ITECH2303 Data Analytics Assessment Answers Australia

Data is recognised as a crucial asset for decision-making in the current business environment, not only as a necessity for day-to-day operations. Raw data, on the other hand, has little value. Before data may be of great value, it must be analysed. If you are enrolled in the ITECH2303 Data Analytics course, you can take assistance from us to complete your ITECH2303 Data Analytics assessment answers.

Data analytics generate knowledge from data, enabling sophisticated data-driven decision-making. Students will grasp the fundamental components of the data analytics process, numerous data analytics approaches, knowledge of the applications and technical elements of data analytics, and the ability to apply data analytics to real-world situations in this course.

This course provides a moderate introduction to data analysis ideas, the work of a Data Analyst, and the tools required to carry out everyday tasks. You'll learn about the data ecosystem as well as the principles of data analysis, including data collection and data mining. Then you'll learn about the soft skills needed to successfully convey data to stakeholders and how developing these abilities can help you become a data-driven decision-maker.

What are the Learning Outcomes of this Course?

Studying the data analytics course can help you to understand the business demands and keep the data secure. This will also enhance your understanding of business and make you ready for a job in the same field. The ITECH2303 Data Analytics assignment help providers have added here some learning outcomes of this course for you. Take a quick look at it -

• Describe the reason for data analytics and the necessity for it.
• Describe the features of important data analytics process components.
• Discuss the concepts of various data analytics approaches and activities.
• Describe the procedure of performance review.
• Identify the most important and growing application areas.
• Evaluate and analyse case studies that connect hands-on expertise with real-world applications.

What Topics are Covered in this Course?

The outline of this course is vast; it requires a lot of time to comprehend the topics covered in this course. If you wish to get an overview of what type of topics are going to be in the course, then we can help you with that. Here are some topics mentioned by our ITECH2303 academic assistance service experts. Take a look at it -

• The requirement for data analytics
• Data ethical issues
• Model building
• model representation.
• Important components of data analytics
• Characteristics of data analytics
• Various operations for data analytics.
• Principles of various methods for data analytics.
• Performance evaluation and management
• Case studies in data analytics.
• Key and emerging application areas.

Study Materials for Completing the Course

If you are looking for some study materials to complete your course effectively, then we have some best study materials with us. We will help you to complete some good study materials for you. Take a look at it -

• Selvanathan S, Selvanathan S and Keller G, Business Statistics: Australia New Zealand Edition 8 ISBN 9780170439527
• Tsai, Chun-Wei, Chin-Feng Lai, Han-Chieh Chao, and Athanasios V. Vasilakos. "Big data analytics: a survey." Journal of Big data 2, no. 1 (2015): 1-32.
• Runkler, Thomas A. Data analytics. Springer Fachmedien Wiesbaden, 2020.
• Russom, Philip. "Big data analytics." TDWI best practices report, fourth quarter 19, no. 4 (2011): 1-34.
• Elgendy, Nada, and Ahmed Elragal. "Big data analytics: a literature review paper." In Industrial conference on data mining, pp. 214-227. Springer, Cham, 2014.
• Zakir, Jasmine, Tom Seymour, and Kristi Berg. "Big Data Analytics." Issues in Information Systems 16, no. 2 (2015).
• Fisher, Danyel, Rob DeLine, Mary Czerwinski, and Steven Drucker. "Interactions with big data analytics." interactions 19, no. 3 (2012): 50-59.
• Aggarwal, Charu C. "An introduction to social network data analytics." In Social network data analytics, pp. 1-15. Springer, Boston, MA, 2011.
• Singh, Dilpreet, and Chandan K. Reddy. "A survey on platforms for big data analytics." Journal of big data 2, no. 1 (2015): 1-20.

Sample Assignment Done Under the Guidance of Our Experts

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What is the Difference between Data Analytics and Data Analysis?

Most people get confused about the terms data analytics and data analysis. Both of these look very common, but they are totally different. To give you a clear idea about it, here are some basic differences between these two mentioned by our online assignment help experts. Take a look at it -

• Data analytics is a common type of analytics that is utilised in a variety of settings, including the health sector, business, communications, and insurance, to make data-driven choices and take action. Data analysis is a specialised kind of data analytics that is used to examine data and extract usable insights from it in organisations and other domains.
• Data analytics is gathering data and inspecting it in general, with one or more applications, whereas data analysis entails creating a dataset, doing research, cleaning the data by deleting Na values or any outliers and converting the data to give a useful result.
• We utilise data analytics to anticipate what will happen next or what will be next whenever someone wants to know what will happen next or what will be next. In contrast, data analysis examines previous datasets to determine what has occurred thus far. Both data analytics and data analysis are required to comprehend data; one is valuable for forecasting future demands, while the other is necessary for undertaking data analysis to look into the past.

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