Review of Python Courses (Part 19)

In Part 18, I summarized my Datacamp courses 53-55. Today I will continue with the next three.

As a reminder, I introduced you to my recent work learning Python here.

My course #56 was Writing Efficient Code with pandas. This course covers:

• The need for efficient coding (time.time(), list comprehensions faster than for loop)
• Locate rows: .iloc[] (generally faster for rows) and .loc[] (generally faster for columns)
• Select random rows (built-in sample() function faster than numpy random integer generator)
• Replace scalar values using .replace() (much faster than using .loc[] to find values and reassigning them)
• Replace values using lists (.replace() faster than using .loc[] )
• Replace values using dictionaries (faster than using lists)
• Looping through the .iterrows() function [for loop using .range() is faster than the smarter/cleaner/optimized .iterrows()]
• Looping through the .apply() function (faster iterating along rows while native pandas .sum() faster along columns)
• Vectorization over pandas series [vectorization method .apply() works faster than .iterrows()]
• Vectorization using NumPy arrays using .values() (summing arrays is faster than summing series)
• Data transformation using .groupby().transform (.transform() cleaner and much faster than native Python code)
• Missing value imputation using .transform() (.transform() much faster than native Python code)
• Data filtration using the .filter() function (.groupby().filter() faster than list comprehension + for loop)

My course #57 was Credit Risk Modeling in Python. This course covers:

• Understanding credit risk
• Outliers in credit data
• Risk with missing data in loan data (finding, counting, and replacing missing data)
• Logistic regression for probability of default
• Predicting the probability of default
• Credit model performance
• Model discrimination and impact
• Gradient boosted trees with XGBoost
• Column selection for credit risk
• Cross validation for credit models
• Class imbalance in loan data
• Model evaluation and implementation (from sklearn.calibration import calibration_curve)
• Credit acceptance rates
• Credit strategy and maximum expected loss

My course #58 was Analyzing IoT Data in Python. This course covers:

• Introduction to IoT data
• Understand the data
• Introduction to data streams (import paho.mqtt.subscribe as subscribe)
• Perform EDA
• Clean data
• Gather minimalistic incremental data
• Prepare and visualize incremental data
• Combining data sources for further analysis
• Correlation
• Outliers (from statsmodels.graphics import tsaplots)
• Seasonality and trends
• Prepare data for machine learning
• Scaling data for machine learning
• Develop machine learning pipeline (from sklearn.pipeline import Pipeline)
• Apply a machine learning model

I will review more classes next time.