Here's how you can dive into real-world data sets as a beginner.

I recommend starting your journey by exploring real-world data sets. Tap into public repositories and government databases—they’re gold mines of free data ripe for analysis. Begin with straightforward data sets that pique your interest, whether it’s sports stats, economic indicators, or social media trends. This foundational step is pivotal—it lays the groundwork for your hands-on experience with actual data. Remember, curiosity and exploration are your allies!

Starting with real-world data sets can be exciting! Begin by exploring publicly available datasets on platforms like Kaggle or Google Dataset Search. Choose a topic you're interested in, and start with simple analyses to get a feel for the data. Gradually tackle more complex tasks as you grow more comfortable. Don't hesitate to join online communities or forums for support and tips. The key is to dive in, experiment, and keep learning!

As a beginner, we just need to spend some extra effort on understanding the "use case" of what we are actually trying to do.( take help of gpts or google ) For Example: Analyzing traffic for an intersection - this dataset might have intersection details, Intersection_ID, Datetime, Traffic signals, pedistrian volume, bicycle volume, LaneConfiguration and other details. Once we understand the problem statement and look at the data attributes (column headers) it will start making sense. Once this is done, we can later proceed with further cleaning preprocessing, transformation(ETL) OR PCA+Train+Test+Repeat(ML) depending on further use case.

As a beginner, exploring a public BigQuery dataset from Google is a good start to familiarize yourself with large datasets at no cost. You can import such a dataset into Microsoft Power BI, which is also available for free download and local use. This allows you to analyze and visualize large datasets, gaining a good feeling for the data and the process without requiring much prior experience.

Diving into Real-World Datasets as a Beginner Start with Public Datasets: Use sources like Kaggle, UCI Machine Learning Repository, or government data portals. Choose a Tool: Learn tools like Excel, Python (with Pandas), or R for data analysis. Set a Clear Goal: Define what you want to achieve with the data (e.g., finding patterns, making predictions). Clean the Data: Handle missing values, remove duplicates, and correct errors. Visualize: Create charts and graphs to understand the data better. Practice Regularly: Work on different datasets to improve your skills.

Learn the programming languages, such as Python or R, that are frequently used in data analysis. These languages include large libraries that make data processing, analysis, and visualization easier (e.g., Pandas, NumPy, Matplotlib, Seaborn in Python; tidyverse packages in R). Tools for Analyzing Data Learn how to use well-known data analysis programs like RStudio, Jupyter Notebooks, and the IDEs (Integrated Development Environments) for R and Python. Resources for Data Visualization: Investigate data visualization programs such as ggplot2 in R and matplotlib/Seaborn in Python, as well as Tableau, Power BI, and others. Online tutorials and courses: To acquire tools and techniques for data analysis, sign up for online tutorials or courses.

Escolha duas soluções de BI , a Líder e a que mais existe oportunidades de trabalho onde estiver, se for seu objetivo procurar empregos remotos internacionais , powerBI e Tableau , uma vez que tiver proficiência nelas, pense em conhecer as demais , será bem amis facil o aprendizado , não comentarei sobre Excel/Google Sheets, saber eles é o minimo para quem quer começar com dados, mas verá que uma vez iniciado no self-service BI raramente precisará deles. Antes de abraçar uma linguagem de programação, aprenda a lógica de programação, a linguagem que escolher será mais facil depois disso , Python esta bem na moda.

Once you get your hands on some data, starting to analyze and explore it within Excel is a great place to start. No matter how many other tools you grow to utilize, you'll always find yourself leveraging Excel in some fashion, so learning various formulas and how to create and explore large datasets using Pivot Tables will take you pretty far. Next I'd recommend diving into something like Postgres as it's free and open source, but widely utilized. Finally, spending time with PowerBI to learn visualize the data you're exploring is a powerful skill to develop. On a Windows based PC, you'll be able to download and use the desktop PowerBI version for free!

Managing Missing Values: Locate any missing values in your dataset and determine the best course of action. The options are to use advanced imputation techniques, remove rows with missing values, or impute missing values with a computed value (e.g., mean, median). Eliminating Duplicates: Make sure each observation in your dataset is distinct by searching for and eliminating any duplicate rows. Format Standardization: If necessary, standardize the variables' formats. Make sure category variables follow the same naming conventions, for instance, or convert date/time variables to a standard format. Handling Outliers: Recognize any outliers in your data and determine the best course of action.

Before analyzing or presenting, data cleansing & preprocessing is important. Ensuring data is accurate, consistent, and complete by identifying and correcting errors, handling missing data & removing duplicates. Use clean trim, columns in top-down hierarchy, upper case, replacing blanks.

ETL, pipeline de dados , palavras bem comum do dia dia, escolha soluções cloud, de preferencias por tecnologias azure e aws, mas fique atento nas vagas de emprego, sempre tem uma ou outra que pode valer a pena observar. muito dizem para usar Python sempre, mas não é bem assim, vale a pena observar o mercado. Quando limpar sua base, lembre-se de ter entendimento da sua origem, documentação e objetivo de execução. Pontos básicos de limpeza são: Nulos , duplicidades , caracteres especiais , espaços brancos, tipo de dados e etc

Data cleaning means examining the data for errors, inconsistencies, or missing values that could distort your analysis. Think of it as detective work – use sorting and filtering tools in your analytics software to identify and address these issues. While it might take time, cleaning your data is the foundation for trustworthy results.

comece bem fazendo o feijao com arroz, entenda com os campos que possui , quais são suas medidas, seja elas indiretas ou diretas , no caso da indireta, o que você pode conseguir com com a combinaçao com as diretas , por exemplo, com o custo e unidades , vc tem o valor de custo, se deduzir do valor de venda , você passa a ter o valor de margem.... Uma vez que consiga fazer isso com naturalidade, passe para coisas mais avançadas como correlações e etc

To gain an understanding of the central tendency, dispersion, and form of your data, compute summary statistics like mean, median, mode, standard deviation, and so on. Produce data visualizations to examine patterns, trends, and connections in your data, such as heatmaps, box plots, scatter plots, and histograms. Regression analysis, hypothesis testing, clustering, classification, and other statistical techniques can be applied to your data to help you understand it better and find answers to certain research problems.

Mantenha padrões de cores para suas medidas, se esta usando verde para valores, use o verde em todos os grafico que falam de valores, não cai na pressão que os clientes querem dashboards super lindos e coloridos , eles querem informação, de preferencia clara , direta e com coerência, dashboard de curso é só para vender matricula, no dia dia , pouco sobra tempo para isso, pois estará focado em criar e gerar lucro, resultado e agregar valor a companhia, lembre-se menos é mais se bem organizado e mostrando o que o negócio e as pessoas que vão utilizar precisam.

Analyzing and querying data is a very powerful skill, but leveraging data visualization tools takes things to another level. You'll be able to help others understand data and the insights buried within by presenting it in a digestible way. I'd recommend starting with PowerBI which can be used for FREE by downloading the desktop version on any Windows PC.

I've found the best way to improve your ability to interpret data is leveraging a data visualization tool like PowerBI. Attempting to visualize your data in different ways is a great way to challenge yourself and your own understanding of a particular dataset. To take things a step further, presenting those visualizations and findings to others and then fielding their questions time and time again will grow your skills and that of those around you.

As a beginner, diving into real-world data sets involves: 1. Start Small: Begin with manageable, smaller datasets to build confidence. 2. Use Public Data: Access public datasets from sources like Kaggle, UCI Machine Learning Repository, and government databases. 3. Leverage Tools: Use data analysis tools like Python, R, and SQL, and explore platforms like Hugging Face for NLP tasks. 4. Follow Tutorials: Use online tutorials and courses to guide you through practical examples. 5. Join Communities: Participate in data science forums and groups for support and advice. 6. Practice Projects: Apply your skills on real projects to gain hands-on experience. These steps help you effectively work with real-world data sets.