How can you identify the most important variables in your portfolio with exploratory data analysis?

Example : Database can have new jersey state as: NEWJERSEY : All upper Characters New Jersey : First Letter Upper case new jersey : All Lower case New Jesey: Missing "r" So cleaning data is the first step of any analysis.

When prepping data for exploratory data analysis, I always remember that less can be more. Each change, from handling outliers to filling gaps, shapes the analysis. Regarding Post-cleaning, I explore patterns and anomalies through visualizations, using correlations and statistics to identify influential variables, guided by my domain knowledge. I emphasize clear data standards, engaging with providers to resolve inconsistencies and documenting each decision. This meticulous approach ensures clarity and consistency, laying a solid foundation for insightful analysis.

When preparing data for EDA, don't overdo it. Every time you remove a data point or fill in missing values, you're making an arbitrary decision that will influence the final shape of your analysis. Make sure that every modification to the original dataset is 100% necessary before doing it - it will save you time and will benefit the future analysis.

"Antes de sumergirse" en el Análisis exploratorio de Datos, se debe tener claro cual es el objetivo del análisis y cuál es el problema que se intenta resolver. ?Se va a entrenar un modelo? ?Se va a construir una visualización? ?Cuál es el problema que lleva a realizar este EDA? Son precisamente la exploración de los datos y la definición del problema, los puntos de partida para definir que clases de transformación deben realizarse sobre los datos, la "limpieza" está incluso asociada al tipo de modelo o técnica de machine learning que se va a realizar, y en muchos casos será un proceso iterativo a medida que se avance en el desarrollo.

Before diving into any data analysis, it's essential to start at the source. Understanding the business processes generating the data lays the groundwork for a successful analysis. By unraveling how the data is created, you can gain invaluable insights into its reliability, potential biases, and limitations. This knowledge forms the backbone for a more informed and effective data exploration, ensuring our analysis aligns seamlessly with the business's objectives.

If you have a dataset of daily temperatures in your city, univariate analysis involves examining each day's temperature on its own. This helps you identify trends or patterns in temperature fluctuations.

Univariate analysis is the analysis of a single variable in a dataset to understand its individual characteristics. For example, analyzing the returns of each stock separately before considering their combined impact on portfolio. This univariate approach helps investors or portfolio managers to understand the characteristics, risk, and potential returns associated with each asset independently, contributing to more informed portfolio management strategy.

In univariate analysis, I delve into each variable's story using statistics like mean and median, and visual tools like histograms. This approach is key in retail, for tracking trends or evaluating new products. It's more than just number-crunching; it's about understanding each variable deeply, guiding decisions on data transformations and setting the stage for complex analyses. As the foundation of EDA, univariate analysis is where I begin to unravel the data's narrative, paving the way for insightful, broader data exploration.

I prefer using visuals over descriptive statistics for univariate analysis. This is because visuals provide quick insights into the distributions of variables and potential outliers, which may be missed in descriptive statistics. Visuals also offer a rapid overview of the skewness of numeric variables and the frequencies of each category in categorical variables. These insights inform the necessity and nature of further analysis when transitioning to multivariate analysis. For example, in a dataset of house prices, features like price and size might exhibit right-skewness. Exploring the relationship between these two features can be further investigated in bivariate analysis.

Univariate analysis, from my experience, is a part of a cleaning process, rather than actual EDA. It allows you to see distributions and missing values of variables, but nothing more, so the biggest value in this step is ability to assess quality of data in each variable.

Bivariate analysis is fun, but be careful not to do it mechanically - if you just cross every variable with another one, you won't really understand your data, which is definitely not what analysis is about. Instead, ask yourself: what are the most interesting and useful questions I can answer in the analysis, and talk with data using visualisations to get an answer.

Misinterpretation of correlation is one of the biggest risks... Watch out for variables such as age and gender. Broad generalizations and quick assumptions are dangerous. Of course there are many tools that can help identify patterns via visualizations or even text analysis on e.g social media. Yet most important when performing analysis is to figure out context and involve additional expertise to prevent bias.

It‘s like a detective game for me, thrilling and full of discoveries. It's not just about pairing variables, but uncovering their hidden stories. Each correlation coefficient I calculate, like Pearson's r or Spearman's rho, feels like deciphering a secret code :) Using scatter plots and heatmaps, I dive into uncovering intriguing patterns, each visualization revealing a unique aspect of the relationship. Like in exploring the link between height and weight, it's not just plotting points; it's visual storytelling, where every dot weaves part of a larger human narrative. This process excites me, as it's more than mere analysis; it's about bringing to life the stories hidden within the data, finding joy in each discovery and insight.

Geralmente crio 2 categorias para a variável resposta e depois crio tabelas de contingência com cada independente. Cálculo o % coluna (cada coluna deve somar 100%) . Depois cálculo o Risco Relativo . Com o RR procuro entender as rela??es .

While bivariate analysis shines in assessing linear or monotonic relationships with Pearson's r and Spearman's rho, don't forget its versatility! It can also uncover non-linear patterns through scatter plots and identify potential interactions between variables using heatmaps. This comprehensive exploration helps you discover the full spectrum of connections, beyond simple linear trends, within your data.

PCA es una herramienta muy útil para reducción de dimensiones pero hay que tener en cuenta que es sensible a datos atípicos. Adicionalmente, deben verificarse en gráficos bivariados haciendo combinaciones de las componentes que no haya patrones en la nube de puntos. De presentarse se debe retirar y reportar los valores atípicos y hacer transformaciones de variables. También hay PCA robusto. Por otro lado PCA asume linealidad en los datos algo que no siempre se cumple. Existe KPCA donde se utilizan kernels para lidiar con la no linealidad.

One thing I've noticed is that the use of Principal Component Analysis (PCA) complements EDA by reducing dimensionality, capturing the variance in data, and highlighting key features. This aids in focusing on the most influential variables, simplifying interpretation, and improving model performance. Also, data cleaning is equally essential; by addressing missing values, outliers, and inconsistencies, you ensure the reliability of your analysis, contributing to the accuracy of variable importance assessments during EDA and subsequent modeling processes.

In my opinion Multivariate analysis examines the simultaneous relationships between multiple variables to uncover patterns, dependencies, and interactions within a dataset. For example in a marketing context, it might explore how advertising spend, product price, and online presence collectively influence sales performance. Techniques like multivariate regression or principal component analysis help discern complex relationships among variables, providing a comprehensive understanding beyond individual correlations. This approach is valuable across various fields, such as economics, biology, and social sciences, enabling researchers and analysts to grasp the intricate dynamics shaping the outcomes of interest in multidimensional datasets.

Multivariate analysis examines relationships among multiple variables simultaneously, offering a comprehensive view of complex datasets. While it demands sophisticated statistical techniques and can be complex to interpret, its strength lies in revealing intricate connections between factors. By providing a holistic understanding, it enables informed decision-making and predictive insights, making it an essential tool for organizations seeking nuanced and comprehensive insights from their data

When using scikit-learn + pandas (&/or tensorflow) for multivariate analysis, there are several pitfalls that continue to trap me. Some of the common ones are: 1. Data scaling: Many dimensionality reduction techniques are sensitive to feature scaling & unscaled features can lead to errors or suboptimal results. Remember to "import StandardScaler" and use "scaler.fit_transform(data)". 2. Missing values: Missing values do not work with dimensionality reduction algorithms. Using "data.fillna(method='ffill', inplace=True)" in pandas can help. 3. Dimensionality reduction technique & evaluation: Choosing the right algo requires experience and iteration but evaluating the performance is most crucial.

In my ?EDA journey“, I cherish the variety of tools at my disposal. Python, with its powerful libraries like pandas and numpy, brings data to life, while R offers its own specialized magic. For quick insights, I often turn to familiar Excel or Google Sheets. When it comes to captivating visualizations, Tableau is my go-to for intuitively presenting complex data relationships. Each tool has its place. Python and R for in-depth analysis, Spreadsheets for a tangible overview, and Tableau or Power BI for stunning visuals. These tools enrich my experience, allowing me to uncover the stories hidden in data and gain profound insights. ????

When it comes to exploratory data analysis (EDA), you have several options for tools and resources to choose from based on your preferences and expertise. Some popular choices include Python, known for its versatility and a wide range of libraries like pandas, numpy, and seaborn; R, a specialized language with extensive data analysis and visualization packages; Excel, a familiar spreadsheet software with powerful data analysis features; and Tableau, a user-friendly tool for creating interactive data visualizations and dashboards. Whether you prefer coding in interactive environments or using no-code platforms, these options enable you to conduct EDA effectively and present your insights professionally.

Examining and preparing data prior to conducting a thorough analysis is known as exploratory data analysis, or EDA. The first steps involve making sure the data is ready and clean, fixing any missing values, outliers, and formatting problems. Univariate analysis, which focuses on individual variables using descriptive statistics and visualizations, is the first step in the EDA process. Bivariate analysis uses graphics and correlation coefficients to examine relationships between two variables. Multivariate analysis goes one step further by employing dimensionality reduction strategies and regression models to look at relationships between several variables.

I like to use Python and Jupyter Notebooks for an interactive EDA process. Jupyter Notebooks provide an interactive way?to write and run code in?independent?cells, giving you the chance to see what each line of code does. This experience is a good view during EDA, as you can run code selectively, visualise results instantly, and easily iterate over analyses. As you explore the data, you can adjust parameters, rerun cells, and observe real-time changes, creating a more dynamic EDA process.

Leveraging tools like Tableau, Power BI, and Excel for visualizations, allowing for a more intuitive understanding of complex relationships within the data.

The process for EDA presented in the article will not provide you with useful knowledge, but rather a lot of random noise. Instead, I'd suggest following the following steps: 1. Know your product 2. Know your variables (how exactly was each one collected) 3. Generate interesting and useful ideas for research questions in the dataset 4. Learn answers to the questions using various EDA techniques and visualising the answers 5. Based on answers, create new questions and iterate until the dataset no longer has any data to answer you 6. Sum up your findings in a clear presentation (this will force you to structure your thoughts and get the most value out of EDA).

Other very important aspects I would consider to carry out are: 1. Outliers detection: Most datasets I come across have these. Other than just relying on visual inspections using box plots, scatters or histos, it might be worth the effort to use Z-scores or IQR to determine if the points are indeed outliers or not - in which case it might be a data scarcity issue (then imputation comes in). 2. Trend and seasonality analysis: Dig in the EDA with time series analysis and detect cyclical patterns. Use moving averages to smooth out the trend for analysis. In business and demand analytics, the most involved ones are implementing forecasting models like ARIMA (AutoRegressive Integrated Moving Average) for predictive analysis.

It always helps me to start with hypothesis → project them on data by answering the question "if it was true, what would be the expected data behavior?" → check if it is a real matter. Not to waste time on finding a needle in a haystack, start with a hypothesis!

So in summary, forget the boring correlation matrices and regression models. Use unorthodox methods like interpretive dance, trampoline time, and ghost stories to uncover the most meaningful variables in the portfolio. The wackier the approach, the deeper the insights will be!

Certainly! In my role as a data analyst at XYZ, I would like to share how leveraging advanced analytics has significantly enhanced our understanding of customer preferences and market trends, contributing to more informed decision-making.