Difference between Data Analysis and Data Analytics

We get mostly confused over these two terms and believe people mostly use anyone of these very confidently without understanding major difference.

Both terms means a crunching data . . .doing research over data . .  .making sense of data . . . . .putting your head down in data and finally come out with a result/ decision/ visualization based on hard work done.

But major difference is Analysis is for past data and Analytics is for predictions. For example:

a) Creating a Line Chart out of data analysis and with slope of line, understanding how was the pace of business.

a’) Creating a Line Chart our of data analytics and with slope of line, predicting upcoming pace of business.

b) Imagine websites showing data analysis on raising and dropping of share markets stocks.

b’) Similarly imagine experts showing data analytics on raising and dropping of share markets stocks future for viewers to decide better.

Analytics is not something of a rocket science. It is simple if we use simple techniques like statistics, math, qualitative analysis.

 

So People in the field of Data Analysis and Data Analytics are different, might use different tools, may earn different money.

So be along with this Data Science journey. More clarification and learning topics are on the way.

 

Introduction to Data Science and What to Learn

Data science is now a days most talked about buzzword. As said by Harvard Business Review “Data Scientist is the Sexiest Job of 21st Century”, its becoming popular and useful everyday. Even when no one is working in office Data Science add to companies efficiency by adding new customer or get more work done.

We have learned/ remember a lot of Math and statistics in our childhood and always hated it. Reason being “We are never gonna use it”. But you must be happy to know that Data Scientists use it on daily basis. Even some of the most advanced mathematical algorithms and advanced statistics.

As asked by many people, I have summarized list of topics we must study to be a Data Scientist.  These topics are fundamental of learning data science and are not tools.

1. Understand Data Science
2. Math (eg. Probability, Permutation, Combination etc.)
3. Statistics (e.g. Histogram, Skewness etc.)
4. Python / R  – programming language for implementation
5. Advanced Statistics (e.g. Regression, Cluster etc.)
6. Machine Learning/ Deep Learning
7. Position of Data Scientist

Data science is needed to check your data and predict the future based on it. It helps companies to build machine learning / deep learning models to automate pattern recognition and provide better direction for company.

For example giving out same discounts to all customers will be utterly useless in most of the cases but discount coupon based on a customer’s buying habit, purchase power (or any other variable) will be better for companies business.

So keep on the pace and join us (by FOLLOW button) to be in this journey to learn Data Science.