Sample Computer Science Paper on Coding and Data Analysts


Steve Jobs was right in saying that every individual in the country had to learn ways to
programme a computer as, in the long run, it teaches us to think. During the module, the Python
programming language was introduced to indicate how data can be processed and visualised
(Demšar et al., 2013, p. 2355). Without knowledge of a primary coding language, it would turn
out to be more laborious and ineffective when analysing and obtaining meaning from data.

Steve Jobs Quote

Learning to code focusses on various factors not only about using a programming
language to manipulate data but also, to create a system where critical thinking, decision making,
as well as relationships between data are examined. It therefore means that for one to be a good
computer programmer, the above skills have to be implemented and put to good use. The
interpretation of data opens other pathways where different computer enthusiasts can create their
own data forms, and manipulate them to gain results. The base case for the argument by Steve
Jobs means that an avid programmer is able to think about systems, concepts and ideas that relate
to data analysis and in the process, be able to solve the problems that need to be solved.
The statement by Steve Jobs, therefore, still stands as most data science assignments and
duties require a set of computer programming skills (Demšar et al., 2013, p. 2354). In having the
skills, an individual is, therefore, able not only to clean the data, but also to analyse it and
implement the programming algorithms that are necessary. In my case, therefore, having
knowledge regarding Python gives rise to a greater domain on how databases are manipulated to
give rise to meaning (Demšar et al., 2013, p. 2355).

Coding 3
Taking the case of the Python programming language for example, engaging in studies
that regard to it creates and impacts an individual with a set of skills that as more exercise is
done, more data engagement possibilities are created. For example, it is through Python
programming language that a programmer is able to understand the methods and forms that can
be used in gaining knowledge regarding a set of data (Demšar et al., 2013, p. 2356). The
relationships between data are able to be exposed as data is cleaned, and if there are missing data
forms, they are noted and replaced. The functions therefore require an individual that is capable
of thinking out of the box, generate ideas, confirm if they correspond with the needed results, as
well as create a general and specific description of the data.
Today, learning institutions and business organisations, and technology firms have
increased the demand for computer code. In many fields, the ability to perform specific tasks on
data has improved the manner, in which we think and make decisions (Demšar et al., 2013, p.
2355). The complex data structures that are being generated day in day out require to be
complemented with avid and sophisticated algorithms (McKinney, 2012). Just as the mind acts
as a computer, and we do not try as hard to understand it, the same should be the case with data
science (McKinney, 2012). Through knowing the various syntaxes of a programming language,
for example, Python, the mathematical concepts that are at times predefined in the computing
language change our thinking abilities in a manner as when more complex and demanding
functions are used, the less effort our brains can use in solving problems (Grover, Jackiw, and
Lundh, 2019, p. 106). This means that an individual without a technical background cannot view
and analyse data the same way as the person who understands a coding language.

Coding 4
The argument by Steve Jobs therefore stands as the visualisation on the seaboards and
plotting using the tableau for example, enables a person interested in data to think of the ways to
handle the various mega data sets to be understood, despite the creation and development of a
generalised picture of data (Demšar et al., 2013, p. 2355). But for the programming languages,
such as Pythion, the exploration of in-depth data and the filtering of useful and needed
information would be tiresome and maybe impossible (Demšar et al., 2013, p. 2356). Therefore,
if the population is well versed with the programming languages that could create, manipulate,
and implement data, then the process of generating analysis, useful for the purpose of learning
and business, would not be sufficient (Demšar et al., 2013, p. 2357).
Personal Experiences

Particularly, learning Python has made it easier to quickly develop prototypes, perform
data mining services, as well as be able to manipulate the platforms that are currently there on
big data (Grover, Jackiw, and Lundh, 2019, p. 116). The reliance on Python is major as it
promotes the environment that one is in to be more productive, organised, and result driven,
making it one of the ideal languages to be used for data analysts (McKinney, 2012). The learning
of computer coding, therefore, promotes thinking as there is a large community and support base,
promoted by the open source language (Grover, Jackiw, and Lundh, 2019, p. 116). This means
that ideas can be generated while analysing and giving meaning to either small or large sets of
Upon learning Python, there has been an improvement on the knowledge and
understanding that I have regarding data. For instance, the ability to effectively code has
increased the desire to dig deep into other interesting topics, such as Big data and Machine
learning (García-Peñalvo et al., 2016, p. 19). Through diving into machine learning, it has

Coding 5
become easier to integrate, correspond, as well as contribute to KAGGLE (Grover, Jackiw, and
Lundh, 2019, p. 106). Through it, I have been able to discuss and analyse successfully the titanic
disaster that took place long ago.
Skills such as problem-solving, using logical and methodical approaches, as well as the
ability to model and interpret data have increased my level of mathematical and coding abilities.
Through coding, the skills of developing conditional statements that can systematically follow
each other and generate ideas imposed on the data, therefore, creating a system of conditional
thinking (Grover, Jackiw, and Lundh, 2019, p. 108). Throughout the module, attention to detail
has been emphasised and through its application to coding, has assisted in understanding what to
look for in data so as to attain desired accomplishments. The same can be said about abstract
thinking, which has sharpened my focus on a particular subject, project, or object.


In conclusion, Steve Jobs was right, noting that computer coding can teach you to think.
Data analysts have to be prudent coders, who can regard data as objects that can be integrated
into instructions, conditions, and sequences to be used to generate desired results. The
performance of the functions means that there cannot be an effective data analyst that cannot
write computer code well.

Coding 6


Demšar, J. et al., 2013. Orange: data mining toolbox in Python. The Journal of Machine
Learning Research. 14(1), pp. 2349-2353.
García-Peñalvo, F.J. et al., 2016. A survey of resources for introducing coding into schools. In
García-Peñalvo, F.J. et al. Proceedings of the Fourth International Conference on
Technological Ecosystems for Enhancing Multiculturality (pp. 19-26).
Grover, S., Jackiw, N. and Lundh, P., 2019. Concepts before coding: non-programming
interactives to advance the learning of introductory programming concepts in middle
school. Computer Science Education. 29(2-3), pp. 106-135.
McKinney, W. (2012). Python for data analysis: data wrangling with Pandas, NumPy, and
IPython. " O'Reilly Media, Inc.".
McKinney, W., 2010. Data structures for statistical computing in Python. In McKinney, W.
Proceedings of the 9th Python in Science Conference (pp. 51-56).
Mihas, P., 2019. Qualitative data analysis. In Mihas, P. Oxford Research Encyclopedia of
Vee, A., 2017. Coding literacy: how computer programming is changing writing. MIT Press.
Yunker, L.P. et al., 2019. PythoMS: a Python framework to simplify and assist in the processing
and interpretation of mass spectrometric data. Journal of Chemical Information and
Modelling. 59(4), pp. 1295-1300.