Business & Data Analytics Module 1
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Questions and Answers

What does BA stand for?

Business Analytics

What is a key benefit of Business Analytics?

  • Making decisions based on evidence rather than guesses (correct)
  • Ignoring customer behavior
  • Ignoring key business metrics reports
  • All of the above

    • Business Analytics is a recent invention, only emerging in the last few decades.

    False

    Which type of Business Analytics focuses on understanding past trends and patterns?

    <p>Descriptive Analysis</p> Signup and view all the answers

    What is the question that Descriptive Analysis focuses on finding answers to?

    <p>What has happened in the past?</p> Signup and view all the answers

    What is the main goal of Predictive Analysis?

    <p>Understanding future events</p> Signup and view all the answers

    Predictive Analytics is exclusively based on human intuition and experience.

    <p>False</p> Signup and view all the answers

    Which type of Business Analytics focuses on providing recommendations for future actions?

    <p>Prescriptive Analysis</p> Signup and view all the answers

    Machine learning algorithms are solely based on explicit instructions to perform tasks.

    <p>False</p> Signup and view all the answers

    Name at least 3 software programs used for Business Analytics.

    <p>MS-Excel, SPSS, R, SAS, E-Views</p> Signup and view all the answers

    The Business Analytic Process is a cyclical process. Which of these steps is NOT part of it?

    <p>Ignoring Customer feedback</p> Signup and view all the answers

    How many major components are involved in any analytics process?

    <p>6</p> Signup and view all the answers

    Which of these is NOT a major component of Business Analytics?

    <p>External Audit</p> Signup and view all the answers

    Data Mining involves analyzing data to uncover existing and new trends, patterns, and relationships.

    <p>True</p> Signup and view all the answers

    What are some common applications of data mining?

    <p>All of the above</p> Signup and view all the answers

    What are the three main phases of the data mining process?

    <p>Data Pre-processing, Data Extraction, Data Evaluation and Presentation</p> Signup and view all the answers

    The term 'Big Data' refers to data that can be easily handled and processed by traditional systems.

    <p>False</p> Signup and view all the answers

    Where can Big Data be found?

    <p>All of the above</p> Signup and view all the answers

    Data Science is a field that focuses exclusively on the technical aspect of data analysis, neglecting the business context.

    <p>False</p> Signup and view all the answers

    What are some challenges associated with Big Data?

    <p>All of the above</p> Signup and view all the answers

    The Data Science process is a linear, one-time process, with no room for iteration or improvement.

    <p>False</p> Signup and view all the answers

    What is the first step in the Data Science Process?

    <p>Defining Research Goals and Creating a Project Charter</p> Signup and view all the answers

    Retrieving data is always straightforward and readily available within organizations.

    <p>False</p> Signup and view all the answers

    What are some key steps in preparing data for analysis?

    <p>All of the above</p> Signup and view all the answers

    Exploratory Data Analysis primarily involves using statistical models and advanced algorithms.

    <p>False</p> Signup and view all the answers

    What are some common types of visualizations used in Exploratory Data Analysis?

    <p>All of the above</p> Signup and view all the answers

    Building models is the only step in the Data Science process where technical expertise is crucial.

    <p>False</p> Signup and view all the answers

    What are the two final steps in the Data Science process?

    <p>Presenting Findings and Building Applications, and Deployment</p> Signup and view all the answers

    What does CRISP-DM stand for?

    <p>Cross-Industry Standard Process for Data Mining</p> Signup and view all the answers

    The Business Understanding phase in CRISP-DM involves defining business objectives and understanding the project scope.

    <p>True</p> Signup and view all the answers

    Underfitting occurs when a model is too complex and fails to generalize to unseen data.

    <p>False</p> Signup and view all the answers

    What are the characteristics of an underfitting model?

    <p>High Bias and Low Variance</p> Signup and view all the answers

    What is one common technique to address underfitting?

    <p>Increase model complexity</p> Signup and view all the answers

    Overfitting occurs when a model learns the underlying patterns and makes accurate predictions on unseen data.

    <p>False</p> Signup and view all the answers

    Study Notes

    ITDX 41-Business & Data Analytics - Module 1 Introduction

    • Business analytics (BA) encompasses skills, technologies, and practices for continuously developing insights into business performance, leveraging data and statistical methods.
    • BA emphasizes exploring organizational data, focusing on statistical analysis, to support data-driven decision-making.

    Significance of Business Analytics

    • Data-driven decisions replace guesswork, improving decision-making.
    • Converting data into valuable information.
    • Gaining faster answers to business questions.
    • Understanding customer behavior.
    • Accessing key business metrics.

    Uses of Business Analytics

    • Improving business profitability.
    • Growing market share and revenue.
    • Enhancing shareholder return.
    • Reducing overall costs.
    • Maintaining a competitive position.
    • Monitoring key performance indicators (KPIs).
    • Responding to changing market trends in real-time.

    Business Analytics Challenges

    • Reliance on high-quality data volumes.
    • Ensuring data quality.
    • Increasing data storage needs for data warehousing.
    • Influence of business analytics on customer interactions.
    • The need for advanced technology infrastructure.
    • Adapting to business changes brought by analytics.

    BA Types

    • Descriptive analysis.
    • Predictive analysis.
    • Prescriptive analysis.

    Descriptive Analysis

    • This analysis method examines past events to answer questions like "What has happened in the past?"
    • Descriptive analysis summarizes and describes raw data for easier understanding.

    Predictive Analysis

    • Aims to forecast future events.
    • Leverages forecasting techniques and statistical models.

    Predictive Analysis Employ

    • Relies on modeling and machine learning techniques.
    • Machine Learning (ML): algorithms allowing computers to perform tasks without explicit instructions, using patterns and inferences.

    Prescriptive Analysis

    • Aims to provide advice on possible future outcomes, answering "What should we do?"
    • Employs optimization and simulation algorithms.
    • It allows users to recommend multiple solutions and guide decisions.

    Few Software used for Business Analytics

    • MS-Excel (spreadsheet)
    • SPSS Statistics (statistical package)
    • R (programming language)
    • SAS (statistical analysis software)
    • E-views (analytical tool)

    Components of Business Analytics

    • Data mining
    • Text mining
    • Forecasting
    • Predictive analytics
    • Optimization
    • Visualization

    Data Mining

    • Discovers trends, patterns, and previously unknown relationships in vast datasets.
    • Techniques like classification, regression, clustering, associations, and sequencing models are used.

    Text Mining

    • Extracts useful insights and patterns from textual data, extracting meaningful patterns and relationships from text collections.

    Forecasting

    • Analyzing and forecasting processes over time, using historical patterns to predict future outcomes.

    Predictive Analytics

    • Developing predictive scoring models (e.g., customer churn, credit scoring).
    • Using simulations for optimal business decision-making.
    • Providing visualizations of analysis results.

    Optimization

    • Use of simulations to identify best scenarios.
    • Optimizing inventory levels.

    Visualization

    • Employing interactive and visual graphics for presenting insights from analysis results.

    Data Mining as a Process

    • Data integration and cleaning.
    • Data pre-processing.
    • Data selection and transformation.
    • Data mining.
    • Data evaluation and presentation.

    Benefits of Data Mining

    • Improved decision-making.
    • Increased efficiency.
    • Enhanced competitiveness.
    • Improved customer service.

    Disadvantages of Data Mining

    • Excessive work intensity and staff training requirements.
    • Large investments required for data collection and management.
    • Security risks to data, including private customer details.
    • Mistakes that can be made from inaccurate data.
    • Managing large databases.

    Big Data & Data Science

    • Big data involves analyzing a huge volume of complex data in various formats.
    • Data science uses advanced technologies like machine learning to analyze large datasets to gain insights for important decisions.

    Source of Big Data

    • Social media
    • IoT devices
    • Customer feedback
    • E-commerce
    • GPS data
    • Machine-generated data
    • Online shopping, emails etc.

    Challenges with Big Data

    • Data sharing and access.
    • Privacy and security.
    • Technical challenges like data quality, fault tolerance and scalability.
    • Analytical challenges.

    Data Science Process

    • Business understanding
    • Data understanding
    • Data preparation
    • Data exploration
    • Model building
    • Model evaluation
    • Deployment

    Steps for Data Science Processes

    • Defining research goals
    • Defining a clear research goal
    • Understanding research context and mission
    • Identifying analysis tools and resources
    • Proof of concept or demonstrating work feasibility
    • Identifying deliverables related to project objectives
    • Defining project timeline and success metrics

    Retrieving Data

    • Finding data within a company can be challenging.
    • Data may be stored in different repository forms (databases, data marts, warehouses, lakes).
    • Company policies may require time to access data.

    Cleansing, Integrating, and Transforming Data

    • Data cleaning removes errors in data.
    • Data is integrated to combine information from diverse sources.

    Joining & Appending Tables

    • Joining combines data between tables with shared values.
    • Appending merges data from multiple sources together.

    Handling Data Preparation steps

    • Data is structured and processed for better model understanding before analysis.
    • Variable elimination or reduction.
    • Handling categorical and continuous data types is performed in the data preparation process.
    • Numerical data type handling.
    • Dummy variable creation to accommodate categorical variables for algorithms that cannot directly use categorical data types.

    Exploratory Data Analysis (EDA)

    • Analysis to identify trends, correlations, and outliers in the data.
    • Use of graphical representations like plots and charts to aid data understanding.

    Building Models

    • Building and evaluating different models to find the best fit for prediction objectives identified earlier.
    • Selecting predictive models.
    • Testing and evaluation.

    Presenting Findings

    • Presenting models' results and findings to stakeholders, focusing on conveying complex data effectively.
    • Decision-making and visualization.

    Evaluation of Models

    • Assessing if models successfully fulfil business success criteria.
    • Review of the entire model-development process with summary of model findings and necessary corrections.
    • Identifying subsequent steps based on model outcomes (deployment, further iteration, or new projects)

    Data Mining - Architecture

    • Front end
    • Graphical user interface
    • Pattern evaluation
    • Knowledge Base
    • Database server
    • Data cleansing
    • Data warehouse

    Deployment

    • Planning for deploying the models.
    • Monitoring and maintaining operational phase of model.

    Overfitting and Underfitting

    • Overfitting: When a model learns too much from noise and inaccurate data. It performs well on training data but poorly on unseen data.
    • Underfitting: when a model is too simple to capture the complexities of data. Perform poorly on training and unseen data.

    Reasons for Underfitting

    • Too simple model.
    • Inadequate features,
    • Insufficient data size.
    • Excessive regularization
    • Features not scaled.

    Techniques to Reduce Underfitting

    • Increasing model complexity
    • Engineering more useful features.
    • Handling Data Noise.
    • Increasing training duration.

    Reasons for Overfitting

    • High variance, low bias.
    • The size of the training data is too little.
    • The model is too complex.

    Techniques to Reduce Overfitting

    • Improving data quality,
    • Increasing data volume,
    • Reducing model complexity,
    • Early stopping in training,
    • Regularization (Ridge and Lasso)
    • Dropout in neural networks.

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    ITDX 41-BDA MOD1 PDF

    Description

    This quiz covers the fundamentals of Business Analytics, including its significance, uses, and the challenges faced in the field. Understand how data-driven decision-making can enhance business performance, improve profitability, and adapt to market trends effectively.

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