1 – PYTHON FOR DATA SCIENCE
- Using Modules
- Listing Methods in a Module
- Creating Your Own Modules
- List Comprehension
- Dictionary Comprehension
- String Comprehension
- Python 2 vs Python 3
- Sets (Python 3+)
- Python Idioms
- Python Data Science “Ecosystem”
- NumPy
- NumPy Arrays
- NumPy Idioms
- pandas
- Data Wrangling with pandas‘ DataFrame
- SciPy
- Scikit-learn
- SciPy or scikit-learn?
- Matplotlib
- Python vs R
- Python on Apache Spark
- Python Dev Tools and REPLs
- Anaconda
- IPython
- Visual Studio Code
- Jupyter
- Jupyter Basic Commands
- Summary
2 – APPLIED DATA SCIENCE
- What is Data Science?
- Data Science Ecosystem
- Data Mining vs. Data Science
- Business Analytics vs. Data Science
- Data Science, Machine Learning, AI?
- Who is a Data Scientist?
- Data Science Skill Sets Venn Diagram
- Data Scientists at Work
- Examples of Data Science Projects
- An Example of a Data Product
- Applied Data Science at Google
- Data Science Gotchas
- Summary
3 – DATA ANALYTICS LIFE-CYCLE PHASES
- Big Data Analytics Pipeline
- Data Discovery Phase
- Data Harvesting Phase
- Data Priming Phase
- Data Logistics and Data Governance
- Exploratory Data Analysis
- Model Planning Phase
- Model Building Phase
- Communicating the Results
- Production Roll-out
- Summary
4 – REPAIRING AND NORMALIZING DATA
- Repairing and Normalizing Data
- Dealing with the Missing Data
- Sample Data Set
- Getting Info on Null Data
- Dropping a Column
- Interpolating Missing Data in pandas
- Replacing the Missing Values with the Mean Value
- Scaling (Normalizing) the Data
- Data Preprocessing with scikit-learn
- Scaling with the scale() Function
- The MinMaxScaler Object
- Summary
5 – DESCRIPTIVE STATISTICS COMPUTING FEATURES IN PYTHON
- Descriptive Statistics
- Non-uniformity of a Probability Distribution
- Using NumPy for Calculating Descriptive Statistics Measures
- Finding Min and Max in NumPy
- Using pandas for Calculating Descriptive Statistics Measures
- Correlation
- Regression and Correlation
- Covariance
- Getting Pairwise Correlation and Covariance Measures
- Finding Min and Max in pandas DataFrame
- Summary
6 – DATA AGGREGATION AND GROUPING
- Data Aggregation and Grouping
- Sample Data Set
- The pandas.core.groupby.SeriesGroupBy Object
- Grouping by Two or More Columns
- Emulating the SQL’s WHERE Clause
- The Pivot Tables
- Cross-Tabulation
- Summary
7 – DATA VISUALIZATION WITH MATPLOTLIB
- Data Visualization
- What is matplotlib?
- Getting Started with matplotlib
- The Plotting Window
- The Figure Options
- The matplotlib.pyplot.plot() Function
- The matplotlib.pyplot.bar() Function
- The matplotlib.pyplot.pie () Function
- Subplots
- Using the matplotlib.gridspec.GridSpec Object
- The matplotlib.pyplot.subplot() Function
- Hands-on Exercise
- Figures
- Saving Figures to File
- Visualization with pandas
- Working with matplotlib in Jupyter Notebooks
- Summary
8 – DATA SCIENCE AND ML ALGORITHMS IN SCIKIT-LEARN
- Data Science, Machine Learning, AI?
- Types of Machine Learning
- Terminology: Features and Observations
- Continuous and Categorical Features (Variables)
- Terminology: Axis
- The scikit-learn Package
- scikit-learn Estimators
- Models, Estimators, and Predictors
- Common Distance Metrics
- The Euclidean Metric
- The LIBSVM format
- Scaling of the Features
- The Curse of Dimensionality
- Supervised vs Unsupervised Machine Learning
- Supervised Machine Learning Algorithms
- Unsupervised Machine Learning Algorithms
- Choose the Right Algorithm
- Life-cycles of Machine Learning Development
- Data Split for Training and Test Data Sets
- Data Splitting in scikit-learn
- Hands-on Exercise
- Classification Examples
- Classifying with k-Nearest Neighbors (SL)
- k-Nearest Neighbors Algorithm
- k-Nearest Neighbors Algorithm
- The Error Rate
- Hands-on Exercise
- Dimensionality Reduction
- The Advantages of Dimensionality Reduction
- Principal component analysis (PCA)
- Hands-on Exercise
- Data Blending
- Decision Trees (SL)
- Decision Tree Terminology
- Decision Tree Classification in Context of Information Theory
- Information Entropy Defined
- The Shannon Entropy Formula
- The Simplified Decision Tree Algorithm
- Using Decision Trees
- Random Forests
- SVM
- Naive Bayes Classifier (SL)
- Naive Bayesian Probabilistic Model in a Nutshell
- Bayes Formula
- Classification of Documents with Naive Bayes
- Unsupervised Learning Type: Clustering
- Clustering Examples
- k-Means Clustering (UL)
- k-Means Clustering in a Nutshell
- k-Means Characteristics
- Regression Analysis
- Simple Linear Regression Model
- Linear vs Non-Linear Regression
- Linear Regression Illustration
- Major Underlying Assumptions for Regression Analysis
- Least-Squares Method (LSM)
- Locally Weighted Linear Regression
- Regression Models in Excel
- Multiple Regression Analysis
- Logistic Regression
- Regression vs Classification
- Time-Series Analysis
- Decomposing Time-Series
- Summary
9 – LAB EXERCISES
- Lab 1 – Learning the Lab Environment
- Lab 2 – Using Jupyter Notebook
- Lab 3 – Repairing and Normalizing Data
- Lab 4 – Computing Descriptive Statistics
- Lab 5 – Data Grouping and Aggregation
- Lab 6 – Data Visualization with matplotlib
- Lab 7 – Data Splitting
- Lab 8 – k-Nearest Neighbors Algorithm
- Lab 9 – The k-means Algorithm
- Lab 10 – The Random Forest Algorithm
