Python for Real-World Data Analysis

In this project-based course, you will learn how to use the Python programming language and Pandas as a data analyst. A data analyst analyzes and visualizes data, as well as communicates the findings and insights effectively across an organization. In many cases, these findings are meant to answer a business question. For this project-based course, Airbnb is looking for excellent deals to promote hosts in New York City using a new social media ad campaign. We will do this by obtaining, cleaning, and analyzing existing data to help Airbnb decide which hosts will be promoted. Data analysis is a valuable skill to have if you want to use open-source data to help make business decisions. This project will help an aspiring data analyst use Python and Pandas to load, clean, and use data to answer important business questions.

In this project, you'll serve as a data analyst at a marketing firm specializing in social media brand promotion. Your task is to use Python to extract, clean, and analyze tweets in specific categories (health, family, food, etc.) and generate visualizations. Your analysis will provide valuable insights to help clients enhance their social media performance and allow the firm to deliver tweets on time and within budget, leading to faster results. There isn’t just one right approach or solution in this scenario, which means you can create a truly unique project that helps you stand out to employers. ROLE: Data Analyst SKILLS: Python PREREQUISITES: Python, Numpy, Matplotlib or Seaborn, Git, Jupyter Notebook

In this project, you'll serve as a data analyst at an online retail company helping interpret real-world data to help make key business decisions. Your task is to explore and analyze this dataset to gain insights into the store's sales trends, customer behavior, and popular products. Upon completion, you’ll be able to demonstrate your ability to perform a comprehensive data analysis project that involves critical thinking, extensive data analysis and visualization, and making data-driven business decisions. There isn’t just one right approach or solution in this scenario, which means you can create a truly unique project that helps you stand out to employers. ROLE: Data Analyst SKILLS: Python PREREQUISITES: Python, Numpy, Matplotlib or Seaborn, Git, Jupyter Notebook

In this 2-hour course, we'll learn to analyze customer reviews for an online women's clothing shop. Our task is determining which clothing category (Tops, Bottoms, Jackets, Dresses, or Intimate) has higher customer satisfaction. The data consists of text reviews, and we'll use Python with pandas for data manipulation and the NLTK module for text preprocessing and sentiment analysis. Prior knowledge of Python and pandas is required. By the end of the course, learners will gain practical experience in text data analysis and customer sentiment evaluation. This project is aimed at learners interested in Natural Language Processing (NLP) using Python.

Data visualization is a powerful tool for exploring and communicating insights from data effectively. Seaborn, a Python visualization library built on top of Matplotlib, offers a wide range of features for creating attractive and informative statistical plots. This course provides a comprehensive overview of Seaborn, covering basic plotting techniques as well as advanced statistical visualizations. Participants will learn how to leverage Seaborn to visualize data distributions, relationships, and patterns, enabling them to convey complex information visually with confidence. Data visualization is a powerful tool for exploring and communicating insights from data effectively. Seaborn, a Python visualization library built on top of Matplotlib, offers a wide range of features for creating attractive and informative statistical plots. This course provides a comprehensive overview of Seaborn, covering basic plotting techniques as well as advanced statistical visualizations. Participants will learn how to leverage Seaborn to visualize data distributions, relationships, and patterns, enabling them to convey complex information visually with confidence. Participants should have a basic understanding of Python programming and fundamental data visualization concepts before enrolling in this course. Familiarity with Python's data manipulation libraries such as Pandas, and an introductory knowledge of Matplotlib, will be beneficial. This foundational knowledge will enable learners to quickly grasp Seaborn's functionalities and apply them effectively in their data visualization tasks. By the end of this course, learners will be equipped to explain the critical role of data visualization in data analysis and interpretation. They will gain practical skills in creating basic plots using Seaborn to visualize data distributions and relationships. Additionally, learners will explore advanced statistical plots for deeper data analysis and develop the ability to customize and enhance Seaborn visualizations, ensuring their data stories are communicated clearly and impactfully.

This course equips you with professional version control strategies essential for collaborative data science projects. Beginning with fundamental GitHub integration for Jupyter notebooks, you'll establish a solid foundation in tracking, sharing, and managing analytical code. As you progress, you'll discover how to leverage generative AI to streamline documentation processes and implement specialized branching strategies that support data science experimentation. Through hands-on labs using the EngageMetrics project, video instruction, and interactive sessions, you'll build a comprehensive version control workflow that enhances both individual productivity and team collaboration. Upon completion, you'll be able to: • Integrate Jupyter notebooks with GitHub for systematic version tracking and collaboration • Generate comprehensive documentation efficiently using AI-assisted techniques • Implement specialized branching strategies that support parallel data science experimentation • Create a professional-grade version control workflow that maintains project integrity while enabling innovation

This project-based course challenges you to demonstrate your data science mastery through a comprehensive real-world project using TrendWave Media's dataset. Beginning with industry expert insights into professional workflows, you'll establish a structured analytical approach to media engagement data before applying your skills independently in a guided project. The course combines authentic business context with technical application, allowing you to integrate and showcase the full spectrum of your Python data science capabilities. Through hands-on analysis and focused assessment, you'll experience the complete lifecycle of a data science project—from initial exploration to delivering actionable recommendations—creating tangible evidence of your ability to derive value from complex datasets in professional contexts. Upon completion, you'll be able to: • Apply Python data science techniques to extract meaningful insights from real-world media engagement data • Implement professional data science workflows that follow industry best practices • Develop a portfolio-ready project that demonstrates your ability to solve business problems through data analysis • Demonstrate mastery of key data science concepts and techniques through comprehensive assessment

In this course, you'll elevate your analytical capabilities with advanced statistical methods and testing procedures. You'll learn to conduct hypothesis tests, design and analyze A/B tests, and automate analytical workflows. Working with real employee and medical data, you'll gain hands-on experience in applying sophisticated analytical techniques to solve complex business problems. Upon completion, you'll be able to: • Perform hypothesis testing and correlation studies. • Design, conduct, and analyze A/B tests to compare different versions of an approach • Automate repetitive tasks, generate professional reports, and document workflows effectively using automated tools. • Tackle a challenge that integrates statistical analysis, A/B testing, and workflow automation to simulate real-world data problems.

In this course, you'll set up a powerful development environment, master essential Python syntax, and learn to leverage GitHub for seamless collaboration. By the module's end, you'll be equipped with the same foundational skills used by industry pros, including cutting-edge GenAI applications. Get ready to transform from a coding novice to a confident data explorer. Upon completion, you'll be able to: • Explain the role of Python in data science and how GitHub integrates into a modern data workflow. • Describe Python’s core syntax, data types, control structures, and functions. • Set up and navigate Jupyter Notebooks as an interactive development environment. • Implement basic version control workflows using Git repositories, commits, and branches for data science projects. • Apply collaborative Git practices through clear commit messages and branching strategies. • Demonstrate the ability to set up a notebook environment and perform initial version control tasks through a challenge lab.