Data Science with Python Training Overview
This Applied Data Science with Python training course teaches attendees the fundamentals of using Python to program tasks in data science, data engineering, business analytics, and data visualization.
Location and Pricing
Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.
In addition, some courses are available as live, instructor-led training from one of our partners.
Objectives
- Use Jupyter Notebooks
- Understand Python
- Work with NumPy and pandas
- Repair and normalize data
- Work with data visualization in Python
- Perform data splitting
- Work with the Random Forest algorithm
- Understand the k-Means algorithm
Prerequisites
Participants must have a working knowledge of Python or experience with a modern programming language and familiarity with core statistical concepts (i.e., variance, correlation, etc.).
Outline
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Introduction to Python for Data Science
- Python Data Science-Centric Libraries
- NumPy
- SciPy
- Pandas
- Creating a pandas DataFrame
- Fetching and Sorting Data
- Scikit-learn
- Matplotlib
- Seaborn
- Python Dev Tools and REPLs
- IPython
- Jupyter
- Anaconda
Defining Data Science
- What is Data Science?
- Data Science, Machine Learning, AI?
- The Data-Related Roles
- The Data Science Ecosystem
- Tools of the Trade
- Who is a Data Scientist?
- Data Scientists at Work
- Examples of Data Science Projects
- An Example of a Data Product
- Applied Data Science at Google
- Data Science Gotchas
Data Processing Phases
- Typical Data Processing Pipeline
- Data Discovery Phase
- Data Harvesting Phase
- Data Priming Phase
- Exploratory Data Analysis
- Model Planning Phase
- Model Building Phase
- Communicating the Results
- Production Roll-out
- Data Logistics and Data Governance
- Data Processing Workflow Engines
- Apache Airflow
- Data Lineage and Provenance
- Apache NiFi
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
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
Data Visualization in Python
- Data Visualization
- Data Visualization in Python
- Matplotlib
- Getting Started with matplotlib
- Subplots
- Using the matplotlib.gridspec.GridSpec Object
- The matplotlib.pyplot.subplot() Function
- Figures
- Saving Figures to a File
- Seaborn
- Getting Started with seaborn
- Histograms and KDE
- Plotting Bivariate Distributions
- Scatter plots in seaborn
- Pair plots in seaborn
- Heatmaps
- ggplot
Data Science and Machine Learning (ML) Algorithms in Scikit-Learn
- In-Class Discussion
- Types of Machine Learning
- Terminology: Features and Observations
- Representing Observations
- Terminology: Labels
- Terminology: Continuous and Categorical Features
- Continuous Features
- Categorical Features
- Common Distance Metrics
- The Euclidean Distance
- What is a Model
- Supervised vs. Unsupervised Machine Learning
- Supervised Machine Learning Algorithms
- Unsupervised Machine Learning Algorithms
- Choosing the Right Algorithm
- The scikit-learn Package
- Scikit-learn Estimators, Models, and Predictors
- Model Evaluation
- The Error Rate
- Confusion Matrix
- The Binary Classification Confusion Matrix
- Multi-class Classification Confusion Matrix Example
- ROC Curve
- Example of a ROC Curve
- The AUC Metric
- Feature Engineering
- Scaling of the Features
- Feature Blending (Creating Synthetic Features)
- The 'One-Hot' Encoding Scheme
- Example of 'One-Hot' Encoding Scheme
- Bias-Variance (Underfitting vs. Overfitting) Trade-off
- The Modeling Error Factors
- One Way to Visualize Bias and Variance
- Underfitting vs. Overfitting Visualization
- Balancing Off the Bias-Variance Ratio
- Regularization in scikit-learn
- Regularization, Take Two
- Dimensionality Reduction
- PCA and isomap
- The Advantages of Dimensionality Reduction
- The LIBSVM format
- Life-cycles of Machine Learning Development
- Data Splitting into Training and Test Datasets
- ML Model Tuning Visually
- Data Splitting in scikit-learn
- Cross-Validation Technique
- Hands-on Exercise
- Classification (Supervised ML) Examples
- Classifying with k-Nearest Neighbors
- k-Nearest Neighbors Algorithm
- Regression Analysis
- Simple Linear Regression Model
- Linear Regression Illustration
- Least-Squares Method (LSM)
- Gradient Descent Optimization
- Multiple Regression Analysis
- Evaluating Regression Model Accuracy
- The R2 Model Score
- The MSE Model Score
- Logistic Regression (Logit)
- Interpreting Logistic Regression Results
- Decision Trees
- Properties of Decision Trees
- Decision Tree Classification in the Context of Information Theory
- The Simplified Decision Tree Algorithm
- Using Decision Trees
- Random Forests
- Support Vector Machines (SVMs)
- 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)
- Global vs. Local Minimum Explained
- XGBoost
- Gradient Boosting
- A Better Algorithm or More Data?
Introduction to Python for Data Engineers
- What is Python?
- Additional Documentation
- Which version of Python am I running?
- Python Dev Tools and REPLs
- IPython
- Jupyter
- Anaconda
- Python Variables and Basic Syntax
- Variable Scopes
- PEP8
- The Python Programs
- Getting Help
- Variable Types
- Assigning Multiple Values to Multiple Variables
- Null (None)
- Strings
- Finding the Index of a Substring
- String Splitting
- Triple-Delimited String Literals
- Raw String Literals
- String Formatting and Interpolation
- Boolean
- Boolean Operators
- Numbers
- Looking Up the Runtime Type of a Variable
- Divisions
- Assignment-with-Operation
- Relational Operators
- The if-elif-else Triad
- Conditional Expressions (a.k.a. Ternary Operator)
- The While-Break-Continue Triad
- The for Loop
- try-except-finally
- Lists
- Main List Methods
- Dictionaries
- Working with Dictionaries
- Sets
- Common Set Operations
- Set Operations Examples
- Finding Unique Elements in a List
- Enumerate
- Tuples
- Unpacking Tuples
- Functions
- Dealing with Arbitrary Number of Parameters
- Keyword Function Parameters
- The range Object
- Random Numbers
- Python Modules
- Creating a Runnable Application
- List Comprehension
- Zipping Lists
- Working with Files
- Reading Command-Line Parameters
- Accessing Environment Variables
- What is Functional Programming (FP)?
- Terminology: Higher-Order Functions
- Lambda Functions in Python
- Lambdas in the Sorted Function
- Regular Expressions
- Python Data Science-Centric Libraries
Conclusion
Training Materials
All Python Data Science training attendees receive comprehensive courseware.
Software Requirements
- Anaconda Python 3.6 or later
- Spyder IDE and Jupyter notebook (Comes with Anaconda)
