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Difference between Data Science, Data Analytics and Machine Learning

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So, it’s 2018 and the word is spread about Data boom. There are Tech Giants like Facebook, Amazon, and Google constantly working in the field of Machine learning and Data science.

We all know that Machine learning, Data Sciences, and Data analytics is the future. There companies like Cambridge Analytica, and other data analysis companies who not only help businesses predict the future growth and generate revenue but also find the application in other fields like survey, product launch, elections and what not. Stores like Target and Amazon constantly keep a track of user data in forms of their transactions, which in turn helps them to improve their user experience and deploy custom recommendations for you on your login page.

Well, we have discussed the trend, so let’s get a little deeper and explore their differences. While Machine Learning, Data Sciences, and Data analytics can’t be exclusively separated, as they are pretty much originating from the same concepts just different applications. They all go hand in hand with each other, and you’ll easily find an overlap between them too.

Data science

So, what is this data science?

Data science is a concept used to tackle and monitor huge amounts of data or big data. Data science includes process like data cleansing, preparation, and analysis. A data scientist would collect data from multiple sources like surveys, physical data plotting. He would then make the data pass through the vigorous algorithms to extract the critical information from the data and make a data set. This dataset could be further be fed to analyzing algorithms, to make more meaning out of it. Which is what basically Data analytics is pretty much for.

What skills are required to make Data scientist?

Some key skills that you’d need :
Deep knowledge of Python, Scala, SAS.
Knowledge of databases like SQL.
Good knowledge in the field of Mathematics and statistics.
Understanding of analytical functions.
Knowledge and experience in machine learning.

Now, you might be wondering ” What is data analytics then?”

Talking in terms of a layman, if Data science is a house that consists of all the tools and resources. Data analytics would be a specific room. It is more specific in terms of functionality and application. Instead of just looking for connections like we do in Data science, a data analyst have a specific aim and goal. Data analytics is often used by the companies to search for trends in their growth. It often moves data insights to impact by connecting the dots between trends and pattern while Data science is more about just insights. You could say that this field is more focused on businesses and organizations and their growth. You would need skills like, Python, Rlab, Statistics, Economics, and Mathematics to become a Data analyst.

Data analytics further bifurcates into branches like Data mining, which involves sorting through datasets and identify relationships.
Predictive analytics. This generally includes predicting customer behavior and product impact. Helps during the market research. Makes the data collected from surveys more usable and accurate in predictions. This finds application in a number of places. From weather report generation to predicting a students behavior in schools to predict the outbreak of disease.

To conclude, one can obviously not draw a definite and clear line between Data analytics and Data science, but a Data scientist would have pretty much the same concepts and skills as an experienced data scientist. The difference between both of them would be the area of applications.

Machine learning

Remember how you learned to ride a bicycle? A machine could learn that with the help of algorithms and datasets. Datasets of values basically.

Machine Learning, basically comprises of set of algorithms that could make software and program learn from it’s past experiences and thus make it more accurate in predicting outcomes. This doesn’t need to be explicitly programmed, as the algorithm improves and adapts itself overtime.

Skills that you’d need for Machine learning

Expertise in coding fundamentals
programming concepts
Probability and stats
Data modeling

There are overlaps and differences between Machine Learning and Data science.

Machine learning and data analytics are a part of data science. Because the machine learning algorithm obviously depends on some data to learn. Data science is a broader term and would not only focus on implementing algorithms and statistics but it includes the entire data processing methodology.

Thus, data science is a broader term that could incorporate multiple concepts like data analytics, machine learning, predictive analytics and Business analytics.

However, Machine learning finds applications in the fields where Data science can’t standalone like Face ID, fingerprint scanner, voice recognition, robotics. Recently, Google taught it’s robot to walk, the algorithms only had constraints and physical parameters of the contour on the robot was supposed to walk. There was no other dataset included, the Machine walked through many different cases and made its dataset of the values it could refer to. Hence, after a few trials and errors, It learned to walk in a few days. This is the best example of Machine learning, that machine actually learns and changes its behavior.

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