Difference Between Data Science, Data Analytics, and Machine Learning

We all know that Machine learning, Data Science, and Data analytics is the future. There are companies who not only help businesses predict future growth and generate revenue but also find applications for data in other fields like surveys, product launches, elections, and more. Stores like Target and Amazon constantly keep 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 dig a little deeper and explore their differences. Machine Learning, Data Science, and Data analytics can't be completely separated, as they are have origins in the same concepts but have just been applied differently. 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 processes like data cleansing, preparation, and analysis. A data scientist collects data from multiple sources like surveys, physical data plottings, etc. They then pass the data through vigorous algorithms to extract critical information from the data and make a data set. This dataset could be further fed to analyzing algorithms to make more meaning out of it. This is where data analytics comes in.

What Skills Are Required to Be a 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.

Data Analytics

In layman's terms, 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 has a specific aim and goal. Data analytics is often used by companies to search for trends in their growth. It often uses data insights to make an impact by connecting the dots between trends and patterns 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 splits off into branches like data mining, which involves sorting through datasets and identifying relationships.

Another branch of data analytics is predictive analytics. This generally includes predicting customer behavior and product impact. Predictive analytics helps during the market research phases, and makes the data collected from surveys more usable and accurate in predictions. Predictive analytics has applications in a number of places, from weather report generation to predicting students' behavior in schools, to predicting 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 analyst will usually have much the same knowledge and skills as an experienced data scientist. The difference between both of them would be the area of application.

Machine Learning 

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

Machine learning basically comprises of set of algorithms that could make software and programs learn from 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 statistics.

• Data modeling.

Machine Learning vs. Data Science 

Machine learning and data analytics are a part of data science. Because the machine learning algorithm obviously depends on having data to learn, data science is a broader term and does not only focus on implementing algorithms and statistics but also 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 scanners, voice recognition, robotics, etc. Recently, Google taught a robot to walk, using only the algorithms that allowed it to take in the constraints and physical parameters of its surroundings. 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, it learned to walk in a few days. This is the best example of machine learning, where a machine actually learns and changes its behavior.