Course Description

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Data Science Projects with Python will help you get comfortable with using the Python environment for data science. This course will start you on your journey to mastering topics within machine learning. These skills will help you deliver the kind of state-of-the-art predictive models that are being used to deliver value to businesses across industries.

Data Science Projects with Python 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.

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
  • 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
  • Final Thoughts on Delivering the Predictive Model to the Client

Learner Outcomes

At the end of this program, you will be able to:

  • 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


Before you start this course, make sure you have installed the Anaconda environment as we will be using the Anaconda distribution of Python. Install Anaconda by following the instructions at this link: https://www.anaconda.com/distribution/
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