Tomdebaes

Improving Query Performance in SQL Server: - Introduction - Aliasing - Query order - Filtering with WHERE - Filtering with HAVING - Interrogation after SELECT - Managing duplicates - Sub-queries - Presence and absence - Alternative methods 1 - Alternative methods 2 - Time statistics - Page read statistics - Indexes - Execution plans - Query performance tuning: final notes - Conclusion

Dates in Python: - Math with Dates - Turning dates into strings - Adding time to the mix - Printing and parsing datetimes - Working with durations - UTC offsets - Time zone database - Starting Daylight Saving Time - Ending Daylight Saving Time - Reading date and time data in Pandas - Summarizing datetime data in Pandas - Additional datetime methods in Pandas

Cleaning data in Python: - Data type constraints - Data range constraints - Uniqueness constraints - Membership constraints - Categorical variables - Cleaning text data - Uniformity - Cross field validation - Completeness - Comparing strings - Generating pairs - Linking DataFrames

Intermediate importing data in Python: - Importing flat files from the web - HTTP requests to import files from the web - Scraping the web in Python - Introduction to API's and JSON's - API's and interacting with the world wide web - The Twitter API and Authentication

Introduction to importing data in python: - The importance of flat files in data science - Importing flat files using NumPy - Importing flat files using pandas - Introduction to other file types - Importing SAS/Stata files using pandas - Importing HDF5 files - Importing MATLAB files - Introduction to relational databases - Creating a database engine in Python - Querying relational databases in Python - Querying relational databases directly with pandas - Advanced querying: exploiting...

User-defined functions Multiple Parameters and Return Values Scope and user-defined functions Nested functions Default and flexible arguments Lambda functions Introduction to error handling Introduction to iterators Playing with iterators Using iterators to load large files into memory List comprehensions Advanced comprehensions Introduction to generator expressions Wrapping up comprehensions and generators Using Python generators for streaming data Using pandas' read_csv iterato...

Introduction to Seaborn Using pandas with Seaborn Adding a third variable with hue Introduction to relational plots and subplots Customizing scatter plots Introduction to line plots Count plots and bar plots Creating a box plot Point plots Changing plot style and color Adding titles and labels Using the distribution plot Regression Plots in Seaborn Using Seaborn Styles Colors in Seaborn Customizing with matplotlib Categorical Plot Types Regression Plots Matrix Plots Using Face...

Datacamp introduction to Data Visualization with Matplotlib: - Customizing your plots - Small multiples - Plotting time-series data - Plotting time-series with different variables - Annotating time-series data - Quantitative comparisons: bar-charts - Quantitative comparisons: histograms - Statistical plotting - Quantitative comparisons: scatter plots - Preparing your figures to share with others - Sharing your visualizations with others - Automating figures from data - Where to go n...

Datacamp joining data with pandas: - Inner join - One to many relationships - Merging multiple relationships - Left join - Other joins - Merging a table to itself - Merging on indexes - Filtering joins - Concatenate DataFrames together vertically - Verifying integrity - Using merge_ordered() - Using merge_asof() - Selecting data with .Query() - Reshaping data with .melt() - Course wrap-up

Datacamp data manipulation with Pandas: * Introducing DataFrames * Sorting and subsetting * New columns * Summary statistics * Counting * Grouped summary statistics * Pivot tables * Explicit indexes * Slicing and subsetting with .loc and .iloc * Working with pivot tables * Visualizing your data * Missing values * Creating DataFrames * Reading and writing CSVs * Wrape-up