One of the important aspects of the data science approach is the process of extracting and interpreting data. When data is extracted we develop perceptions or best to say cultivating possibilities out of that extracted data. In data science, these possibilities are interpreted with the help of statistics and the term is known as statistical analysis.
With digital transformation and the growing importance of decisions and functions based on data, statistics holds a critical position in the process of analyzing, controlling, and presenting data. Statistics is the only key to control, manage, and learn from data and also determine problems that often lead to incorrect solutions.
Data has become an important part of everybody's life. Without data we are nothing. Data mining for digging insights has marked the demand to be able to use data for business strategies. Therefore, the field of data science is growing with increasing demand. Data science is not limited to only consumer goods or tech or healthcare. There is a high demand to optimize business processes using data science from banking and transport to manufacturing.
We often think about how data which are in the form of images, text, videos, or any unstructured form gets processed and interpreted by machine learning models. What is the process? How does it work? How is it so easy to process data?
The process includes the conversion of data into a numerical form which is not exactly data but its numerical equivalent. So, this brings us to the importance of statistics in data science.
When the data is converted into a numerical form, it provides us with interminable possibilities to interpret the information out of it. Statistics is the key to extract and process data and bring successful results. Detecting structure in data, large or small and making predictions are critical stages in data science that can either make or break research. Statistics provides measures and methods to evaluate insights out of data by getting the right mathematical approach for data.
How is Statistics Important for Data Science?
Data is classified into accurate, observable, and mining fields with the help of statistical methods. For all companies' classification and organization are important to make predictions and create business plans. Some data are operational and some are unusable, statistics help in classifying and filtering out unusable data for further process.
These statistical methods are pathways to grasp the basics of machine learning and algorithms like logistic regressions. Cross-validation and LOOCV methods of statistics have been brought into the field of Machine Learning and Data Analytics for reasoning-based research, and hypothesis testing.
Almost every company deals with huge stacks of data received from various sources. Statistics in this process helps in detecting oddities and structures in the data. This enables researchers to reject inapplicable data at the early stage and thus reduces wasting of time, effort, and resources.
Visualization in data is the depiction and elucidation of structures, models, and perceptions found in interactive, comprehensible, and effective formats. These formats must be easy to process. For data visualization and representation statistical formats like graphs, pie charts, and histograms are used. It makes data understandable and also helps in intensifying them when required.
Detailing on values and networks without statistical methods of distribution can lead to evaluations that are not accurate and reliable. Statistics helps in identifying distinguishing structures and clusters in data that are dependent on variable factors like space, time, etc.
Data is a series of complex interactions between factors and variables. To represent these or display them logically and accurately, statistical methods using graphs and networks are the only key.
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