What exploratory data analysis is!
If you like the world of statistics and mathematical sciences, you will have heard of exploratory data analysis. If this is not the case, I’ll tell you everything you need to know about this method that is used to analyze and summarize data sets.
Keep reading!
exploratory data analysis
The exploratory data analysis aims to identify the most appropriate model to represent the population from which the sample data comes. Let me tell you what exploratory data analysis consists of and how you can train in statistics in the most comfortable and quick way possible. Do you dare to discover these and other aspects of interest?
Let’s go there!
What is an exploratory data analysis?
Exploratory data analysis (EDA) involves the use of graphs and visualizations to explore and analyze a data set. It helps determine the best way to manipulate data sources to get the answers you need, enabling data scientists to discover patterns, spot anomalies, test a hypothesis, or test assumptions. The goal of exploratory data analysis is to explore, investigate, and learn, not to confirm statistics.
EDA or Exploratory Data Analysis was originally developed by the American mathematician John Tukey in the 1970s, although it is still a widely used method in the data discovery process today.
This analysis is primarily used to see what data can be revealed beyond the formal modeling task or hypothesis testing, and provides insight into dataset variables and the relationships between them. It also allows you to determine whether the statistical techniques you are considering for data analysis are appropriate.
What techniques is used in exploratory data analysis?
Techniques that can be used with EDA tools include:
Clustering and dimension reduction techniques, which allow you to create graphical displays of large data, which in turn contain many variables.
Univariate, display of each field in the raw dataset.
Bivariate visualizations and summary statistics, which allow you to assess the relationship between each variable in your dataset and the target variable you’re looking for.
Multivariate visualizations, to correlate and understand the interactions between the different fields in the data.
K-means Clustering is an unsupervised learning clustering method, where data points are assigned to K groups, that is, the number of clusters, spread in distance from the centroid of each group. This technique is often used in market segmentation, pattern recognition, and image compression.
Predictive models, for example linear regression, use statistics and data to predict results..
Why is it important to study Data Analytics or Data Science?
Data science whose main objective is to study Data from a statistical point of view. What is Data Science used for?
In the labor field
The strategic plan of an organization is fundamentally based on forecasting and budgeting studies. The control mechanisms, in charge of the compliance sectors, are applied based on the historical outcomes got from statistical studies.
In personal bonds
A person’s financial planning is the best example of the application of Data Science in daily life.
In sports
Athletes’ records are made based on their performance for the number of games or matches in which they have participated. The statistical data accumulated establish objective elements that lead to the best use of resources and training. This is how athletes attain their supreme performance.
On sales
The world of sales is planned based on detailed analyzes of consumer needs, tastes and preferences. The measurement of the quality of the service, the level of customer satisfaction and the sales strategies themselves are determined by the application of Data Science techniques.
Route optimization
Statistical Data is essential to calculate transport routes and optimize them. They can be from the delivery routes of logistics businesses to the air traffic of commercial aircraft.
Why study Data Science or Data Analytics?
The main reason for taking these studies is its high rate of employability, in addition to being one of the best paid salaries. In addition to:
Interdisciplinary flexibility
We can say that it is a meeting point of all field with Data.
Analytical capacity
Data Science allows an effective development of the skills of analysis and interpretation of numbers and data.
Add value
It is the professional who sheds light on the hypotheses raised by the professionals of the strategy. They are capable of proposing predictive models, providing solutions and predicting the benefits and risks of actions.
Strategic vision
Their mastery of Data and their global vision of scientific and social processes gives them a strategic capacity and intuition above the rest of the professionals.
Employment of the future
The integration of technology in economic activities and the sophistication of data processing systems make it possible for statistical activity to extend to more companies and businesses.
And these are just some of the reasons why training in Data Science and Data Analytics is a good option.
