Quelles techniques avancées d’analyse de données utilisez-vous ou souhaitez-vous apprendre?

Machine learning is a technique that enables computers to detect patterns, similar to how we teach young minds to understand the world. Just as a child learns to differentiate between shapes, colors, and sounds, a machine learns from vast amounts of data. With time, it can make "intelligent" predictions and decisions. With machine learning, we are not simply programming computers to follow instructions but nurturing them to make informed judgments based on experience.

Machine learning is a force that propels us into a future where computers autonomously learn and adapt, forging new paths in understanding and problem-solving. It's a discipline where creativity meets computation, and its potential is bound only by the data we feed it and the imaginations of those who wield it.

There is a widespread misunderstanding regarding data analysis and analytics. Data analysis typically pertains to the past, while analytics is oriented towards the future. Analysis centers on KPIs, reports, metrics, and dashboards, whereas machine learning delves into SVMs, neural networks, k-means, etc. It is essential to clarify these terms to prevent confusion.

In a practical scenario, you have a deadline and a business problem to solve. It's always important to understand the business problem before you start training the data with any random ML models. I have observed many Data Scientist traing a XGboost model or Neural network when the problem at hand can be very easily solved by Data Statistical summary or a basic data aggregation. So it's crucial that you have an in-depth understanding of the problem that you want to solve and then train the data with the most appropriate model.

Most learning resources online teach you greatly on how to perform machine learning. But very rarely on the complexities of operationalization of these techniques from the perspective of the end user. I want to learn how to seamlessly integrate these models into production pipelines, ensuring their seamless deployment and continuous optimization, ultimately driving the transformation of data science into actionable solutions that drive tangible results in the field.

Another definition of Data visualization: is the graphical representation of data through visual elements such as charts, graphs, and maps. The purpose of data visualization is to convey complex information in an accessible and easily understandable manner, enabling individuals to identify patterns, trends, and insights within the data. It involves the transformation of raw data into visual formats that facilitate analysis, interpretation, and communication. Effective data visualization not only enhances the clarity of information but also aids decision-making processes by providing a visual context for understanding the significance of the data.

When creating data visualizations, I keep the following questions at the forefront of my mind: What type of chart will best display the data? What will stakeholders be experiencing when they see this visualization? Will users be able to easily understand what is going on? What background knowledge do I have on this visual that won't be obvious to someone seeing it for the first time? What questions might stakeholders have? Can I address those questions within the visual itself? These questions can apply to any data visualization software you may be using.

Data visualization can be one of the hardest parts of using data. Not only do you need to understand the data yourself, but you need to format your data in a manner that less technically involved members can understand it as well. A good data visualization starts with a clear understanding of the end goal. What is the purpose of this report? What exactly am I trying to convey? Once this goal is clearly formed, next is the audience. Who am I making this report for? Is this for fellow IT? Or maybe this is for upper management? Understanding your audience is crucial to developing a good product with adequate data visualization. After this, the visualization part is about having the correct tool to make the correct graph.

Think of a data warehouse as a giant storage place for information. It's like a big library where all the important information from a company or organization is kept in one organized spot. This makes it easy for people to find and use the information they need, like looking up books in a library. It is a helpful way to keep everything in order to make smarter decisions based on the collected information.

Big data is often comprised of data from many sources. Each dataset "fit for use" in its originating domain but in the new combined "big data" domain it can have issues. This reality led to my 3 rules for working with big data: #1 Know the data, you need to understand the limitations of the source data. #2 Performance = moving the least amount of data the shortest (network) distance. #3 Everybody’s data sucks! You can have the most pristine data sources but when you use them in new ways the imperfections will come out. Additional data quality & cleansing work will be required.

- One thing I notice more in data science one has to be an investigator and a creator using data. Gathering relevant data from various sources, which may include databases, APIs, sensors, and more, helps in the investigation of the given business problem.

Data Science is like being a detective for numbers and information. It is about using computer skills to collect and study lots of information to find hidden patterns and useful facts. Just like detective mysteries, data scientists use data to solve puzzles and make predictions. They help us understand things better and make smart choices based on what the numbers tell us.

Lo primero que debes considerar al momento de comenzar el ciclo de los datos es el objetivo que necesitas alcanzar y la audiencia (desde quienes va a consumir esos datos hasta los que se van a beneficiar de los insights resultantes del análisis). Mientras más claro tengas el por qué, tanto la calidad como el proceso de la obtención y del análisis de negocio van a generar más valor para todos los stakeholders. Pregúntate todo el tiempo ¿para qué hacemos el análisis? ¿Qué tipo de datos necesito? ¿Cómo puedo obtenerlos? ¿Cómo puedo hacer una visualización correcta? ¿El análisis responde a mi problema de origen? Tener feedback constante también te ayudará a comprender mejor si tus acciones están generando valor y el impacto de las mismas.

One of the best tasks I was ever assigned was working with a demo desktop that ran two NVIDIA Teslas, 768 GBs of RAM, and a processor that more than matched. Though I was able to meet the needs of our project, I still wish I had more skill with C++ to push the limits of the code I wrote and time to optimize the program to my standards.