You’re looking to make your data-driven projects more effective. What tools should you use?

Sin datos, no hay paraíso! 🔍✨ ETL/ELT comienza con la emocionante extracción desde nuestras queridas fuentes. Opinando: SQL: 🚀 El caballero líder en consultas, perfecto para conquistar bases de datos relacionales. ¿Su superpoder? Extraer, filtrar, unir y transformar datos con elegancia. Python: 🐍💡 El héroe para analistas inmersos en la ingeniería y ciencia de datos. ¡Cuidado con el web scraping! Más allá de sus poderes técnicos, recordemos las cuestiones éticas y los permisos legales. Formulario de Google: 📝🌐 Un guerrero eficaz, gratuito y amigable, pero ¿es suficiente para las batallas más profesionales? Yo recomiendo integrar servicios que fusionen la magia de la inteligencia artificial con el procesamiento natural del lenguaje.

Before collecting data, understand its purpose and availability. This strategic approach saves time and ensures relevance, paving the way for meaningful insights. Where to Start? Let's break it down: Purpose-Driven Data: Identifying the objective of your data analysis guides the type of data you need. It prevents unnecessary collection and saves time. Efficiency in Collection: Knowing what data is relevant also informs the tools and methods for collection, making the process more efficient. Data Availability: It's vital to confirm the availability and accessibility of the needed data before committing resources to its collection.

- Streamline data flow with APIs & connectors, wrangle messy data with cleaning tools, and scrape hidden gems from webpages. - Use SQL and Python libraries crunch numbers, find patterns, and build models. - Use Tableau or Power BI to create stunning dashboards that tell your data's story. - Version control (Git) and project management tools keep everyone on the same page. - Deploy your analyses for real-time insights and wider reach.

The choice of tools for effective data-driven projects depends on factors like data type, source, volume, collection methods, timeline, budget, and expertise. In telecom data analytics, Python stands out. Its versatility enables diverse tasks simultaneously, surpassing PostgreSQL limited to relational databases. Python excels in data cleaning, processing, and analysis, addressing growing complexity and increasing features for efficient and timely projects. Unlike counterparts facing constraints with intricate data, Python shines in descriptive, diagnostic, predictive, and prescriptive analytics. Its attributes make Python highly suitable for advanced AI platform integration.

As ferramentas de coleta de dados desempenham um papel fundamental em projetos orientados a dados, oferecendo uma maneira estruturada e eficiente de reunir informações cruciais.

Data Visualization: Tableau, Power BI: User-friendly tools for creating interactive and insightful visualizations. Matplotlib, Seaborn (Python), ggplot2 (R): Libraries for creating static and dynamic visualizations. Machine Learning and Predictive Analytics: Scikit-learn (Python): Simple and efficient tools for machine learning. TensorFlow, PyTorch: Deep learning frameworks for building and training neural networks. RapidMiner, KNIME: Platforms for end-to-end data science and machine learning. Collaboration and Version Control: Git/GitHub, GitLab: Version control systems for tracking changes in your codebase. Jupyter Notebooks: Interactive notebooks for sharing and collaborating on code and analysis.

One another tool - Alteryx stands out as a user-friendly data processing and analysis tool. Unlike traditional SQL, it offers a no-code approach, allowing users to execute various data processing tasks effortlessly. What sets Alteryx apart is its integration of machine learning algorithms, easily applied through a drag-and-drop technique. A noteworthy feature is the Browse tool, which provides a rapid overview of the dataset. The key advantage of Alteryx, in my perspective, lies in its automation capabilities. Even without any programming knowledge, users can construct intricate workflows and automate their execution with a simple click. This empowers users to create and manage complex data processes efficiently on a daily basis.

Excel is great for simple stuff like sorting and filtering data. But when it comes to doing more complex things ETL processes (Extract, Transform, Load) Plays important roles.

It important to understand the data during the processing stage. Also, choose the right tool for data processing depends on the complexity of the task. Excel is user-friendly for basic operations and quick analyses, making it suitable for lightweight processing. R, on the other hand, excels in handling complex transformations and manipulations, making it ideal for sophisticated data wrangling.

After collecting raw data, processing and cleaning need to done, to ensure accuracy and reliability. Data processing tools assist in transforming and preparing data for analysis. These tools help handle missing values, standardize formats, and perform other preprocessing tasks. Examples: Microsoft Excel/Google Sheets: For smaller datasets, spreadsheet software is effective for data cleaning and basic transformations. Functions and formulas in Excel or Google Sheets can be applied to clean and preprocess data. OpenRefine: A powerful tool for cleaning messy data, OpenRefine allows users to explore, clean, and transform data efficiently. Trifacta: This data wrangling tool automates the process of cleaning and structuring raw data.

Power Bi is OK for smaller or medium sized data. But I think it has a limit to the number of rows it can handle at least in the unlicensed version. So from the scalability point of view it is not very ideal. Same issue I faced with Qliksense. For some reason they seem to have a limit to the amount of data that can be filtered out at least in the version I used. But then again these are mainly data visualization tools. The ETL needs to be done elsewhere before transferring data to these tools I think. But then it affects how deeper the reports can be drilled down.

A Data Analysis Plan and clear communication are essential. Utilize Jupyter Notebooks, Apache Spark, Pandas, and SQL databases to enhance data-driven projects within a diverse toolset.

1. Descriptive Analytics: • Microsoft Excel, Google Sheets: Ideal for spreadsheet-based data analysis, pivot tables, basic charts. 2. Diagnostic Analytics: • Tableau, Power BI • SQL: Essential for data querying in databases. • Python/R (basic use): For more customized data analysis, including use of libraries like Matplotlib, Seaborn (Python) or ggplot2 (R). 3. Predictive Analytics: • Python/R: Utilizing libraries like scikit-learn, TensorFlow (Python), or caret, shiny (R) for machine learning and statistical modeling. • SPSS, SAS: advanced statistical analysis. 4 Prescriptive Analytics: • Python/R : Building complex, custom machine learning models. • Advanced AI platforms: TensorFlow, PyTorch for deep learning.

Tableau: 🎨📊 No solo crea informes; es una obra maestra interactiva y el más visual. Conecta, arrastra y suelta para dar vida a cuadros, gráficos y mapas. ¿La clave? Personalización sin límites con filtros, cálculos y parámetros. Power BI: ⚡🌐 El líder del BI (Gartner y yo). Desde modelar datos hasta narrar historias visuales, muy poderoso con con su práctico editor Power Query. SAS: 🚀📈 La suite para los amantes de la analítica avanzada. Desde minería de datos hasta análisis predictivo, ofrece una plataforma robusta para modelar, implementar y triunfar en el mundo de los datos. Añado Looker y Qlik Sense. Cada uno con sus ventajas según los objetivos del proyecto.

Power BI and Tableau are great for medium-sized data, but they face challenges when dealing with larger datasets, which limits the level of detail you can achieve in reports. When it comes to analytics, there are a variety of tools available. For simpler tasks, you can use Excel, while for more advanced analysis, you can use Python/R or even more complex platforms like TensorFlow. I think it is important to select the appropriate tool for the size and complexity of your data.

In my viewpoint, data based marketing research using data collection, data analytics tools give a great perspective yet Research can’t beat entrepreneur spirit and passion adds the following success tools : 1. Capability to take calculated risks 2. Speed to take corrective actions based on initial experience 3. Just not blindly follow the marketing research but use your GUT. Finally market research is important but not a recipe of success. Still even 30% right helps you achieve the objective - WELL BEGUN IS HALF DONE.

Mas que dominio técnico: Dominio del Negocio: 🌐📊 Entender del negocio es como tener un mapa en medio de un viaje. Proporciona el contexto necesario para interpretar datos de manera cualitativa y significativa. Estrategia de Datos: 🚀🔍 La planificación cuidadosa, desde la recopilación hasta el análisis, es clave. ¿Qué datos son cruciales? ¿Cómo se alinean con los objetivos? La estrategia guía cada paso. Inteligencia Emocional: ❤️📈 Los cálculos nos conducen a contar historias detrás de los números. La inteligencia emocional agrega el toque humano, permitiendo no solo analizar datos, sino también saber escuchar, comprender las expectativas, y hasta el impacto emocional de las decisiones de negocio.

Choosing the right data analytics tool requires careful consideration. Start by defining your goals, data size, and technical expertise. Evaluate features, functionality, scalability, security, and community support. Research, compare, and try out tools before committing. Remember, flexibility and adaptability are key as your needs evolve. By following these steps, you'll find a tool that empowers you to unlock the insights hidden within your data.

Existem ferramentas consolidadas para gerenciamento de projetos e que podem contribuir para projetos orientados a dados, tais como: Microsoft Project, Jira, Trello e Asana. O Microsoft Project, provavelmente, é a ferramenta mais antiga e difundida para gerenciamento de projetos. As melhorias e integrações da ferramenta com o Microsoft Teams e com o Power BI facilitam a comunicação e colaboração entre membros da equipe e possibilitam a criação de Dashboards e relatórios personalizados, dando uma visão abrangente e baseada em dados para a tomada de decisão consistente e consciente.