Big Data Analytics Project Report
As a part of my Course work at FMSR, I was assigned to upskill myself in the field of my choice but with emphasis on Analytics.
This activity has been one of the most exciting and self reflective exercises in my MBA Program.
One couldn't utilize his time in a better way, and even be graded for it. I am thankful to my teacher for the quick adoption of this project over assignments and activities which prove to be off little significance in a persons professional life.
Here, is a brief Summary of the 3 courses I have done due to the sufficient time provided by my teacher along with their relevant details such as the instructor the course details and my learnings from it.
About the Instructor
Dr Sebastian Binnewies
Join educator, Sebastian - a researcher and teacher from Griffith University
A faculty member in the School of Information and Communication Technology at Griffith University. I joined Griffith after completing my PhD in the area of Artificial Intelligence. His research interests include data mining, learning analytics, and knowledge representation.
This course explored big data analytics and its applications. Topics covered included the key concepts of big data; an introduction to the data analytics cycle; the relationship between big data and social media; the opportunities and challenges presented by big data analytics; and practical ways big data analytics can solve problems in a variety of industries.
STUDY REQUIREMENT 2 weeks, 4 hours per week
LEARNING OUTCOMES SYLLABUS
• Explain the key concepts of big data analytics
• Evaluate data from different sources
• Identify implications of big data analytics Describe how big data analytics can solve problems across disciplines
• Introduction to big data and where it comes from
• Overview of the data analytics cycle
• Social media platforms and types of data Applications of big data across different industries
• Opportunities and challenges for big data analytics
My learnings
This was a brush up for everything my teacher thought me, and a revision before I went to higher level courses they were practical examples which improved my insights about the subject and its relative depth and extensive practicality of it in our lives.
About the Instructor -
Jungwoo Ryoo
Jungwoo Ryoo teaches IT, cyber security, and risk analysis at Penn State.
Jungwoo is a professor of information sciences and technology (IST) at the Pennsylvania State University (Altoona College). He is also a graduate and affiliated faculty member of the College of Information Sciences and Technology at Penn State, and a tenured faculty member of the B.S. degree program in security and risk analysis at Penn State Altoona
Course details
The explosion of data in recent years has made the field of data science—in which professionals work to glean insights from this abundant information—increasingly more vital. If you're looking to pursue a career or to work with experts in this rapidly-growing field, it's crucial that you familiarize yourself with the tools of the trade. In this course, instructor Jungwoo Ryoo helps to acquaint you with some of the most well-known data science tools in the areas of cloud computing, distributed file storage, distributed processing, and machine learning. Throughout this course, Jungwoo provides coverage of Proxmox, Hadoop, Spark, and Weka, discussing how to install and leverage each tool in your data science workflow. To wrap up, he explains how Hadoop, Spark, and Weka can work collaboratively to produce the best results.
Learning objectives
Enabling technologies in data science
Cloud computing and virtualization
Installing and working with Proxmox, Hadoop, Spark, and Weka
Managing virtual machines on Proxmox
Distributed processing with Spark
Fundamental applications of machine learning
Distributed systems and distributed processing
How Hadoop, Spark, and Weka can work together
Skills covered in this course
My Learnings -
It made me better to understand as well relate to the practical experience of having an on hand experience with data science tools, which we missed out due to uncertainties.
I took me into the shallow waters of coding I am glad it didn't go into the depths and setting up virtual machines on a Linux system and how we could interpret data better.
The Lead Educator
Fernando Lucini
The educator on this course is Fernando Lucini, who is the Artificial Intelligence Lead for Accenture United Kingdom and Ireland. Based in London, Fernando is a passionate and experienced senior leader with extensive experience in Artificial Intelligence software and business solutions. He has spent 18+ years in the enterprise software industry, creating technologies to automate and understand text, speech and video data. He has integrated these technologies into business solutions for Fortune 100 companies including those in Financial Services, High Tech, Pharmaceuticals, leading Government and Defense Regulators as well as other public sector entities.
Here’s what Fernando has to say about Artificial Intelligence,
“There is opportunity in AI beyond our imagination, the winners will be the ones getting to production early”
Mentors
Serina A. is a mentor on the course. Serina A. has worked for Accenture within Digital AI for the past 2 years across industries - in Health and Public Service, Communications, Media and Products. Her projects have included working with a large telephone network to optimise their loyalty app, using dashboards to enable efficient reporting, as well as working with the police to analyse crime data to drive insights and improve efficiency within the police force. She specialises in Data Science, and has used tools such as Tableau, Python, Alteryx & SQL, to implement AI. Reach out to her for any AI or Data Science related questions.
Susannah M. is a mentor on this course. Susannah is a Technology Consultant, specialising in Intelligent Automation solutions for Public Sector and Education clients. She is passionate about Data Science, Machine Learning and Artificial Intelligence; particularly the ethics surrounding AI and the practical applications of AI in industry.
DIGITAL SKILLS: ARTIFICIAL INTELLIGENCE ACCENTURE 96% AVERAGE TEST SCORE
This online course helped discover the potential of Artificial Intelligence (AI) and how it can change the workplace. It enhanced understanding of AI with interesting facts, trends, and insights, and helped to explore the working relationship between humans and AI.
STUDY REQUIREMENTs - 3 weeks, 2 hours per week
LEARNING OUTCOMES SYLLABUS
Week 1: Introduction to Artificial Intelligence
Week 2: Artificial Intelligence in Industry
Week 3: Adapting your skills to work with Artificial Intelligence
• Describe the origins and advent of AI Explain the relationship between AI and Automation
• Reflect on the application of AI to your own context
• Identify key shifts in the workplace influenced by AI
• Assess the impact shifts in the workplace may have on roles and responsibilities
• Identify how the relationship has changed between AI and humans
• Identify future skills required to work and interact with AI
• Produce an action plan to adapt your skills for the future
• What is Artificial Intelligence and where did it come from?
• AI in Action
• What does this mean for me?
• Impact of AI on Individuals
• What does this mean for me?
My Learnings -
This was my last course on the list wish I could add in more but there are other assignments piling up, to sum this course gave me a new perspective of the world of Artificial Intelligence and busted the fact that AI will take away jobs but rather it would create around 66 Million people will loose their jobs due to AI but around 133 Million jobs will be created gradually due to AI.
Thank you, for this amazing journey.
Anas Ahmad Khan
18 MBA 56
Gi9233
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