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this<00:00:07.480><c> course</c><00:00:07.799><c> is</c><00:00:08.080><c> intended</c><00:00:08.519><c> to</c><00:00:08.800><c> equip</c>

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this course is intended to equip
 

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this course is intended to equip
students<00:00:09.760><c> with</c><00:00:10.040><c> skills</c><00:00:10.480><c> such</c><00:00:10.639><c> as</c><00:00:10.840><c> programming</c>

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students with skills such as programming
 

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students with skills such as programming
in<00:00:11.599><c> Python</c><00:00:11.920><c> and</c><00:00:12.120><c> R</c><00:00:12.799><c> uh</c><00:00:12.920><c> to</c><00:00:13.160><c> analyze</c><00:00:13.679><c> big</c><00:00:13.920><c> data</c>

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in Python and R uh to analyze big data
 

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in Python and R uh to analyze big data
we<00:00:14.799><c> also</c><00:00:15.160><c> focused</c><00:00:15.559><c> a</c><00:00:15.679><c> lot</c><00:00:15.839><c> on</c><00:00:16.119><c> covering</c><00:00:16.840><c> uh</c><00:00:17.000><c> the</c>

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we also focused a lot on covering uh the
 

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we also focused a lot on covering uh the
entire<00:00:17.760><c> process</c><00:00:18.520><c> uh</c><00:00:18.640><c> of</c><00:00:18.880><c> data</c><00:00:19.439><c> analysis</c>

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entire process uh of data analysis
 

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entire process uh of data analysis
starting<00:00:21.000><c> from</c><00:00:21.560><c> uh</c><00:00:21.720><c> web</c><00:00:21.960><c> scraping</c><00:00:22.480><c> for</c>

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starting from uh web scraping for
 

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starting from uh web scraping for
example<00:00:23.080><c> using</c><00:00:23.519><c> python</c><00:00:24.279><c> then</c><00:00:24.480><c> we</c><00:00:24.599><c> try</c><00:00:24.840><c> to</c><00:00:25.039><c> put</c>

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example using python then we try to put
 

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example using python then we try to put
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a structure on messy data sets that
 

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a structure on messy data sets that
often<00:00:28.519><c> comes</c><00:00:28.960><c> as</c><00:00:29.039><c> a</c><00:00:29.240><c> result</c><00:00:29.560><c> of</c><00:00:29.759><c> web</c><00:00:30.000><c> scraping</c>

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often comes as a result of web scraping
 

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often comes as a result of web scraping
and<00:00:30.960><c> then</c><00:00:31.160><c> when</c><00:00:31.320><c> we</c><00:00:31.480><c> have</c><00:00:31.599><c> a</c><00:00:31.759><c> structured</c><00:00:32.320><c> data</c>

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and then when we have a structured data
 

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and then when we have a structured data
set<00:00:32.840><c> we</c><00:00:33.040><c> apply</c><00:00:33.440><c> a</c><00:00:33.559><c> lot</c><00:00:33.719><c> of</c><00:00:33.920><c> data</c><00:00:34.280><c> visualization</c>

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set we apply a lot of data visualization
 

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set we apply a lot of data visualization
techniques<00:00:35.879><c> to</c><00:00:36.280><c> observe</c><00:00:36.719><c> basic</c><00:00:37.160><c> patterns</c><00:00:37.960><c> and</c>

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techniques to observe basic patterns and
 

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techniques to observe basic patterns and
lastly<00:00:38.640><c> we</c><00:00:38.760><c> do</c><00:00:39.160><c> a</c><00:00:39.320><c> rigorous</c><00:00:39.800><c> economic</c>

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lastly we do a rigorous economic
 

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lastly we do a rigorous economic
analysis<00:00:40.840><c> to</c><00:00:41.039><c> test</c><00:00:41.480><c> hypothesis</c><00:00:42.480><c> about</c><00:00:43.000><c> uh</c><00:00:43.120><c> the</c>

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analysis to test hypothesis about uh the
 

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analysis to test hypothesis about uh the
data<00:00:43.640><c> set</c><00:00:44.399><c> in</c><00:00:44.600><c> this</c><00:00:44.760><c> module</c><00:00:45.160><c> you</c><00:00:45.239><c> will</c><00:00:45.399><c> learn</c><00:00:45.640><c> a</c>

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data set in this module you will learn a
 

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data set in this module you will learn a
variety<00:00:46.399><c> of</c><00:00:46.600><c> skills</c><00:00:47.320><c> uh</c><00:00:47.440><c> including</c><00:00:47.960><c> how</c><00:00:48.079><c> to</c><00:00:48.559><c> uh</c>

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variety of skills uh including how to uh
 

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variety of skills uh including how to uh
transform<00:00:49.399><c> data</c><00:00:49.680><c> sets</c><00:00:50.039><c> from</c><00:00:50.320><c> unstructured</c><00:00:51.039><c> to</c>

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transform data sets from unstructured to
 

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transform data sets from unstructured to
structured<00:00:52.120><c> and</c><00:00:52.280><c> lastly</c><00:00:52.680><c> we</c><00:00:52.760><c> will</c><00:00:52.960><c> perform</c><00:00:53.399><c> a</c>

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structured and lastly we will perform a
 

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structured and lastly we will perform a
lot<00:00:53.640><c> of</c><00:00:53.800><c> rigorous</c><00:00:54.520><c> uh</c><00:00:54.719><c> data</c><00:00:55.039><c> analysis</c><00:00:55.920><c> using</c>

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lot of rigorous uh data analysis using
 

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lot of rigorous uh data analysis using
uh<00:00:56.640><c> regression</c><00:00:57.120><c> techniques</c><00:00:57.520><c> and</c><00:00:57.680><c> training</c>

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uh regression techniques and training
 

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uh regression techniques and training
machine<00:00:58.440><c> learning</c><00:00:58.800><c> models</c><00:00:59.519><c> these</c><00:00:59.640><c> are</c>

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machine learning models these are
 

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machine learning models these are
building<00:01:00.280><c> blocks</c><00:01:00.680><c> to</c><00:01:01.079><c> uh</c><00:01:01.199><c> learn</c><00:01:01.879><c> uh</c><00:01:02.079><c> more</c>

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building blocks to uh learn uh more
 

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building blocks to uh learn uh more
about<00:01:02.680><c> artificial</c><00:01:03.280><c> intelligence</c><00:01:03.879><c> in</c><00:01:04.080><c> future</c>

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about artificial intelligence in future
 

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about artificial intelligence in future
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courses here at work when we think about
 

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courses here at work when we think about
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uh teaching data science we focus a lot
 

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uh teaching data science we focus a lot
on<00:01:10.040><c> applications</c><00:01:11.040><c> including</c><00:01:11.720><c> uh</c><00:01:11.880><c> economic</c>

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on applications including uh economic
 

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on applications including uh economic
applications<00:01:13.600><c> uh</c><00:01:13.720><c> so</c><00:01:14.040><c> together</c><00:01:14.320><c> with</c><00:01:14.520><c> this</c>

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applications uh so together with this
 

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applications uh so together with this
class<00:01:14.960><c> you'll</c><00:01:15.200><c> be</c><00:01:15.360><c> taking</c><00:01:15.680><c> econometrics</c><00:01:16.520><c> B</c><00:01:17.280><c> uh</c>

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class you'll be taking econometrics B uh
 

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class you'll be taking econometrics B uh
which<00:01:17.600><c> covers</c><00:01:18.080><c> econometric</c><00:01:18.759><c> techniques</c><00:01:19.240><c> how</c>

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which covers econometric techniques how
 

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which covers econometric techniques how
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to rigorously analyze economic data and
 

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to rigorously analyze economic data and
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we will show you applications of these
 

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we will show you applications of these
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techniques to data science and big data
 

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techniques to data science and big data
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sets we designed the course thinking
 

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sets we designed the course thinking
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about skills that employers will look
 

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about skills that employers will look
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for in the labor market in the next few
 

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for in the labor market in the next few
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years in particular we want to teach
 

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years in particular we want to teach
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students not just computer science
 

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students not just computer science
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techniques but also how to interpret
 

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techniques but also how to interpret
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data and how to provide uh economic
 

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data and how to provide uh economic
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analysis and uh these skills combined
 

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analysis and uh these skills combined
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will be very valuable to employers
 

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will be very valuable to employers
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