Data Analytics Lifecycle Stages

<p>Data Preparation (D)</p> Signup and view all the answers

<p>Social media analytics (B)</p> Signup and view all the answers

<p>Visualizing data (D)</p> Signup and view all the answers

<p>Creating data models (A)</p> Signup and view all the answers

<p>Ensure data quality and relevance (A)</p> Signup and view all the answers

<p>Hadoop (C)</p> Signup and view all the answers

<p>Data Visualization (A)</p> Signup and view all the answers

<p>To interpret and compare results (A)</p> Signup and view all the answers

<p>Excel (A)</p> Signup and view all the answers

<p>Did we succeed or fail? (B)</p> Signup and view all the answers

<p>Interpreting the results (A)</p> Signup and view all the answers

<p>Using a default value (B)</p> Signup and view all the answers

<p>To determine techniques, workflow, and methods (B)</p> Signup and view all the answers

<p>Model performance results (A)</p> Signup and view all the answers

<p>It ensures the accuracy and reliability of data (D)</p> Signup and view all the answers

<p>Using a statistical model or assumption (C)</p> Signup and view all the answers

<p>Storing data on physical media (C)</p> Signup and view all the answers

<p>Use smaller test sets to validate approaches. (B)</p> Signup and view all the answers

<p>Employ fast hardware and parallel processing. (A)</p> Signup and view all the answers

<p>They allow for easier identification of flaws in the model. (D)</p> Signup and view all the answers

<p>To speed up processing times and streamline workflows. (B)</p> Signup and view all the answers

<p>It enables simultaneous processing to improve overall speed. (C)</p> Signup and view all the answers

<p>Defining the process for updating and retraining the model (B)</p> Signup and view all the answers

<p>A process to update and retrain the model must also be defined (C)</p> Signup and view all the answers

<p>Randomly changing model parameters without evaluation (B)</p> Signup and view all the answers

<p>To ensure consistency and reliability in the model's outcomes (B)</p> Signup and view all the answers

<p>Feedback from end-users regarding model predictions (B)</p> Signup and view all the answers

Using small datasets to test and improve machine learning models before using large datasets.

Using powerful hardware and parallel processing to speed up model training and workflows in machine learning.

The Data Analytics Lifecycle is a structured process for extracting knowledge and insights from data. It involves a series of interconnected stages.

The first stage of the Data Analytics Lifecycle involves identifying the specific business problem or question that the analysis aims to address.

This stage focuses on gathering, cleaning, and transforming raw data into a usable format. This may involve handling missing values, removing duplicates, and converting data types.

In this phase, different techniques are applied to explore and understand the patterns and relationships within the data.

This stage involves building models and algorithms based on the analyzed data to generate predictions, classifications, or other insights.

A process of cleaning up messy or inconsistent data, often involving removing duplicates, handling missing values, and correcting errors.

The process of changing data from one format to another to make it more usable or compatible with different systems.

A powerful programming language used for querying and manipulating data in databases.

An open-source framework used for processing large datasets in a distributed manner, often used for data transformation and analysis.

A programming model used with Hadoop for processing large datasets by dividing them into smaller chunks and distributing the processing across multiple computers.

Replacing missing values with a predetermined value, often the most common value.

Replacing missing values with the average or median of the existing data.

Replacing missing values with randomly generated values based on the distribution of the existing data.

The phase focuses on defining the overall approach to manage data, including techniques, workflow, and methods.

Techniques used to handle missing values in a data set.

The final stage of the model building process where you present your findings to stakeholders, explain the model's performance, and discuss its implications.

Examining the output of your model, analyzing key metrics, and drawing conclusions based on the data.

Comparing the results of your model to the initial questions or hypotheses you had at the beginning of the process.

A programming language widely used for statistical analysis and data visualization. It's popular in the field of data science and machine learning.

A powerful software suite used for data analysis and model building. It's known for its comprehensive features and wide adoption in businesses.

The process of making a machine learning model available for use in a real-world application.

The process of updating a deployed machine learning model with new data or changes to its algorithm.

The process of retraining a deployed machine learning model with new data to improve its performance.

The process of removing a deployed machine learning model from service when it is no longer needed or performs poorly.

An organized plan for managing the lifecycle of a machine learning model from development to deployment, including updating, retraining, and retirement.