Typos in slides/glossary/glossary_of_big_data_and_mapreduce.md and a few other files (typo only ticket)

[willy-b]

Hello @mahmoudparsian !

Thanks very much for sharing all your course materials online (I worked with you at Illumina and came to follow you on Github back then; I have not had the pleasure of taking the courses you teach, but, remembering your expertise in the workplace, am always interested to learn from what you share).

In reviewing your recently updated content I noticed some typos, so I thought I would share those with you in case you are interested to fix them (or if you would want me to send a PR to fix them, which I would be happy to do). Note this list is NOT intended to be an exhaustive list of typos or errors in the repo or the files mentioned.

(I will be reviewing the material further and can report other items as I find them but figured the following items are relatively unambiguous, can be confirmed with little effort on your part, and may be of interest to you to know about immediately.)

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Typos in slides/glossary/glossary_of_big_data_and_mapreduce.md

• In the bullet point
  

  big-data-modeling/slides/glossary/glossary_of_big_data_and_mapreduce.md

  Line 373 in 065f92b

  find frequecy of every unique word

  For example, given a set of text documents, find frequecy of every unique word

  "find frequecy" should be "find the frequency".

• In the lines
  

  big-data-modeling/slides/glossary/glossary_of_big_data_and_mapreduce.md

  Line 5067 in 065f92b

  performed. For RDD, anction is defined as

  Action: when we want to work with the actual dataset, at that point Action is performed. For RDD, anction is defined as the Spark operations that return raw values.

  "at that point Action is performed" should be "at that point an Action is performed",
  and "anction" should be "an Action".

• In the sentence
  

  big-data-modeling/slides/glossary/glossary_of_big_data_and_mapreduce.md

  Line 541 in 065f92b

  Greedy algorithm is designed to achieve optimum solution

  Greedy algorithm is designed to achieve optimum solution for a given problem (here US Change problem).

  it may be worth noting that the greedy solution is NOT optimal when making change for a given total with the fewest coins outside the US system (e.g. in Tanzanian coins - I saw an example on ALGS2xx at UCSD on that), in general to my understanding the optimal change problem requires other techniques like dynamic programming.

  Also "Greedy algorithm is" should probably be "A greedy algorithm is", and "achieve optimum solution" should be "achieve an optimum solution" to be grammatical.
  Also "In greedy algorithm approach" should be "In the greedy algorithm approach", and for "As being greedy" should be "When being greedy". There are in general perhaps some opportunities also to improve the grammar in this area of the text but I have focused on the typos here.

• In the sentence
  

  big-data-modeling/slides/glossary/glossary_of_big_data_and_mapreduce.md

  Line 709 in 065f92b

  (https://www.cs.odu.edu/~toida/nerzic/content/recursive_alg/rec_alg.html)

  [recursive algorithm] (https://www.cs.odu.edu/~toida/nerzic/content/recursive_alg/rec_alg.html)

  should have a space removed so it shows as at link rather than showing the underlying markdown with an extra space.

• In the sentence
  

  big-data-modeling/slides/glossary/glossary_of_big_data_and_mapreduce.md

  Line 1158 in 065f92b

  which are assocaited with each other

  For example, given a DNA data, find genes, which are assocaited with each other

  "assocaited" should be "associated" and "given a DNA data" should be "given DNA data".

• In the bullet point
  

  big-data-modeling/slides/glossary/glossary_of_big_data_and_mapreduce.md

  Line 1272 in 065f92b

  * **Interprete**: Last but not least you have one of

  Interprete: Last but not least you have one of the most important steps: it is time to interpret your results.

  "Interprete" should be "Interpret" (the preceding image also has this typo).

• In the line
  

  big-data-modeling/slides/glossary/glossary_of_big_data_and_mapreduce.md

  Line 6777 in 065f92b

  ETL (Extract, Transform and Load) is thhe process

  ETL (Extract, Transform and Load) is thhe process of extracting raw data, transforming by cleaning and enriching the data to make it fit operational needs and loading into the appropriate repository for the system’s use.

  "thhe" should be "the".

• In the line
  

  big-data-modeling/slides/glossary/glossary_of_big_data_and_mapreduce.md

  Line 8622 in 065f92b

  your desired final RDD/DataFrame, and eventuallly

  Once you create an RDD/DataFrame, then you may apply a set of transformations on RDD/DataFrame to generate your desired final RDD/DataFrame,
  and eventuallly you may save it or persist it in an external data source.

  "eventuallly" should be "eventually".

• In the line
  

  big-data-modeling/slides/glossary/glossary_of_big_data_and_mapreduce.md

  Line 1015 in 065f92b

  read ratings from a data source, and fianlly

  For example, read users data from a data source, read movies from another data source, read ratings from a data source,
  and fianlly find median rating for movies rated last year

  "fianlly" should be "finally".

• Nit: In the line
  

  big-data-modeling/slides/glossary/glossary_of_big_data_and_mapreduce.md

  Line 1000 in 065f92b

  data together into a common athenaeum for the

  Data aggregation refers to the collection of data from multiple sources to bring all the data together into a common athenaeum for the purpose of reporting and/or analysis.

  It may be worthwhile to double check whether "athenaeum" is the clearest word to use to explain data aggregation. It is not a commonly used word.

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Other miscellaneous typos

• In the sentence
  

  big-data-modeling/slides/mapreduce_progs/telecom/README.md

  Line 22 in 065f92b

  calls. By indentifying such subscribers, telcom company wants

  By indentifying such subscribers, telcom company wants to offer them STD (Long Distance) Pack which would efficient for them instead spending more money without that package.

  "indentifying" should be "identifying", and "telcom company" should be "the telcom company".

• In the filename
  https://github.com/mahmoudparsian/big-data-modeling/blob/065f92b84ee02664889e74a95ad6f0d62671d7cd/slides/glossary/images/lucence-flow-1.png

  lucence-flow-1.png

  "lucence-flow-1.png" should be named "lucene-flow-1.png" (it is about Lucene, https://en.wikipedia.org/w/index.php?title=Apache_Lucene&oldid=1361196287 ).

• In the heading
  

  big-data-modeling/slides/mapreduce/combiners/README.md

  Line 408 in 065f92b

  Revided Reducer:

  Revided Reducer:

  "Revided" appears to have been intended to be "Revised"
  (compare to

  big-data-modeling/slides/mapreduce/combiners/README.md

  Line 424 in 065f92b

  Final Output of Revised Reducers:

  )

• In the filename
  https://github.com/mahmoudparsian/big-data-modeling/blob/065f92b84ee02664889e74a95ad6f0d62671d7cd/slides/pyspark/pyspark-rdd-sessions/understaning_partitions_and_mapPartitions.md

  understaning_partitions_and_mapPartitions.md

  "understaning" should be "understanding"

• In the line
  

  big-data-modeling/slides/pyspark/dataframes_tutorial/module01_intro.md

  Line 127 in 065f92b

  >>> spark.sql("select country, avg(salary) as avg_salary, max(salary) as max_salry from people group by country").show()

  spark.sql("select country, avg(salary) as avg_salary, max(salary) as max_salry from people group by country").show()

  "salry" should probably be "salary".

• In the line
  

  big-data-modeling/slides/pyspark/dataframes_tutorial/module01_intro.md

  Line 136 in 065f92b

  >>> spark.sql("select country, avg(salary) as avg_salary, max(salary) as max_salry from people group by country").show()

  spark.sql("select country, avg(salary) as avg_salary, max(salary) as max_salry from people group by country").show()

  "salry" should probably be "salary".

• In the line
  

  big-data-modeling/data/movies/README.md

  Line 18 in 065f92b

  For ouput, we do not want all pairs to be printed.

  For ouput, we do not want all pairs to be printed. Will give you instructions later on.

  "ouput" should be "output".

• In the comment line
  

  big-data-modeling/slides/mapreduce/word_count_in_python/word_count_python.py

  Line 8 in 065f92b

  # returns a dictionary of (word, frequecy)

  returns a dictionary of (word, frequecy)

• In the heading
  

  big-data-modeling/syllabus/2026-Fall/README.md

  Line 36 in 065f92b

  * Cluster Comouting

  Cluster Comouting

  "Comouting" should be "Computing".

• In the code comment
  

  big-data-modeling/slides/glossary/data_analytics_glossary.html

  Line 111 in 065f92b

  /* categorgy filter template */

  /* categorgy filter template */

  "categorgy" should be "category"

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Thanks very much!

I would be happy to review any PR to fix these if it would be useful OR to be invited to send a PR to fix these typos from my end.