Things you can’t miss about Data Analysis
When we tend to talk and discuss the information about Data or Analysis with the Customers or our Community there is one thing which remains Constant. We always try to come up with a list of resources that everyone uses. There have been few Awe-inspiring Blogs withholding huge information about Data Science.
Also, we have incorporated Few amazing blogs and Sources that would nurture and add up into the Customers Knowledge of Data Analysis.
Let’s Begin.
Data analysis
Data analysis is a savvy approach to data exploration and laying out conclusions based on the evaluations. It helps the Business Organizations to inspect, clean, transform and Model the Data from the Useful information. It unfolds multiple micro views which helps the Organizations to dig deeper into the insights.
With Data Analysis you get to decide what is working in favor, what did not work and more importantly what is expected out of a product and service.
Data Analysis has a Major Contribution to the Businesses in Decision Making.
Two Major Components of Data Analysis:
Quantitative data analysis:
Quantitative data analysis, it is the Process where you’re expected to turn raw numbers or Data into meaningful data. Here, you’ve to use your Rational and Critical Thinking. Quantitative Data analysis is an approach the Factors and Variables that will support or withdraw from the Data Model or the Hypothesis. It includes the calculation of frequencies of variables and also finds out the differences between variables. Careful Judgement is an essential step while working on a Data Model as there can be lot many interpretations of your Data set. Quantitative research deals in numbers, logic, and an objective stance.
What is the Purpose of Quantitative Data Analysis?
- To classify features of the Data
- To keep count of the Data
- To construct statistical models in an attempt to explain what is observed
- To Identify the research problem
Qualitative data analysis:
Qualitative data analysis is an integral part of qualitative research. You need a proper collection of Data whenever you begin with the Research Process. When you proceed to Organize and Analyze the Information collected during the Process it is called Data Analysis.
Qualitative Data comes in Unstructured Nature, analyzing this qualitative Data can be very Confusing. However, if we use the Correct Methodology the Data Analysis can be Pulled in the Right Manner.
Qualitative Data Analysis: is the Process of Analysis where you examine the Qualitative Data for Deriving a Specific explanation. Using Qualitative Analysis, you’ll get an understanding of how to carry out your Research Process as it helps the Users Reveal Patterns and Themes in the Data
What is the Purpose of Qualitative Data Analysis?
- To organize the Data
- To interpret the Data
- To Identify the Patterns in the Data
- To Tie the field data to research objective(s)
- To Form the basis for informed and verifiable conclusions
Data Analysis is a Giant Process encompassing diverse Features and Comings. It is used Differently in Different Business Scenarios.
To have a Better understanding about Data Analysis you must understand 2 Fundamentals really well.
Data Analysis Process
The Data Analysis Process goes through these important Stages:
Let’s look at these Stages in detail:
- Deciding and Identifying the Goal:
The first step of the Data Analysis Process is to decide upon the Goals and the Objectives of your Business. You’ll require a significant amount of Data Collection and Analysis as Data is something all the Organizations look up to for taking any decision in the Favor of the Business. You must be clear on one part i.e. if the Business and the Business Process is making Advancements or not.
- Data Requirements Specification
You need to know the Data Requirements and Specifications. What the input Data is Supposed to be and how the Analysis is supposed to be identified. A Specific Variable or an Output is expected at the end. You can obtain specific results only if you’ve Put in the right Data Requirements and Specifications.
- Transform data (Aggregate/Join Data)
Transforming the Data is an essential piece of analysis. One important step of Data Analysis when you’ve Large Data set is efficient summarization of the Data.
- Data Collection
Data Collection is one of the integral steps of Data Analysis. In this Process, you collect the Data from various Sources or you can say the data is pulled and extracted from various Sources. These sources could be Organizational Databases or informative Databases. Once the Data is accumulated, it is sent for Data Cleaning and Processing.
- Data Processing
Once the data is Pulled, you need to properly organize it for the Analysis. To organize the Data, you’ve to structure the Data with the help of the Data Analysis Tools. Some cases might demand a Data Model to be Created. In such cases, you’ll need to take the Help of Data Analysis Tools and do the Data Processing.
- Data Cleaning
Now, consider a Case where you’ve a Data Model. This Data Model contains multiple Rows and Columns. Now, there are chances that this Data Model contains Duplicates, errors or maybe some incomplete information. Before you proceed for the Data Analysis, you need to do the Data Cleaning. Now, you even need to Consider the Data Type before you do Data Cleaning.
- Data Analysis
Once the Data Processing is done, you need to Properly organize the Data and clean it. Once the Data has gone through this Process, it is ready for the Analysis. Now, using the various Data Analysis Methods you can relate, interpret and analyze the Data.
There are Data Analysis tools Available that will help you gain a few Additional insights of the Data.
- Communication/ Recommendations / Story Boarding
The Data models are descriptive in nature and they help in simplifying the analysis and communicate results to the end users. This Result is to be passed in a specific Format so that the Users are able to evaluate their Business Goals and proceed with the Decision Making. Feedback of the Users is really important as it helps the Organizations in making Additional Analysis. You can choose the analysis tools which provides facility to highlight the extracted information after the Analysis and give a good Presentation to the Data using color codes and formatting and Data Visualization Techniques in tables and charts.
To learn about the next important Fundamental i.e. the Data Analysis Tools. Continue Reading Further *Click Here*
