Data Analytics Lifecycle Stages

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Questions and Answers

What is one of the essential steps before data transformation and cleansing?

  • Implementing machine learning models
  • Ignoring data outliers
  • Familiarizing yourself with the data (correct)
  • Deploying database systems

What is the first stage in the Data Analytics Lifecycle?

  • Data Analysis
  • Data Visualization
  • Data Collection (correct)
  • Data Processing

Which of the following is commonly used for data transformation and cleansing?

  • Alpine Miner (correct)
  • Slack
  • Adobe Photoshop
  • Microsoft Excel

Which phase of the Data Analytics Lifecycle is primarily focused on cleaning and transforming data?

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

Which technique is NOT associated with data transformation and cleansing?

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

What activity is part of data conditioning?

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

What is a critical activity that occurs during the Data Analysis phase?

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

In the context of the Data Analytics Lifecycle, what does Phase 2: Data Preparation aim to achieve?

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

Which tool is primarily utilized for handling big data in transformation and cleansing processes?

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

During which stage of the Data Analytics Lifecycle is data typically transformed into visual formats?

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

What is the primary purpose of Phase 5 in the model building process?

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

Which tool is NOT typically used for model building?

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

In Phase 5, which key question should be addressed regarding the model's performance?

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

Which of the following actions is included in Phase 5?

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

What is one method used to handle missed values in data?

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

Which of the following best describes the main objective of Phase 3: Data Planning?

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

What should be compared to initial hypotheses during Phase 5?

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

In the context of data integrity, why is consistency important?

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

What might be an appropriate technique for filling in missed values aside from using averages?

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

Which option is NOT a method for handling missed values in data?

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

What is a recommended strategy to validate approaches effectively?

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

Which of the following is suggested to optimize the environment for model building?

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

Why is it beneficial to use smaller test sets during the validation process?

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

What is the purpose of employing fast hardware in model building?

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

How does parallel processing benefit model building and workflows?

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

What is the primary focus when implementing a model in a production environment?

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

What should happen when it is necessary to retire a model?

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

Which aspect is NOT a part of maintaining a model in a production environment?

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

Why is it important to define a process for retraining the model?

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

Which factor should be considered when deciding to update a model?

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

Flashcards

Validate Approach

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

Optimize Environment

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

Data Analytics Lifecycle

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

Phase 1: Business Understanding

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

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Phase 2: Data Preparation

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.

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Phase 3: Data Exploration & Analysis

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

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Phase 4: Modeling & Evaluation

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

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Data Cleansing

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

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Data Transformation

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

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SQL (Structured Query Language)

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

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Hadoop

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

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MapReduce

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

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Default Value Imputation

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

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Average/Median Imputation

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

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Random Value Imputation

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

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Data Planning Phase 3

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

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Missing Value Techniques

Techniques used to handle missing values in a data set.

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Communicate Results (Phase 5)

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

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Interpret the Results

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

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Compare to Initial Hypotheses

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

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R

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

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SAS Enterprise Miner

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

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Model Deployment

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

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Model Updating

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

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Model Retraining

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

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Model Retirement

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

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Model Lifecycle Management

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

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Study Notes

Data Analytics Lifecycle Stages

  • The lifecycle involves several key stages: discovery, data prep, model planning, model building, operationalize, and communicate results which are all key to creating and running a useful data analytic project.

Data Preparation (Phase 2)

  • The primary goal of this phase is to construct a powerful and robust analytics environment for the team.
  • A dedicated analytics sandbox with at least 10 times the capacity of the existing Enterprise Data Warehouse (EDW) is necessary.
  • Extract-Load-Transform (ELT) processes are essential for identifying and executing data transformations.
  • Big ELT and ETL processes (Extract-Load-Transform and Extract-Transform-Load) aid this data manipulation.
  • Data transformations and cleansing are performed using tools like SQL, Hadoop, MapReduce, and Alpine Miner.
  • Data should be thoroughly examined to ensure its quality. Data conditioning, visualization, and surveys help in this analysis.
  • Visualization tools include R packages (base, ggplot2, lattice), Gnuplot, and tools like Ggobi/Rggobi, Spotfire, and Tableau.

Data Cleaning (Phase 3)

  • Data cleaning (or cleansing) is the process of preparing raw data for analysis effectively.
  • Identifying and managing incomplete data, removing noise, and fixing duplicates are crucial parts of this phase..
  • Ensuring data integrity and consistency through validation helps ensure accurate results.
  • Missing values are addressed using default values, averages, or random methods as appropriate based on the data issue and its nature overall.

Data Planning (Phase 3)

  • Determine the appropriate techniques, work processes, and methods to achieve desired outcomes using information gleaned from data structure, volume, and potential hypotheses.
  • Tools like R/Postgres SQL, SQL Analytics, Alpine Miner, SAS/ACCESS, and SPSS/OBDC assist this analytical effort.
  • Data exploration is a crucial component, which includes various steps like variable selection and model fitting.
  • Best performance is gained when converting to SQL or a database language when appropriate, choosing the right technique based on the goals set for the model.

Model Building (Phase 4)

  • Data sets are prepared for various purposes such as training, testing, and production and should address all needs and expectations.
  • Smaller test sets are employed to validate approaches.
  • The optimal environment uses fast hardware for parallel processing.
  • R, PL/R, SQL, Alpine Miner, and SAS Enterprise Miner, are among the tools that are useful here.

Communication of Results (Phase 5)

  • Assess project success and determine any areas needing improvement.
  • Comparing results to previously held hypotheses (initial hypotheses) is a vital step.
  • Identify significant points and measure the overall contribution of the project to the business.

Operationalization (Phase 6)

  • Running a pilot project to gain initial experience is an important first step.
  • Assessment of project benefits is essential to gauging its overall value.
  • Model deployment in a production environment is essential for long-term functionality and utility.
  • The implementation of a defined process helps in keeping the model current. This process accounts for any retraining or retirement that may be needed at a later time.

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