Fall 2023 PDEV 2302-0 10190 Data and Computing Skills (6
ECTS)
School of Education, ADA University
Instructor: Etibar Vazirov
Teaching hours:
- Wed 01:00 PM - 02:15 PM, Building B, B302
- Fri 01:00 PM - 02:15 PM, Building B, B301
Office Hours:
- Office hours:
 Tue/Thu from 03:00 PM – 03:45 PM, E311
 Wed/Fri from 03:00 PM – 03:45 PM, E311
-
E-mail addresses: evazirov@ada.edu.az
Course Description
Overview of course
 Data and Computing Skills is an introductory course in basic computer use,
meticulously crafted to provide a foundational understanding of key concepts relating
to computing and data analysis. This course seamlessly integrates two pivotal areas of
study: the advanced functionalities of Microsoft Excel and the foundational principles
of Python programming. In the initial phase, students will be equipped to identify,
describe, and organize key concepts of computing and data analysis within Microsoft
Excel worksheets. They will learn to procure and systematically arrange relevant data
and computational elements, ensuring that their Excel skills are honed to perfection.
This segment is not just about understanding Excel but mastering its advanced
techniques to solve intricate data-related challenges. Transitioning to the second
phase, the course delves into the world of Python programming. Here, students will
be introduced to the core concepts of computational thinking, enabling them to
organize and manipulate data effectively within Python programs. They will be
guided to apply basic data analysis and computing concepts, solving complex realworld scenarios using Python. This segment emphasizes not just the technicalities of
Python but its application in solving multifaceted computing problems. By the end of
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this course, students will be adept at evaluating the reliability, plausibility, and
effectiveness of solutions in both Excel and Python, based on data calculations,
manipulations, visualizations, and computations. They will be trained to identify
relevant aspects of various data and computing scenarios, formulating assessments
that reflect a deep understanding of the multifaceted nature of data challenges. In
essence, this course implies a complete learning experience, ensuring that students are
well-prepared to tackle any challenge in the domains of Excel and basic Python,
backed by a strong foundational identification of computing and data analysis.
Prerequisites:
 No specific prerequisites are necessary for the Data and Computing Skills course.
Minimum passing grade:
 Data and Computing Skills Course is a pass/fail course and minimum passing grade is
60 (D)
Technology Requirements:
Devices:
 Laptops: Students are mandated to use laptops during class sessions for assignments
and practice tasks. Tablets, phones, or other devices are not acceptable
substitutes! Ensure your laptop meets the minimum system requirements for the
software used in this course.
Software:
 Microsoft Office Excel: Essential for data analysis and visualization tasks.

Python: Version 3.11 or the latest. Ensure you have the necessary libraries and
extensions installed for course-related tasks.

Language Requirement: All software interfaces and installations must be in
English. Using software in languages such as Turkish or Russian will be considered a
violation of course policies.
Course level learning outcomes:
At the completion of this course the student should be able to:

Identify and describe key concepts of computing and data analysis and organize data
and computational elements effectively within Microsoft Excel worksheets and
Python programs.

Procure and organize relevant data and computational elements within Microsoft
Excel worksheets and Python fundamentals effectively and with the support of the
teacher.

Apply basic data analysis and computing concepts to solve complex scenarios or
problems using Excel skills and Python programming.
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
Evaluate the reliability, plausibility and effectiveness of solutions to problems in
Microsoft Excel and Python programming that are based on data calculations,
manipulations, visualizations, and computations while being guided by the teacher.

Identify relevant aspects of data and computing scenarios and formulate basic
assessments of the multifaceted nature of data and computing problems using Excel
and Python fundamentals.

Apply initial data analysis and computing solutions that show a degree of originality
and creative problem-solving capacity to multifaceted real-world scenarios or
problems while receiving the structured support of the teacher and using advanced
Excel techniques and foundational Python programming.
Methods of instruction:

The class will be taught through class sessions/lectures, including practices around
case studies, examples, Q&A sessions, video tutorials and homework assignments.
Teaching Methodology:
 Application-Oriented: Lessons will be structured around real-world applications,
ensuring students can directly relate theoretical knowledge to practical scenarios.

Function-Oriented: Emphasis will be placed on understanding the core functions and
utilities of tools and software, enabling students to adapt to various tasks and
challenges.

Active Learning: Students will be encouraged to actively participate in class
discussions and off-class discussions in the Blackboard. This approach fosters deeper
understanding and retention of course material.
Workload:
 Study Commitment: On average, students should allocate 2-5 hours per week for
revision, understanding core concepts, and completing homework assignments.

Consistent Engagement: While the aforementioned hours are a guideline, consistent
engagement with course materials is crucial for success. This includes not just
homework but also reviewing class reading material, participating in discussion
groups, and seeking clarification when needed.

Time Management: Given the intensive nature of the course, students are advised to
plan their study schedules in advance, ensuring they can balance coursework with
other commitments.
Materials
Main Readings for "Data and Computing Skills" Course:
1. Microsoft Excel 365 Bible by Dick Kusleika and Michael Alexander
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
Description: A comprehensive guide to Microsoft Excel 365, this book covers
all the essential features and functionalities of the software. Written by experts
in the field, it serves as a definitive resource for users looking to master Excel
365.
2. Excel 2019 All-in-One For Dummies 1st Edition by Greg Harvey

Description: A complete guide to Excel 2019, this book covers everything
from basic functionalities to advanced features. Written in a user-friendly
style, it's designed to help readers of all levels get the most out of Excel 2019.
3. Head First Learn to Code by Eric Freeman

Description: A visually rich and engaging introduction to coding, this book
uses a unique approach to teach programming concepts. With its interactive
style, readers are encouraged to dive head-first into the world of coding,
making the learning process fun and intuitive.
Supplemental Readings and online sources for "Data and Computing Skills" Course:
1. Microsoft Excel Video Training

Description: A comprehensive video training series provided by Microsoft to
help users get acquainted with Excel and its various functionalities. This
training is suitable for beginners to advanced users.

Link: Excel Video Training (Free)
2. GCFGlobal Excel Tutorials

Description: A series of tutorials by GCFGlobal that covers the basics of
Microsoft Excel. These tutorials are designed to help users learn Excel at their
own pace, from beginner to advanced topics.

Link: GCFGlobal Excel Tutorials (Free)
3. Python by Example: Learning to Program in 150 Challenges

Description: An engaging approach to learning Python, this book presents 150
challenges that gradually introduce the reader to the fundamentals of
programming with Python. It's a hands-on way to build programming skills
through practical exercises.
University Policies
Plagiarism
 Academic Honesty: The academic community assumes that you understand the
ethical violation of plagiarism. Successful academic and professional writing involves
careful reading and composing skills so as to avoid any semblance of plagiarism. Be
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sure to give yourself plenty of time to complete various assignments in order that you
will never be so overwhelmed that you are tempted to, or inadvertently, claim
another’s work as your own. Clearly, you will not learn or benefit cognitively by
plagiarizing. Strict standards of academic honesty will be enforced in this course.
Serious repercussions will be issued if you are caught plagiarizing. The consequences
may include failure of this course.

ADA University HONOR CODE: “Student members of the ADA University
community pledge not to cheat, plagiarize, steal, or lie in matters related to academic
work.”
Grade Appeal
 The responsibility to assign grades lies with the course instructor. Students who
contend that their grade is not an accurate reflection of their accomplishments in a
class should first discuss their grade assessment with the instructor. If after the
discussion the instructor is persuaded to change the grade, he/she must immediately
inform the Registrar and the Dean as soon as possible. In the case of data input or
communication error, notification to the Registrar will be sufficient. If after
discussing the grade with the instructor the student remains dissatisfied, it is possible
to initiate a grade appeal. This appeal is admissible in a case where the student feels
the instructor's grade is in error. A grade appeal must be filed within five working
days after the reception of the final grade. The appeal must be sent to the Dean of
the college in which the course is offered and must include a detailed description of
why the student feels the grading assessment was in error. The student may withdraw
the appeal at any point during the process. It is the Dean who will make the decision
of whether or not the student's appeal has merit. If the Dean decides the appeal is
unfounded, the appeal is denied; however, if the dean finds the appeal has merit,
he/she will convene a committee consisting of the Dean and two neutral faculty
members to discuss the appeal. The committee shall have the right to consult with
both the instructor and the student during the appeal process. The Dean will make a
decision on the case within one week after the reception of the appeal. The decision
will be made in writing and will be communicated to both the student and the
instructor. The committee's decision is final. It is important that the student be alerted
to the fact that the committee's decision may result in the original grade being
lowered. If a grade change is decided that decision must be sent to the Registrar's
Office at once.
Disability Statement
 ADA University provides upon request appropriate academic accommodations for
qualified students with documented disabilities. Any student who feels s/he may need
an accommodation based on the impact of a disability should notify the Office of
Disability Services and Inclusive Education about his/her needs before the start of the
academic term. Please contact Mr. Elnur Eyvazov, Director of the Office of
Disability Services and Inclusive Education; Phone: 4373235/ext249; Email:
eeyvazov@ada.edu.az
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Class Policies
Grading procedures:

Instead of receiving a letter-based grade, students will either receive a passing grade
or a failing grade at the end of term. The passing grade for Data and Computing
Skills course is 60% or above.
NO.
1
2
3
4
5
6
Graded activity
Attendance
Participation
Quizzes
Homework(s)
Midterm exam
Final exam
Weight %
5%
10%
15%
20%
20%
30%
Attendance:
 Attendance is an indispensable element of the educational process. In compliance
with Azerbaijani legislation, instructors are required to monitor attendance and inform
the Registrar and the Dean of the student’s respective school when students miss
significant amounts of class time. Azerbaijani legislation mandates that students who
fail to attend at least 75% of classes will fail the course.

Besides that, as an ADA University instructor, I will strictly follow ADA’s Academic
Regulations for the attendance policy too. Attendance policy excuses two (2) student
absences. More than two (2) absences will result in the lowering of a student’s grade.
For each additional absence, students will lose 1.25% of attendance grade. After
four (4) absences without any excuse, students lose attendance grade, which makes
5% percent of the overall grade. Rare exceptions will apply only in extreme and
objectively verifiable circumstances and must be discussed with the instructor before
the occurrence.

Emergencies: Students who face emergencies, such as a death in the family, the
serious illness of a family member, hazardous weather that makes attendance
impossible, or other situations beyond their control that preclude class attendance,
should notify their instructors in advance.

Attendance in Online Learning Courses: Being merely logged into an online lesson
may not be a sufficient indicator of academic attendance by students. To be
considered in academic attendance, faculty may require the students to turn their web
cameras on. Faculty reserve the right to mark a student as absent if he/she fails to
provide their camera view when required. The Blackboard Collaborate Ultra may
automatically put a late mark for the student who joins the session 5 minutes late. In
case student joins the online session 20 minutes late, will get an absent mark
automatically by the system.
Class participation
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
Class Participation is critical to any course and a significant portion of your overall
grade. Students are encouraged to contribute to class discussion. A certain percent of
the course grade will depend upon contributions to class sessions/online sessions.
Class participation provides the opportunity to practice speaking and persuasive skills,
as well as the ability to listen. What matters is the quality of one's contributions, not
the number of times one speaks.
Class Participation Rubrics:
Outstanding
contributor
810%
Good contributor
5-7%
Adequate
contributor
2-4%
Non-participant
0-1%
Full participation means coming(joining) to all classes(sessions)
with computer and complete tasks on time, contributing as both a
listener and a speaker to class quizzes and asking questions when
something is unclear on any aspect of the class. Clearly
demonstrates the understanding of the lesson, completes all
requirements, provides an insightful explanation, extends aspects
of the task. Participating in all discussion forums on the
blackboard.
Attends class (online sessions) regularly and sometimes
contributes to the discussion in the aforementioned ways and
demonstrates the understanding of the task, completes some
requirements, provides an insightful explanation or solution of
the task, using ideas sample task. Participating in some
discussion forums on the BB.
Attend class (online sessions) regularly but rarely complete
classwork on time and participate in discussion in the
aforementioned ways and reflect satisfactory preparation.
Demonstrates only the partial understanding of the task or using
text incorrectly. Participating in discussion forums on the BB
rarely
Attends class (online sessions) regularly but never complete
classwork or participate in discussion in the aforementioned
ways. Demonstrates minimal understanding of the lesson, does
not meet requirements, and shows vague reference or no use of
the computer. Avoiding all discussion forums on the BB.
Exams and quizzes:
 Final exam, midterm exam and several online quizzes (minimum two, maximum three
quizzes) are expected.

In-class quizzes will be closed book (no laptops or other devices) tests consisting of
multiple-choice, complete-the-sentence and/or open-ended questions.

Instructor will notify students in advance [online quizzes], in the case laptops, other
equipment is required for an exam or will provide students with computer lab laptops
without informing beforehand [pop-up quizzes]

Final exam and midterm exam will be computer-based or paper-based (based on
situation) exam where students will use lab computers/their computers to answer
theoretical questions and complete practical tasks. Students are allowed to use only
the required software during the examination.
Assessment Overview:
 The assessment criteria for this course are meticulously designed to align with the
Student Learning Objectives. By the end of this course, students are expected to
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exhibit proficiency in a range of skills, from identifying key concepts in computing
and data analysis to creatively solving real-world problems using advanced Excel
techniques and foundational Python programming. The criteria provide a structured
framework to gauge students' capabilities in these areas, ensuring that they not only
acquire knowledge but also effectively apply it in practical scenarios. Each
assessment criterion is categorized into five performance levels, from "Excellent" to
"Academic Fail." Students are encouraged to familiarize themselves with these
benchmarks to align their efforts with the course's overarching objectives and achieve
academic excellence.
Assessment Criteria and Grade Descriptions:
 Identify and describe key concepts of computing and data analysis and organize data
and computational elements effectively within Microsoft Excel worksheets and
Python programs.
Assessment
Criteria
The student
identifies key
concepts of
computing and
data analysis.
The student
describes key
concepts of
computing and
data analysis.
The student
organizes key
concepts of
computing and
data analysis.

Excellent
Good
Satisfactory
Poor
Acad. Fail
The expected key
number of
concepts of
computing and
data analysis
areas are
identified.
All or nearly all
of the identified
concepts of
computing and
data analysis are
described.
A considerable
number of
concepts of
computing and
data analysis are
identified.
Some (more than
50%) of concepts
of computing and
data analysis are
identified.
Some (less than
50%) of concepts
of computing and
data analysis are
identified.
Hardly any or no
of concepts of
computing and
data analysis are
identified.
Many of the
identified
concepts of
computing and
data analysis are
described.
Many of the
identified
concepts of
computing and
data analysis are
described.
An insufficient
number of
concepts of
computing and
data analysis are
described.
All or nearly all
of the identified
concepts of
computing and
data analysis are
adequately
organized
Many of the
identified
concepts of
computing and
data analysis are
adequately
organized.
Many of the
identified
concepts of
computing and
data analysis are
adequately
organized.
Some (more
than 50%) of the
identified
concepts of
computing and
data analysis are
described.
Some (more than
50%) of the
identified
concepts of
computing and
data analysis are
adequately
organized.
An insufficient
number of
concepts of
computing and
data analysis are
adequately
organized.
Procure and organize relevant data and computational elements within Microsoft
Excel worksheets and Python fundamentals effectively and with the support of the
teacher.
Assessment
Criteria
The student
procures relevant
data and
computational
elements within
Microsoft Excel
worksheets and
Python
fundamentals
Excellent
Good
Satisfactory
Poor
Acad. Fail
Relevant data and
computational
elements of
sufficient scale
are procured that
are highly
significant and
that is obtained
using a
considerable
number of the
most essential
resources
Relevant data and
computational
elements of some
scale are
procured that are
significant and
that is obtained
using a sufficient
number of the
most essential
resources.
A certain amount
of relevant data
and computational
elements are
procured that is
significant and
that is obtained
using some of the
most essential
resources.
A certain amount
of relevant data
and
computational
elements are
procured that is
partly significant
and that is
obtained using
only a few of the
most essential
resources.
Hardly any or no
relevant data and
computational
elements are
procured. Hardly
any or none of the
essential
resources are
used.
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The student
organizes the
procured relevant
data and
computational
elements within
Microsoft Excel
worksheets and
Python
fundamentals

Most of the
newly procured
relevant data and
computational
elements are
organized in a
manner that is
largely systematic
and logical.
Most of the newly
procured relevant
data and
computational
elements are
organized in a
manner that is
largely systematic
and logical.
Some of the
newly procured
relevant data and
computational
elements are
organized in a
manner that is
somewhat
systematic and
logical.
Newly procured
relevant data and
computational
elements are not
organized or in a
manner that is
neither systematic
nor logical.
Apply basic data analysis and computing concepts to solve complex scenarios or
problems using Excel skills and Python programming.
Assessment
Criteria
The student
applies basic data
analysis to solve
complex scenarios
or problems.
The student
applies basic
computing
concepts to solve
complex scenarios
or problems.

All or nearly all
of the newly
procured relevant
data and
computational
elements are
organized
systematically and
in a logical
manner.
Excellent
Good
All or nearly all
of the essential
data analyses are
adequately
applied.
Most of the
essential data
analyses are
applied in manner
that is mostly
adequate.
Essential
computing
concepts of
sufficient breadth
are adequately
applied.
Essential
computing
concepts of some
breadth are
applied in a
manner that is
mostly adequate.
Satisfactory
Some (more than
50%) of the
essential data
analyses are
applied in a
manner that is
mostly adequate.
Some of the
essential
computing
concepts are
applied in a
manner that is
mostly adequate
Poor
Acad. Fail
Some (less than
50%) of the data
analyses are
applied in a
manner that is
partly adequate.
Hardly any or no
essential data
analyses are
applied.
Some of the
essential
computing
concepts are
applied in a
manner that is
partly adequate.
Hardly any or no
essential
computing
concepts are
applied.
Evaluate the reliability, plausibility and significance of the problems relevant to data
and computing skills based on simple Microsoft Excel functions and Python
fundamentals being guided by the teacher.
Assessment
Criteria
The student uses
Microsoft Excel
functions and
Python
fundamentals to
evaluate the
problems relevant
to data and
computing skills.
The student uses
Microsoft Excel
functions and
Python
fundamentals to
evaluate the
reliability,
plausibility and
significance of
the problems
relevant to data
and computing
skills.
Excellent
Good
Satisfactory
Poor
Acad. Fail
The Microsoft
Excel functions
and Python
fundamental skills
used show
sufficient breadth.
The Microsoft
Excel functions
and Python
fundamental
skills used show
breadth.
The Microsoft
Excel functions
and Python
fundamental
skills show a
degree of breadth.
There is only a
small number of
Microsoft Excel
functions and
Python
fundamental
skills used.
Hardly any or no
Microsoft Excel
functions and
Python
fundamental
skills are used.
These Microsoft
Excel functions
and Python
fundamental skills
are fully
conclusive and
used to
comprehensively
evaluate the
reliability,
plausibility and
significance of the
problems relevant
to data and
computing skills.
These Microsoft
Excel functions
and Python
fundamental
skills are largely
conclusive and
used to evaluate
the reliability,
plausibility and
significance of
the problems
relevant to data
and computing
skills.
These Microsoft
Excel functions
and Python
fundamental
skills are largely
conclusive and
used to partly
evaluate the
reliability,
plausibility and
significance of
the problems
relevant to data
and computing
skills.
These Microsoft
Excel functions
and Python
fundamental
skills are partly
conclusive and
used to partly
evaluate the
reliability,
plausibility and
significance of
the problems
relevant to data
and computing
skills.
The Microsoft
Excel functions
and Python
fundamental
skills are not
conclusive. They
do not serve to
evaluate the
reliability,
plausibility and
significance of
the problems
relevant to data
and computing
skills.
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
Identify the relevant computational thinking facts and formulate a basic assessment of
a range of data problems that is essential to the computing concepts using Excel skills
and Python programming.
Assessment
Criteria
The student
identifies the
relevant
computational
thinking facts.
The student
formulates a basic
assessment of a
range of data
problems.

Excellent
Good
Satisfactory
Poor
Acad. Fail
All or nearly all
of the relevant
computational
thinking facts are
identified.
Most of the
relevant
computational
thinking facts are
identified.
The assessment is
formulated with a
considerable
degree of clarity.
It is fully
adequate to the
range of data
problems.
The assessment is
formulated with a
reasonable degree
of clarity. It is
largely adequate
to the range of
data problems.
Some (more than
50%) of the
relevant
computational
thinking facts are
identified.
The assessment is
formulated with a
reasonable degree
of clarity. It is
partly adequate to
the range of data
problems.
Some (less than
50%) of the
relevant
computational
thinking facts are
identified.
The assessment is
formulated
without much
clarity. It is
difficult to
maintain.
Hardly any or
none of the
relevant
computational
thinking facts are
identified.
No assessment is
provided, or it is
formulated
without any
clarity. The
formulated
assessment
cannot be
maintained.
Apply primary solutions in computing to a previously identified set of computational
problems and scenarios that play key role to the data and computing concepts using
Excel worksheets and Python programming.
Assessment
Criteria
The student
applies primary
solutions in
computing to a
previously
identified set of
computational
problems.
The applied
solutions in
computing tend to
be original.
Excellent
Good
Satisfactory
Poor
Solutions are
proposed and
applied that
adequately
address the
computational
problems.
Solutions are
proposed and
applied that
largely address
the computational
problems.
Solutions are
proposed and
applied that
largely address
the computational
problems.
Solutions are
proposed and
applied that partly
address the
computational
problems.
The applied
solutions in
computing are
largely original.
The applied
solutions in
computing show
some originality.
The applied
solutions in
computing are
mostly
conventional.
The applied
solutions in
computing are
mostly
conventional.
Acad. Fail
No solutions are
proposed and
applied, or the
proposed
solutions are not
applicable to
computational
problems.
The proposed
solutions in
computing are
very
conventional.
Assignment/problem sets:
 Practice tasks and home assignments will be provided once the necessary topics are
concluded. Over the course of the term, you will receive 4 different assignments.
Detailed information and due dates for these assignments and tasks will be provided
during the term.
Missed or late assignments/extensions:
 Students will be responsible for submitting their assignments before the deadline.
These assignments are very important to evaluate student’s contribution on a related
course. Try not to submit them in late. Please, note that works that are submitted after
deadline will lose 20% of total grade for the assignment for each day it is late.
Example Situation: John, a student in the course, was supposed to
submit his assignment on Monday. However, due to unforeseen
10 | P a g e
circumstances, he was only able to submit it on Wednesday, making
it 2 days late.
Based on the course policy for missed or late assignments, John's
assignment will lose 20% of the total grade for each day it is late.
Since he is 2 days late, this means his assignment will lose a total of
40% from its total grade.
Other requirements:
 No other requirements.

Instructor will provide information for another requirement if it’s needed.
Standards for academic honesty and penalties for infractions:
 If student found guilty of academic dishonesty first time, he or she would fail the
course. If the case repeated, student will be expelled due to university regulation. For
further information please read the Honor Code.
Schedule
Learning
Objective
Week 1
12 – 14
Sep.
Week 2
Introduction
Data representation:
1. Formatting
26 – 28
Sep.
Week 4
3–5
Oct.
Week 5
10– 12
Oct.
Introduction on Data and
Computing Skills, course
materials and extracurricular activities
1.1 Format cells
1.2 Worksheets
19 Sep - 21
Sep.
Week 3
Activities
Calculation:
2. Formulas and
Functions
2.1 Using Formulas and
Functions
Calculation:
2. Formulas and
Functions
2.1 Using Formulas and
Functions
Data visualization:
3. Charts
3.1 Creating Charts
3.2 Formatting Charts
Task
1.1.1 Apply conditional formatting.
1.1.2 Create and apply custom number formats.
1.1.3 Split text to columns.
1.2.1 Copy move worksheets between spreadsheets.
1.2.2 Split a window. Move, remove split bars.
1.2.3 Hide, show rows, columns, worksheets.
1.2.4 Save a spreadsheet as a template, modify a template.
2.1.1 Use date and time functions: today, now, day, month,
year.
2.1.2 Use logical functions: and, or, not.
2.1.3 Use mathematical functions: operators, rounddown,
roundup, sumif
2.1.4 Use statistical functions: countif, countblank, rank.
2.1.5 Use text functions: left, right, mid, trim, concatenate.
2.1.6 Use lookup functions: vlookup, hlookup.
3.1.1 Create a combined chart like: column and line,
column and area.
3.1.2 Create, change, delete a sparkline.
3.1.3 Add a secondary axis to a chart.
3.1.4 Change the chart type for a defined data series.
3.1.5 Add, delete a data series in a chart.
3.2.1 Re-position chart title, legend, data labels.
3.2.2 Change scale of value axis: minimum, maximum
number to display, major interval.
3.2.3 Change display units on value axis without changing
data source: hundreds, thousands, millions.
3.2.4 Format columns, bars, pie slices, plot area, chart area
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Week 6
4.Analysis
17 Oct – 19
Oct.
Week 7
4.1 Using Tables
4.2 Sorting and Filtering
4.2.1 Sort data by multiple columns at the same time.
4.2.2 Create a customized list and perform a custom sort.
4.2.3 Automatically filter a list in place.
4.2.4 Apply advanced filter options to a list.
4.2.5 Use automatic, manual outline features: group,
ungroup.
4.3 Scenarios
4.3.1 Create named scenarios.
4.3.2 Show, edit, delete scenarios.
4.3.3 Create a scenario summary report.
Learning Objective
5.Validating and
Auditing
24 – 26
Oct.
Week 8
to display an image.
4.1.1 Create, modify a pivot table/datapilot.
4.1.2 Modify the data source and refresh the pivot
table/datapilot.
4.1.3 Filter, sort data in a pivot table/datapilot.
4.1.4 Automatically, manually group data in a pivot
table/datapilot and rename groups.
Activities
5.1 Validating
Task
5.1.1 Set, edit validation criteria for data entry in a
cell range like: whole number, decimal, list, date,
time.
5.1.2 Enter input message and error alert.
5.2 Auditing
5.2.1 Trace precedent, dependent cells. Identify
cells with missing dependents.
5.2.2 Display all formulas in a worksheet, rather
than the resulting values.
5.2.3 Insert, edit, delete, show, hide
comments/notes in a worksheet locally, online.
7.1.1 Compare and merge spreadsheets.
7.1.2 Add, remove password protection for a
spreadsheet: to open, to modify.
7.1.3 Protect, unprotect cells, worksheet with a
password.
7.1.4 Hide, unhide formulas.
8.1.1 Name cell ranges, delete names for cell
ranges.
8.1.2 Use named cell ranges in a function.
8.1.3 Activate, deactivate the group mode.
7.Collaborative Editing
7.1 Reviewing and Security
8.Enhancing
Productivity
8.1 Naming Cells
31 Oct –2
Nov.
Week 9
8.2 Paste Special
8.2.1 Use paste special options: add, subtract,
multiply, divide.
8.2.2 Use paste special options: values /numbers,
transpose.
8.Enhancing
Productivity
8.3 Linking, Embedding and
Importing
8.3.1 Insert, edit, and remove a hyperlink.
8.3.2 Link data within a spreadsheet, between
spreadsheets.
8.3.3 Update, break a link.
8.3.4 Import delimited data from a text file.
9.Computing Term:
Key concepts
9.1 Key Concepts
9.1.1 Define the term computing.
9.1.2 Define the term computational thinking.
9.1.3 Define the term program.
9.1.4 Define the term code. Distinguish between
source code, machine code.
9.1.5 Understand the terms program description and
specification.
9.1.6 Recognise typical activities in the creation of
a program: analysis, design, programming, testing,
7 Nov
Week 10
14 – 16
Nov.
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Week 11
10.Computing: Starting
to code
10.1 Getting Started
21 – 23
Nov.
10.2 Variables and Data Types
Week 12
28 –30
Nov.
10.2.4 Use appropriately named variables in a
program for calculations, storing values.
10.2.5 Use data types in a program: string,
character, integer, float, Boolean.
10.2.6 Use an aggregate data type in a program
like: array, list, tuple.
Week 13
5–7
Dec.
Week 14
11.Computing:
Building blocks
12 – 14
Dec.
Week 15
19-21
Dec.
enhancement.
9.1.7 Understand the difference between a formal
language and a natural language.
9.1.8 Define the programming construct term
sequence. Outline the purpose of sequencing when
designing algorithms.
9.1.9 Recognise possible methods for problem
representation like: flowcharts, pseudocode.
9.1.10 Recognise flowchart symbols like: start/stop,
process, decision, input/output, connector, and
arrow.
10.1.1 Describe the characteristics of wellstructured and documented code like: indentation,
appropriate comments, descriptive naming.
10.1.2 Use simple arithmetic operators to perform
calculations in a program: +, -, /, *.
10.1.3 Understand the term parameter. Outline the
purpose of parameters in a program.
10.1.4 Define the programming construct term
comment. Outline the purpose of a comment in a
program.
10.1.5 Use comments in a program.
10.2.1 Define the programming construct term
variable. Outline the purpose of a variable in a
program.
10.2.2 Define and initialize a variable.
10.2.3 Assign a value to a variable.
Final overview
11.1 Logic
11.1.1 Define the programming construct term
logic test. Outline the purpose of a logic test in a
program.
11.1.2 Recognise types of Boolean logic
expressions to generate a true or false value like: =,
>, <, >=, <=, <>, !=, ==, AND, OR, NOT
11.2 Iteration
11.2.1 Define the programming construct term
loop. Outline the purpose and benefit of looping in
a program.
11.3 Conditionality
11.3.2 Use IF...THEN...ELSE conditional
statements in a program.
Review chapters
Revision session
Tips for Success:

Engage with the Material: Regularly review the course readings and materials. This
not only solidifies your understanding but also prepares you for meaningful
participation in class discussions.

Seek Clarification: If a topic seems challenging, don't wait. Attend office hours or
approach your peers for discussions. The sooner you address your doubts, the better
your grasp on the subject will be.
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
Stay Organized: Keep a study schedule and stick to it. Organizing your study time
helps in consistent learning and reduces last-minute cramming.

Collaborate and Discuss: Engage with your classmates. Group discussions can offer
new perspectives and insights into topics that you might not have considered.

Practice Regularly: Theoretical knowledge is vital, but practice ensures that you can
apply what you've learned. Regularly work on assignments, projects, or even self-set
tasks to hone your skills.

Stay Curious: Beyond the syllabus, explore related articles, videos, or seminars.
Expanding your horizons can make the learning experience more enriching and
enjoyable.
Disclaimer

The course schedule is subject to change, as necessary.
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