In the real world data analysis become a very important topic. We use our real data for inspecting, cleansing, transforming, and modeling data to discover meaningful information, informing conclusions, and supporting decision-making and predictions after analysis.

A simple example of data analysis can be seen whenever we decide on our daily lives by evaluating what has happened in the past or what will happen if we make that decision. This is the process of analyzing the past or future and making a decision based on that analysis.

Why it is becoming more important day by day?

We can see the factors why data analysis become more important in business or other organizations-

• For better Customer Targeting

• Through data analysis, your business can get a better idea of your target audience’s spending habits, disposable income, and most likely areas of interest. By this, You Will Know Your Target Customers Better.

• It Reduces Operational Costs

• Data analysis helps businesses make the right choices and avoid costly pitfalls.

• It helps businesses acquire relevant, accurate information, suitable for developing future marketing strategies, and business plans and realigning the company’s vision or mission.

The process of data analysis or we can say it the methodology of the data analysis of the project-

• Data collection

• Data cleaning

• Data analysis

• Data visualization.

Data collection is the process of gathering and measuring information from countless different sources. Many types of data are collected to develop a machine-learning project. They can be in the form of text, tables, images, videos, etc. Some of the main types of data collected to feed a predictive model are categorical data, numerical data, time-series data, and text data. Raw data that is collected in the data-gathering stage is neither in the proper format nor in the cleanest form. It needs to undergo pre-processing steps such as:

• Splitting data into training, validation, and testing sets

• Handling missing values

• Dealing with outliers present in the data

• Taking care of categorical data/features

• Scaling and normalizing the dataset

Data cleaning:

Collected data is messy. So, we need to prepare it for further steps. Data preparation is a step where we put our data into a suitable place and prepare it to use in our machine learning training.

In this step, first, we put all data together, and then randomize the ordering of data. This step can be further divided into two processes:

Data exploration: It is used to understand the nature of data that we have to work with. We need to understand the characteristics, format, and quality of data.
A better understanding of data leads to an effective outcome. In this, we find Correlations, general trends, and outliers.

Data pre-processing: Now the next step is pre-processing of data for its analysis.

After data pre-processing data wrangling is done. It is the process of cleaning the data, selecting the variable to use, and transforming the data into a proper format to make it more suitable for analysis in the next step.

Data analysis:

Now the cleaned and prepared data is passed on to the analysis step. This step involves:

• Selection of analytical techniques

• Building models

• Review the result

The aim of this step is to build a machine learning model to analyze the data using various analytical techniques and review the outcome. It starts with the determination of the type of the problems, where we select the machine learning techniques such as Classification, Regression, Cluster analysis, Association, etc. then build the model using prepared data, and evaluate the model.

Now the next step is to train the model, in this step we train our model to improve its performance for better outcome of the problem.

We use datasets to train the model using various machine learning algorithms. Training a model is required so that it can understand the various patterns, rules, and, features.

Visualize the result:

After finishing data analysis process we have our insights of the probel. The final step of the data analytics process is to share these insights with the wider .It’s very important that the insights are 100% clear and unambiguous.

The data analysts can choose data visualization techniques, such as tables and charts, which help in communicating the message clearly and efficiently to the users. The analysis tools provide facility to highlight the required information with color codes and formatting in tables and charts.

Some data visualization tools are Microsoft exel , python -Jupyter notebook(data science),tableau,Microsoft power BI etc.