Xavier Loera Flores

Types of Analytics Overview Data analysts solve various problems for businesses of different types depending on what the business needs. Some of the most common types of analytical analysis are: Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics Generally speaking, it is harder to gain insights on future and it is also harder to understand how or why those events would happen. For that reason, the types of analytics are ordered from the easiest to the hardest based on the type of insights the analysis type is trying to find....

Key Skills: Data Analysis Phases of the Data Analytics Lifecycle Analytical Tools Master various analytical tools for data gathering, cleaning and visualization Analytical Methods Identify and implement data analysis methods Data Analytics Lifecycle: Business Understanding (Discovery) Data Acquisition (Collection) Data Cleaning (Preparation) Data Exploration (Exploratory Data Analysis) Predictive Modeling (Data Modeling) Data Mining (Machine Learning) Data Reporting & Visualization (Representation & Communication) Tools: Python R SQL Tableau Methods: Machine learning algorithms Statistical analysis techniques

This will be a deeper dive into the different phases of the data analytics lifecycle and serves as a continuation for the Data Analytics Lifecycle Overview. Data Analytics Lifecycle Overview Stages of the Data Analytics Lifecycle Business Understanding (Discovery) Data Acquisition (Collection) Data Cleaning (Preparation) Data Exploration (Exploratory Data Analysis) Predictive Modeling (Data Modeling) Data Mining (Machine Learning) Data Reporting & Visualization (Representation & Communication) Business Understanding This phases involves gaining a business understanding of the problem or mission objective....

Data Analytics Lifecycle The data analytics lifecycle consist of the entire process from understanding the business problem and objectives to the communication and visualization of the data and analysis. Upon gaining a business understanding of the objective after collaborating with stakeholders, data analyst will then begin collecting and gathering data from various sources that will undergo cleaning and preprocessing steps to address errors, inconsistencies, and missing values ensuring the data is of great quality....

Foundation Models (FMs) are large deep learning neural networks that are trained on large amounts of data. They are also know as “general purpose ai” or “GPAI” since they are designed to be general-purpose and serve as the starting point for future AI development. Foundation vs Traditional Models Common forms of foundation models include large language models (LLMs) or generative AI models. FMs differ from traditional ML models or “Narrow AI models” in that they are pretrained on a wide range of tasks and data, allowing them to perform a variety of tasks unlike the specialized models of the past....

The request-response model is a communication pattern used in computing where a client sends a request to a server, and the server responds to the request. This model is used in various communication protocols and APIs to exchange data between clients and servers widely used in web development, networking, and distributed systems. Examples of this request response pattern can be seen in following use cases: Protocols HTTP DNS SSH RPC TCP APIS...

Retrieval-Augmented Generation (RAG) is a process that optimizes the output of LLMs do that it utilizes and references a specific knowledge or domain base that may not have been included in the LLM’s training data. RAG can be seen as a cost-efficient extension of the LLM’s abilities using an organization’s knowledge base while improving the outputs so that it remains relevant, accurate, and useful in various contexts without needing to retrain the LLMs....