As many of you probably know, being a data scientist requires a large skill set . . .

Credit: Swami Chandrasekaran

To master all of that at a high level would probably take a lifetime! I’m sure many data scientists would love to be highly skilled in all of these areas if possible, but busy Ph.D. students like me (that are trying to graduate in an efficient manner!) don’t have enough time to focus on all of these skills.

So which of these skills are most employers actually looking for?

On DataScienceCentral, I saw a link to this posting by Kumaran Ponnambalam where he examined this question with R by scraping job postings. I thought it was an interesting idea. I wanted to find out this information for myself, as updated as possible, so I could see any changing trends in the job market.

It would also be nice to see if different cities have different skills they like to emphasize (i.e. does the Silicon Valley market have different skills they prefer compared to New York City?)

Data like this isn’t going to be easily accessible through a .csv or database. Sometimes, you are going to have to get it yourself. That may require web scraping, which automates the process of collecting data from websites. I’ve always thought this sounded very cool, but I didn’t know how to do it.

Luckily, Greg Reda at Datascope Analytics had a great blog post about web scraping that helped me complete this project (see it here). He did a great job! I’m not going to go into as much detail about web scraping as he did in this post, so I would recommend going to his blog post if you want to learn the basics.

So, in this post, I am going to scrape job postings from Indeed.com for data science jobs and see which skills employers want the most (Python or R? Are they interested in Spark yet? How dominant are NoSQL databases? Are they using proprietary software like SAS or are companies preferring open source now?) To make it even better, I will create the program so that I can have a detailed breakdown by city.

Program Setup

The basic workflow of the program will be:

Enter the city we want to search for jobs in matching the title “data scientist” (in quotes so it is a direct match) on Indeed.com

See the list of job postings displayed by the website

Access the link to each job posting

Scrape all of the html in the job posting

Filter it to only include words

Reduce the words to a set so that each word is only counted once

Keep a running total of the words and see how often a job posting included them

We will create two functions. The first will scape an individual job posting for the HTML, clean it up to get the words only, then output the final list of words. The second will manage which URLs to access via the job postings Indeed’s website links to.

In this post, we will use the urllib2 library to connect to the websites, the BeautifulSoup library to collect all of the HTML, the re library for parsing the words and filtering out other markup based on regular expressions, and pandas to manage and plot the final results.

The First Function: Cleaning a Website

This function will be called every time we access a new job posting. Its input is a URL for a website, while the output will be a final set of words collected from that website.

In this post, I am going to import all of the libraries necessary first.

from bs4 import BeautifulSoup # For HTML parsing import urllib2 # Website connections import re # Regular expressions from time import sleep # To prevent overwhelming the server between connections from collections import Counter # Keep track of our term counts from nltk.corpus import stopwords # Filter out stopwords, such as 'the', 'or', 'and' import pandas as pd # For converting results to a dataframe and bar chart plots % matplotlib inline

Now create our first website parsing function.

def text_cleaner ( website ): ''' This function just cleans up the raw html so that I can look at it. Inputs: a URL to investigate Outputs: Cleaned text only ''' try : site = urllib2 . urlopen ( website ) . read () # Connect to the job posting except : return # Need this in case the website isn't there anymore or some other weird connection problem soup_obj = BeautifulSoup ( site ) # Get the html from the site for script in soup_obj ([ "script" , "style" ]): script . extract () # Remove these two elements from the BS4 object text = soup_obj . get_text () # Get the text from this lines = ( line . strip () for line in text . splitlines ()) # break into lines chunks = ( phrase . strip () for line in lines for phrase in line . split ( " " )) # break multi-headlines into a line each def chunk_space ( chunk ): chunk_out = chunk + ' ' # Need to fix spacing issue return chunk_out text = '' . join ( chunk_space ( chunk ) for chunk in chunks if chunk ) . encode ( 'utf-8' ) # Get rid of all blank lines and ends of line # Now clean out all of the unicode junk (this line works great!!!) try : text = text . decode ( 'unicode_escape' ) . encode ( 'ascii' , 'ignore' ) # Need this as some websites aren't formatted except : # in a way that this works, can occasionally throw return # an exception text = re . sub ( "[^a-zA-Z.+3]" , " " , text ) # Now get rid of any terms that aren't words (include 3 for d3.js) # Also include + for C++ text = text . lower () . split () # Go to lower case and split them apart stop_words = set ( stopwords . words ( "english" )) # Filter out any stop words text = [ w for w in text if not w in stop_words ] text = list ( set ( text )) # Last, just get the set of these. Ignore counts (we are just looking at whether a term existed # or not on the website) return text

As you can see in the code above, a lot of cleaning for the raw html is necessary to get the final terms we are looking for. It extracts the relevant portions of the html, gets the text, removes blank lines and line endings, removes unicode, and filters with regular expressions to include only words. To see what the final result looks like, let’s try calling this function on a sample job posting. The one I am using is a job posting for a Data Scientist at Indeed itself!

If you are reading the IPython Notebook interactively, the example job posting may have disappeared so you can try your own to see how the function works.

sample = text_cleaner ( 'http://www.indeed.com/viewjob?jk=5505e59f8e5a32a4&q= %22 data+scientist %22 &tk=19ftfgsmj19ti0l3&from=web&advn=1855944161169178&sjdu=QwrRXKrqZ3CNX5W-O9jEvWC1RT2wMYkGnZrqGdrncbKqQ7uwTLXzT1_ME9WQ4M-7om7mrHAlvyJT8cA_14IV5w&pub=pub-indeed' ) sample [: 20 ] # Just show the first 20 words

[ 'professionally.' , 'code' , 'cj' , 'indeed' , 'competitive' , 'month' , 'label' , 'scientist' , 'frequency' , 'per' , 'human' , 'keywords' , 'follow' , 'alt' , 'viewthroughconversion' , 'find' , 'access' , 'style' , 'retirement' , 'candidate' ]

Now that we can extract terms from the website with our text_cleaner function, let’s build another function that will call this and automatically loop through all of the websites on Indeed for us.

The Second Function: Accessing the Job Postings

This next function will allow us to search for “data scientist” jobs in a particular city (or nationally if we want to see everything!) and plot the final results in a bar chart so we can see which skills are most frequently desired.

This second function is fairly long, so I will try to explain how everything works through a lot of commentary. The basic idea is to look through Indeed’s pages of job results and click on all of the job links, but only in the center of the page where all of the jobs are posted (not on the edges). See an example here. I just want the URLs in the “center” column of the website. You can get an idea of how Indeed organized the website by using a browser like Firefox or Chrome. Right click on the page to see the “Inspect Element” option in Firefox and the HTML will now be visible to you.

def skills_info ( city = None , state = None ): ''' This function will take a desired city/state and look for all new job postings on Indeed.com. It will crawl all of the job postings and keep track of how many use a preset list of typical data science skills. The final percentage for each skill is then displayed at the end of the collation. Inputs: The location's city and state. These are optional. If no city/state is input, the function will assume a national search (this can take a while!!!). Input the city/state as strings, such as skills_info('Chicago', 'IL'). Use a two letter abbreviation for the state. Output: A bar chart showing the most commonly desired skills in the job market for a data scientist. ''' final_job = 'data+scientist' # searching for data scientist exact fit("data scientist" on Indeed search) # Make sure the city specified works properly if it has more than one word (such as San Francisco) if city is not None : final_city = city . split () final_city = '+' . join ( word for word in final_city ) final_site_list = [ 'http://www.indeed.com/jobs?q= %22 ' , final_job , ' %22 &l=' , final_city , ' %2 C+' , state ] # Join all of our strings together so that indeed will search correctly else : final_site_list = [ 'http://www.indeed.com/jobs?q="' , final_job , '"' ] final_site = '' . join ( final_site_list ) # Merge the html address together into one string base_url = 'http://www.indeed.com' try : html = urllib2 . urlopen ( final_site ) . read () # Open up the front page of our search first except : 'That city/state combination did not have any jobs. Exiting . . .' # In case the city is invalid return soup = BeautifulSoup ( html ) # Get the html from the first page # Now find out how many jobs there were num_jobs_area = soup . find ( id = 'searchCount' ) . string . encode ( 'utf-8' ) # Now extract the total number of jobs found # The 'searchCount' object has this job_numbers = re . findall ( ' \ d+' , num_jobs_area ) # Extract the total jobs found from the search result if len ( job_numbers ) > 3 : # Have a total number of jobs greater than 1000 total_num_jobs = ( int ( job_numbers [ 2 ]) * 1000 ) + int ( job_numbers [ 3 ]) else : total_num_jobs = int ( job_numbers [ 2 ]) city_title = city if city is None : city_title = 'Nationwide' print 'There were' , total_num_jobs , 'jobs found,' , city_title # Display how many jobs were found num_pages = total_num_jobs / 10 # This will be how we know the number of times we need to iterate over each new # search result page job_descriptions = [] # Store all our descriptions in this list for i in xrange ( 1 , num_pages + 1 ): # Loop through all of our search result pages print 'Getting page' , i start_num = str ( i * 10 ) # Assign the multiplier of 10 to view the pages we want current_page = '' . join ([ final_site , '&start=' , start_num ]) # Now that we can view the correct 10 job returns, start collecting the text samples from each html_page = urllib2 . urlopen ( current_page ) . read () # Get the page page_obj = BeautifulSoup ( html_page ) # Locate all of the job links job_link_area = page_obj . find ( id = 'resultsCol' ) # The center column on the page where the job postings exist job_URLS = [ base_url + link . get ( 'href' ) for link in job_link_area . find_all ( 'a' )] # Get the URLS for the jobs job_URLS = filter ( lambda x : 'clk' in x , job_URLS ) # Now get just the job related URLS for j in xrange ( 0 , len ( job_URLS )): final_description = text_cleaner ( job_URLS [ j ]) if final_description : # So that we only append when the website was accessed correctly job_descriptions . append ( final_description ) sleep ( 1 ) # So that we don't be jerks. If you have a very fast internet connection you could hit the server a lot! print 'Done with collecting the job postings!' print 'There were' , len ( job_descriptions ), 'jobs successfully found.' doc_frequency = Counter () # This will create a full counter of our terms. [ doc_frequency . update ( item ) for item in job_descriptions ] # List comp # Now we can just look at our final dict list inside doc_frequency # Obtain our key terms and store them in a dict. These are the key data science skills we are looking for prog_lang_dict = Counter ({ 'R' : doc_frequency [ 'r' ], 'Python' : doc_frequency [ 'python' ], 'Java' : doc_frequency [ 'java' ], 'C++' : doc_frequency [ 'c++' ], 'Ruby' : doc_frequency [ 'ruby' ], 'Perl' : doc_frequency [ 'perl' ], 'Matlab' : doc_frequency [ 'matlab' ], 'JavaScript' : doc_frequency [ 'javascript' ], 'Scala' : doc_frequency [ 'scala' ]}) analysis_tool_dict = Counter ({ 'Excel' : doc_frequency [ 'excel' ], 'Tableau' : doc_frequency [ 'tableau' ], 'D3.js' : doc_frequency [ 'd3.js' ], 'SAS' : doc_frequency [ 'sas' ], 'SPSS' : doc_frequency [ 'spss' ], 'D3' : doc_frequency [ 'd3' ]}) hadoop_dict = Counter ({ 'Hadoop' : doc_frequency [ 'hadoop' ], 'MapReduce' : doc_frequency [ 'mapreduce' ], 'Spark' : doc_frequency [ 'spark' ], 'Pig' : doc_frequency [ 'pig' ], 'Hive' : doc_frequency [ 'hive' ], 'Shark' : doc_frequency [ 'shark' ], 'Oozie' : doc_frequency [ 'oozie' ], 'ZooKeeper' : doc_frequency [ 'zookeeper' ], 'Flume' : doc_frequency [ 'flume' ], 'Mahout' : doc_frequency [ 'mahout' ]}) database_dict = Counter ({ 'SQL' : doc_frequency [ 'sql' ], 'NoSQL' : doc_frequency [ 'nosql' ], 'HBase' : doc_frequency [ 'hbase' ], 'Cassandra' : doc_frequency [ 'cassandra' ], 'MongoDB' : doc_frequency [ 'mongodb' ]}) overall_total_skills = prog_lang_dict + analysis_tool_dict + hadoop_dict + database_dict # Combine our Counter objects final_frame = pd . DataFrame ( overall_total_skills . items (), columns = [ 'Term' , 'NumPostings' ]) # Convert these terms to a # dataframe # Change the values to reflect a percentage of the postings final_frame . NumPostings = ( final_frame . NumPostings ) * 100 / len ( job_descriptions ) # Gives percentage of job postings # having that term # Sort the data for plotting purposes final_frame . sort ( columns = 'NumPostings' , ascending = False , inplace = True ) # Get it ready for a bar plot final_plot = final_frame . plot ( x = 'Term' , kind = 'bar' , legend = None , title = 'Percentage of Data Scientist Job Ads with a Key Skill, ' + city_title ) final_plot . set_ylabel ( 'Percentage Appearing in Job Ads' ) fig = final_plot . get_figure () # Have to convert the pandas plot object to a matplotlib object return fig , final_frame # End of the function

Let’s now try running our new function on Seattle, Washington to see what results we get. Just as a note, all of these results were run on March 8, 2015 (with the exception of the national results that were run the next day).

seattle_info = skills_info ( city = 'Seattle' , state = 'WA' )

There were 73 jobs found , Seattle Getting page 1 Getting page 2 Getting page 3 Getting page 4 Getting page 5 Getting page 6 Getting page 7 Done with collecting the job postings ! There were 74 jobs successfully found .

Looking at the plot above, it seems Python is definitely the most commonly requested skill. Relational databases are used far more commonly than unstructured NoSQL databases. Spark doesn’t seem to have caught on to very many jobs yet (only about 7%) but it is listed more frequently than Pig, which has been around much longer! It also seems R is starting to lose the infamous Python vs. R battle that data scientists like arguing about (in my opinion just use both for their strengths!)

What if we tried a different job market, such as Chicago? How do things change here?

chicago_info = skills_info ( city = 'Chicago' , state = 'IL' )

There were 63 jobs found , Chicago Getting page 1 Getting page 2 Getting page 3 Getting page 4 Getting page 5 Getting page 6 Done with collecting the job postings ! There were 68 jobs successfully found .

In the case of Chicago, it seems Hadoop is the top skill to have. My guess is some of the companies list Hadoop without really understanding that Hadoop is an entire framework and not a specific “skill.” Python has now dropped to third place compared to Seattle. It also seems Spark appears in a greater percentage of job ads from Chicago than it did in Seattle, but Pig is requested more often.

What about the largest Data Scientist job markets like San Francisco and New York City?

silicon_val_info = skills_info ( city = 'San Francisco' , state = 'CA' )

There were 299 jobs found , San Francisco ... # I deleted some of the output for the purposes of this website Getting page 27 Getting page 28 Getting page 29 Done with collecting the job postings ! There were 336 jobs successfully found .

Once again, Python is the top skill demanded, with R in second. In terms of the Hadoop framework, Hive was the most in demand. This makes sense given most companies are still dealing with structured data and Hive’s strong similarity to SQL. Spark also beat Excel (thank goodness!) yet the percentage of job descriptions mentioning Spark was smaller in the Bay Area than in Chicago. A bit surprising since Spark originated at Berkeley, but this is just a snapshot in time after all.

nyc_info = skills_info ( city = 'New York' , state = 'NY' )

There were 300 jobs found , New York ... Getting page 27 Getting page 28 Getting page 29 Getting page 30 Done with collecting the job postings ! There were 335 jobs successfully found .

New York City and San Francisco had almost exactly the same number of job postings, which seems to show the job market is relatively balanced between the two coasts. R finally comes in first place in the Big Apple, with Python close behind. Demand for Spark seems pretty limited still, however. I think Spark has a bright future, so this may change by the end of the year.

Last, let’s see the overall national trend. (I ran this separately because it takes a long time to run).

national_info [ 0 ]

From a national standpoint, I notice a few interesting things:

Python is more in demand than R now for data science jobs. I think this will continue to be the trend as the Python data science community is further developed. Python is the “glue” that can hold almost every aspect of data science together. From data manipulation in pandas, machine learning with scikit-learn, web applications with Flask/Django, and an interface to Spark via PySpark, Python probably has you covered somewhere along the way. Is it always the best tool for the job? No, but it can usually get you there. R is still important in some areas where Python isn’t quite as good yet (such as plotting or statistical libraries). With Microsoft buying Revolution Analytics recently as an example, R still has a role to play which is why it is in second place.

Data visualization doesn’t seem to be in very high demand yet. D3 (or D3.js depending on the terminology the company used) is very low on the list. This surprised me because D3 seems to have a lot of power, yet it doesn’t seem to have won many companies over as a skill they value. Tableau is still more popular, even though it isn’t open source.

Even nationally, Spark is now (at least for this snapshot in time) a more in-demand skill than Excel. Perhaps companies are starting to realize that Excel isn’t exactly the best tool for data science at scale (sorry Microsoft!) and is better suited for a Data Analyst role.

Summary

Designing our own web crawler, we were able to find out which skills companies are most interested in for data science jobs on Indeed. Trends vary somewhat between cities, but the top four skills were Python, R, SQL, and Hadoop pretty consistently. Java, SAS, and Hive would probably be considered the second tier.

Notice, however, that these skills are just for the software/programming languages that a data scientist should know. There are many others, such as machine learning, statistics, mathematics, and an understanding of what insights or products can be helpful to a business. These are also very important skills that are necessary to be a successful data scientist in my opinion, so don’t forget about them!

If you would like the IPython Notebook for this blog post, you can find it here.