Description
Problem #1
Business Intelligence Reporting using Cognos
1. Download the weather dataset available on https://www.kaggle.com/PROPPG-PPG/hourlyweather-surface-brazil-southeast-region?select=sudeste.csv
2. Explore the dataset and identify data field(s) that could be measured by certain factors or
dimensions. (Follow recorded lecture #18, and synchronous session #18)
Example: In a Sales dataset, you may find a measurable field “total sales”, which could be
analyzed by other factors such as, “products”, “time”, “location” etc. These factors are known as
dimensions.
Depending on the data, you may also find possibilities of slice and dice, i.e. analysis
could be possible in more granular level; From total sales by city to total sales by store
3. Write ½ page explanation on how did you select the measurable filed, i.e. fact and what are the
possible dimensions. Include this part in your PDF file.
Winter 2021 saurabh.dey@dal.ca
4. Clean the dataset, if required perform formatting. You can perform the cleaning and formatting
using spreadsheet operation or programming script. If you use program add that in GitLab, if
you use other methods, write the steps in the PDF file.
5. Create Cognos account and import your dataset. Identify the dimensions, and create/import the
dimension tables.
6. Based on your understanding of the domain (please read the information/metadata available
on the dataset source, i.e. Kaggle), create star schema or snowflake schema. Provide justification
of your model creation in the PDF file.
7. In addition to justification, attach screenshot of the model (star schema or snowflake schema)
in the PDF file.
8. Display visual analysis of the data in a suitable format, e.g. bar graph showing temperature
change in terms of a suitable dimension. Add the screenshot of the analysis on the pdf or add a
screen recording of the analysis on your .zip folder.
Problem #2
Sentiment Analysis
9. To perform this task, you need to consider the processed tweets (“messages” or “texts” only,
ignore metadata) that you obtained in previous assignment.
10. Write a script to remove URL and/or any special characters. (If not done in Assignment 3.
Otherwise ignore this step)
11. Write a script to create bag-of-words for each tweet. (code from online or other sources are not
accepted)
e.g. tweet1 = “hey i m happy in Canada not the room”
bow1 = {“hey”:1, “i”:1, “m”:1, “happy”:1, “in”:1, “Canada”:1, “not”:1, “the”:1, “room”:1}
12. Compare each bag-of-words with a list of positive and negative words. You can download list of
positive and negative words from online source(s).
13. Tag each tweet as “positive”, “negative”, or “neutral”. You can add an additional column to
present your finding.
Tweet Message/tweets match polarity
1 hey i m happy in Canada
not the room
Happy, not neutral
Problem #3
Semantic Analysis
14. For this task, consider the processed tweet collection that you created in Assignment
15. Use the following steps to compute TF-IDF (term frequency-inverse document frequency)
a. Suppose, you have 500 tweets (messages only) that are stored in 500 JSON arrays.
You need to consider these data points as the total number of documents (N). In this
case N=500
Now, use the search query “flu”, “snow”, “cold”, and search in how many documents
these words have appeared.
Total Documents 500
Search Query Document
containing
term(df)
Total Documents(N)/ number
of documents term appeared
(df)
Log10(N/df)
flu 20 500/20 1.39
snow 40 500/40 1.09
cold 10 500/10 1.69
b. Once you build the above table, you need to find which document has the highest
occurrence of the word “cold”. You can find this by performing frequency count of the
word per document.
Term Canada
Canada appeared in 20 documents Total Words (m) Frequency (f )
Article #1 6 2
Article #2 10 1
: : :
Article #20 8 1
c. You should print the news article, which has the highest relative frequency. You can
find this by computing (f/m).
Assignment 4 Submission Format:
1) Compress all your reports/files into a single .zip file and give it a meaningful name.
You are free to choose any meaningful file name, preferably – BannerId_Lastname_firstname_5408_A4
but avoid generic names like assignment-4.
2) Submit your reports only in PDF format.
Please avoid submitting .doc/.docx and submit only the PDF version. You can merge all the reports into
a single PDF or keep them separate. You should also include output (if any) and test cases (if any) in the
PDF file.
3) Your executable code/script needs to be submitted on https://git.cs.dal.ca/
