You always need statistical analysis in the pipeline of Data Science. In this article, I am going to share some ideas about statistical analysis and how Data Scientists use statistical analysis in each step of building a Machine Learning model.
Why Use Statistical Analysis?
The insights that Data Scientists provide after doing statistical analysis has immense value. It can prove to be the profits of a company for the next quarter, it can be the analysis of data for a big machine learning project.
Using statistics and statistical data, you can explore, extract and visualize data in a more sophisticated manner and for a
In the data science world, this procedure is termed as Exploratory Data Analysis or EDA for short. Now let us see some of the ways in which EDA and statistics are used by data scientists.
Analyzing Data
The truth is that analysis of data comes in a much later stage. Before that, the process of EDA goes through choosing the data source, extracting the data and cleaning the data. But we are going to focus on the usage after the well defined data has been retrieved.
The process of analyzing the data contains a bit of statistical mathematics. Analyzing and exploring the data by finding the mean, mode and median are very common procedures at this stage. Data scientists and data analysts spend quite a bit of time in this. Often it is said that cleaning and analyzing the takes up to 80% of the whole Machine Learning Project time.
Also, this helps in exploring different types of data. ‘But what type of data?’ You may ask. At a higher level, those can be defined as Categorical data and Numerical data.
Categorical data are those which give the final output as a category or label. For example, when analyzing patient data, we may categorize them either suffering from a certain disease or not suffering from the disease. Whereas, Numerical data provide certain numbers to be analyzed and make predictions based on the analysis.
Visualizing The Data
After the analysis of data is done, then data scientists will move on to visualize the data. The visualization is in the form of plots and charts. These can include bar charts, histograms, scatter plots, pie charts and many more . You can read more about data visualization Here.
But how does visualization help? The answer can be pretty simple here. Analyzing only written data can be useful but when we combine that with a picture, then the whole story becomes clear. There are many times when analysts have the ‘AHA’ moment when doing data visualization which may not have happened if they would have solely focused on the numerical or categorical analysis.
The tools available for data visualization are just numerous. Industry experts use these tools extensively to make better visualizations and thus make better predictions. Some of the well-known tools are Matplotlib, Seaborn, ggplot, Plotly. There are many more as well. If you want to have an in-depth read, you can visit this website here.
Data visualization can be a very crucial step before moving further while doing a project as this provides insights which can be otherwise easily missed.
Building Models And Making Predictions
After data scientists are through with the analysis and visualization of the data, the next step involves building the machine learning models. The model should cater to the need of the project and the necessary optimizations are done throughout the process.
The model which is built in the process should have a minimum threshold performance. This is very important to make future predictions. Data
The story does not end here, obviously. Many future tweaks are made to the model all the while trying to increase the accuracy percentage of the predictions. From here on, new data can be collected to feed into the model to make it even more robust.
If you liked the post and want a bit of different perspective, then you should surely read this article, 7 Ways Data Scientists Use Statistics.
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