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IGTC01OS - Python for Data Science Essential Training Part 1 (EN)
01 - Introduction
02 - What you should know (0:39)
01 - Data science life hacks (0:33)
Ex_Files_Python_Data_Science_EssT_Pt_1
Ex_Files_Python_Data_Science_EssT_Pt_1
02 - 1. Introduction to the Data Professions
01 - Introduction to the data professions (9:07)
02 - The four flavors of data analysis (3:16)
03 - Why use Python for analytics (4:22)
04 - High-level course road map (1:28)
03 - 2. Data Preparation Basics
01 - Filtering and selecting (15:25)
02 - Treating missing values (15:07)
03 - Removing duplicates (6:18)
04 - Concatenating and transforming (10:08)
05 - Grouping and aggregation (4:52)
04 - 3. Data Visualization 101
01 - The three types of data visualization (7:40)
02 - Selecting optimal data graphics (6:17)
03 - Communicating with color and context (4:38)
05 - 4. Practical Data Visualization
02 - Defining elements of a plot (9:06)
01 - Creating standard data graphics (8:42)
03 - Plot formatting (12:09)
04 - Creating labels and annotations (14:30)
05 - Visualizing time series (6:33)
06 - Creating statistical data graphics (9:59)
06 - 5. Basic Math and Statistics
02 - Basic linear algebra (6:17)
01 - Simple arithmetic (6:53)
03 - Generating summary statistics (9:46)
04 - Summarizing categorical data (12:25)
05 - Parametric correlation analysis (15:15)
06 - Non-parametric correlation analysis (13:25)
07 - Transforming dataset distributions (9:54)
08 - Extreme value analysis for outliers (11:05)
09 - Multivariate analysis for outliers (5:53)
07 - 6. Data Sourcing via Web Scraping
01 - BeautifulSoup object (18:28)
02 - NavigableString objects (10:01)
03 - Data parsing (12:42)
04 - Web scraping in practice (10:48)
05 - Introduction to NLP (12:31)
06 - Cleaning and stemming textual data (7:47)
07 - Lemmatizing and analyzing textual data (10:15)
08 - 7. Collaborative Analytics with Plotly
02 - Create statistical charts (3:28)
01 - Introduction to Plotly (4:31)
03 - Line charts in Plotly (6:29)
04 - Bar charts and pie charts in Plotly (7:24)
05 - Create statistical charts (15:00)
09 - Conclusion
01 - Next steps (0:54)
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03 - Why use Python for analytics
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