Podcast
Questions and Answers
What does BA stand for?
What does BA stand for?
Business Analytics
What is a key benefit of Business Analytics?
What is a key benefit of Business Analytics?
Business Analytics is a recent invention, only emerging in the last few decades.
Business Analytics is a recent invention, only emerging in the last few decades.
False (B)
Which type of Business Analytics focuses on understanding past trends and patterns?
Which type of Business Analytics focuses on understanding past trends and patterns?
Signup and view all the answers
What is the question that Descriptive Analysis focuses on finding answers to?
What is the question that Descriptive Analysis focuses on finding answers to?
Signup and view all the answers
What is the main goal of Predictive Analysis?
What is the main goal of Predictive Analysis?
Signup and view all the answers
Predictive Analytics is exclusively based on human intuition and experience.
Predictive Analytics is exclusively based on human intuition and experience.
Signup and view all the answers
Which type of Business Analytics focuses on providing recommendations for future actions?
Which type of Business Analytics focuses on providing recommendations for future actions?
Signup and view all the answers
Machine learning algorithms are solely based on explicit instructions to perform tasks.
Machine learning algorithms are solely based on explicit instructions to perform tasks.
Signup and view all the answers
Name at least 3 software programs used for Business Analytics.
Name at least 3 software programs used for Business Analytics.
Signup and view all the answers
The Business Analytic Process is a cyclical process. Which of these steps is NOT part of it?
The Business Analytic Process is a cyclical process. Which of these steps is NOT part of it?
Signup and view all the answers
How many major components are involved in any analytics process?
How many major components are involved in any analytics process?
Signup and view all the answers
Which of these is NOT a major component of Business Analytics?
Which of these is NOT a major component of Business Analytics?
Signup and view all the answers
Data Mining involves analyzing data to uncover existing and new trends, patterns, and relationships.
Data Mining involves analyzing data to uncover existing and new trends, patterns, and relationships.
Signup and view all the answers
What are some common applications of data mining?
What are some common applications of data mining?
Signup and view all the answers
What are the three main phases of the data mining process?
What are the three main phases of the data mining process?
Signup and view all the answers
The term 'Big Data' refers to data that can be easily handled and processed by traditional systems.
The term 'Big Data' refers to data that can be easily handled and processed by traditional systems.
Signup and view all the answers
Where can Big Data be found?
Where can Big Data be found?
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.
Data Science is a field that focuses exclusively on the technical aspect of data analysis, neglecting the business context.
Signup and view all the answers
What are some challenges associated with Big Data?
What are some challenges associated with Big Data?
Signup and view all the answers
The Data Science process is a linear, one-time process, with no room for iteration or improvement.
The Data Science process is a linear, one-time process, with no room for iteration or improvement.
Signup and view all the answers
What is the first step in the Data Science Process?
What is the first step in the Data Science Process?
Signup and view all the answers
Retrieving data is always straightforward and readily available within organizations.
Retrieving data is always straightforward and readily available within organizations.
Signup and view all the answers
What are some key steps in preparing data for analysis?
What are some key steps in preparing data for analysis?
Signup and view all the answers
Exploratory Data Analysis primarily involves using statistical models and advanced algorithms.
Exploratory Data Analysis primarily involves using statistical models and advanced algorithms.
Signup and view all the answers
What are some common types of visualizations used in Exploratory Data Analysis?
What are some common types of visualizations used in Exploratory Data Analysis?
Signup and view all the answers
Building models is the only step in the Data Science process where technical expertise is crucial.
Building models is the only step in the Data Science process where technical expertise is crucial.
Signup and view all the answers
What are the two final steps in the Data Science process?
What are the two final steps in the Data Science process?
Signup and view all the answers
What does CRISP-DM stand for?
What does CRISP-DM stand for?
Signup and view all the answers
The Business Understanding phase in CRISP-DM involves defining business objectives and understanding the project scope.
The Business Understanding phase in CRISP-DM involves defining business objectives and understanding the project scope.
Signup and view all the answers
Underfitting occurs when a model is too complex and fails to generalize to unseen data.
Underfitting occurs when a model is too complex and fails to generalize to unseen data.
Signup and view all the answers
What are the characteristics of an underfitting model?
What are the characteristics of an underfitting model?
Signup and view all the answers
What is one common technique to address underfitting?
What is one common technique to address underfitting?
Signup and view all the answers
Overfitting occurs when a model learns the underlying patterns and makes accurate predictions on unseen data.
Overfitting occurs when a model learns the underlying patterns and makes accurate predictions on unseen data.
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.
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
Related Documents
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.
