Data ScienceIntermediate
Data Science with Python
Analyze real data with NumPy and pandas, right in your browser.
19 chapters76 lessons48 challenges
Course curriculum
Chapter 1: Introduction and NumPy
- ▸What is data science
- ▸NumPy: arrays and operations
- ▸Array indexing and slicing
- ▸Boolean masks
Chapter 2: The Series
- ▸The Series
- ▸Statistics on a Series
- ▸Custom indexes
- ▸Counting values
Chapter 3: The DataFrame
- ▸The DataFrame
- ▸Columns and dimensions
- ▸Exploring the data
- ▸New columns and loc/iloc
Chapter 4: Selection and filtering
- ▸Selecting columns and rows
- ▸Filtering with conditions
- ▸isin and text conditions
- ▸Missing values
Chapter 5: groupby and aggregations
- ▸Grouping with groupby
- ▸Aggregations and sorting
- ▸Multiple aggregations
- ▸Sorting and taking the top
Chapter 6: Data cleaning
- ▸Missing values (NaN)
- ▸Dropping missing: dropna
- ▸Filling missing: fillna
- ▸Duplicates and types
Chapter 7: Data transformation
- ▸apply
- ▸map and replace
- ▸String operations: .str
- ▸Dates and time
Chapter 8: Combining data
- ▸concat
- ▸merge: the join
- ▸Join types
- ▸join on the index
Chapter 9: Reshaping data
- ▸pivot_table
- ▸melt
- ▸stack and unstack
- ▸Tidy data
Chapter 10: Descriptive statistics
- ▸Measures of center
- ▸Measures of spread
- ▸Correlation
- ▸Distribution and outliers
Chapter 11: Time series
- ▸Dates with to_datetime
- ▸The datetime index
- ▸resample
- ▸Rolling windows
Chapter 12: Data visualization
- ▸Why and how to visualize
- ▸Chart types
- ▸Preparing data for the chart
- ▸Customizing and best practices
Chapter 13: Introduction to Machine Learning
- ▸What Machine Learning is
- ▸The fit / predict flow
- ▸Train / test split
- ▸Features and target (X and y)
Chapter 14: Regression
- ▸What regression is
- ▸Linear regression
- ▸Predicting and evaluating
- ▸Multiple regression
Chapter 15: Classification
- ▸What classification is
- ▸Logistic regression
- ▸Decision trees
- ▸predict and predict_proba
Chapter 16: Metrics and validation
- ▸Accuracy and confusion matrix
- ▸Precision, recall, F1
- ▸Overfitting and underfitting
- ▸Cross-validation
Chapter 17: Preprocessing and pipelines
- ▸Why preprocess
- ▸Standardization
- ▸Encoding categories
- ▸Pipelines
Chapter 18: Advanced models and clustering
- ▸Ensembles and Random Forest
- ▸Hyperparameter tuning
- ▸Clustering with KMeans
- ▸Dimensionality reduction: PCA
Chapter 19: End-to-end project
- ▸The project workflow
- ▸A complete classification flow
- ▸A complete regression flow
- ▸Senior best practices
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