What are the most common data analysis workflows for exploratory data analysis?

Data cleaning is like tidying up before a crucial meeting – it ensures your information is accurate, organized, and ready for analysis. Just as you wouldn't want distractions in a meeting room, clean data minimizes errors and inconsistencies, providing a solid foundation for meaningful insights. It's essentially the essential step in setting the stage for effective data-driven decision-making.

Data is stored in forms that are convinient for storage but not necessarily be convinient for analysis. The first step is to prepare you data for analysis, this includes cleaning raw data intorelevant form, declaring variables, naming column names, correcting any error in data and dealing with missing values and duplicates. Data cleaning is a crutial step as without cleaning raw data our analysis can show undesirable and untrue results. These inconsistencies are to be avoided in data science.

Data cleaning falls under the process of Exploratory Data Analysis (EDA), which involves understanding data and finding solutions to problems. Even data visualization is a part of EDA. EDA is all about exploring the data, data visualization, handling missing values, data standardization, and improving the quality of data by doing a few pre-processing techniques. Finally, this helps to manipulate data, and this is a critical factor in enhancing the accuracy yeah, it entirely depends on the context of the data, and it may improve or may not.

Apart from the basis checks on the data types and data quality in terms of missing, nulls, and zeros; the most important factor is to have a bird's eye understanding of the data distribution in terms of certain percentiles and measures of central tendencies. Having set the exploratory process in place to get the percentile values and distinct values in each variable. Having understood the univariate distribution of the data it is very important to understand the interaction between variables. So intelligent hypotheses (after correlation analysis) around the interaction of two variables for bi-variate analysis. Optimal visualization with a storyboard to present the findings would help in having a comprehensive understanding through EDA

Data cleaning is important and most time consuming task in Data Science project workflow. Effective data cleaning leads to good model performance. Data cleaning involves outliers handling, missing value handling, text cleaning, range normalisation, encoding, data type conversion, scaling, normalisation, standardization, duplicate data removal. Numpy, Pandas, sklearn, regex gives us flexibility to clean data effectively.

Data exploration is akin to exploring a new territory before embarking on a journey – it provides a map of your data landscape. Just as a traveler observes landmarks and potential obstacles, exploring data unveils patterns, trends, and potential outliers, offering valuable insights into what to expect during the analytical journey. It's the compass that guides you through the intricacies of your dataset, making the path to meaningful analysis clearer and more informed.

3 types of EDA are there, Univariate, Bivariate and Multivariate. In Univariate analysis we study single feature like statistical summary including mean, median, mode, standard deviation, variance, IQR, distribution, skewness. In Bivariate analysis we study correlation of two features with each other, in which we find correlation, categorical distribution. In Multivariate Analysis we study correlation of multiple features with each other in which we use correlation matrix. Different plots are used for analysis for better representation like histogram, kdeplot, barchart, boxplot, scatter plot, line plot, regplot, heatmap, catplot, relplot. Matplotlib, seaborn, pandas plotting can be used for this.

Data exploration is the process of analyzing and visualizing data to understand its characteristics, patterns, and relationships. It involves examining the data from different perspectives to uncover insights and inform decision-making. This can include summarizing the data, identifying trends, and detecting outliers or anomalies. Data exploration is an important step in the data analysis process and helps to guide further analysis and modeling.

A análise das características de um conjunto de dados fornece informa??es valiosas que ajudam a compreender os dados. Muitas dessas características podem ser obtidas por de fórmulas estatísticas simples.Outras podem ser observadas por meio do uso de técnicas de visualiza??o. Por isso, antes de aplicar algoritmos de Machine Learning em um conjunto de dados, é importante que os dados sejam analisados.

Une étape importante à ne pas négliger est la définition explicite de nos données. Cette étape consiste à comprendre et à définir les données que l'on va utiliser dans le cadre de notre projet. Pour cela, on va construire un ? dictionnaire de données ? en répertoriant toutes les variables utiles. Ainsi pour chaque variable, il faudra : identifier sa signification, son type (numérique, catégoriel, ...), ses différentes contraintes (exemple : une taille ne peut pas être négative), sa potentielle corrélation avec d'autres colonnes ... Cette étape est importante car elle permet de comprendre clairement les données que l'on va utiliser dans le cadre de notre contexte et de pouvoir les exploiter au mieux dans le cadre de notre projet.

Data transformation is akin to sculpting raw data into a refined form, optimizing it for analysis or modeling. It's the art of adjusting scales, crafting new variables, and translating categorical nuances into numerical harmony. Ultimately, it's the key to unlocking deeper insights and aligning your data with the nuances of analytical methods and algorithms.

Data transformation is crucial for the effective creation of models. Various techniques are employed to transform data, enhancing its suitability for model development. Common transformation methods include Log Transformation, which is utilized to address right-skewed data, and techniques to handle outliers. Additionally, scaling or normalization transformations are commonly applied to ensure data consistency and optimize its compatibility with diverse modeling algorithms. These transformations play a pivotal role in preparing the data for robust model creation, contributing to the overall success of the modeling process.

Data transformation is the process of converting data from one format or structure to another. This can involve changing the data type, cleaning and filtering the data, or aggregating and summarizing it.

There were many Data Scaling techniques, each will gives you different performances of the model, so that techniques we have to check And feature extraction, feature engineering in that interaction method etc., also we need to consider while building a model

Data transformation is a crucial step in EDA. Features Engineering techniques can be used to transform the data, extract more insights using mathematical operations or functions. The transformed data can be then reported to the stakelholder or maybe used by the datascientist to build strong model on relevant data.

Data visualization is another crucial part of the workflow. Once you have done the EDA, you would need to see the highlights, and understand a particular pattern or trend. Data visualization helps with getting the bigger picture. Also not everyone in the team would be as data savvy as you, so in order to share your analysis with other stakeholders, data visualization aids to visualize the insights. Some commonly used Data Visualization tools are matplotlib, pyplot, dash, plotly, ggplot2, powerbi etc. Commonly used (yet powerful) data visualizations are Bar graph, pie chart, Boxplots, histogram, Scatterplot

Data visualization in EDA is like using a magnifying glass. Tools: Histograms reveal how values are distributed (Ex: customer ages). Scatter plots show relationships between variables (Ex: ad spending vs. sales). Box plots highlight outliers and distributions (Ex: product prices). Bar charts compare categories (Ex: customer ratings). Start simple, then zoom in: Begin with overviews, then create focused visualizations to investigate specific areas. Interact to uncover secrets: Interactive visualizations let you filter, zoom, and change perspectives to reveal new insights. Combine multiple views: Use multiple visualizations to see different aspects of the data, like a detective using multiple lenses.

Exploratory data analysis (EDA) is the art of turning dry numbers into captivating stories. Data visualization is your storytelling tool, using colors, shapes, and labels to craft narratives that resonate. It is not just about charts and graphs. It is about conveying a message. With EDA, you become the storyteller, unraveling patterns, trends, and outliers in a way everyone can grasp. EDA transforms your analyses into narratives that captivate and inform, making your data speak a universal language.

"A picture is worth a thousand words" It helps to communicate complex patterns and insights in a more understandable and accessible way. And, here're the common types of data visualization used in DS: - Bar Charts - Scatter Plots - Histograms - Heatmaps - Line Charts - Treemaps Choosing the right type of visualization depends on the nature of the data and the specific insights you want to convey.

According to my view, Data visualization is easy to understand for even not tech background, it means Case1: Seeing what in x-axis,y-axis we can assume there is a relation between these 2 axis, & then later with some information present in it will makes them to interpret in their way, does its correct or not that has to examined by the data analysis people Yes, in every data analysis process everyone will do EDA, but what's their understanding is biggest ? Based on the algorithms output we can say that it is good model & it's not a good one. But How, visualization will plays a easier role to understand each & every change in x & y- axis makes you to understand in a better/easier way.

Data modeling in EDA is like building a miniature city to understand how it works. Relationships: Create simple models like scatter plots or correlation matrices to see how variables "talk" to each other. Cluster the residents: Group similar data points together to identify patterns and segments. Predict trends: Build basic models like linear regression to see how one variable might influence another (e.g., price vs. demand). Anomalies: Use outlier detection techniques to spot unusual data points that might need further investigation. EDA models are not built for high accuracy, but for rapid exploration. They're your temporary city models, helping you visualize and understand the data landscape before diving into analysis.

Data Modelling is combination of statistical & ML Algorithms, the model will learn statistical formulas & patterns of ML, gives you better model, model can be built for generic or particular product related according to the clients requirements. Initially we can say, by seeing dataset features & records that the model will perform well or not, based on balanced or imbalanced dataset. After building model, we needs to check whether my model is giving precised results or not, if not we need to do tuning until it gives you a better one.

Data reporting in EDA is like sending a postcard from your data journey! Key findings: Highlight the impactful insights, patterns, and surprises uncovered during your exploration. Storytelling: Use charts, graphs, and tables that are clear, concise, and tell the story of your data effectively. Context: Explain the purpose, data source, and limitations encountered. Actionable: Conclude with further analysis or questions to investigate. Audience: Tailor your tone and level of detail to your audience, from technical experts to business stakeholders. Think of your report as a bridge between your exploration and the next steps. It's not just about documentation, but about sharing the discoveries and paving the way for further action.

O relatório de dados é o produto resultante de todos os outros processos na análise de dados. é a parte onde você mostra o conhecimento que você obteve através da análise de dados e como esse conhecimento pode ser aplicado na tomada de decis?es. é a parte que engloba todos os processos da análise de dados e também é a parte em que as pessoas est?o interessadas em ver pois é onde o cientista/analista de dados mostra o seu valor e como é possível obter insights valiosos a partir dos dados.

Common Exploratory Data Analysis (EDA) Workflows: Data Cleaning: Address missing values and outliers. Clean data for better analysis. Data Exploration: Select numerical and categorical features. Identify patterns and relationships. Data Transformation: Handle missing values. Address imbalanced datasets and outliers. Scale down data for consistency. Data Visualization: Use Python libraries (Matplotlib, Seaborn). Create visualizations for insights. Data Modeling: Convert categorical features. Explore feature engineering. Use correlation, K Neighbors, Chi-square, generic algorithms, and feature importance for selection. Data Reporting: Summarize findings. Communicate insights to stakeholders.

En el Análisis Exploratorio de Datos (EDA) en la industria retail, el "contexto del dominio" es esencial. Al explorar datos de ventas, por ejemplo, entender las dinámicas del mercado y factores estacionales es crucial. En mi experiencia, fusionar datos con conocimientos del sector reveló insights valiosos. En el EDA retail, el contexto del dominio destaca como el factor clave que da sentido y dirección a los datos.