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Data science vs machine learning which career option is better for you? Are both fields the same? Has this question ever came to your mind? Or are you confused about what to learn among them? Then you are in the right place. When it comes to choosing it as a career option, it is mandatory to have a fundamental understanding of the similarities and differences between data science and machine learning.
Data Science vs Machine Learning
The term data science was first used in early 1974 as an alternative for computer science. But later on in the 1980s, the term data science was used as an alternative for statistics.
Well, in modern times, we can describe data science as a combination of statistics, programming, business, and communication. So we can say Data science is multidisciplinary. It includes data analytics, software engineering, data engineering, machine learning, etc. We have to be clear about the difference between data science vs machine learning.
When we talk about data science vs machine learning it would be completely unethical if we don’t discuss this Venn diagram.
This diagram pictorially represents what we discuss about data science before.
Have you ever wondered how Facebook knew your neighbor and showed him in your suggestion? Or how Netflix puts the movies that you like on your home screen? This is all the magic of ML.
ML can be explained as the practice of implementing algorithms to extract data, learn from the data and find the future trends for that domain.
The description may find a little bit confusing so, let’s try to understand it with an example.
Data Science Process
Suppose If you are working as a data scientist in amazon prime music. The problem that you have to solve as a data scientist would be to frame a model that will suggest to users the songs that they are more likely to be loved by the user so that the user experience will be improved.
Now we know what exactly is the problem, next we need to have some raw data. But this raw data is rarely used as there will be some false data that may give rise to erroneous results. After filtering out the useless and false data, the data scientist has to explore the data to identify the information that is contained within the data. From the whole dataset, you have to choose only what is relevant to the problem you need to solve.
Suppose you plan to make a model that will suggest song according to the gender and age of the user, collect the following data from the dataset: age, gender, music genre. Now we have the required data. we can use ML algorithms to predict a list of songs that the user is more likely to enjoy.
As we can see, ML comes only in a later part of the data science process. We can understand that ML comes under the wide umbrella of data science.
Data Science VS Machine Learning In Terms Of Salary?
Data science vs machine learning, which has a higher salary? Data science and ML have attracted most people today since both the fields are interesting and the job profiles under them are high-paid jobs. Even though the salary paid depends on many factors like location, company, experience, and skill, on average, we can say entry-level data scientists are paid 69000$ (as per ZipRecruiter) annually and increases with experience and skill.
In 2019 indeed identified machine learning as the most promising job with an average base salary of 146,085$ annually.As we can see, even though ML is a subdomain of data science, ML engineers get paid more. This happens because of the job trend, the number of job openings for data scientists is higher than that of ML engineers.
So, who is the winner between data science vs machine learning in terms of salary? The answer is machine learning.
Data Science VS Machine Learning Comparison :
|Topics||Data Science||Machine Learning|
|Definition||It is a combination of statistics, business, programming, and communication.||It is the practice of implementing algorithms to extract data, learn from the data and find the future trends for that domain.|
|Salary||On average entry-level data scientists is paid 69000$.||An average base salary of an ML engineer is 146,085$.|
|Skills||Statistics, Data visualization, Data mining and cleaning, Unstructured data management techniques, Programming languages like R and python.||Computer science fundamentals, Statistical modeling, Data evaluation and modeling, Understanding and application of algorithms, Natural language processing.|
|Useful Tools||Excel, MATLAB, Hadoop, Hive, Pig, Matplotlib, TensorFlow, SAS, Jupyter||Sci-kit Learn, TensorFlow, Jupyter, Google Cloud ML, Azure machine learning studio|
|Popular Books||An introduction to statistical learning: with application in R by Trevor Hastie.|
Python Data Science Handbook: Essential Tools for Working with Data by Jake VanderPlas
|Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, by Aurelien Geron|
Deep Learning by Yoshua Bengio
*all universities provide masters only
|Southern Methodist University, University of Denver, University of California, University of Dayton||Carnegie Mellon University, University of California — Berkeley, Stanford University|
|Courses||MicroMasters Program in Statistics and Data Science|
Data Science Specialization Course
HarvardX’s Data Science Professional Certificate
|The Complete Machine Learning Course with Python|
Data Science: Machine Learning
|Hiring Companies||Cognizant Technology Solutions, WallmartLabs, American Express, Cisco Systems, T-mobile, Verizon, JP Morgan Chase, HSBC, Adobe.||Adobe, Apple, Facebook, Google, Linkedin, Qualcomm, Twitter, Spotify, Nvidia,|
|Future Scope||With each passing year, the data will only continue to increase and add to the already massive pile of data. It is not possible for traditional computing tools to analyze such a vast volume of unstructured datasets – they demand more advanced and intelligent analytical tools for storing, processing, and analyzing data.||The future scope of ML is on its way to make a drastic change in the world of automation. Thus, you can make a lucrative career in the field of ML to contribute to this growing digital world.|
|Hardware Specifications||Medium level specs. The Tools used are not too heavy.||High-level specs required. Hardware that can work well with extensive computations is required.|
Now that we have a basic understanding of the topic “data science vs machine learning”, let’s conclude by answering the question from which we started. Both data science and machine learning are hot cakes in the industry right now. People having good knowledge of these topics are being hired by top tech companies at jaw-dropping salaries.
When it comes to a comparison between data science vs machine learning we have to say machine learning engineers are being paid more than data scientists. So in terms of salary machine learning is the clear winner. But there are more job openings for data scientists than Ml engineers. Both data science and Ml is a promising career opportunity
Applications of data science and ML are vast. But they purely depend on the quality of data and skills of the employees.
FAQ Section :
Are Data Science And Machine Learning Same?
No. Data science is a field of study that aims to use a scientific approach to extract meaning and insights from data.ML, on the other hand, refers to a group of techniques used by data scientists that allow computers to learn from data.
Which Is Better Machine Learning Or Data Science?
If we compare data science vs machine learning in the case of salary ML wins the game.
If we compare data science vs machine learning in the number of job openings data science wins the game.
Is Data Science Required For Machine Learning?
In both ML and data science, it will be necessary for you to do a lot of data analytics and pre-processing. This is so that you can make sense of what the data is showing so that you can modify the data so that it works effectively with the ML models and so that you can remove unnecessary features in the data.
Data Science VS Machine Learning, Which One Pays You Better?
ML engineer is paid more since there are more job openings for data scientists than ML engineers. On one hand, Machine Learning Engineers get slightly more paid than Data Scientist, on the other hand, the demand or the Job openings for a Data Scientist is more than that of an ML Engineer
Can A Data Scientist Become A Machine Learning Engineer?
Yes. A data scientist could become an ML engineer if he learns the working and application of ML algorithms like KNN, linear regression, etc.
What To Learn First Machine Learning Or Data Science?
We would suggest starting with data science since when you start learning ML, many of the materials will assume that you have knowledge of how to do data analytics in a certain programming language.