Have you ever wondered how data scientists solve huge data problems and give accurate results in the least time? It's because they have significant experience with data analysis and statistical analysis. One helps collect and analyze data, while the other benefits to find inferences based on undiscovered patterns that could be a game-changer for business and organization.
For a better understanding, let's take a practical example of the use of Google Search Engine. Suppose you want to know the best restaurant near your place, it shows you all the restaurants within a radius of five km. When you run this search, you're analyzing the data, But that is enough to decide you want to bump into the restaurant. You would read the reviews, look for the items they are famous for, and if the same interests you and your family, you add that in your list to maybe go.
But when your friends or family members ask you to try a particular item, you see the best review for that item they have on their website and some offers running on it. You would go for it or order it, seeing the limited-time offer online. Even restaurants practice the same by collecting the data from users or seeing the number of orders and reviews they get is the best application of statistical analysis.
What is Data Analysis?
The process that involves cleaning, transforming, and data modelling to discover hidden patterns for business decision-making is called data analysis. The sole purpose of data analysis is to extract useful information from data by taking calculated risks based upon data analysis decisions.
Since data is the game of numbers, figures, and statistics, not everyone understands it. Data analysts play a pivotal role in decision making and finding loopholes where lies the most significant opportunity. Data analysts help people and higher authorities explain new possibilities and risks in decision-making.
Importance of Data Analysis
You can see tons of data everywhere, from google sheets, where sales are in the pipeline, to social media platforms. Businesses interact directly with their customers, survey forms, user tickets about ongoing issues. It is crucial for any business, and proper analysis leads to their respective success.
Every business has specific goals and objectives to market its products and services while maintaining its brand awareness and enhancing customer experience. Data analysis shows how to do them effectively with calculated risks and where you need to focus on finding new opportunities and broadening your business.
What is Statistical Analysis?
Many businesses rely on statistical analysis to organize their data and predict future and hidden trends based on current and previous information. Statistical analysis uses numbers to clear unwanted data that adds no value to your business and makes your decision-making easy.
This method examines every single piece of data in the population rather than an overview of sample data. Statistic analysis is the science and art of collecting, exploring, analyzing, and presenting a large volume of data to reveal hidden patterns.
Importance of Statistical Analysis
Analyzing data means explaining its nature, exploring the relationship of the data with hidden patterns, and developing a model to determine these patterns. It checks for the model's validity for the underlying populations and uses predictive analytics to discover future trends.
Statistical analysis can reveal where the maximum sales can happen, where they have the most value, and what marketing tactics are attached. The statistical analysis gives you every information accurately out there.
Key Difference Between Data Analysis and Statistical Analysis
Data analysis and statistical analysis are two branches of data science, where analysis focuses on exploring the data and statistics infers what is beyond the data analysis — the hidden patterns and future opportunities.
Data Analysis helps you form new hypotheses based on the given data while it improves your questions over time. Statistical analysis will enable you to come to a better conclusion by analyzing the dataset. Statistical analysis tells us whether it is feasible to go with the phenomenon based on the current database's analysis.
As you know, Python has many predefined libraries, and the best part is it is the best fit for data analysis and data science purposes. Whereas in statistical analysis, it's not like Python is not handy; we can still solve many Python problems. Still, the R programming language is ideal for statistical computing and graphics, along with extensive data analysis.
Data analysis is the process of inspecting, presenting, and reporting the data to non-technical people. A statistical analysis process helps you gain an in-depth understanding of a large population of data by analyzing the sample's information.
It's because data is useless when it doesn't solve the purpose when higher authority cannot make a decision or customers feel perplexed to decide which one to buy, and this happens when there is not enough proof. And this is the place where data analysts play the transition role to connect the gap between numbers and figures for the people who need to understand and make crucial decisions.
The statistical analysis gives you solid proof of whether your assumption is correct or not, based on the future prediction. It allows hindrance based on target markets, customers' cohort analysis to predict the behaviour and characteristics of many based on few datasets.
Key takeaways from Data Analysis vs. Statistical Analysis
In this article, you learned about data analysis, statistical analysis, definition, and their importance in data-driven decision-making. You also understand data analysis and statistical analysis with a practical example based on a google search based on restaurants and four critical differences between data analysis and statistical analysis.
The author is a senior data scientist and director at Excelr. Views are personal.