You want to work in data modeling. What statistics skills do you need?

Na minha experiência com startups B2B, a análise descritiva é extremamente útil para auxiliar na segmentação da base de clientes e, em seguida, calcular as estatísticas descritivas para esses grupos. Isso auxilia na visibilidade do cenário e também no direcionamento de atuação.

It's always good to start with understanding data better and selecting model which fits to analyze the data to answer research question. In order to better understand how data distributed we can generate histogram and descriptive statistics including mean, median, standard deviation, and IQR from numeric variables and frequency counts for categorical data distribution. Software's such SAS, R, and Python are great tools to handles the tasks to build the model.

To work in data modeling in the United States, it is recommended to have a combination of technical and soft skills. Descriptive statistics and familiarity with basic probability theory and sampling techniques are necessary for most entry-level positions. However, one must also have a foundation in database concepts, data normalization, relational databases, dimensional modeling, SQL proficiency, and familiarity with data modeling tools. Understanding of correlation and multiple regression techniques are also useful. Strong analytical skills to identify patterns, understand data relationships, and foresee implications of data structure decisions on integrity and quality are needed (critical thinking and problem-solving).

To successfully work in data modelling you need to combine two elements: domain knowledge and statistical knowledge. Considering the statistical knowledge part, I can recommend to increase your knowledge of statistical inference as many research questions can be solved through linear models and statistical inference.

Hay una frase que me ha marcado mi vida profesional durante años. En el promedio se ahogan los enanos!. Frase célebre acuñada por uno de mis primeros jefes y hace referencia a que el promedio no es todo. Es importante entender y interpretar la variabilidad de la información (desviación estándar y varianza). Una buena descripción estadística de la información te permitirá comparar distintos segmentos o cluster y poder encontrar patrones que te permitan hacer inferencias. Esto es una radiografía inicial que haces a los datos a fin de entender con qué estás tratando este entendimiento te ayudará para buscar la mejor visualización posible y así poder extraer más información

I have been teaching causal inference for over 15 years. One basic area that goes understudied in data analytics are the assumptions of regression. It is a failure of our discipline to focus obsessively on prediction without teaching students about the dreaded "omitted variable bias." There are a lot of canned programs that can be used for prediction, but the data scientists makes their money on understanding causal relationships. How do you know if your model is right, if you do not know what it looks like when it goes wrong? An omitted variable bias does not just bias your estimates, but can lead to conclusions that are the complete opposite of reality. More time needs to be spent here.

When aiming for a career in data modeling, mastering Inferential Statistics is essential. It enables us to make predictions and draw conclusions about a population from sample data, which is at the heart of data modeling. This skill empowers you to understand the relationships between variables, test hypotheses, and make informed decisions based on data. Inferential statistics bridges the gap between data collection and actionable insights, making it a cornerstone for any aspiring data modeler.

Inferential statistics are considered the "topping cream" on a cake!; The users require knowledge of the philosophy of statistics (meaning that the computer-generated results only, may not take you to an accurate conclusion), expertise in the population where the data is collected, and a profession in that relevant field. Above all, please remember that the hypothesis, anti-hypothesis and predictions based on the statistical results may also fall outside the scope and reality. So, please do follow-up tests and checks to increase the confidence interval while staying focused on the original purpose of these tests and analyses.

Educate yourself about problems of process that lead to biases results: data-mining, p-hacking, survivor bias, look-ahead bias…. See chapter 1 in my book “Smarter Investing” (Palgrave/Macmillan, 2019) for a discussion of what constitutes good research: problems and solutions.

In data modeling, inferential statistics plays a vital role in drawing conclusions and making predictions based on sample data. Key skills include hypothesis testing, where you assess whether observed differences or relationships in data are statistically significant. Understanding confidence intervals helps in estimating population parameters from sample data, providing a range within which the true value likely lies. Regression analysis is essential for modeling relationships between variables and making predictions. Additionally, proficiency in analysis of variance (ANOVA) is valuable for comparing means across multiple groups.

In data modeling, a strong understanding of probability theory is crucial. This includes the ability to calculate and interpret various probability measures like probability mass and density functions, as well as understanding key concepts like expected value, variance, and standard deviation. Proficiency in applying fundamental probability rules – the addition rule, multiplication rule, conditional probability, and Bayes' theorem – is also necessary. These skills allow you to effectively model and handle uncertainty and variability in data, which is fundamental in creating accurate and reliable data models. For example, in predictive modeling, probability theory is used to estimate the likelihood of future events based on historical data.

I don't understand why people still believe that data manipulation is required for modeling. Visualization helps us to create story line of outcomes of model. Except these two all are Mandatory skill for data modeling. Statistics helps to understand what happened in past and probability will guide what may happen in future. Keep in mind interpretation of numbers and coefficients with respect to given scenario matters a lot. Sometimes basic Statistics like mean, median and mode may be sufficient to explain scenario. Manipulation of data create biased information. Instead of this think about bidirectional transformation of data from one space to another space.

Practice and knowledge in Probability theory helps identify the likelihood of events and outcomes, and which probability distribution function to best apply to the data set, for more accurate statistical analysis.

In data modeling, a strong grasp of probability theory is essential. Key skills include understanding probability distributions, such as normal, binomial, and Poisson, which help in modeling data behaviors and outcomes. Mastery of conditional probability and Bayes' theorem is crucial for making predictions based on prior knowledge. Familiarity with random variables and their expected values enables quantification of uncertain events. Skills in calculating and interpreting variance and standard deviation are important for assessing data variability. Proficiency in these areas allows for the development of accurate, predictive models and for making informed decisions based on probabilistic assessments of data.

Probability theory is foundational in data modeling, providing tools to quantify uncertainty. It encompasses concepts like probability distributions, which describe the likelihood of different outcomes, and random variables, representing uncertain quantities. Understanding probability mass functions and cumulative distribution functions aids in analyzing data uncertainty. Moreover, grasping conditional probability enables modeling dependencies between events, crucial for making accurate predictions in data modeling tasks.

Um exemplo de disposição dos dados que gosto de seguir para apresentar resultados é seguir a sequência: apresentação KPI principal no tempo + apresentação do componente do KPI que está sendo patrocinador do resultado do KPI + apresentação do componente que está sendo detrator do KPI. Esse racional da análise no tempo, seguida de análise estática dos componentes ajuda: (i) a contextualizar a evolução do que está analisando, e (ii) a fazer um estudo mais profundo do período de referência, como uma foto dos componentes que compõem o KPI principal. Na prática, a análise estática, ajuda a entender o que está contribuindo positivamente ou negativamente para o que foi apresentado e então ajuda no direcionamento das decisões.

Data visualization, with its power to and clearly present complex trends, is crucial for any data analysis. By mastering tools like Power BI, one can turn raw data into captivating visual stories that illuminate and influence decision-making

In data modeling, effective data visualization skills are crucial for communicating complex information clearly and intuitively. Key skills include understanding of data distribution and being able to select the appropriate type of graph or chart (e.g., histograms, scatter plots, line graphs) for different data types and relationships. Proficiency in summarizing data patterns and trends through visual means is essential. Familiarity with color theory and design principles helps in making visualizations more accessible and impactful. Mastery of data visualization tools and software, such as JMP or Python libraries like Matplotlib and Seaborn, is necessary for creating insightful and interactive visual representations of data models.

Data visualization is the graphical representation of information and data. It uses visual elements like charts, graphs, and maps to provide an accessible way to see and understand trends, outliers, and patterns in data.

El proceso de un experto en estadística debe manejar todos los tópicos planteados en este artículo: 1. Entendimiento del fenómeno en estudio. 2. Verificación de fuentes de datos fidedignas. 3. Extracción y limpieza de datos. 4. Visualización de datos: Es la parte más importante, nuestro trabajo es poder explicar la situación a partir de los resultados, y proponer los distintos planes de acción y los resultados esperados en cada caso.

Cleaning is a must, NEVER TRUST THE DATA YOU ARE GIVEN WITHOUT DUE DILIGENCE. - SQL has been a powerful software when starting out - visualize your tables and variable types as this makes all the difference for outliers - ask the experts who may know what you don’t when it comes to data. In my case this is usually clinicians or subject experts

I love the data cleaning process. It can sometimes be frustrating, but it’s truly rewarding to review each column to assess the quality and status of the data. I particularly enjoy this part because it involves a variety of activities such as data cleansing, name normalization, data optimization for each column, handling missing values, exploring distribution, data imputation, and creating new columns. Many believe that almost three-quarters of a data project is in this phase (and it’s true), but it’s essential to achieve the best data quality according to the general and specific objectives of the project in question.

Data cleaning is a critical precursory step to formal analysis and modelling. It entails checking for logical consistencies, spotting errors and preparing data for further use. Yet, in practice, this is often the most overlooked practical aspect of the modelling process. Failing to detect or correct input errors during this phase may lead to error propagation resulting in a model with obscured validity.

Data cleaning is crucial for credible statistical analysis. As one of our mentors used to say and I quote, "Garbage in, garbage out"

Data cleaning is a crucial part of Machine learning or Deep learning project. Any model accuracy is highly dependant on good data. In data cleaning essential statistical skills include understanding measures of central tendency i.e mean, median, mode to summarize data and identify outliers. Knowledge of measures of dispersion (range, variance, standard deviation) is crucial for assessing data variability and consistency. Skills in handling missing data through imputation techniques or removal, based on the data distribution, are necessary. Familiarity with data normalization and transformation methods helps in correcting skewness and making data more comparable.

It is worth your time to learn how to use pivot tables in Excel or Tidyverse in R. Tidyverse has the advantage of allowing you to think from left to right when manipulating data. Don't you hate having to embed/nest a bunch of functions together? Suppose we want to find the mean, min, and max of a variable by group. without pipes %>% summarize(group_by(filter(X, year >2000), team_id), mean=mean(W), min=min(W),max=max(w)))) or X %>% filter(year >2000) %>% group_by(team_id) %>% summarize(mean=mean(W), min=min(W),max=max(w)) It is very clear to see the logical steps using pipes than when we nest functions together.

Understanding the manipulation operations ( selecting, subsetting, grouping, summarizing, joining, pivoting, and reshaping) is essential for your data a data modeller. Not only to develop your querys/code but also to think in value that a transformed data can add to your data model. Data Manipulation skills added to business knowledge will permit for a professional to develop models that will bring the right data to the right person.

Pivoting data between long and wide formats is a data manipulation technique I find myself employing almost on a daily basis. Why? Because tailoring the data format to suit your specific use case significantly streamlines the data flow and enhances efficiency.

A manipulação eficaz de dados é essencial para orientar em decisões estratégicas, impulsionar a eficiência operacional, proteger contra riscos e promover a inovação, havendo melhoria contínua em uma empresa. Sem sombra de dúvidas saber manipular dados é uma habilidade crítica, que permite que as empresas prosperem em um ambiente de negócios cada vez mais complexo e competitivo.

That's the most time demanding and tiring step. It's very important to guarantee the robustness of the data. It's important to check integrity, formats, sources and connections. If needed, this is the time step where you should transform data to, then, start creating the variables. It's important also to check if the content is meaningful, which is, are the variables full of zeros? are the variables having different content for each group? etc.

One of the things I have found helpful is: Translating business requirements into data definitions. This is where hypothesis testing comes handy.Hypothesis testing is a rigorous and systematic way of statistical inferenc to see if the sample data sufficiently supports the hypothesis we have about the population. The following framework is generally useful in approaching any data modelling exercise: - Understand business objective - Translate it into one or more hypothesis. - Use various statistical tools from basic descriptive statistics to more advanced ones involving hypothesis testing and carry it out on the data. - Draw inferences and evaluate the results. - Perform iterations of the above steps until desired results are achieved.

Saindo do contexto acadêmico, uma habilidade muito importante para trabalhar com a modelagem de dados nas empresas é ter conhecimento do negócio. Na maioria das vezes a respostas/ideias de boas modelagens vão vir de conversas com os times e debates do dia a dia, muito mais do que qualquer significância estatística que um modelo apresente.

Data can narrate any story you wish once you become adept at manipulating them. "Big data" doesn't simplify this process; hence, before you begin, you should possess a fundamental understanding of the relationships between the variables. At the same time, be prepared to uncover new connections that you hadn't anticipated.

To work with Data Modeling, the main thing is to understand the reason and/or the main goal of working with this data. What will help the business? What do I want to answer? Regardless of data analysis techniques, it is necessary to understand how to connect the technical part with the problem to be solved. Whether in a financial or health-related database, exploring the data for modeling will meet what you want to answer.

In my experience one of the most important skill is ability to interpret the outcomes of the modeling and tell the data story supported by the outcomes of the analyses.