Variable Identification - identify Predictor (Input) and Target (output) variables
Univariate Analysis -
Bi-variate Analysis - finding out the relationship between two variables. Here, we look for association and disassociation between variables at a pre-defined significance level and performing various tests (z-test, t-test, ANOVA, chi-sq test)based on the data type of each variable.
Missing values treatment - treating missing various using various imputational techniques (central imputation, ffill, bfill, KNN imputation).
Outlier treatment - Detecting outliers using boxplots and scatter plots and taking appropriate measures to treat them. Standard formula but not limited to detect outliers is: IQR(+,-)1.5.
Variable transformation - i.e. replacement of a variable by a function. For instance, replacing a variable x by the square / cube root or logarithm x is a transformation. In other words, we can say transformation is a process that changes the distribution or relationship of a variable with others.
Variable creation - process to generate a new variables / features based on existing variable(s). For example, say, we have date(dd-mm-yy) as an input variable in a data set. We can generate new variables like day, month, year, week, weekday that may have better relationship with target variable. This step is used to highlight the hidden relationship in a variable
Analysing results.
Reporting the results back to the relevant members of the business.
Identifying patterns and trends in data sets.
Working alongside teams within the business or the management team to establish business needs.
Creating Dashboards and reports using various Data Analysis tools such as Tableau, PowerBI etc.
