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DS-U3-KT&F-Note

Keywords

  • Data Analytics Lifecycle
  • Discovery
  • Data Preparation
  • Model Planning
  • Model Building
  • Communicating Results
  • Operationalize
  • Data Collection
  • Data Cleaning
  • Data Transformation
  • Exploratory Data Analysis (EDA)
  • Data Integration
  • Data Reduction
  • Data Analysis
  • Data Interpretation
  • Data Visualization

Flashcards

Flashcard 1

Front: What are the six key stages of the Data Analytics Lifecycle? Back: Discovery, Data Preparation, Model Planning, Model Building, Communicating Results, Operationalize

Flashcard 2

Front: What is the purpose of the Discovery phase in the Data Analytics Lifecycle? Back: To understand the business problem, identify data sources, and formulate initial hypotheses.

Flashcard 3

Front: Why is Data Preparation important in the data analytics process? Back: It ensures data quality by cleaning and transforming raw data, increasing the reliability and accuracy of models.

Flashcard 4

Front: Name three common data cleaning techniques. Back: Removing duplicates, handling missing values, and correcting inconsistencies.

Flashcard 5

Front: Define Exploratory Data Analysis (EDA). Back: EDA is the process of analyzing data sets to summarize their main characteristics, often using visual methods.

Flashcard 6

Front: What are the main challenges of Data Integration? Back: Handling heterogeneous data sources, ensuring data consistency, and managing data redundancy.

Flashcard 7

Front: What is Data Reduction, and why is it important? Back: Data Reduction reduces the volume of data while retaining important information, enhancing computational efficiency.

Flashcard 8

Front: Give examples of Data Transformation techniques. Back: Normalization, encoding categorical variables, and aggregation.

Flashcard 9

Front: Describe the purpose of the Model Building phase. Back: To build and train predictive models using selected algorithms and iteratively improve model accuracy.

Flashcard 10

Front: What principles should be followed for effective Data Visualization? Back: Clarity, accuracy, and relevance.

Learning Terms Definitions

Data Analytics Lifecycle

A structured approach comprising six phases: Discovery, Data Preparation, Model Planning, Model Building, Communicating Results, and Operationalize, aimed at systematically conducting data analytics projects.

Discovery

The initial phase focused on understanding the business problem, identifying relevant data sources, and formulating hypotheses.

Data Preparation

The process of collecting, cleaning, and transforming data to ensure it is suitable for analysis.

Model Planning

The phase where appropriate modeling techniques and tools are selected, and exploratory data analysis (EDA) is conducted to understand patterns within the data.

Model Building

The phase where predictive models are developed and trained using various algorithms, followed by iterative improvement and validation.

Communicating Results

The phase where model outputs are interpreted and insights are communicated to stakeholders through visualizations and reports.

Operationalize

The final phase where the model is deployed into production, integrated into business processes, and maintained for ongoing effectiveness.

Data Collection

The process of gathering data from various sources, which can include surveys, web scraping, sensor data, and transactional data.

Data Cleaning

The process of identifying and correcting errors, inconsistencies, and missing values in the data to improve quality and reliability.

Data Transformation

The process of converting data into a suitable format for analysis, which can include normalization, encoding, and aggregation.

Exploratory Data Analysis (EDA)

A method of analyzing data sets to summarize their main characteristics, often using visual methods to identify patterns, anomalies, and relationships.

Data Integration

The process of combining data from different sources to provide a unified view, addressing challenges such as heterogeneity and redundancy.

Data Reduction

The process of reducing the volume of data while retaining its essential characteristics, often through techniques like dimensionality reduction, sampling, and aggregation.

Data Analysis

The application of various techniques to examine data sets, including descriptive, predictive, and prescriptive analysis, to derive insights and support decision-making.

Data Interpretation

The process of making sense of analysis results by extracting meaningful insights and translating them into actionable business decisions.

Data Visualization

The representation of data in graphical or pictorial format to make information clear and easy to understand, using tools such as Tableau, Power BI, and Matplotlib/Seaborn.

These flashcards and definitions should provide a solid foundation for mastering the concepts and stages of the Data Analytics Lifecycle, enhancing both understanding and recall for exam preparation.