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Reflective journal on capstone project experience and professional exposure

Professional Skills Developed During Professional Exposure. Map your outcome to ACS CBOK knowledge

a) Identify the specific skills that your specific practical exposure best matches to and state why that is so.

i) Include a good example from your practical exposure to demonstrate how you may have developed in the skills identified.

ii) Justify yourself by filling in the “Graduate Outcomes Table” (Appendix A). The justification should reflect the stage you are in your development of the skill.

b) As you look into the future as a networking graduate, use your professional exposure to address the following questions:

i) Based on your professional exposure what would you like to change in the way and manner you approach your study? Explain.

b) What would you recommend to those who structure your degree program to change to ensure you become a successful networking professional in future?

c) Your plans for your future as a networking graduate.


Technical Skills Developed During Exposure

a) Summary of overall professional exposure

The program [1] helps in understanding the importance of artificial intelligence with reference to the ability in decision making using enormous data generated through routine action. In particular, this program gives insight for the opportunities such as machine learning developer, robotic science for developing prototypes, information and communication technology (ICT) and Big Data analysis to integrate data from various sources and structuring them using query language, Python, as well as statistical Computing language. I also learned that in order to develop predictive models, it is important to have expertise on cloud computing and application, strong computer programming skills, and analytical skills.

b) Prior knowledge to understand the professional expectation

Prior of knowledge regarding technical skills include working experience with programming languages such as Python and Java. in addition to this, I also have special edition in computer aided design and computer aided engineering that uses the software for controlling the machine tool. To link with the newly learned skills, it is important to understand how to arrange “Big Data” into structured language and develop predictive models that can serve decision-making intelligence for any sector [2].

c) New technical skills or knowledge learned

The new skill that is learned in this professional exposure program is SQL that offers a unified platform for managing data. This is helpful in maintaining a database engine that can help in collecting information from third-party sources, ingesting them seamlessly, and helping in storage as well as retrieval of vast amount of data.  Furthermore, these data can be integrated with python and scalable programming that can enable enterprises to gain predictive according to the market requirement and conditions [3]. Other learning includes evidence-based understanding from companies like Amazon and Google that uses deep learning, computer perception, and data representation; using the available information from worldwide sources.

d) Where to use that exposure in future?

My future objective is to develop career as a data scientist in ICT sector. This exposure helps me in understanding what are the key requirements related to the expertise, computer programming, and soft analytical skills that are mandatory in this sector. Furthermore, I also learned about important software such as Hadoop, Spark, MapReduce, and cloud computing which can develop my basic requirement and competency level to develop a carrier in Big Data analytical science. I also learned that, artificial intelligence is the current source of energy for innovation which have many prospects in the near future regarding decision making, predictive models, computer parts, perception and data representation, in which I can grow my career [3].

Professional Skills Developed During Exposure.

a) Specific skills that the specific practical exposure best matches

i) Examples from your practical exposure

One of the practical exposure example is the “Deep learning” concept, in which we can input the data to train a predictive model, test the model for productivity, validate it which statistical meaning, and obtain a robust predictive model to obtain inferred output. This concept is an integration of the fundamental computer science and programming with the mathematical concept of probability and statistics [1]. The development of skill is enforced by the evidences of its application in real life system as well as hands-on training given by the experts. In particular, the data modelling requires clustering of the information, classifying the data set, and using mathematical concepts such as Bayes Network, Hidden Markov modelling and ANOVA test to build and validate the predictive model.

ii) Graduate Outcomes Table



b) Future as a networking graduate

i) Modules to change in the way and manner aligned to studies

It is important to have advanced learning on computer programming, Python and project R. This will give a connected learning of computer program, data management system, as well as statistical interface to develop analytical skills. Thus, in addition to theory related to soft computing and algorithm, there must be sufficient emphasis on practical application of programming and algorithm designing, using real life case analysis.

ii) Recommendation to those who structure the degree program

In particular, it is vital for the students to obtain industrial exposure. The coursework must include industrial visit, hands-on training on AI and deep learning. In addition to this, the course must give focus on computation and algorithm designing for data analysis. This will motivate students to learn the current standards of AI and hence newer development can be introduced from the academic learning programs. Likewise, it will also be effective if students will be encouraged to complete project modules in association with industries. This will give a hands-on experience as well as real-time learning of the industrial requirements.

b) Plans for your future as a networking graduate

i) Types of work environments attract to the most

Particularly, the work environment implemented in Google Inc. is of high interest to me. The reason is not associated with flexibility in work schedule and competitive salary; but the opportunities it offers to their employees for bringing innovation. This include dynamic working environment in a cross-disciplinary environment, quick learning from available market tools, and implementing newer solution to make human life easier. The data analytics prospect in such environment is motivating as they offer ample opportunity for application of computer programing in developing predictive and decision-making models [4].

ii) Specific networking challenges for the future

ICT is one of the commonly used solution in business environment that manages financial, outsourcing, resource management, and communication purpose. However, the market-based decision related predictive modelling is still in its preliminary stage. Common example includes security management, using success and failure profile for future share market analysis, and linking the social dynamics with market value of shared. These areas require further development of AI algorithm [5].

iii) Ideas about your future direction and their development during the practical exposure

Indeed, these discussed prospect are developed during the professional exposure. Here, I learned how AI can be effective as well as harmful for mankind, if not used appropriately. This also mandates that social and business decision making models require input not only from computer programmers but also from other cross-disciple sector to work as a team.

iv) Specific aspects of your professional exposure: Likes and dislikes

I enjoyed the concepts such as deep learning that is based on neural network within human brain. The dislike includes loopholes in the algorithm that still resist the implementation of AI in automated car, market data analysis via internet, and several Google modules. This influences my thinking with motivation to develop soft computing skills and learning statistical modules such that I can contribute to the future as data analyst [5].


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