Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Python for SEO 102 (Advanced)
Introduction
Intro (10:21)
Intro to Colab (5:38)
Dictionaries & Lists
Link to code file used for this section
Dictionaries and lists intro (3:28)
Nested lists (6:25)
List comprehension (6:02)
Dictionaries basics (5:44)
Adding information to dictionaries or overwriting it (3:20)
End of dictionaries and lists (11:51)
Error handling
Link to code file used for this section
Simple try except (5:26)
Handling errors better (12:23)
Error catching finally (4:56)
APIs
Link to code file used for this section
Introduction - what are APIs? (26:21)
APIs hands-on (0:45)
Setting up GSC API authorisation (8:55)
Authorising with Search Console API and our first request (19:41)
Requesting keyword data and working with it (26:13)
GSC wrapping everything into an easy function (3:47)
Getting more data out of search console - row limits and filters (10:28)
Available filters and dimensions and usage limits (7:19)
Google Sheets API - dataframes and Google Sheets (21:24)
Google Sheets API - pulling and changing information (19:09)
API wrap up (2:32)
Requests
Link to code file used for this section
Direct requests intro (3:12)
Requests library simple requests (9:04)
Extracting information with beautifulsoup (15:26)
Pandas to extract tables (3:13)
Selenium to move around web pages (13:04)
Manually working with APIs with requests (15:07)
Advanced work with Pandas
Link to code file used for this section
Pandas advanced intro (6:54)
Pandas merge 1 (7:24)
Pandas merge outer (3:47)
Pandas merge left and right (5:16)
Merge handling duplicates (13:35)
Pandas pivot (9:43)
Pandas Melt (15:24)
Pandas apply introduction (9:06)
Pandas apply for categorising keywords (7:29)
Seaborn bar chart (8:10)
Seaborn scatter charts (5:11)
Plotly interactive (6:19)
Pandas advanced wrap up (1:48)
Local files
Link to code file used for this section
Introduction to working with local files (3:16)
os making and deleting folders (7:33)
os creating whole paths (4:28)
Glob (4:25)
Reading and writing text files (6:20)
Pickle (3:46)
Files wrap up (0:46)
Analysis methods
Link to code file used for this section
Analysis methods introduction (0:51)
NLP introduction (2:38)
NLP Stemming (9:20)
NLP - Lemmatisation (10:01)
NLP - what are ngrams (3:53)
NLP - counting ngrams (11:53)
NLP - counting ngrams across a list (4:56)
NLP - returning ngrams for list (4:56)
NLP - wrap up of ngrams, stemming, and lemmatisation (5:29)
NLP - entity extraction (11:03)
Scikit learn (6:59)
Forecasting (11:21)
Trying to estimate impact - causal impact (10:34)
Trying to estimate impact - making estimates more accurate (13:06)
Analysis methods conclusion (2:19)
Running code in the cloud
Running code in the cloud - intro and platforms (6:56)
Cloud functions - initial settings (11:24)
Writing code in Google Cloud (10:59)
Code after you deployed (7:07)
Cloud scheduling (6:32)
Cloud functions pricing (5:38)
Running code in the cloud conclusion (0:50)
Keyword categorisation
Link to code file used for this section
Different keyword categorisation introduction (7:46)
Different keyword categorisation - preparing our data (10:04)
Different keyword categorisation - extracting data from a dataframe into a dictionary (11:36)
Different keyword categorisation - Comparing similarities of url lists (11:45)
Different keyword categorisation: finding best overlap (19:50)
Different keyword categorisation: adding data to google sheets (8:54)
Conclusion
Conclusion (6:07)
NLP - returning ngrams for list
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enrol in Course to Unlock