ONE OF THE HOTTEST JOBS OF 2021
In today’s world, there is no shortage of data. More than 1 quintillion bytes of data are generated each day. Many Business firms, Social media sites, IoT devices, etc. The common question asked by many enterprises is Can we do something that really adds value to the company and the customer by using the data? The solution that came into the picture is Data Analytics.
What is Data Analytics?
⮚ Data Analytics is the process that involves sorting through a large amount of unstructured data and deriving key insights from the data.
⮚ These insights are very much valuable for the company to make appropriate decisions.
The process involves in Data Analytics:
- Define Problem Statement
Before starting the analysis, you should know what is the problem statement, define it and understand it in a clear manner. Then only you should start planning for the solution.
2) Data Collection:
The second step is to collect data for the solution. It is an important step because your whole analysis depends upon this step. There are two types of sources to collect data.
1) Primary or Internal source:
Data Collection starts with this step. Primary sources consist of data in a structured format where these data are gathered from CRM software, Databases, Marketing automation tools, etc.
2) Secondary or External source:
The second type of data source is the external source or secondary source. The external source consists of data in both structured and unstructured format where these data gathered from social media APIs, Kaggle websites, Google public data, Amazon API, etc.
The one important thing is that you should collect data relevant to the problem statement.
3) Data Preparation:
The data we collected is called raw data. It is in an unstructured format i.e there are no column names, having lots of missing values, duplication of rows, etc. so we need to clean the data and prepare according to model fitness. Many data analysts said that 40% to 50% of time spent in data preparation alone.
Steps involved in Data Preparation:
● Remove duplicate values
● Fix structural errors
● Filter unwanted outliers
● Handling missing data
● Does the data follow the appropriate rules for its field?
4) Analysing the data:
Once the data preparation is done, the next step is analyzing and manipulating the data. For that you can use Data Mining techniques like clustering, classification, prediction, association, etc. to find the hidden patterns within the data and how the data are associated with each other. To implement this we can use python programming language packages like Pandas, Matplotlib, Scikitlearn, etc.
You can use Business Intelligence tools like Power BI and Tableau to analyze and find key insights from the data. It gives you a wonderful visualization to understand data better and also you can create Dashboard and present it to your stakeholders for effective decision making.
Predictive analysis can also be useful to predict the company’s future sales, profit, ROI, etc. You can do this with the help of predictive modeling like regression methods.
Nowadays predictive analysis can be done by many companies to get futuristic results and effective risk management.
5) Interpreting the results:
The final step is interpreting the results from the data analysis. This part is important because it’s how a business will gain actual value from the previous four steps. Analysts and business users should look to collaborate during this process.
I hope that you found something useful in this article. Keep learning and keep practicing.
Learn well and Grow well!!!