Foundation Skills in Data Analysis

This article discusses the foundation skills in data analysis and provides guidance on using charts, tables, and graphs to analyze and present statistical data. It covers the development of evaluation standards for visualizing data and includes examples and evaluations of different data visualizations. The article emphasizes the importance of clear and logical presentation of data and provides tips for achieving effective visualizations.

Running head: FOUNDATION SKILLS IN DATA ANALYSIS 1
Foundation skills in data analysis
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FOUNDATION SKILLS IN DATA ANALYSIS 2
Foundation skills in data analysis
Various scholars have acknowledged the use of charts, tables, and graphs in analysing and
presenting different sets of statistical data (Robbins, 2012). However, the ease of understanding
the information is entirely reliant on the visualisation skills portrayed in the report (Ware, 2012).
Achieving sufficient visualisation of the data when using tools such as charts, graphs, and tables
is made through various set standards or criteria that can govern the development of the charts as
discussed in the sections below.
Part A: Developing Generic Evaluation Standards
Common evaluation standards for charts, tables, and graphs
1) The charts, tables, and graphs should have titles as well as be numbered using appropriate
indices. Titles give an overview of the entire information being analysed or presented (Boers,
2018).
2) The numerical data should have a similar number of significant figures for all the entries or
rounded off to the same number of decimal places for uniformity.
3) Axes should be well labelled with representative variables along the x- and x-axis indicated
as well as the units of measurements for standardised units.
4) The logical presentation of the data should be observed in the charts, tables, and graphs either
alphabetical or based on the chronological occurrence of events (Collazo, Goergen, & Smith,
2018). This could entail the yearly, monthly, or weekly arrangement of events.
5) The sources should accompany the use of the charts, tables and graphs if they are not
generated from raw data. Sources act as a reference that makes the reader authenticate the
information.

FOUNDATION SKILLS IN DATA ANALYSIS 3
6) Using abbreviations, short forms, or codes in the graphs, tables, and charts should be
illustrated to give meaning. Adding explanation reduces potential confusion of the data.
Evaluation standard for Graphs
7) Using a bar or grouped graphs should have a common baseline upon which all the variables
originate.
8) Graphs with multiple variables under investigation should have a legend for distinguishing
the different variables. Use of legends facilitates faster understanding of the data.
9) Using grouped charts with several variables should entail independent assigning of group
names with distinct features that enables tracing of a single variable across the groups. Use of
colour codes for the variables would ensure the distinguishing of variables among the groups
(Borkin et al., 2013).
10) Simple and common scales should be applied to grouped graphs or those with multiple for
uniformity and faster judgment to the data being analysed or presented.
Evaluation standard for Tables
11) Using tables should be accompanied with appropriate labelling of the rows at the top of the
table or the columns on the left side of the table (Gelman, 2011).
12) The columns and rows in the charts should be separated using gridlines or using sufficient
spacing between the entries. Grids or larger spaces between the entries makes the reader trace
elements or entries with ease.
13) The information or data in tables should be simplified and categorised. Streamlining data
eliminates repetition thus avoiding data redundancies during the presentation.
14) Using long tables that extend to the next layout should have the labels of the rows and
columns at the beginning of every layout/ page as well as the title.

FOUNDATION SKILLS IN DATA ANALYSIS 4
15) The cells that have no data entries should not be left blank. Use of abbreviation such “NA” or
other means such a dash (-) which should be explained in the legend or the table key.
Evaluation standards for Charts
16) Using line charts that have many variables should be evaluated using different trend lines.
The trend lines could be of different colours, thickness, dashed, or continuous for fast
isolation.
17) The time series data or information should be presented using line charts whereby the x-axis
to indicate the time (years, months, weeks, days, hours) while the y-axis to depict the
variable under investigation.
18) Pie charts should be used to represent portions of dependent variables of data expressed as
percentages for quantification purposes. Colour codes for different parts contribute to a more
natural distinction among variables.
19) Line charts should be used to analyse data or represent information that has multiple
dependent variables aligned on the y-axis with a single independent variable aligned along
the x-axis.
20) Line charts should be used for forecasting and predicting the outcomes of a time series data.

FOUNDATION SKILLS IN DATA ANALYSIS 5
Part B: Evaluation Report
Evaluation 1
(Source:http://cf.cdn.unwto.org/sites/all/files/pdf/
visa_openness_preliminary_report_africa_2019.pdf)
From the graph, it can be deduced that some criteria have been observed by the researchers. For
instance, the figure is given a title as well as labelling the axes as recommended in the first
standard. Concisely, the researcher has arranged information in a logical way where the y-axis
shows years of occurrence as mentioned in criteria 4. The information about the variables has
been explained in the legend section to assist the reader in evaluating and internalisation the
report, which is a standard, illustrated in eighth criteria from the previous section. Moreover, the
units are given on the y-axis, which ensures the reader understands the currency the information
is presented in as per the third standard.

FOUNDATION SKILLS IN DATA ANALYSIS 6
Evaluation 2
(Source:http://cf.cdn.unwto.org/sites/all/files/pdf/
visa_openness_preliminary_report_africa_2019.pdf)
The grouped bar graph has a detailed legend that aids in understanding the information as a
necessity of criteria 8. The source to the data is indicated for the data in the graph, in essence,
fifth criteria. Sources enable the reader to seek further clarity from the source thus helping in
authenticating the information. The diagram is also numbered (as figure 4.1) with a title below
the index as required by the first standard. Moreover, the scale is simple whereby whole numbers
are used along the y-axis for the variables are required by the tenth criteria, which make the
information user-friendly to when analysing.

FOUNDATION SKILLS IN DATA ANALYSIS 7
Evaluation 3
(Source: http://cf.cdn.unwto.org/sites/all/files/pdf/vietnam_country_report_2012.pdf)
The diagram indicates a grouped bar graph, which is numbered or indexed as well as the title in
the first criteria. Using criteria 16 under the charts sections, the author managed to use colour
codes to represent the variables. It can be observed that a single colour has been assigned to a
single variable that cuts across all the groups. Moreover, the eighth standard is also well utilised
where the legend has been used to distinguish individual variables in the groups. With the source
of the data/ information given, the graph employed the fifth criteria to achieve a more insightful
interpretation of the data.

FOUNDATION SKILLS IN DATA ANALYSIS 8
Evaluation 4
(Source: http://cf.cdn.unwto.org/sites/all/files/pdf/vietnam_country_report_2012.pdf)
The chart presented observes certain evaluation standards. From the diagrams, it can be depicted
that the use of percentages to represent portions has been used which gives the sense of
comparison to the reader at a single glance. Thus, they were developed using the 18th criteria
from part A. Additionally, the titles have been given according to the first standard as well as
numbering of the charts, which enables the audience to make quick referrals to the data. The use
of legend has been used to clarify the meaning of the data, which is ascertained in the eighth
criteria and the source for instant references to the data for the fifth criteria.

FOUNDATION SKILLS IN DATA ANALYSIS 9
Evaluation 5
(Source: http://cf.cdn.unwto.org/sites/all/files/pdf/ce85_5_a_report_of_the_sg._en.pdf)
The diagram above is a section of a large table, which extends to the next layout as shown by the
break as page 4. The table indicates labelling of rows and columns within every plan as a
fulfilment of the 14th standard. Labelling within every page saves the readers’ time who do not
need to scroll back through the report to identify the label for a given set of data in the row in
subsequent layouts. Additionally, the fifth and sixth criteria have been deployed in the table
where indices/ numerical indices are defined in the legend or the key at the end of every page.

FOUNDATION SKILLS IN DATA ANALYSIS 10
Evaluation 6
(Source: https://www.who.int/medicines/mdg/mdg8report2010_engw.pdf)
The table has horizontal grids that enable easier tracking of the information in rows thus
approving the use of the 12th standard. The source to the data is indicated for the data in the
graph, in essence, fifth criteria. Sources enable the reader to seek further clarity from the source
thus helping in authenticating the information. The information has also been simplified through
the given groups that help in eliminating the potential redundancies in the data as explained by
criteria 13. Additionally, the rows and columns are well labelled in the table according to 11th
criteria making the information to be well organised and thus easy to interpret.

FOUNDATION SKILLS IN DATA ANALYSIS 11
Evaluation 7
(Source: http://cf.cdn.unwto.org/sites/all/files/pdf/indonesia_country_report_2012.pdf)
The evaluation of the table in the diagram is seen to observe the first criteria from section A
where the numbering and the title of the table have been performed. Moreover, the columns
representing currency have been labelled by using the financial years where the units of the
currency have been indicated as per the third criteria. On the other hand, grids lines have been
used in the table to distinguish the rows and columns as a fulfilment of the 12th criteria. The
numeric data has observed standards for significant figures as dictated by the second standard in
the first section. Moreover, the acronyms have been defined in the legend for a, b, and c thus
fulfilling the sixth and eighth standards appropriately.

FOUNDATION SKILLS IN DATA ANALYSIS 12
Evaluation 8
(Source:http://cf.cdn.unwto.org/sites/all/files/pdf/
visa_openness_preliminary_report_africa_2019.pdf)
The line graph has been used to represent multiple variables, in essence, many dependent
variables with a single dependent variable. Additionally, the line chart has been used to represent
time series data with the time indicated on the x-axis while the measurement variables on the y-
axis whereby the information has been arranged logically from the earliest point of occurrence to
the most recent. These aspects collectively fulfil the standards mentioned under criteria 4, 16, 17,
and 19 from the first sections of the report. Moreover, the rules under the first and second criteria
that requires charts and graphs to have titles and simple scale across the data have adhered.

FOUNDATION SKILLS IN DATA ANALYSIS 13
Evaluation 9
(Source:http://cf.cdn.unwto.org/sites/all/files/pdf/
visa_openness_preliminary_report_africa_2019.pdf)
The line chart shown above has a common baseline that has its origin at zero, as depicted along
the y-axis as a standard fulfilled under criteria 7. Concisely, the information has been presented
in a logical was along the axes which are labelled as well as the addition of the title to the chart
thus combining criteria 1, 2 and 4. Having used multiple variables under the same chart, the
author further used different trend lines that can make a tracing of the individual variables easier
than using similar trend lines, as per criteria 16 and 19. The combination of these standards has
made the line chart user-friendly representing numerous information in a simple manner
(Hawkins & Olwell, 2012).

FOUNDATION SKILLS IN DATA ANALYSIS 14
Evaluation 10
(Source: https://www.e-unwto.org/doi/pdf/10.18111/9789284411900)
The use of line charts to predict or forecast trends has been explored as per the 20th standard.
The dotted section of the trend lines indicate the predicted values that the variables have to be for
proper development. Additionally, the source to the data has been incorporated as a requirement
of criteria five alongside a detailed legend that interprets the trend lines used in the chart. As
aforementioned, the title, numbering of the chart, and labelling of the axes have been done as
common standards that are observed during the deployment of the descriptive statistics. The use
of a simple scale is also evident in the charts as illustrated in the rule under the tenth criteria.
Conclusion
Visualisation can be achieved through the use of simple analytical techniques such as the tables,
graphs, and charts as demonstrated in the second sections. The primary concern is to establish a
set of standards that would lead into development of data presentation and analysis tools that are
easier to understand with adequate information. The set standard or criteria would ensure that the
author observes the rules that are audience-based in forming the data presentation approaches
that would in turn enable proficient evaluations. Mostly, readers seek to understand information

FOUNDATION SKILLS IN DATA ANALYSIS 15
without struggle as well as with minimal directions and explanations. Therefore, using simple
visualisation tools such as tables, graphs, and charts would help the audience to digest the
findings or the information presented at a glance or within a short time.
References

FOUNDATION SKILLS IN DATA ANALYSIS 16
Boers, M. (2018). Designing effective graphs to get your message across. Annals of the
rheumatic diseases, 77(6), 833-839.
Borkin, M. A., Vo, A. A., Bylinskii, Z., Isola, P., Sunkavalli, S., Oliva, A., & Pfister, H. (2013).
What makes a visualization memorable?. IEEE Transactions on Visualization and
Computer Graphics, 19(12), 2306-2315.
Collazo, R. A., Goergen, C., & Smith, J. Q. (2018). Chain event graphs. Florida: CRC Press.
Gelman, A. (2011). Why tables are really much better than graphs. Journal of Computational and
Graphical Statistics, 20(1), 3-7.
Hawkins, D. M., & Olwell, D. H. (2012). Cumulative sum charts and charting for quality
improvement. Berlin: Springer Science & Business Media.
Robbins, N. B. (2012). Creating more effective graphs. New Jersey: Wiley.
Ware, C. (2012). Information visualization: perception for design. London: Elsevier.