The process of collecting raw data and then preparing to explore and identify patterns to understand them so that they can be used for further decision-making and machine learning model training is called data analysis. In this article, I will take you through how to analyze data.
What is Data Analysis?
Before building a machine learning model, a Data Scientist must understand what data they are using to train a model. Data analysis means exploring the data you are working on. It usually starts with cleaning the data by identifying outliers and missing values.
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After cleaning up the data, we need to properly explore the features available in the dataset and identify the relationship between the features so that we can use the features that are strongly related to the issue we are working on.
One thing a lot of newbies are confused about is which tool you should use for data analysis. You can probably choose any tool you like, like Excel, Tableau, or Python. Python is considered to be one of the best approaches because we can easily work with big data using Python. While other approaches like Excel and Tableau may not be as efficient on Big Data as Python.
So How To Analyze Data?
Hope so far you must have understood what data analysis is. In this section, I will take you through how to analyze data step by step. Here are the main steps to follow when analyzing any type of data:
- Understand your data
- Understand Features of Data
- Identify Patterns
Now let’s understand all three major steps of Data Analysis mentioned above to understand how to analyze data.
Understand Your Data:
So the first step to analyze your data is to properly understand what type of data you are working with. Then you have to understand whether your data has labels or not. Labels are the values of those columns whose values your machine learning model will predict. For example, stock prices, if you will not have the values of historical prices then how you will predict future prices.
So if your dataset does not have any labels then your first step is to find a better dataset.
Understand the Features:
To analyze the data, the next step is to understand the features of the data. It is very important to understand the features of the dataset. Now features can be numeric or categorical. To fully understand the features of the dataset, you need to answer a few questions such as:
- Is the dataset skewed towards a range of values or any specific category?
- What are the minimum, maximum, mean, median, and mode values of the features?
- Are there any missing or null values?
- Are there any outliers in the dataset?
So these were some of the most important questions that you should solve while understanding the features of data.
Identify Patterns:
To analyze the data, the next step is to identify patterns. In this step, we explore the data by visualizing it to identify patterns in the data. Some of the questions that you need to answer in this step are:
- How to deal with missing values? Should you fill in these values? if yes, then what approach you should choose to fill in the missing values?
- What will you do with the outliers?
- Are the features correlated? If yes, then how many are negatively and positively correlated?
- What you should do with the categorical values?
Summary
After you are done with all the steps mentioned above you will be able to understand and identify patterns from the data. This is what Data Analysis is. After analyzing the data we use the most important features for model training.
So this is how you should divide the task of data analysis into steps. You can use these steps for any problem you are working on. I hope you liked this article on how to analyze data. Feel free to ask your valuable questions in the comments section below.
