Machine Learning, Statistics, Data Science, or Analytics – whatever you name it, this discipline is on growth since the last quarter of century essentially owing to increasing data collection techniques and exponential increase in computational power. The field is drawing from the pool of mathematicians, engineers, statisticians, and computer scientists and frequently is demanding multi-faceted strategy for successful execution.
Though, as one journey through his/her profession in analytics by joining data analytics training course, some truths start becoming evident over time. So, it’s worthwhile to discuss some of the facts about data.
Data is Never Clean
Analytics without real data is a small collection of principles and theories. Data supports test them and find the right one suitable in the context of end-use in hand. However, in real-world data is never clean. Even in companies that have well-established data science centers for decades, data isn’t clean. Apart from avoiding or wrong values, one of the biggest problems refers to joining multiple datasets into a coherent entity.
You Will Spend Most of Your Time Cleaning and Preparing Data
The conclusion to above is that a large part of your time will be consumed in just refining and processing data for model consumption. This usually irritates people new to industries. With brilliant mind bursting with complicated machine learning methods, employing three-fourths of the time with just data wrangling seems a waste of talent and time. Often this leads to disappointment and lack of concentration – errors from which can come to bite even the fanciest of the algorithms. To learn how to clean data, join a data science course.
There is no Full Automated Data Science
Since data is not clean and needs quite a lot of data processing, there is no quick set of scripts or keys to push to improve an analytic model. Each data and problem is separate. There is no replacement for exploring data, testing models, and verifying against business sense and field experts.
Academia and Business are Two Different Worlds
This applies to nearly all disciplines and analytics is no exception. Focus in academics is on learning new techniques and proving new theorems. Focus in business is on resolving a difficulty and making money. Doesn’t matter if analytics behind the solution is fancy or not, and no one cares about that anyway. Speed is usually of more essence than accuracy. Every business analytic solution should resolve a real-life problem and directly or indirectly should contribute to the bottom line.
Presentation is the Key
Since decision-maker and end-user is often the non-mathematical person, selling an analytic solution isn’t different from other sells. Being able to describe your approach in simple terms and align with end-users’ interest is art that all data scientists who want to make the significant non-theoretical mark on the world must master.
So, these are the crucial things you need to know about Data Analytics and Data Science. What are you waiting for? Get started with Data Science and switch to a high-paying career.