Before venturing into any advanced analysis of data using statistical, machine learning, and algorithmic techniques, it is essential to perform basic data exploration to study the basic characteristics of a dataset. Data exploration helps with understanding data better, to prepare the data in a way that makes advanced analysis possible, and sometimes to get the necessary insights from the data faster than using advanced analytical techniques.
Simple pivot table functions, computing statistics like mean and deviation, and plotting data as a line, bar, and scatter charts are part of data exploration techniques that are used in everyday business settings. Data exploration, also known as exploratory data analysis, provides a set of tools to obtain a fundamental understanding of a dataset.
The results of data exploration can be extremely powerful in grasping the structure of the data, the distribution of the values, and the presence of extreme values and the interrelationships between the attributes in the dataset. Data exploration also guides applying the right kind of further statistical and data science treatment. Data exploration can be broadly classified into two types—descriptive statistics and data visualization.
Descriptive statistics is the process of condensing key characteristics of the dataset into simple numeric metrics. Some of the common quantitative metrics used are mean, standard deviation, and correlation. read more at data science course
Visualization is the process of projecting the data, or parts of it, into multi-dimensional space or abstract images. All the useful charts fall under this category. Data exploration in the context of data science uses both descriptive statistics and visualization techniques.
In the data science process, data exploration is leveraged in many different steps including preprocessing or data preparation, modeling, and interpretation of the modeling results.
1. Data understanding: Data exploration provides a high-level overview of each attribute in the dataset and the interaction between the attributes. Data exploration helps answers questions like what is the typical value of an attribute or how much do the data points differ from the typical value, or presence of extreme values.
2. Data preparation: Before applying the data science algorithm, the dataset has to be prepared for handling any of the anomalies that may be present in the data. These anomalies include outliers, missing values, or highly correlated attributes. Some data science algorithms do not work well when input attributes are correlated with each other. data science certification Thus, correlated attributes need to be identified and removed.
3. Data science tasks: Basic data exploration can sometimes substitute the entire data science process. For example, scatterplots can identify clusters in low-dimensional data or can help develop regression or classification models with simple visual rules.
4. Interpreting the results: Finally, data exploration is used in understanding the prediction, classification, and clustering of the results of the data science process. Histograms help to comprehend the distribution of the attribute and can also be useful for visualizing numeric prediction, error rate estimation, etc.
