Businesses today need every edge and advantage they can get. Thanks to obstacles like rapidly changing markets, economic uncertainty, shifting political landscapes, finicky consumer attitudes, and even global pandemics, businesses today are working with slimmer margins for error.
Companies that want to not only stay in business but also thrive can improve their odds of success by making smart choices. And how does an individual or organization make these choices? They do it by collecting as much useful, actionable information as possible, then using it to make better-informed decisions!
This strategy is common sense, and it applies to personal life as well as business. No one makes important decisions without first finding out what’s at stake, the pros and cons, and the possible outcomes. Similarly, no company that wants to succeed should make decisions based on ignorance. Organizations need information; they need data.
This need for data is why the discipline of data analysis enters into the picture. This article is your primer on data analysis, what the phrase means, the available types and processes, popular data analysis methods, and how to do data analysis.
Now, before get into the details about the data analysis methods, let us first understand what is data analysis.
Although many groups, organizations, and experts have different ways to approach data analysis, most of them can be distilled into a one-size-fits-all definition. Data analysis is the process of cleaning, changing, and processing raw data, and extracting actionable, relevant information that helps businesses make informed decisions. The procedure helps reduce the risks inherent in decision making by providing useful insights and statistics, often presented in charts, images, tables, and graphs.
It’s not uncommon to hear the term “big data” brought up in discussions about data analysis. Data analysis plays a crucial role in processing big data into useful information. Neophyte data analysts who want to dig deeper by revisiting big data fundamentals should go back to the basic question, “What is data?”
The data analysis process, or alternately, data analysis steps, involves gathering all the information, processing it, exploring the data, and using it to find patterns and other insights. The process consists of:
There are a half-dozen popular types of data analysis available today, commonly employed in the worlds of technology and business. They are:
o Descriptive. Descriptive analysis works with either complete or selections of summarized numerical data. It illustrates means and deviations in continuous data and percentages and frequencies in categorical data.
o Inferential. Inferential analysis works with samples derived from complete data. An analyst can arrive at different conclusions from the same comprehensive data set just by choosing different samplings.
Next, we will get into the depths to understand about the data analysis methods.
Some professionals use the terms “data analysis methods” and “data analysis techniques” interchangeably. To further complicate matters, sometimes people throw in the previously discussed “data analysis types” into the fray as well! Our hope here is to establish a distinction between what kinds of data analysis exist, and the various ways it’s used.
Although there are many data analysis methods available, they all fall into one of two primary types: qualitative analysis and quantitative analysis.
o Content Analysis, for analyzing behavioral and verbal data.
o Narrative Analysis, for working with data culled from interviews, diaries, surveys.
o Grounded Theory, for developing causal explanations of a given event by studying and extrapolating from one or more past cases.
o Hypothesis Testing, for assessing the truth of a given hypothesis or theory for a data set or demographic.
o Mean, or average determines a subject’s overall trend by dividing the sum of a list of numbers by the number of items on the list.
o Sample Size Determination uses a small sample taken from a larger group of people and analyzed. The results gained are considered representative of the entire body.
We can further expand our discussion of data analysis by showing various techniques, broken down by different concepts and tools.
AI is on the rise and has proven a valuable tool in the world of data analysis. Related analysis techniques include:
This is the technique where you find number-crunching data analytics. The techniques include:
We are visually oriented creatures. Images and displays attract our attention and stay in our memory longer. The techniques include:
o Area Chart
o Bubble Chart
o Column Charts and Bar Charts
o Funnel Chart
o Gantt Chart
o Line Chart
o Pie Chart
o Radar Chart
o Word Cloud Chart
o Gauge
o Flow Map
o Heat Map
o Point Map
o Regional Map
If you want to pursue a career in data analytics, you should start by first researching what it takes to become a data analyst. You should follow this up by taking selected data analytics courses, such as the Data Analyst masters certification training course offered by Simplilearn.
This seven-course Data Analyst Master’s program is run in collaboration with IBM and will make you an expert in data analysis. You will learn about data analysis tools and techniques, working with SQL databases, the R and Python languages, creating data visualizations, and how to apply statistics and predictive analytics in a commercial environment.
You can even check out the Post Graduate Program in Data Analytics in partnership with Purdue University and in collaboration with IBM. This program provides a hands-on approach with case studies and industry-aligned projects to bring the relevant concepts live. You will get broad exposure to key technologies and skills currently used in data analytics.
Several years ago, IBM predicted that the demand for data analysts would soar 28% by the end of 2020, resulting in more than 360,000 new positions this year. As per the reports of Salary.com, data analysts earn an annual average of USD 75,724, hitting over USD 85,000 in the high end of the range.
So, if you want a career that pays handsomely and will always be in demand, then check out Simplilearn and get started on your new, brighter future!
Name | Date | Place | |
---|---|---|---|
Data Analyst | Class starts on 12th Mar 2021, Weekdays batch | Your City | View Details |
Data Analyst | Class starts on 13th Mar 2021, Weekend batch | Chicago | View Details |
Data Analyst | Class starts on 14th Mar 2021, Weekdays batch | Houston | View Details |
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