Introduction to Python for data science

Before data can be analyzed, it needs to be clean, structured, and ready for use. This module covers essential data wrangling techniques to handle missing values, remove duplicates, merge datasets, reshape data, and manipulate text. By mastering these skills, we can ensure data quality, streamline analysis, and extract meaningful insights from raw information. Effective data wrangling is a crucial step in any data science workflow, making our datasets reliable and analysis ready.

What you'll learn

• Apply techniques to handle missing and duplicate values in a dataset
• Evaluate best practices for data processing, transformation, and cleaning
• Integrate data from multiple sources and perform necessary transformations
• Implement string manipulation methods to clean and modify text data

In this module, we'll cover key techniques for data exploration and visualization using Python libraries like pandas, NumPy, Matplotlib, and seaborn. We'll learn how to explore datasets, create visualizations, and uncover insights that help in understanding patterns and relationships within the data. By the end, you'll be able to effectively use visual tools to communicate your findings.

What you'll learn

• Understand why data exploration and visualization are essential in data analysis
• Use techniques like summary statistics, outlier detection, and correlation analysis
• Interpret and choose appropriate plots using Matplotlib and Seaborn
• Apply exploration and visualization skills to real-world datasets

In this module, we will explore how to build efficient data pipelines for integrating, transforming, and managing data from various sources. We will cover the fundamentals of RESTful APIs, HTTP protocols, and data extraction techniques. We will learn how to retrieve data from web services, understand the ETL (Extract, Transform, Load) process, and apply web scraping methods. These skills will help us automate data workflows and streamline data processing for analysis and decision-making.

What you'll learn

• Understand the components and processes involved in data pipelines
• Apply techniques to extract data from websites using Python and web scraping methods
• Discuss the principles and architecture behind RESTful services and APIs
• Use the requests library to send HTTP requests and interact with RESTful APIs

In this module, we will cover the essentials of machine learning using Python. We will explore key concepts like supervised and unsupervised learning, model selection, and training, along with popular libraries like scikit-learn. We'll work through the process of preparing data, selecting the right algorithms, evaluating model performance, and applying machine learning techniques to real-world problems. By the end of this module, you'll have a strong foundation in applying machine learning methods and be equipped to take on machine learning projects with confidence.

What you'll learn

• Explain core OOP concepts and how they help structure ML code
• Describe the machine learning process and key terminology
• Understand how scikit-learn's estimator API works
• Choose models and tune hyperparameters using scikit-learn
• Use supervised learning to predict customer churn with real-world data