<jupyter_start><jupyter_text>Question 1 Company: NC4Description: Entry level analyst in an international monitoring center[job website](https://www.indeed.com/cmp/NC4/jobs/Intelligence-Analyst-63198433d1cf563f?sjdu=QwrRXKrqZ3CNX5W-O9jEvShgg173eaGcqumhSWKwuDksYOkjgyFqrDMUWoB3VpBovFKPbFNPRD9jdmWu02ALGEQ5gTHecRETGIXzZQdjmjM&tk=1d5un2nt350kn802&adid=6100457&vjs=3)<jupyter_code>job_ad = open("job.txt")

print(job_ad.read())
job_ad.close()<jupyter_output>Home


Intelligence Analyst - Entry Level
NC4
-
Merrifield, VA


NC4


General Purpose of Intelligence Analyst Role: 
As an entry-level domestic Intelligence Analyst, you will work in one of NC4’s International Monitoring Centers (NIMC) in support of the NC4 Risk Center™ product. Our centers provide critical information—24 hours a day, 365 days a year—to NC4’s diverse client base. In the NIMC, Intelligence Analysts monitor thousands of sources to provide clients with real-time intelligence on natural disasters, fires, terrorist incidents, civil unrest, transportation disruptions, and other incidents which have the potential to impact domestic business continuity, corporate security, travel management, logistics, or government operations. Current openings for entry level Intelligence Analysts are for our night shift roles beginning at 7 PM ET and ending at 7 AM ET. Intelligence Analysts typically work 15 days a month, on a 3-to-5-days-on, 3-to-5-days-off rotation schedule which includes[...]<jupyter_text>Question 2<jupyter_code>from collections import Counter
with open ("job.txt") as job_ad:
    job_ad_list = job_ad.read().split()
    
    counter_result = Counter(job_ad_list)
    
    for word, count in counter_result.most_common(20):
        print(word,count)<jupyter_output>and 18
to 13
of 11
in 10
a 10
Intelligence 9
work 6
the 5
or 5
with 4
Analyst 3
NC4 3
Analysts 3
sources 3
on 3
which 3
security, 3
for 3
Ability 3
Strong 3<jupyter_text>Question 3<jupyter_code>import xlwt
from collections import Counter

mybook = xlwt.Workbook()
sheet = mybook.add_sheet('ad_word_count')
i=0
sheet.write(0,0,'word')
sheet.write(0,1,'count')
with open ("job.txt") as job_ad:
    job_ad_list = job_ad.read().split()
    
    counter_result = Counter(job_ad_list)
    
    for word, count in counter_result.most_common(20):
        i =i+1
        sheet.write(i,0,word)
        sheet.write(i,1,count)

mybook.save('ad_word_count.xls')<jupyter_output><empty_output><jupyter_text>Question 4<jupyter_code>import xlrd
my_book = xlrd.open_workbook('ad_word_count.xls')
my_sheet = my_book.sheet_by_name("ad_word_count")
for i in range(my_sheet.nrows):
    row = my_sheet.row_values(i)
    word, count=row
    print (word,count)<jupyter_output>word count
and 18.0
to 13.0
of 11.0
in 10.0
a 10.0
Intelligence 9.0
work 6.0
the 5.0
or 5.0
with 4.0
Analyst 3.0
NC4 3.0
Analysts 3.0
sources 3.0
on 3.0
which 3.0
security, 3.0
for 3.0
Ability 3.0
Strong 3.0
{'fraction_non_alphanumeric': 0.08531131750945993, 'fraction_numerical': 0.05091159270725834, 'mean_word_length': 4.971252566735113, 'pattern_counts': {'":': 0, '<': 14, '<?xml version=': 0, '>': 14, 'https://': 1, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '484947', 'license': 'MIT', 'n_tokens_mistral': 1148, 'n_tokens_neox': 1028, 'n_words': 303, 'path': 'Lab7.ipynb', 'repo_name': 'AKinkead/IA241', 'size': '2907'}