Data-Driven Decision Making Assignment Example Singapore

Data-driven decisions are more successful when teams have a mixture of experienced data analysts and newcomers. This is according to research from the MIT Centre for Digital Business, one obstacle is that such groups lack people who can provide analytics insights due in large part to their rarity–especially considering how few positions exist within industries looking specifically at this field or qualified candidates with an interest but no experience working as either analyst or statistician.

Data modeling and engineering is the role of data scientists or analysts, business leaders have a critical role to play at both ends of this process. At first, they frame problems with analysis in mind while also considering how well it will work for their company’s needs- after all, no one can make sound decisions without knowing what those are! And secondly, these managers need knowledge about different kinds of disciplines such as statistics too; combining them together makes you an expert on everything needed -whether its marketing platforms or financial models.

As well as this, business leaders should be able to identify opportunities where modeling might help the company– after all there are numerous examples or discussions about how advanced tools can benefit or streamline an organization’s operations. MIT’s research however was only looking at companies with heavy analytics departments and noticed how it had become too complex for a single person to manage as data was changing faster. Still, as data become more important for business leaders should look to expand their knowledge of analytics and the tools used by those who do this kind of work.

When presented with analytics output, business decision-makers commonly have the following questions and uncertainties. It is necessary for the end consumers of data analytics to know how to discern good analytics work from poor one.

In the world of data analytics, there are many ambiguous and uncertain factors that end consumers need clarity on. For example: what does “good” mean? It’s all relative in this industry; one person may have better results than another because their methods work for them or fit who they want themselves to be at any given time–however, these qualities can also lead others astray if not properly thought through before implementation so it becomes necessary for someone making decisions about which tool/service could potentially help solve some problem (whether large-scale like hurricane relief efforts abroad or small scale such as improving customer satisfaction scores) see where each product stands from its own perspective first hand by looking intentlyo at a good number of examples.

Learning Outcomes In Data Driven Decision Making Assignment 

These are not learning outcomes, they’re just questions that sometimes get asked in Data-Driven Decision Making assignments. If you ever receive such a set of questions then feel free to contact us for online assignment help in Singapore.

Assignment Activity 1: Ask the right questions to evaluate model goodness and validity

Every time you populate a model with data, there are many opportunities to introduce biases. Different data sets can contain different variables, different numbers of records per unit of analysis, and will have inherent variability in the included measurements that may or may not be missing. Each time you fill up a model with data, it must undergo some quality control steps to ensure it is free from any one of these influences.

Regardless of whether you’re using analytics software or programs like R & SAS analytical packages for regression models, box plots are always an easy way to set out what might be happening in your models before running them through linear regressions or ANOVA. If they are normal distributions then your analyses should work just fine on suspected parameters but if they’re not then you might want to look at transformations of the data or some type of weighting.

One particular kind of quality control for regression models is checking for outliers. An outlier is an observation that falls far from the rest of the data points in a distribution. Outliers can have a large impact on the results of a regression model, causing an overestimate of the slope (or underestimates in some cases), and potentially making it difficult to find the “true” parameter values.

Assignment Activity 2: Understand competencies required of a data-driven project

Answer: The competencies required for a data-driven project are much the same as ones needed for other kinds of projects, but have to do with working with numbers and data sets. These include key skills such as analytical thinking, communication, statistics, and numeracy.

A person will be able to communicate their findings in a clear manner using statistical analytical toolsets such as R or SAS which require good computational understanding. They will have an understanding of the limitations of sample size – that is that the size of a sample impacts its representational power. They should know what can affect bias in a study or survey design and consult with experts when appropriate about these issues. Data-driven projects also involve generating predictive models, simulations, and forecasting which interlink requires theoretical and practical skills.

The ability to think critically is also important when working with data sets as well as an understanding of the research process and how to find information. Being able to work independently is also a key skill, as well as being able to collaborate effectively with others. All these skills are necessary in order to be successful in a data-driven project.

Assignment Activity 3: Interpret common model outcomes and applications

The model has many applications in fields such as engineering, where optimal design efficiency may be required. It can also be applied to areas of human interest, which hopefully provide a more pleasant experience for the user.

We often realize that certain things are not optimal for our uses after taking into account measurements and observations. For example the thickness of material on wiring may not allow electric current to pass through easily or an object may have poor durability against wear if it’s made with thin material. Albert Shumway created this model, called the Shumway-Tang Model, which provides instructions on how to find an optimum solution for any given problem by solving equations repeatedly until they become solvable at linear order.

Assignment Activity 4:Understand the benefits and risks of applying particular analytical outcomes

In order to understand the benefits and risks of applying any particular analytical outcome, learn what each result means for different people.

This is a really tricky question considering that “benefits” and “risks” are subjective ideas. If you’re asking on behalf of a business, then it might be profitability or customer satisfaction that would affect them the most on some level, but for someone looking out for themselves, weight might have higher priorities than profit margins. For this reason, it’s not possible to give generalized advice on how benefit or risk can be measured as this must first start with understanding who you are asking.

Assignment Activity 5: Understand the difference between various discipline areas such as statistics, machine learning, and operations research

The disciplines overlap and share some overlap. Here is a brief overview: 

Statistics: Compares data sets and builds statistical models. Results show best guess or certainties about probabilities based on the knowledge, assumptions, and probability distributions of the underlying problem; Data analysis; Models to make inferences from data (Hypothesis Testing) such as General Linear Model (for linear relationship), Chi-square Test (for count correlation), ANOVA (to find significant different groups).

Machine Learning: Deals with how computer programs can learn, generalize from experience; programming algorithms that allow programs to automatically determine patterns in the information and use those patterns for inferring new information or discriminating between visible objects. Artificial intelligence summarizes machine learning as “the science of getting computers to act without being explicitly programmed”. Its goal is for machines to be able to teach themselves how to grow and change when exposed to new data.

Operations research: It is the application of mathematical programming, statistical analysis, queueing theory, and real-time systems to produce practical results in business. It plays an influential role in scientific studies of existing operations as well as the design of entirely new manufacturing or service systems. Operations research (OR) is the application of advanced analytical techniques, especially statistical analysis, computer simulation, and mathematical modeling to help solve complex problems. It applies mathematical optimization for decision-making under uncertainty. OR engineers are usually looking for a “fairer” work schedule instead of seeking one that maximizes company profit through the use of complex mathematics to minimize employees’ hours while maximizing earnings per hour or profit margin. 

Assignment Activity 6: Do a quick evaluation of a project given the organization data and analytics maturity level

The project can be evaluated in 5 steps. These steps evaluate the organization’s data analytic maturity level and identify necessary tools to make tangible improvements for this project. 

• Step 1: Collecting Data about The Organization’s Present Needs, By Identifying Risks in the Project Via the Organisation’s Data Collection Mechanisms, Map Out Prioritised Problem Areas & Create a Priority Matrix 
• Step 2: Assessing Key Performance Indicators Via Dashboards & Working with Executives to Prioritise Problems Based on Their Impact to Business Goals via Data Analysis Tools 
• Step 3: Ensuring that Proper Technologies are Deployed Throughout The Implementation Phase of your Project – using BI tools such as Tableau & PowerBI (which were both recently acquired by Microsoft) to Visualise Data & Interact with it in Real-Time
• Step 4: Make Use of Modern Alliances, so as Not to be Limited by an Individual Businesses Limitations – Working with Dropbox or Google Drive (if necessary) for Storing & Sharing any Data that may be Unavailable via Another Cloud Service
• Step 5: Have a Project Manager Who Understands the Importance of Data Analytics, & is able to Liaise with all Teams Involved in order to Facilitate Smooth Implementation.

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