Data is growing at rapid speed and is of great value to every organization. Why? Data can help to improve the product, services. Can help the organization to grow from bankruptcy or to avoid bankruptcy. In fact, today’s global leaders drive data analytics as the new secret weapon for competitive advantage. World’s fastest car use data analytics to improve their performance. This has been named as the sexiest job of the century. Now you must have got the importance of data in today’s world. As rightly said, the world’s most valuable resource is no longer oil, but data. With this, new terms are emerging and it is important to understand the meaning and difference between them to build the career in this field.
You must have listened to these terms nowadays a lot …
Are you thinking to make the career in analytics?? And wondering where to head on… Questions popping out?? what is data science, How it is different from data mining, what is machine learning… want to know the difference between all… So many questions when moving for Analytics…and the courses related to build the career.
We will take an example to understand all these terms in an easy manner. Cricket… got excited, will try to understand analytics through this sport. How players perform on cricket field can be used to determine how they are going to transact in coming matches, all can be attained from the data collected from their past performances.
Let’s move on with one by one term:
For players, what data can be collected to have some findings? It can be how many runs made, which over player got out, How many four or six made, on which position player came to bat etc.. And after collecting we can find some pattern. Let’s consider Virat Kohli as an example. Super excited now. If I collect the data for Virat for all the matches played, I can find a pattern in his batting style.
“Data mining is about collecting data, finding and extracting new information. Pattern Prediction based on past data”
Courses for data mining an initial level to start with are:
University of Illinois at Urbana Champaign, Data Mining Specialization,
I collected all the data for Virat, what I can do now is that I am going to apply some algorithm to find some correlation between the runs made and the position he comes to bat. I can apply same to all the parameters or variables I have with me.
“Data Analytics is about using the algorithm to derive the insights and to find meaningful correlation between collected data”
And the Courses for data analytics where you can find are:
After getting correlation I should analyze it so that I can decide some pattern or I can give some prediction about Virat, some decisions to be made. Sounds cool right…
“Data Analysis is about describing, interpreting, evaluating, representing data for decision-making”
Courses related to that are:
Duke University, Data Analysis course,
University of Michigan, Applied Data Science with Python Specialization
Now I’m done with Virat, now whose next Dhoni, again excited. I will use all the algorithms, methods to analyze, interpret the data collected for Dhoni and now this time I don’t want any human interaction. This sounds really good, everything to be done by computer only… right??
“Machine learning processes the data by using algorithm and techniques to discover patterns that can be later used to analyze new data without human intervention”
Machine learning courses you can find here :
Cricket is not just about Dhoni, Virat or Yuvraj. So many teams and thousands of players… OMG enormous amount of data because data with any player, that stored intentionally or unintentionally over the years, all the digital, papers, whether or not categorized data is Big Data. Essentially no data is useless. Now how to analyze terabyte and petabyte data, definitely not possible to analyze with a regular database.
“Big data is Huge amount of data that is analyzed for results that can be used for predictions, which cannot be discovered using normal spreadsheets and regular tools of database management”
They need special analysis tools like Hadoop, pig, hive etc. so that all the data can be analyzed at one go.
I should just not define myself to some limited pattern or some constraint by applying correlation, I should think from all the different angles. Correct right, I will use all the algorithms, tools to analyze the data and to predict again.
“Data Science is about extracting useful insights from the data, solve complex problems by using various tools, algorithms, and machine learning principles by looking at data differently”
Data scientist job is like whatever you would do may or may not be seen by people outside your company. Your functions would be helping the companies engineer things better.
For this very important part, you can find courses here:
These careers go in this level: