What to learn first machine learning or data science ?

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Getting started with machine learning or data science could be difficult if you are new to these fields. Both career options are quite popular among beginners. 

Many people want to enter these fields, yet most of them find it difficult to start and figure out what skills to master.  

If you’re in the process of deciding which one to learn first, you may find it helpful to know the similarities and differences between each field. Based on that you’ll be able to make an informed decision about which to pursue first. 

Machine learning vs. Data Science

Data Science and Machine learning are different fields and their application areas vary widely. 

Machine learning is mostly used to solve real-life problems, while data science refers to processes of extracting meaningful insights from unorganized data. 

For instance, speech recognition, voice recognition, and social media platform algorithms are based on machine learning concepts. On the other hand, businesses and other organizations use data science analysis and insights to drive business profits and innovations.   

In simple terms, data science refers to transforming unstructured data into structured ones. That’s the second step in developing any machine learning program. All machine learning programs start with collecting data and then structuring data. 

Machine learning and data science are different, still, both are intertwined concepts. Understanding the similarities and differences in skills and scope in each field is crucial before you finalize a field to build a career in

ML vs. data science: Skills required and Scope 

Skills required to become Data Scientist 

Data scientists produce insights from huge sets of data. To do data analysis effectively, you must learn data learning, data wrangling, and data visualization. Therefore, you must master the following skills : 

  • Statistics 
  • Data Visualization 
  • Coding Skills 
  • Data Wrangling 
  • Data analysis on a large scale 

Once you master all these skills, you can become a data analyst, scientist, engineer, and BI analyst. 

Skills required for Machine learning

Machine learning requires data analysis in its initial program development stages. Other than data analysis, you must master these skills: 

  • Programming Skills ( Python, SQL, Java ) 
  • Statistics and probability 
  • Prototyping 
  • Data modeling 

Skilled machine learning engineers can work as business intelligence developers and NLP scientists. 

Can I learn machine learning without data science and vice versa?

Learning data science without machine learning is possible, but you can’t pursue machine learning unless you know data analysis. Data science is a common concept in both fields. 

“Data science is a broader skill set than machine learning.”

Just like we can say, a square is a rectangle, but a rectangle isn’t a square. Similarly, machine learning isn’t possible without data science. 

Machine learning requires multiple aspects of data analysis, such as data collection, cleaning, and visualization.  


What to learn first: Machine learning or Data science? 

ML is still in its early stages, while data science already has a mark in the industry. Telling where to start wouldn’t be right. There is no perfect path to start a career in any one of these fields. Both have their own set of scope, opportunities, and set required skills. 

There is no perfect hierarchy or hard and fast rules, you can start where you want to. 

But here’s the thing you must consider before getting started in any one of these fields – Machine Learning requires knowledge of Data Science for its implementation. You need to know the ins and outs of Data Science before getting started with Machine learning. 


Where to start first?

Despite being entirely different fields, there are multiple areas where both concepts overlap. It is difficult to explain how much these two are linked. What we can say is that these two fields are almost inseparable.

Scientists have been trying to differentiate data science and machine learning for over a decade. Hugh Conway gives the best way to understand the link between ML and data science. He created a Venn diagram of three circles: hacking skills, statistics, and subject expertise.

From the above diagram, we can say irrespective of which field you choose you should learn the following set of common skills: 

  • Math, probability, and statistics 
  • Programming skills 
  • Data science concepts, for example, data cleaning, data analysis, data wrangling, and data visualization. 

If you are confused about what to learn first, start with these common skills. 


What to pursue first? – You can start with whichever field you prefer. It’s suggested to learn data analysis and programming first. Master data analysis when you’re starting, and then if your interest aligns with algorithms, then you can pursue machine learning. Otherwise, you can stick to becoming a data scientist. 

But irrespective of the field you choose, both of them offer ample opportunities. 

If you are looking to become an expert in either machine learning or data science, explore our certification courses in these fields today!


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