Answer 1.
I am Rohith Reddy Gade with master degree holder in Information Assurance. I have done my bachelor’s in Mechanical Engineering. During my three years course on information assurance, my coursework focused on policy and the security of both data and non-digital information. I have gained knowledge of cybersecurity, data analysis and cryptography. As an expert in information assurance, I am well acquainted in setting up policies to protect the most valuable physical and digital materials of an organization. I have extensive knowledge in many areas of computer science, computer security, cybersecurity and internet security, My topic of study involved business continuity planning, disaster recovery, mission assurance, IT risk management, Risk assessment, system engineering and security controls. My bachelor’s degree in mechanical engineer gave me in-depth knowledge on designing, constructing and use of machines. Through my course work, I gained a basic understanding of the use of heavy tools and machinery work. As a mechanical engineer, I designed new batteries, athletic equipment, personal computer, automobile engines, electric power plants and air conditioners. I am interested in the University of the Cumberland as it provides world-class education and for last 125years it is maintaining its position as one of the best educational institute. Its excellent faculty in my chosen area attracted me. This university has an influential school of data sciences and machine learning and the best minds of the country studies over here. So I want to be a part of this university that has such a vast alumni network. Apart from an educational background, this university provides a wide range of facilities at an affordable range. The online class facilities, course content, university library, comfortable and competitive environment made me more confident to chose this university where I can continue my doctoral programme.
Answer 2.
Research says the use in tandem of data science, deep learning, and AI has opened the door for countless possibilities. AI is simply a machine capable of imitating or simulating human thought or behaviour (Brynjolfsson & Syverson, 2017). AI has a vital role to play in defining the benefits we could expect in the future. Under this, there is a subset called machine learning which is now the foundation of the most exciting aspect of AI. Machine learning has made several breakthroughs that once seemed almost impossible by having machines learn how to solve problems on their own. After thorough research on this field, I have chosen my area of research on Data Science for AI and Machine Learning Using Python. The two most influential technologies of the world and that will flourish in future are Data Science and Artificial Intelligence. Today Data Science has brought about a fourth world technological revolution. Data Science is an interdisciplinary area of processes and systems for extracting information or observations in various forms from the data (Van Der Aalst, 2016). In other words, data science helps AIs to find solutions to problems by connecting related data for future use by pulling my interest in this field. However, machine learning is the AI branch which works best on data science. Data science covers many associated areas such as mathematics, statistics and programming on which I have a keen interest.
Along with it, Machine learning is an artificial intelligence branch where a class of data-driven algorithms allows software applications to become highly accurate in predicting results without any specific programming requirement (Jordan & Mitchell, 2015). The most significant use of AI in Robotics. The processors in these robots have been fed a substantial quantity of data on the method of harvesting lettuce. This AI revolution would improve not only productivity but also open doors to new possibilities. Robotics is a significant growth area for the AI community, so it is no surprise that there are plenty of startups researching to develop the field further. The creativity of Olis Robotics is currently manifesting in a plug-and-play controller equipped with tech framework, which is powered by AI. Using 4G/5G networks, the controller and proprietary software will run tethered robots on the ocean floor, satellite servicing robots using high latency space satellite connections or industrial robots cleaning up a hazardous chemical spill on the ground. Innovation can broaden the role of robots exponentially in shaping human development and exploration. As per my coursework in masters and bachelors level, I want to gain insight into an autonomous vehicle. When it comes to AI innovations, this topic takes most of the spotlight.
PwC estimates that by 2030, 40% of Europe’s miles will be reached by autonomous vehicles. According to the US Transportation Department, 63.3% of the $1.139 billion goods transported in 2017 were pushed on the highways. This makes road freight one of the world’s largest producer of pollution. My research area will be on how AI impacts on highly automated driving. The advantages of self-driving cars and lorries in this context are significant. The concept of travelling for hours without losing concentration and optimize fuel usage and routes to help improve energy and time management. Until AI takes over the driver’s seat entirely, it’s being used as a co-pilot to win customers, regulators, manufacturers’ trust. AI can be useful in circumstances where flesh and blood drivers are susceptible to make human errors by analyzing data feeds through their sensors. In cases where flesh and blood drivers are vulnerable to make human mistakes, AI can be useful by analyzing data feeds through its sensors. I want to establish myself as a data scientist who should also have good knowledge of algorithms for machine learning. These algorithms are Artificial Intelligence. Artificial Intelligence is the intelligence which the machines possess. This is based upon the innate wisdom that animals and humans share. Artificial Intelligence uses algorithms to carry out autonomous actions (Brynjolfsson & Mcafee, 2017). There are countless variables without managing the external environment, so AI requires a lot of learning. Various companies are researching AI’s driving applicability, but Waymo and Tesla have made the most impressive achievements. Based on evidence and my interest, I want to extend my hand in this research.
Answer 3.
The position of a data scientist is a tricky profession now. This has staying power in the marketplace and provides ways to make meaningful contributions to their businesses and communities at large for people studying data science. The area of data science and AI will see many opportunities; there is currently a massive gap between consumer demand and qualified practitioners; it’s time to get upskilled. My current skills match the requirement and make me a good fit for the programme. I can contribute mainly to this programme as I have a vivid knowledge of cybersecurity, internet security, computer science, risk management, risk assessment. Being a mechanical engineer, I am capable enough to get through the basics of course on the autonomous vehicle quickly. My good grasp over computer knowledge along with engineering knowledge, will make my struggle a bit easier than any other novice. Few things that made me realize to chose as my doctoral programme are:
Companies are struggling to manage their data- As a data scientist; I can help businesses make changes with the data they collect, making them pay off quickly as well as overtime.
New Data Privacy Legislation The data scientists need more- Due to the need for real-time monitoring and safely storing data, the GDPR ( General Data Protection Regulation) increased the dependence businesses have on data scientists.
Data Science still evolving- Data science seems to have plenty of prospects for evolution in the next decade or so. Since it shows no signs of slowing down, that’s good news for those who want to get into the industry.
Data scientists have expertise on demand- Since 2013, they have increased by 256 per cent, showing that businesses understand the importance of data scientists and want to add them to their teams.
A staggering rise in data- According to a report on current and potential growth in technology, 5 billion customers engage with technology every day. That figure will rise to 6 billion by 2025, reflecting three-quarters of the world’s population. The output of data is on the rise and data scientists will be at the forefront of helping businesses make successful use of it.
High probability of career growth prospects- Recently, LinkedIn named data scientist as its most promising career of 2019. One of the reasons it got the top spot was because the average salary is $130,000 for people in the position. According to LinkedIn’s study also looked at the probability that people as data scientists will get promotions and gave a career development score of nine out of 10.
In recent years, python programming has been one of the most popular Data Science languages. And Python offers an ideally efficient and versatile framework to build on when it comes to developing Machine Learning systems.
Answer 4.
My master’s degree in information assurance and bachelors degree in mechanical engineering will help me to build a strong foundation and connect easily with the technical points that form the centre of Data Science practice. My other skills include programming, Knowledge of Sas and other Analytical tools, Adept at dealing with unstructured information.
Programming- I have vivid knowledge of programming language like Perl, C/C++, Python, Java and SQL. I have an additional certification course on Python. The programming language will help me to form an unstructured set of data.
Knowledge of Sas and other Analytical Tools- A clear understanding of analytical software comprehension is what will help me to derive the useful insights from the washed, massaged, and structured data collection. The most common data analytics tools used by data scientists include SAS, Hadoop, Spark, Hive, Pig, and R, and I have a good grip on most of them. In addition to this, certifications helped me to be an expert in this field.
Adept at working with understand data- To become a data scientist, one must have the ability to understand and manage data in an unstructured manner that comes from different channels. My capability to well adept unstructured data can make myself fit for this doctoral programme.
Not only the technical skills, but it is also necessary to have non-technical skills which refer to personal skills, educational qualifications, certifications and many more.
My skill as business acumen and making up a successful business model will improve my channel technical skills and produce effectively. My effective communication skill is an added benefit to present myself perfect candidate to apply for this programme. During my coursework, my great data intuitions helped me to score well in my subject and have made my basics strong.
Apart from this, my degrees helped me to gain a good hold on fundamentals that include Matrices and linear Algebra functions, ETL (Extract Transform Load), Relational Algebra, Hash Functions and Binary Tree. My grip on statistics included Bayes theorem, skewness, Exploratory Data Analysis, Probability Theory, Probability theory, Percentiles and Outliers, Cumulative Distributions function (CDF). I hold excellent hands-on knowledge of various visualization tools such as Tableau, google charts, Datawrapper and Kibana. I am trained in Data ingestion tools such as Apache Flume and Apache Sqoop. I have been an expert in MS Excel, R and Python, Tableau, Spark.
Answer 5.
As my long term goal, I want to get to the point where I can call myself a data scientist with enough expertise and projects under my belt. I want to become a manager in a particular position I’d built in my career as a consultant. Also, somewhere down the road, I would like to dabble in a startup if I get a good idea and enthusiastic people along with it. Data science aims to develop the means to extract business-focused insights from the data. This includes an understanding of the flow of value and knowledge in a market and the ability to use that understanding to find opportunities for growth. I will get a full understanding of a data science project’s life cycle in five years. To get the secret patterns in the details, I would understand and appreciate the beauty of visualization. I will be well versed in Python by then. I will slowly understand how inconsistent a data can be and ask if it makes sense to me as a human being before feeding it into a computer. I would be an expert by then, and my curiosity in learning would not be extinguished, and I will continue to explore more specialized techniques like NLP (Britz, 2015). It will be a great trip to teach and learn with them. I wish to connect with people from many backgrounds, gain an understanding of their domain and combine it with your experience to create a solution that fits the company’s needs. I’ll be a developer, a statistician, an informatics engineer and a customer’s voice all at once. Five years down the road, you’re likely to be leading a small team, teaching them a few things and learning a lot more from them while addressing more complicated issues and domain-specific problems. Whenever data holds the key to solving a problem, I want to be the go-to person. I dream that it will be the secret to solving tomorrow’s most difficult problems because almost all will have collected data against it in about a decade. Data science is a huge field and continues to grow every day. Remaining tuned.
References
Britz, D. (2015). Understanding convolutional neural networks for NLP. URL: http://www. wildml. com/2015/11/understanding-convolutional-neuralnetworks-for-nlp/(visited on 11/07/2015).
Brynjolfsson, E., & Mcafee, A. N. D. R. E. W. (2017). The business of artificial intelligence. Harvard Business Review, 1-20.
Brynjolfsson, E., Rock, D., & Syverson, C. (2017). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics (No. w24001). National Bureau of Economic Research.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
Van Der Aalst, W. (2016). Data science in action. In Process mining (pp. 3-23). Springer, Berlin, Heidelberg.
