Dive into my GitHub repository dedicated to Exploratory Data Analysis (EDA). Uncover insights from diverse datasets, enhancing your data comprehension skills. Learn visualisation techniques, data preprocessing, and statistical analysis.
1.Data collection: Gather the data you want to analyze from various sources, such as databases, APIs, or files.
2.Data cleaning: Clean the data by identifying and handling missing values, removing duplicates, and dealing with outliers.
3.Data transformation: Transform the data by converting data types, creating new variables, or aggregating data.
4.Data visualization: Visualize the data using graphs, histograms, and other visualization techniques to explore the data and identify patterns and trends.
5.Descriptive statistics: Compute descriptive statistics such as mean, median, mode, standard deviation, variance, and skewness to summarize the data.
6.Inferential statistics: Conduct statistical tests, such as hypothesis testing, correlation analysis, and regression analysis, to draw inferences about the data and test hypotheses.
7.Data modeling: Create models to predict outcomes or explain relationships between variables.
8.Communication of findings: Communicate your findings by creating visualizations and presenting your analysis in a clear and concise manner.
The EDA process is iterative and may require going back and forth between steps to refine the analysis and gain new insights. The purpose of EDA is to gain an understanding of the data and generate insights that can be used to inform decisions or guide further analysis.