Foundations of Data Analysis with Pandas and Python

Foundations of Data Analysis with Pandas and Python

This course is part of Data Analysis with Pandas and Python Specialization

Instructor: Packt - Course Instructors

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3,135 already enrolled

3 modules

Gain insight into a topic and learn the fundamentals.

30 reviews

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

30 reviews

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Explain how to navigate and utilize Jupyter Lab for Python programming.
Write Python code, including functions and data structures.
Manipulate and analyze data using Pandas Series and DataFrames.
Apply data cleaning and sorting techniques to prepare datasets for analysis.

Skills you'll gain

Tools you'll learn

Details to know

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Assessments

4 assignments

Taught in English

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This course is part of the Data Analysis with Pandas and Python Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 3 modules in this course

Embark on a comprehensive journey into data analysis with Python and Pandas. Learn to set up Anaconda and Jupyter Lab on macOS and Windows, navigate Jupyter Lab's interface, and execute code cells.

- You'll start by mastering essential Python programming concepts, including data types, operators, variables, functions, and classes. - Then, dive into Pandas to create and manipulate Series and DataFrames. The course covers data importing from sources like CSV, Excel, and SQL databases, along with techniques for sorting, filtering, and data extraction. - Advanced analysis methods, including group-by operations, merging, joining datasets, and pivot tables, are also explored to equip you with the skills for efficient and sophisticated data analysis. Ideal for aspiring data analysts and scientists, no prior programming knowledge is necessary with the included Python crash course.

In this module, we will guide you through the initial setup required for this course, including installing the Anaconda distribution on both macOS and Windows, and creating Python environments using Anaconda Navigator. You'll also learn to unpack the provided course materials, navigate the Jupyter Lab interface, execute code cells, and import necessary libraries to get you started on your data analysis journey.

What's included

8 videos3 readings1 assignment

8 videos Total 74 minutes

Introduction to the Specialization 11 minutes
macOS - Download and Install the Anaconda Distribution 4 minutes
Windows - Download and Install the Anaconda Distribution 4 minutes
Use Anaconda Navigator to Create a New Environment 10 minutes
Unpack Course Materials + The Startdown and Shutdown Process 12 minutes
Intro to the Jupyter Lab Interface 10 minutes
Code Cell Execution 14 minutes
Import Libraries into Jupyter Lab 8 minutes

3 readings Total 30 minutes

Introduction to the Course 'Foundations of Data Analysis with Pandas and Python' 10 minutes
Full Specialization Resources 10 minutes
Note for Learners - Accessing the Pandas Folder 10 minutes

1 assignment Total 15 minutes

Installation and Setup Assessment 15 minutes

In this module, we will cover the essentials of Python programming, starting with the use of comments to enhance code readability. You'll gain familiarity with Python's basic data types, operators, variables, and built-in functions, laying the groundwork for effective coding. We will delve into custom functions, string methods, lists, indexing and slicing, dictionaries, and classes to build your programming skills. Finally, you will learn to navigate and use Python libraries within Jupyter Lab, a critical skill for data analysis.

What's included

12 videos1 assignment

12 videos Total 184 minutes

Comments 8 minutes
Basic Data Types 15 minutes
Operators 21 minutes
Variables 13 minutes
Built-in Functions 9 minutes
Custom Functions 20 minutes
String Methods 25 minutes
Lists 15 minutes
Index Positions and Slicing 18 minutes
Dictionaries 17 minutes
Classes 10 minutes
Navigating Libraries using Jupyter Lab 12 minutes

1 assignment Total 15 minutes

Python Crash Course Assessment 15 minutes

In this module, we will explore the creation and manipulation of Pandas Series objects from different data sources like lists and dictionaries. We will delve into essential methods and attributes of Series, understand the use of parameters and arguments, and learn techniques to import data into Series using 'pd.read_csv'. Additionally, we will cover methods for inspecting, sorting, and extracting Series values, along with advanced operations like broadcasting and applying functions to Series elements.