8: Data Analytics and Modeling
- Page ID
- 45516
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)- 8.0: Introduction
- This page emphasizes the importance of data analytics for businesses in making informed decisions by extracting insights from large data sets. It highlights how companies utilize data to understand consumer behavior, market trends, and sales patterns, which helps optimize marketing and improve products. This process of analytics drives business intelligence, enhancing performance and decision-making capabilities, and ultimately sets modern companies apart in competitive markets.
- 8.1: The Business Analytics Process
- This page discusses the fundamentals of data analytics and the role of big data in modern business. It covers the evolution from traditional analytics to big data integration, addressing challenges like data volume and governance. Ethical considerations in data collection are emphasized, alongside the business analytics process from problem definition to implementation.
- 8.2: Foundations of Business Intelligence and Analytics
- This page highlights the role of business intelligence (BI) and analytics in enhancing decision-making and operational performance across organizations. It covers predictive analytics for trend identification and inventory optimization, exemplified by companies such as Netflix and Amazon. BI tools increase efficiency in decision-making, financial analysis, and customer experience.
- 8.3: Analytics to Improve Decision-Making
- This page emphasizes the vital role of analytics in decision-making, highlighting methodologies like decision trees, regression, neural networks, and clustering. It underscores the importance of ethical practices, including data privacy and algorithmic fairness.
- 8.4: Web Analytics
- This page discusses the importance of web analytics for businesses to optimize their online presence through data collection and analysis of user interactions. Key metrics like traffic and conversion rates guide strategy refinement. A/B testing is emphasized as a method to enhance user engagement and site performance by comparing different webpage versions.
- 8.5: Key Terms
- This page defines key terms related to data analysis and business strategies, including A/B testing, APIs, and various types of analytics (descriptive, diagnostic, predictive, prescriptive). It explains data mining, ETL processes, and metrics like bounce rate, conversion rate, and key performance indicators for assessing performance.
- 8.6: Summary
- This page outlines the evolution of analytics through three eras, culminating in Analytics 3.0, which utilizes big data for in-depth market insights. It addresses challenges related to data volume and quality while detailing the business analytics process, including problem definition and data interpretation. The foundations of business intelligence (BI) are discussed, focusing on trend identification and ethical considerations.
- 8.7: Review Questions
- This page explores data analytics and business intelligence, defining data analytics as the examination of data for insights and highlighting challenges related to big data's volume and variety. It details the business analytics process and the role of business intelligence in decision-making and efficiency.
- 8.8: Check Your Understanding Questions
- This page discusses the challenges organizations face with big data, such as data quality, privacy, and expertise. To leverage big data for decision-making, companies can invest in technology, implement governance policies, and cultivate a data-centric culture. Big data enables competitive advantages through operational optimization, personalized customer experiences, and trend forecasting. Predictive analytics requires careful objective setting, data gathering, model selection, and evaluation.
- 8.9: Application Questions
- This page discusses tasks related to data analysis and its applications, including the impact of big data on markets, examples of predictive analytics in decision-making, organizational forecasting methods, personal reflections on data sharing comfort levels, and producing a video on SEO best practices. Each task emphasizes the significance of understanding data in both business and personal contexts in today's environment.