Review of Python Courses (Part 15)

In Part 14, I summarized my Datacamp courses 41-43. Today I will continue with the next three.

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

My course #44 was Python for Spreadsheet Users. This course covers:

• Welcome to Python
• Dataframes and their methods
• Filtering rows and creating columns
• Grouping and summing: the beginner’s pivot table
• Grouping by multiple columns
• More ways to condense information (using .groupby().head() to get top rows of each group)
• Working with multiple sheets [pd.ExcelFile(), .sheet_names attribute, .parse()]
• Preparing to put tables together
• Merging: the VLOOKUP of Python
• How visualization works in Python
• Building up the barplot
• The power of hue

My course #45 was Preprocessing for Machine Learning in Python. This course covers:

• Preprocessing data for machine learning (count number of missing values in column)
• Working with data types (converting column types)
• Training and test sets (stratified sampling with train_test_split)
• Standardizing data and log normalization
• Scaling data (from sklearn.preprocessing import StandardScaler)
• Standardized data and modeling (from sklearn.neighbors import KNeighborsClassifier)
• Feature engineering
• Encoding categorical variables [lambda function, from sklearn.preprocessing import LabelEncoder, pd.get_dummies()]
• Engineering numerical features
• Engineering features from text (from sklearn.feature_extraction.text import TfidfVectorizer)
• Feature selection
• Removing redundant features
• Selecting features using text vectors
• Dimensionality reduction (from sklearn.decomposition import PCA)
• UFOs and preprocessing

My course #46 was Cluster Analysis in Python. This course covers:

• Unsupervised learning: basics
• Basics of cluster analysis (from scipy.cluster.hierarchy import linkage, fcluster; from scipy.cluster.vq import kmeans, vq)
• Data preparation for cluster analysis (from scipy.cluster.vq import whiten)
• Basics of hierarchical clustering
• Visualize clusters
• How many clusters (from scipy.cluster.hierarchy import dendrogram)?
• Limitations of hierarchical clustering
• Basics of k-means clustering
• How many clusters?
• Limitations of k-means clustering
• Dominant colors in images (import matplotlib.image as img)
• Document clustering
• Clustering with multiple features

I will review more classes next time.