What are some common data analytics projects and how can you prepare for them?

Common data analytics projects often start with Exploratory Data Analysis (EDA), a crucial step in understanding your data's story. To prepare effectively, focus on honing your skills in data cleaning, visualization techniques, and statistical methods. Familiarize yourself with tools like Python's pandas, matplotlib, and seaborn for thorough data exploration. By mastering these fundamentals, you'll be well-equipped to uncover insights, spot trends, and drive informed decisions from your data.

Common data analytics projects cover various areas, and one popular dataset for learning is the Titanic dataset. This dataset compiles information about Titanic passengers, like age, gender, and survival status. Many use it to practice data analysis and prediction skills. By exploring the data, you can identify factors influencing survival. It's a helpful tool for grasping how data aids predictions, making it a great starting point for hands-on learning in data analytics projects.

Common data analytics projects include customer segmentation, sales forecasting, and sentiment analysis. To prepare, enhance your skills in data collection, cleaning, and analysis using tools like Python, SQL, and Excel. Learn data visualization tools like Power BI or Tableau to present insights clearly. Understanding business goals and maintaining good communication with stakeholders are crucial. Stay updated with industry trends and continuously improve your skills to succeed in the field.

This step is why I fell in love with data analytics. It’s an opportunity for you to really dive in and investigate correlation and causation. Coming from a finance and revenue cycle background personally, this is where I tend to start. There also typically a material amount of opportunity here. A key that makes all the difference in this step is understanding the data, why it’s important, and the workflows that supports what you are looking at.

Although data cleaning and transformation in any project is important, I believe that the designing of the data collection tool is just as important because it will ensure that avoidable data errors can be reduced and time spend on data cleaning can be lessened

In any data project you'll always begin with data cleansing. To avoid unnecessary pain in this process , you should focus on your planning stage, to understand the data that you have and the amount of effort to clean and manipulate that data. After that, you're now ready to use the tools recommended.

Basado en mi experiencia en proyectos de datos, un consejo crucial es dedicar tiempo suficiente a la limpieza de datos. Es tentador pasar rápidamente a la etapa de análisis, pero una limpieza exhaustiva es esencial para la precisión. Comprender el contexto de los datos y documentar cada paso de limpieza son prácticas fundamentales. Además, no subestimes la validación manual; la combinación de herramientas automatizadas con revisión humana es clave para detectar problemas sutiles. Involucrar a expertos del dominio ayuda a interpretar los datos de manera más acertada. Recuerda, la limpieza meticulosa de los datos establece la base para conclusiones precisas y decisiones informadas.

Data modelling has a more basic, foundational component compared to statistical modelling. In reality, much of the data will come to you semi-structured at best. 80% of descriptive analytics can be handled by organizing your data in one of the four classic models: Hierarchial model Network model Entity Relationship (E-R) model Relational model Organizing data in these models not only serves basic analysis needs but also lays the groundwork for more complex statistical and predictive analytics. Clean, well-organized data is crucial for effective advanced analytics. Sometimes you will encounter a database already structured to serve your goal. In most cases, you'll have build this foundation.

To practice these techniques and gain hands-on experience, the best approach is to work on self-initiated projects. A more effective method is to obtain data from the Kaggle website and use it for model building and related tasks. A valuable opportunity arises from the fact that experts have completed similar projects using these datasets. By studying their work, we can manage our projects and learn new concepts and techniques from the datasets.

To ensure thorough and insightful visualization, it is essential to have data analysts with a fundamental grasp of best practices in data visualization. Simultaneously, they need the time and mental capacity to think critically and analyze the data they are handling. Unfortunately, more frequently than I would prefer to acknowledge, we encounter teams that are overburdened and stakeholders with minimal levels of data literacy. It is crucial to foster a culture in which data professionals are afforded the necessary time to work meticulously with data, rather than merely generating graphs with the mindset of assembly line workers.

Remember always to make things simple. You need to express the insight as soon as possible to your audience to pursue the solution to the business problem.

I would add that design should not happen in a silo; the dashboard and/or report builder must continue to meet with stakeholders and seek feedback throughout the design process. Taking requirements and going off to build a solution that is only presented to the stakeholder when it's close to being completed, is a recipe for failure. The best and most utilized dashboard and reports are those where stakeholders are involved through the design and build process - not just at the tails.

En visualización y comunicación de datos, adapta las visualizaciones a tu audiencia para una comunicación efectiva. Simplifica con gráficos claros, destaca puntos clave para contar una historia clara y evita la saturación visual. La elección de visualización depende del mensaje y datos que quieras mostrar. Contextualiza para una narrativa relevante que guíe a través de la información. La retroalimentación de la audiencia es crucial; ajusta las visualizaciones según sus comentarios para mejorar la comunicación. Solicitar al usuario que dibuje cómo le gustaría ver los datos puede ser un efectivo punto de partida en este proceso.

Understanding the type and role of key audiences is integral in developing data visualisations and data comms, this is because it determines the level of detail that needs to be included, for example, a funder might want to see a high level summary of the data; specifically data that tells them whether or not their investment is paying off. While a project manager might be interested even in the small details that could have a big impact on project implementation. Considering the different types of audiences ensures that data being presented drives evidence-based decision making.

Yo incorporaría la Optimización de Procesos de Datos: Este proyecto implica mejorar la eficiencia y precisión en la gestión de datos. Incluye la identificación y aplicación de estrategias para optimizar la recopilación, almacenamiento, procesamiento y acceso a los datos. Implica trabajar en la automatización de flujos de trabajo, mejorar la calidad de los datos en tiempo real y garantizar la integración adecuada de diferentes fuentes de datos para su análisis.

For any data analytics project to be successful, more effort is required in developing data collection tools. To ensure that you are collecting necessary data that will help you answer your questions. A good data collection tool will ensure that you get rich enough data to guide make decisions

Als allerersten Schritt sollten Sie sich Ihre Fragestellung klar machen und welche Informationen Sie zur Antwort benötigen. Dann können Sie sich einen Überblick über den Datenbestand und die Struktur der Daten verschaffen. Welche Variablen gibt es? Welcher Art sind sie? Welche Werte können sie annehmen? Die einzelnen Teile der Datenanalyse sind kein linearer Prozess, den man Schritt für Schritt abarbeiten sollte. Sie werden bei einer komplexeren Analyse immer wieder zu früheren Schritten zurückkehren und diese mit neuen Erkenntnissen verfeinern. Beispielsweise ist bereits bei der ersten Analyse eine Visualisierung der Daten sehr sinnvoll um Zusammenhänge zu erkennen, anhand von denen Sie Annahmen für Ihre ersten Modelle treffen können.

All data analytics projects start the same way — identify the business problem(s) to solve, form hypotheses, and collect the data needed to prove or disprove the hypotheses. Preparation for data analytics projects thus include: 1. Getting the buy-in and support of stakeholders for the projects. 2. Getting the necessary data ready for analysis. 3. Getting ready a suitable team to work on the projects.