis are crucial when it comes to the responsibilities of data scientists , Because they have to be dealt with .
4. Use things like artificial neural networks , Machine learning techniques like clustering can help you gain experience , In order to apply for data science work to play their own advantages . Need at least 5 To 7 Years of experience in statistical modeling and data processing . 
5. To learn data science , Need distributed data and computing tools （ for example Hadoop,Spark,MySQL,Python） And visualization and representation of data , So , You need a course in Data Science .

 2. Requirements for machine learning Engineers ：

Just like a data scientist , Most companies prefer a machine learning engineer with a master's degree in any subject related to technology . however , Because the field is a relatively new one , So there's a shortage of people with these skills , As a result, recruiters tend to be more considerate when recruiting candidates for data science positions , And often willing to make exceptions . But that doesn't mean less requirements for other parameters , Because machine learning engineers should be familiar with something that can be done through libraries ,API, The concept of learning by means of package, etc , For example, machine learning algorithms . Some other skills that machine learning engineers should have are as follows .

1. Must have visual processing , Deep neural network and reinforcement learning experience . in addition , Also need to Python,Java,R,C ++,C,JavaScript,Scala And so on programming language has enough understanding . 
2. It's important to know the probability and statistics . Similarly , In mathematics , Because of the need for algorithmic theory , So we need deep knowledge , At the same time, decrypt complex machine learning algorithms to help machine learning and communication . 
3. Use things like MATLAB Programming tools like that , And etcd Distributed system tools work together with a wealth of engineering and technical knowledge and strong analytical skills and rich experience ,Zookeeper It's also crucial . Through data science , It's easy to learn all this knowledge , These courses are easily available online and in Institutions .
4. When processing large amounts of data and working in high throughput environments , It should also be flexible and have no problems . Besides , The broad knowledge of machine learning assessment indicators is really important as a skill . 

 3. The role and responsibilities of data scientists ：

Compared with statisticians , Data scientists know more about programming than they do , And against software engineers , Data scientists know more about statistics than they do . The roles and responsibilities of data scientists include storing and cleaning up large amounts of data , Explore data sets to identify patterns by investigating valuable insights , Run the data science project . Details of the responsibilities of data scientists are as follows .

1. The primary role and responsibility of data scientists involves the research and development of statistical models for data analysis , This is an important part of learning data science . 
2. It is the primary role and responsibility of data scientists to understand customers' needs and design models or guide them to seek solutions . Besides , By working with the company's management and engineering departments , Data scientists can also understand a company's needs or how it can help it grow . 
3. Communicate decisions to key business owners , Plans and concepts belong to the role and responsibility of data scientists . Identify new opportunities or trends in the industry , And design models to keep that in mind , This will help the company's improvement process , That's what data scientists should be aware of , And it's usually something taught in data scientists . 
4. It is also one of the responsibilities of data scientists to use the appropriate database and project design to optimize the solutions involved in the project . Again , To learn data science , Handle , It is also important to clean up and verify the integrity of the data to be used for data analysis , Because they will contribute to future data science work . 

 4. The role and responsibilities of machine learning engineers ：

The responsibilities of machine learning engineers will be related to the specific project they are working on at a certain point in time . however , If you notice carefully , You will acknowledge that machine learning engineers are usually responsible for creating algorithms based on statistical modeling processes . Now? , Let's see what these machine learning engineers are doing every day .

1. The first task is to research and transform data science and technology prototypes , And design machine learning model . Besides , Working with data engineers to develop data and model pipelines is also considered part of one of the most recognized data science efforts .
2. To design distributed systems , It's going on In data science （ It is best to ） Learning data science and the application of machine learning technology . 
3. From writing production level code to make it suitable for production , To participate in the code review and learn from the code what changes to make , Machine learning engineers are trying to improve the existing machine learning model . 
4. Choose the right data set and the right data representation , Run machine learning tests and experiment with them , Use these test results for statistical analysis and fine tuning , Is the key to making up for the role and responsibility of these machine learning Engineers .