How is the algorithm of data analysis?

The data analysis algorithm can be defined as a stepwise process and procedure that identifies, retrieves, and cleans data, followed by analysis and interpretation to draw out valuable conclusions and assist in decision-making. It is not an algorithm but rather a full workflow coupled with analytical methods.

stage-by-stage data analysis algorithm.

Define the problem

Evidently define the question or the objective.

Example: What is the cause of sales going down?

Data collection

Collect data in the form of databases, Excel spreadsheets, surveys, APIs, or logs.

Data cleaning

This is among the most significant measures:

Remove duplicates

Handle missing values

Correct errors

Standardize formats

Good results come as a result of clean data.

Exploratory data analysis (EDA) refers to a technique for examining data sets, particularly numerical data, aiming to identify patterns and trends that underpin conclusions about the data (Context Analytics, 2005). <Exploratory Data Analysis (EDA) Exploratory data analysis (EDA) is a method used to analyze a data set, most commonly numerical data, to determine the patterns and trends that can be used to draw conclusions about the data (Context Analytics, 2005).

Examine data to get an idea about patterns and trends:

Mean, median, and standard deviation.

Correlations

Outliers

Charts and graphs: Visual analysis.

Data transformation

Ready data for further analysis:

Normalization and scaling

Categorical variables can be encoded.

Creation and aggregation of features.

Implement algorithms of data analysis.

Other algorithms are varied depending on the purpose:

Mean, median, frequency analysis: Descriptive analysis.

Analysis of diagnoses: Correlation analysis, root cause analysis.

Predictive analysis: Linear regression, decision trees, and time-series models.

Prescriptive analysis, optimization, and simulation methods.

Clustering: hierarchical clustering, K-means.

Classification: random forest, Naive Bayes, logistic regression.

Interpretation and visualization.

Show current insights through dashboards and reports to enable the stakeholders to comprehend and take action on the results.

Decision-making

Apply the insights to make processes better and forecast or inform business strategy.