MBA FPX 5008 Assessment 4 Presenting Data Analysis Results Effectively
MBA FPX 5008 Assessment 4 Presenting Data Analysis Results Effectively Student name Capella University MBA-FPX5008 Applied Business Analytics Professor Name Submission Date Presenting Data Analysis Results Effectively Slide 1: Hello, I’m ________, and today I’m going to be analyzing the performance of NVIDIA Corporation’s stock over the last decade, and I’m going to be using a full data-driven approach to detailing the performance and strategic business implications of the stock. Slide 2: Reporting back the results of data analysis well will assist in making business decisions to various audiences and demystify the complexity of the data. To display NVIDIA’s stock price data, which contains more than 2,500 observations, over a decade and clearly illustrate the stock price’s exponential growth, it is essential to have clear visuals that communicate this data effectively. When sharing data with non-technical stakeholders, well-designed, visually appealing graphics can show trends in data that would otherwise not be clear if relying on raw statistics alone, according to Hull (2022). A visualization of the descriptive statistics of NVIDIA’s mean closing price for this period ($39.67) and its standard deviation ($54.94) will help make those descriptive statistics into actionable strategic insights. By translating these numbers into clear visual narratives, stakeholders can more readily grasp both the growth potential and the volatility associated with NVIDIA’s stock. Company Context Slide 3: A firm’s cost of capital is very dependent on the context under which the firm was created—a fact that can have a significant impact on a wide strategic evaluation from which stock performance can be evaluated in comparison with its similar competitors. Established in 1993, NVIDIA, a leading global innovator in artificial intelligence (AI) system architecture and infrastructure, has evolved from the video game graphics chip industry into a company with a global footprint. NVIDIA was founded in 1993 and has become the world’s global leader in the development of artificial intelligence system architecture and infrastructure (NVIDIA, 2026). As of FY24, NVIDIA’s market share of the total global AI graphics processing unit (GPU) circuit microprocessor (MPU) market is estimated to be ~86-92%, and it is estimated to generate ~$60.9 billion in revenues (Vendrell-Herrero et al., 2025). A deep dive into NVIDIA stock performance data, compared against its historical performance, can help analysts and decision-makers gain a better understanding of the company’s stock performance. The business strategy, in which a company operates, can offer useful ideas on what factors are affecting its business performance and how it stands in the business world. With the strategic transformation of its business and step into the world of Artificial Intelligence infrastructure, cloud computing, and data centre technology, NVIDIA is in a far better competitive position compared to AMD, which accounts for only 8% – 10% of the market for GPUs (Nasdaq, 2026). The boom of demand for AI hardware after 2023 had instant and direct consequences on the share price of NVIDIA, climbing from below 40$ to a maximum value of 207.04$ and bringing an unprecedented level of trust in investing in NVIDIA (Vendrell-Herrero et al, 2025). Putting these changes in the context of the semiconductor industry helps all stakeholders fully understand the implications these changes have on their businesses, given the data analysis provided. Graphical Interpretations Slide 4: Comparative Table (NVIDIA vs. AMD vs. Intel) Table 1 Comparative Table (NVIDIA vs. AMD vs. Intel) Variable NVIDIA AMD Intel AI/Data-Center Market Share 86–92% (AI GPU) 8–10% (GPU/AI) ~70% (CPU) Total Revenue (2024) $60.9 billion (FY2024) $25.8 billion $54.2 billion Employees ~29,600 ~26,000 ~124,800 Total Assets ~$65 billion ~$67 billion ~$191 billion Sources: Vendrell-Herrero et al. (2025); Nasdaq (2025, 2026); NVIDIA (2026) Comparative data from various organizational parameters is analyzed on this benchmark performance, and stakeholders can be provided an unbiased platform over which they can evaluate the competency of their organization in comparison with other organizations. As of the end of February 2024, for instance, NVIDIA commands ~86-92% of the global AI GPU market and generates $60.9 billion of revenues during FY2024 as compared to AMD with $25.8 billion of revenues and Intel with $54.2 billion (Vendrell-Herrero et al., 2025). Nevertheless, even though Intel has an employee base nearly 5x larger than NVIDIA’s (124,800 vs. 25,000) as well as total assets of approximately $191 billion (also more than 5x NVIDIA), Intel’s growth rate in this rapidly growing and evolving technology area has been slower than AMD and NVIDIA (Nasdaq, 2025). Finally, the comparative analysis using more than a single variable provides the corporate leadership with an empirical basis to make decisions on investment, partnerships, and a comparison with the competition. Slide 5: Competitor and Industry Graph (NVIDIA vs. AMD vs. Intel Stock Price 2016–2026) Figure 2 Competitor and Industry Graph (NVIDIA vs. AMD vs. Intel Stock Price 2016–2026) It’s important to see how a company’s stock has fared relative to its counterparts over a period of time, as this will give you an idea of the speed of the market and if consumers trust the market in general. Specifically, at its highest valuation in 2023, NVIDIA’s stock price was as high as $207.04 compared to Intel, which was in a considerably lower range and was fairly stable (Nasdaq). Meanwhile, although AMD’s stock price rose, it wasn’t by much, proving that the demand for AI had a significant impact on just NVIDIA’s market value in comparison to its semiconductor rivals (AlShekh et al.). These competitor performance graphics will allow organizations to evaluate their relative performance in the industry to see where strategic opportunities lie, as well as accurately understand their own competitive advantage. Slide 6: Descriptive Statistics – Closing Price Distribution Table 1 Descriptive Statistics – Closing Price Distribution Close/Last Mean 39.66617276 Standard Error 1.095485345 Median 13.465 Mode 177.82 Standard Deviation 54.93834431 Sample Variance 3018.221675 Kurtosis 1.103967289 Skewness 1.578617793 Range 206.2365 Minimum 0.8035 Maximum 207.04 Sum 99760.4245 Count 2515 Descriptive Statistics will help you to recognize movement in stocks both numerically and visually (graphically), which will enable you to make a data-based decision in business. The total population means share closing price ($39.67) is then
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