Reacquaint yourself with simple yet important data analysis tools to transition from abstract numbers to quantifiable success. In today’s digital era, we are inundated with information—it’s overwhelming. Leaders at the top helm make decisive decisions daily. Sorting through avalanche of data to assist management’s decision making is the key. Objective is to sort and  interpret  and report findings. Toward this end, tools or techniques for statistical data analyses achieve this objective. Buzzwords we hear today are big data, hadoop, and AI; however, rudimentary tools need to be deployed to their maximum capability, which is not the case in most firms. Propel your  data analysis   program by mastering the following 5 basic techniques. Review these mundane statistical techniques, yet so vital. Techniques Merits Demerits 1. Hypothesis Testing Hypothesis tests are used in multiple disciplines such as science, research, economics, etc. Popularly kn...

Data Analysis Plan for Quantitative and Qualitative Research: Data Analytics is the quest of excavating an understanding or inference from unprocessed data via dedicated computer applications. These applications alter, shape, and model the data to infer deductions and ascertain patterns. Although Data Analytics can be uncomplicated, nowadays the term is most frequently used to define the study of huge capacities of data ( quantitative and qualitative data ) and/or high-speed data, which offers unique mathematical and data-juggling obstacles. Proficient data analytics pros who essentially possess a strong know-how in business statistics, are known as data scientists. Data Analytics is a generic word for any type of data manipulation that considers historical data over a time frame; however, as the volume of organizational data increases, the concept of data analytics is developing to support big data-capable systems. The dawn of big data dramatically altered the need f...

With more and more companies using big data, demand for professional data analysts has witnessed exponential growth in the recent years. “Learners of data analysis and interpretation need to have an in-depth understanding of the subject along with the statistical acumen and working knowledge of tool sets to make significant progress in the field of data analytics” – Dr. Venkatesh, Statistician. To gain good expertise in Data analysis and interpretation, the beginner learner requires good foundation in basic statistics, and better skills of how to input, process and analyze data by employing a range of data analytical and visualization tools. The objectives of data analysis and interpretation are to manipulate the data, both qualitative as well as quantitative, to obtain utilizable information that is critical to making informed decisions. It involves the usage of many different methods and procedures that can, Describe and summarize data Evaluate and enhance qua...