Data Science Projects with Python

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Course Description

Data Science Projects with Python training course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and, unseen data. As you delve into later chapters, you’ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data.

LEARNING OUTCOMES

• Install the required packages to set up a data science coding environment
• Load data into a Jupyter Notebook running Python
• Use Matplotlib to create data visualizations
• Fit a model using scikit-learn
• Use lasso and ridge regression to reduce overfitting
• Fit and tune a random forest model and compare performance with logistic regression
• Create visuals using the output of the Jupyter Notebook
• Use k-fold cross-validation to select the best combination of hyperparameters

Course Outline

Lesson 1: Data Exploration and Cleaning

• Python and the Anaconda Package
• Management System
• Different Types of Data Science Problems
• Loading the Case Study Data with Jupyter and pandas
• Data Quality Assurance and Exploration
• Exploring the Financial History Features in the Dataset

Lesson 2: Introduction to Scikit-Learn and Model Evaluation

• Introduction
• Model Performance Metrics for Binary Classification

Lesson 3: Details of Logistic Regression and Feature Exploration

• Introduction
• Examining the Relationships between Features and the Response
• Univariate Feature Selection: What It Does and Doesn’t Do
• Building Cloud-Native Applications

Lesson 4: The Bias-Variance Trade-off

• Introduction
• Estimating the Coefficients and Intercepts of Logistic Regression
• Cross Validation: Choosing the Regularization
• Parameter and Other Hyperparameters
• DevOps Patterns for Cloud-Native Architecture
• Choosing the Best CI/CD Tools

Lesson 5: Decision Trees and Random Forests

• Introduction
• Decision trees
• Random Forests: Ensembles of Decision Trees

Lesson 6: Imputation of Missing Data, Financial Analysis, and Delivery to Client

• Introduction
• Review of Modeling Results
• Dealing with Missing Data: Imputation Strategies

Learning Path

• Please contact us for more suggestions.

• Applied Data Science and Big Data Analytics
• Applied Data Science with Python
• Big Data Analysis with Python
• Data Wrangling with Python

Ways to Attend

• Attend a public course, if there is one available. Please check our schedule, or register your interest in joining a course in your area.
• Private onsite Team training also available, please contact us to discuss. We can customise this course to suit your business requirements.