Python for Data Analysis: Step-By-Step with Projects

Packt via Coursera

Go to class Write review

Overview

Coursera Flash Sale

40% Off Coursera Plus for 3 Months!

Grab it

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this hands-on course, you will learn how to use Python for data analysis through practical, step-by-step projects. You will start with Python basics, including data types, functions, and loops, and then dive into the powerful Pandas library to load, manipulate, and clean data. As you explore data, you'll master techniques like combining datasets, renaming columns, sorting data, and cleaning text. The course then covers exploratory data analysis (EDA) using statistical methods and the Seaborn library to visualize and interpret relationships between variables. You’ll also gain experience working with time series data, learning how to resample data, handle time-based analysis, and apply rolling windows. Throughout the course, you’ll apply your skills to real-world datasets, including NBA games, Czech bank data, and Olympic Games data, providing valuable project experience. The course will also guide you in addressing common challenges in data analysis, such as handling missing data and outliers. This course is perfect for beginners interested in data analysis or anyone looking to gain practical experience in using Python for data science. While no prior experience is required, familiarity with basic programming concepts is helpful. By the end of the course, you will be able to clean and transform data, perform exploratory data analysis, and visualize relationships within datasets, all while working with real-world data projects.

Syllabus

Introduction

In this introductory section, we will walk you through the course overview and provide context for the hands-on projects you'll be working on. You'll get a sense of the practical applications of Python for data analysis that will be demonstrated and practiced throughout the course.

Python Crash Course

In this section, we will cover the foundational concepts of Python programming. From setting up the Python environment to understanding core data types and structures, this section will help you get comfortable with Python syntax and build a strong base for working with data.

Importing Data

In this module, you'll learn how to import, preview, and export data with Python. We’ll focus on using Pandas to load datasets and explore the different data structures that Pandas offers, helping you manipulate data effectively for analysis.

Exploring Data

This section focuses on exploring and manipulating data. You'll learn how to combine datasets, sort data, select specific columns and rows, and modify values. The aim is to develop your skills in data exploration and preparing datasets for deeper analysis.

Capstone Practice Project I

In this practice project, you’ll get the chance to apply what you’ve learned in a real-world context by working with NBA games data. You’ll clean, explore, and analyze the data, following a project workflow that includes key steps in data analysis.

Cleaning Data

In this section, we’ll focus on the crucial task of data cleaning. You will learn how to handle missing values, remove outliers, and clean text data, ensuring that your dataset is ready for analysis and modeling.

Transforming Columns/Features

This section covers various transformation techniques, such as extracting date and time information, applying binning, and mapping values. You will also learn how to apply functions to modify data, making it more suitable for analysis.

Capstone Practice Project II

In this project, you will work with data from a Czech bank. The project will provide hands-on experience in cleaning, transforming, and analyzing a real-world financial dataset, helping reinforce your learning from the previous sections.

Exploratory Data Analysis

This section focuses on exploratory data analysis (EDA). You’ll learn how to aggregate statistics, use groupby and pivot tables, and visualize the relationships between variables using Python’s Seaborn library, enhancing your ability to derive insights from data.

Capstone Practice Project III

In this capstone project, you’ll analyze data from the Olympic Games. You’ll apply EDA techniques, such as aggregation and visualization, to uncover insights and present your findings, simulating a real-world data analysis scenario.

Dealing with Time Series Data

In this section, we’ll dive into time series data analysis. You’ll learn how to work with datetime objects, resample time series data, and use rolling windows to smooth and analyze trends over time, a crucial skill in fields like finance and sales forecasting.

Thank You

In this final module, we’ll review the key concepts and skills you’ve learned, provide tips for continued learning, and offer guidance on how to apply your new data analysis skills in real-world projects.