People tend to use both the terms interchangeably but both the terms are quite different. While both the terms help in the process of decision-making by uncovering the patterns behind the data and analyzing it. To put it in a limpid manner, one method looks towards the past and the other towards the future.
Examining the Past and Predicting the Future
Both terms are confounding and here is the example which can elucidate the difference between the two terms.
First of all, let us consider the colloquial definition of both the terms which posits that “Analysis” is the comprehensive examination of elements or structure of given data. “Analytics ” on the other hand is defined as the organized computational analysis of the data.
Let’s review and example
Now, take into consideration the retailer of the clothing shop who analyzes the profit earned from the previous year by the sales of different apparel. Here he is analyzing various data from the previous year to gain insights and understand various patterns.
Let's dive into the same example again to understand the concept of data analytics. The retailer would combine the results obtained from the analyses of last year’s sales data with “systematic computational” reasoning. By taking into consideration the pragmatic approach for this they will employ advanced machine learning algorithms and tools to anticipate the pattern of future sales.
WHAT, WHERE, AND WHY?
Let’s see what is Qualitative data and Quantitative Data
- The quantitative analysis quantifies data to test hypotheses or predict the future whereas qualitative analysis seeks to get a deeper understanding of why certain things occur
- Qualitative analysis is exploratory and subjective
- Quantitative analysis is conclusive and objective
- For example, The researcher conducts interviews or surveys to collect qualitative data whereas in quantitative analysis the research conducts experiments, observations, and measurements.
Tools for Data Analytics
- R programming:-It is the leading analytics tool in the industry and widely used for statistics and data modeling.
- Python:-Python is an object-oriented scripting language that is easy to read, write, maintain, and is a free open source too. Also, Python has very beneficial machine learning libraries viz. Scikitlearn, Theano, Tensorflow, and Keras. Another important feature of Python is that it can be assembled on any platform like SQL server, a MongoDB database or JSON.
- Apache Spark:-Spark is built on data science and its concept makes data science effortless. Spark is also popular for data pipelines and machine learning models development.
- KNIME:- It is leading open-source, reporting, and integrated analytics tools that allow you to analyze and model the data through visual programming.
- Tableau Public:-It is free software that connects any data source be it corporate Data Warehouse, Microsoft Excel or web-based data, and creates data visualizations, maps, dashboards, etc. with real-time updates presenting on the web.
- Orange:-It provides interactive data visualization and clever reporting includes the workflow history of every widget and visualization. Also, Intelligent visualization with a great scatter plot is possible.