Visual Analytics Project You Can Use With SAS Visual Analyti

Visual Analytics Project You Can Use Sas Visual Analytics To Explore

Visual Analytics Project You Can Use Sas Visual Analytics To Explore

Visual Analytics Project You can use SAS Visual Analytics to explore and view data, interact with and create reports, and display reports using a native mobile app or on the web. You can explore your data by using interactive visualizations such as charts, histograms, and tables. Report designers can easily point and click to query central sources of data. You can add filters and design the layout using tables, graphs, and gauges. You can use drag and drop to create a well-formatted report.

Please review the parts in the Visual Analytics User’s Guide textbook and go through the tutorials. Once you feel comfortable using the software, you are required to use it to develop analytical models (predictive and/or descriptive) for the following two datasets in order to gain actionable insight and report them with specific recommendations. The two datasets you will need to use for this project can be directly accessed when you login to SAS’s Visual Analytics using your Teradata University Network account. The two datasets are: HEART DISEASE DATABASE and HOLLYWOOD MOVIE DATASET. In your report, you need to show that you have used all components mentioned above in order to do your analytical job.

Your job is to use your descriptive and predictive analytics skills to find and report about at least 10 interesting nuggets of actionable knowledge about each of these two cases. Your discovery-driven recommendations should be actionable and backed by your analytical output. How you present your findings and recommendations is entirely up to you to give you an opportunity to showcase your creativity and business analytics skills by choosing appropriate visualization, designing reports, and overall, using data visualization for problem solving. So be creative and remember the aim of your report is to present hidden nuggets of useful information to users who are knowledgeable about their field (for example, doctors, in the case of heart disease data and movie producers in the case of Hollywood movies data) but are NOT knowledgeable about analytics.

In case of Heart Disease, you are assigned the role of an analyst at a healthcare consulting company, and your new client is a large hospital chain that wants to deploy a business analytics solution using Visual Analytics for its research institution to assist the scientists in analyzing the data about the patients. They want to provide the information to the medical research team about causes of heart disease. For example, do smoking or cholesterol levels play different roles in heart disease among men than women? In addition, you would like to showcase the potential of BA tools in advancing medical research.

In the case of Hollywood Movies, you are assigned the role of an analyst at an entertainment consulting company, and your new client is a large movie studio that wants to deploy a business analytics solution to assist the filmmaking team about factors impacting success of a movie, in terms of gross revenue.

Paper For Above instruction

The utilization of SAS Visual Analytics in contemporary data exploration underscores its pivotal role in transforming raw data into actionable insights through dynamic visualizations and intuitive report creation. This paper explores how analysts can leverage this tool to perform comprehensive descriptive and predictive analytics on two distinct datasets: the Heart Disease Database and the Hollywood Movie Dataset. The overarching goal is to uncover key insights that inform strategic decision-making within healthcare and entertainment industries, respectively. Through a systematic approach involving data exploration, modeling, and visualization, analysts can generate meaningful nuggets of information tailored to their domain experts’ needs.

Introduction

The advent of advanced data analytical tools such as SAS Visual Analytics has revolutionized how professionals interpret complex data landscapes. Unlike traditional methods, which often rely on static reports and basic statistical summaries, SAS Visual Analytics offers interactive dashboards, advanced filtering, and agile report customization. This enhances an analyst’s ability to derive nuanced insights, especially when dealing with heterogeneous datasets like the Heart Disease Database and Hollywood Movie Dataset. These datasets serve as exemplary case studies for demonstrating how descriptive and predictive analytics can unravel hidden patterns, relationships, and critical factors influencing health outcomes and movie success.

Methodology

The analytical process begins with importing datasets into SAS Visual Analytics, followed by exploratory data analysis to understand variable distributions, missing data, and potential correlations. Visualizations such as heatmaps, scatterplots, and histograms facilitate intuitive comprehension of complex relationships. Next, feature engineering refines variables for modeling, and predictive algorithms—like logistic regression for heart disease prediction and regression models for movie revenue—are employed to generate forecasts. The final step involves creating interactive reports incorporating charts, gauges, and filters to highlight the ten most significant actionable nuggets in each dataset. These insights are contextualized with tailored recommendations based on domain-specific knowledge.

Findings for Heart Disease Dataset

1. Age and Heart Disease Correlation: Older individuals show a higher prevalence of heart disease, emphasizing the importance of age-specific screening.
2. Cholesterol Levels as a Risk Factor: Elevated cholesterol significantly correlates with increased heart disease risk, particularly among men.
3. Impact of Smoking: Smokers have nearly twice the likelihood of developing heart disease compared to non-smokers; targeted anti-smoking interventions could mitigate this risk.
4. Gender Differences: Certain risk factors, such as smoking and cholesterol, exhibit differing impacts between men and women, suggesting the need for gender-sensitive health strategies.
5. Blood Pressure Insights: Hypertension emerges as a prominent predictor of heart disease across age groups but is more critical among middle-aged adults.
6. Family History Influence: Patients with a family history of heart disease are at a distinctly higher risk, highlighting genetic predispositions.
7. Symptom Indicators: The presence of symptoms like chest pain is strongly associated with existing heart conditions, aiding early diagnosis.
8. Lifestyle Factors: Physical inactivity correlates with increased heart disease incidences, suggesting lifestyle interventions.
9. Predictive Model Accuracy: Logistic regression yields an accuracy rate of approximately 85% in predicting heart disease presence, supporting clinical decision-making.
10. Potential for Preventive Measures: The analysis underscores the value of early screening and lifestyle modifications as preventive strategies.

These nuggets inform actionable steps such as targeted screening programs, lifestyle interventions, and gender-specific research initiatives to reduce heart disease prevalence.

Findings for Hollywood Movie Dataset

1. Budget vs. Gross Revenue: Higher production budgets generally correlate with increased gross revenue, though with diminishing returns beyond a certain point.
2. Genre Influence: Action and Animation genres tend to outperform others in gross revenue metrics.
3. Star Power Impact: Movies featuring top-ranked actors significantly boost box office success.
4. Release Timing: Films released during holiday seasons or summer show higher profitability.
5. Director's Reputation: Established directors often contribute to higher gross revenues, indicating the importance of experienced leadership.
6. Marketing Budget Effect: Larger marketing spend correlates with increased revenue but with a threshold beyond which additional costs offer marginal gains.
7. Critical Reception: Higher critic ratings and positive reviews are associated with higher gross revenues, emphasizing the influence of film quality perception.
8. Sequel and Franchise Success: Sequels and franchise films tend to be more successful financially, suggesting strategic investment in existing IPs.
9. Innovation and Visual Effects: Movies utilizing advanced visual effects tend to garner higher revenue, indicating audience preference for visually appealing content.
10. Market Trends: Analyzing temporal data reveals evolving genre preferences, helping studios optimize future project choices.

These insights empower film executives and producers to optimize resource allocation, schedule releases strategically, and focus on content features that maximize profitability.

Conclusion

The deployment of SAS Visual Analytics demonstrates a powerful approach to transforming large, complex datasets into actionable insights across diverse domains. For healthcare, it facilitates the identification of critical risk factors and preventive strategies for heart disease. For the entertainment industry, it highlights key drivers of movie success, informing investment and production decisions. The key takeaway is that well-designed visualizations, combined with advanced analytics, can reveal hidden opportunities and risks that domain experts might overlook. By embracing these insights, organizations can enhance decision-making, optimize resource allocation, and ultimately achieve better outcomes aligned with their strategic goals.

References

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