Machine learning is indeed shaping the world in many ways beyond imagination. Look around yourself and you will find yourselves immersed in the world of data science, take Alexa for example, a beautifully built user-friendly AI by none other than Amazon and Alexa is not the only one, there are more such AIs like Google Assistant, Cortana, etc. So, how were they developed and the most crucial question of all, why were they developed in the first place? Well, we will try to dive into all such questions and will also come up with some very reasonable yet technical answers. The first and foremost question at hand here is what is Machine Learning and Data Science?
Many have the notion that data science is a superset of Machine Learning. Well, those people are partly correct as data science is nothing but a vast amount of data and then applies machine learning algorithms, methods, technologies to these data. Therefore, to master data science you should be an expert in mathematics, statistics and also in subject expertise. Well, what is subject expertise? Subject expertise as the name gives it away is nothing but the knowledge about the domain to be able to abstract and calculate the same. So basically these three concepts are considered the cornerstones of data science and if you manage to ace all of them, well then congratulate yourself because you are an A grade Data Scientist. Let us understand this with the help of a diagram that was curated by Hugh Conway.
Now, you are familiar with the term data science and what it comprises of. So, if that lit a spark in you to pursue this field as a career there are a couple of things that you might need to watch out for! To become a data scientist you will need immense knowledge in three prominent domains and those are Analytics, Programming and Domain Knowledge. But you see the data science can’t be mastered just because you have certain knowledge but you will require critical skills as well and to carve out the data scientist in you and to hone your skills there are a couple of skills you can practice and which will help you in your journey:
- Expert level Python skills, SAS, R, SCALA
- Hands-on expertise in SQL coding.
- Capacity and Ability to deal with the unstructured data.
- Ability to understand various analytical functions.
- The last but not the least, knowledge of Machine Learning
As we said that the Machine Learning could be said to be a subset of Data Science but the definition does not end here. A very simple and reasonable machine learning could be that Machine Learning provides techniques to extract data and then appends various methods to learn from the collected data and then with the help of some well-defined algorithms to be able to predict future trends from the data.
Machine Learning or traditional machine learning had its core revolving around spotting patterns and then grasp the hidden insights of the available data. Well, that was the elaborated definition of Machine Learning but how do we justify this definition? A real-life functional example proves to be very good in such cases and here the exemplar would be GOOGLE. Google is the quintessential example for machine learning as GOOGLE records the number of searches you have made and then suggests you similar searches when you google something in the future. Similarly, AMAZON recommends your products based on your previous searches and so does NETFLIX, based on the TV show or Movies that you watched, you get a similar type of suggestions.
It is not an unknown fact now, that Machine Learning’s domain is growing exponentially worldwide, so if you wish to pursue a career in this field, there are a couple of skills that are critical for you to trump this domain.
- Good expertise in computer fundamentals.
- Well-versed programming skills.
- A good amount of knowledge about probability and statistics.
- You will also need to improve the Data Modeling skills.