ANL551 Data Analytics for Decision Makers Assignment SUSS Sample Singapore

Data Analytics for Decision Making is a course that teaches you how to use data analytics in order to make decisions.

The class goes over CRISP-DM, which has been developed by industries across all sectors and covers every industry’s process for mining information from different sources using machine learning techniques like association rule finding or predictive modeling & response combination models – among other things! 

You’ll learn about these projects during your time studying at Data Analytics Academy because they provide an excellent way of getting acquainted with key aspects within Business Intelligence tool sets while also gaining experience turning important insights into improved organizational performance outcomes.

Get Assignment Sample for ANL551 Data Analytics for Decision Makers SUSS Singapore

The data analytics course at SUSS is designed for decision makers who want to make use of available information and insights in their work. Need help with your ANL551 course? We offer sample assignments that will give you an idea of what’s expected from each assignment.

Assignment Task 1: Design Analytics Solutions using the CRISP-DM framework

CRISP-DM is a six-step data mining process that can be used for any business analytics problem. The steps are as follows:

• Business understanding: In this step, you identify the business problem that you want to solve and gather relevant data.
• Data preparation: This step involves cleansing and transforming the data so that it is ready for analysis.
• Model selection and development: In this step, you select the appropriate modeling technique and develop models to solve the business problem.
• Evaluation and deployment: This step involves evaluating the models and deploying them into production.
• Monitoring and improvement: This step involves monitoring the models in production and making improvements as needed.
• Closing the loop: The final step involves closing the loop and repeating all of the steps above if necessary to help you solve your business problem.

CRISP-DM is a process for managing data-mining projects. It stands for “CRISP-Data Mining Process,” where CRISP stands for “Columbus Radcliffe Institute of Science and Policy.” The acronym was first published in a book by William S. Cleveland and, later, extended to include the “M” for marketing in 2000.

Assignment Task 2: Appraise the suitability of analytics techniques in different contexts

There is no one-size-fits-all answer to this question because the suitability of analytics techniques depends on the specific business context and data set. However, some general considerations include:

• The appropriateness of analytics techniques often depends on the type of data being analyzed. For example, numeric data can be effectively analyzed with statistical methods, while text data can be analyzed with natural language processing algorithms.
• The accuracy of analytics results depends on the quality of the data being used. Inaccurate or incomplete data will produce inaccurate or misleading results.
• The complexity of analytics algorithms varies, and some algorithms are more suited to certain types of data than others. It’s important to choose an algorithm that fits with the complexity of your data set.
• The implementation time for a given analysis technique varies depending on the algorithm being used and the size of your dataset. For example, some analyses can take several days or even weeks to complete, while others may only take a few hours or minutes.

Assignment Task 3: Evaluate performance of analytics models

Before you can evaluate the performance of analytics models, you need to make sure that you’re measuring the right things.

One of the biggest problems with evaluating analytics models is that there are so many ways to do it wrong. For example, you might measure how well a model performs on average, but this doesn’t tell you anything about how well it performs when used in real-world applications.

Another common mistake is trying to measure too much at once. Instead of trying to measure the accuracy, usability, and efficiency of a model all at once, try focusing on one or two measures at a time. This will help you avoid getting overwhelmed and will make it easier to identify which aspects of a model need improvement.

Additionally, it’s important to measure the right things. For example, if you’re trying to build a predictive model for customer churn, measuring how well the model predicts people who will leave in the future is not very helpful.

Assignment Task 4: Assess the quality of data for analytics

The quality of data for analytics can be assessed in terms of its completeness, accuracy, timeliness and relevance.

Completeness means that all the relevant data is included in the dataset. Accuracy means that the data is correct and free from errors. Timeliness means that the data is up-to-date and processed in a timely manner. Relevance means that the data is aligned with the business objectives.

The quality of data for analytics can be improved by cleansing and standardizing the data, and by reducing the amount of noise in the dataset.

The term “data quality” can also refer to the assessment of data accuracy and precision. This use of the word is synonymous with the concept of data precision, which is used in fields such as computer graphics, machine learning and image processing. For example, in personalization systems like Google Search or content delivery networks like Akamai Technologies’ Dynamic Network Delivery, high precision is required because small changes in the requested URL can result in very different pages.

Assignment Task 5: Prepare data for mining and analysis

Before you can begin mining and analyzing data, you need to prepare it for those activities. That means getting it into a form that is easy to work with.

There are a number of ways to do that, but one of the most common is to split it up into manageable chunks called data frames. Each data frame should include all the information you need to perform the desired analysis, including the variables you want to study and the associated values for each.

Once your data is in this format, you can then use various software tools to mine it for patterns and insights. With the right tools, you can quickly and easily find out things like how different factors are related to one another or what trends are emerging over time.

However, data frames are just one way to prepare your data for analysis. There are other formats that you can use. And certain situations might give you no choice but to work with them. This article explains some of the different ways you can transform your data so it’s ready for mining and provides examples of when each can be useful.

There are a few key things to keep in mind when preparing data for mining and analysis:

• Make sure the data is clean and organized. This will make it easier to work with and will reduce the chances of errors.
• Make sure all data is accounted for. This includes both original data and any derived or calculated values.
• Use standard formats whenever possible. This will make it easier to compare datasets and to run analytical algorithms on them.
• Use descriptive variable names. This will help you understand the data more easily and will make it easier for others to do the same.

Assignment Task 6: Construct an analytics solution using application software

A good analytics solution will help you to track, analyze and report on data gathered from your business applications. It should be able to provide insights that will help you make better decisions about how to improve your business performance.

There are a number of different application software packages available on the market, so it’s important to choose one that is best suited to your needs. Make sure to consider the features and functionality that you require, as well as the size and complexity of your organization.

The best analytics solution will be able to integrate with your existing applications and systems, so it’s important to find one that offers this level of compatibility. Also be sure to get a demo of the software before making your final decision. This will give you the opportunity to try out the application and assess how easy it is to use.

The main benefit of analytics software is that it will help you improve your online marketing campaigns by providing detailed statistics and reports. You can also use this information to make better decisions about which keywords, adverts and search terms would work best for your business.

Once you know what data you need to collect, you’ll need to choose the software that can help you track it. There are a variety of different analytics tools out there, so it’s important to choose one that will fit your needs. Google Analytics is a popular choice for small businesses as it’s free and easy to use.

Once you have your software in place, be sure to set up tracking goals and conversion funnels so you can see how well your marketing efforts are performing.

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