This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library.

The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively.

This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

Course Objectives

What You Will Learn

• Python Programming
• Numpy
• Pandas
• Data Cleansing

Ideal if you have experience with basic Python

Module 1: Fundamentals of Data Manipulation with Python

In this module you’ll get an introduction to the field of data science, review common Python functionality and features which data scientists use, and be introduced to the Coursera Jupyter Notebook for the lectures. All of the course information on grading, prerequisites, and expectations are on the course syllabus.

• Data Science
• The Coursera Jupyter Notebook System
• Python Functions
• Python Types and Sequences
• Python More on Strings
• Python Demonstration: Reading and Writing CSV files
• Python Dates and Times
• Advanced Python Objects, map()
• Advanced Python Lambda and List Comprehensions
• Advanced Python Demonstration: The Numerical Python Library (NumPy)

Module 2: Basic Data Processing with Pandas

In this module of the course you’ll learn the fundamentals of one of the most important toolkits Python has for data cleaning and processing — pandas. You’ll learn how to read in data into DataFrame structures, how to query these structures, and the details about such structures are indexed.

• Introduction
• The Series Data Structure
• Querying a Series
• The DataFrame Data Structure
• DataFrame Indexing and Loading
• Querying a DataFrame
• Indexing Dataframes
• Missing Values

Module 3: More Data Processing with Pandas

In this module you’ll deepen your understanding of the python pandas library by learning how to merge DataFrames, generate summary tables, group data into logical pieces, and manipulate dates. We’ll also refresh your understanding of scales of data, and discuss issues with creating metrics for analysis

• Merging Dataframes
• Pandas Idioms
• Group by
• Scales
• Pivot Tables
• Date Functionality

Module 4: Answering Questions with Messy Data

In this module of the course, you’ll be introduced to a variety of statistical techniques such a distributions, sampling, and t-tests.

• Introduction
• Distributions
• More Distributions
• Hypothesis Testing in Python

1. Beginners in Python: If you have little to no experience with Python programming, this course is an excellent choice. It provides a solid introduction to Python and covers essential programming techniques, making it accessible for beginners.
2. Aspiring Data Analysts: If you are interested in becoming a data analyst, this course is highly relevant. It focuses on data manipulation and cleaning techniques using pandas, which are essential skills for data analysts. By completing this course, you will gain the foundational knowledge needed to work with data and perform basic analysis.
3. Professionals Transitioning to Data Science: If you are currently working in a related field and looking to transition into data science or data analysis roles, this course can help you acquire the necessary skills. Python is widely used in the data science field, and proficiency in data manipulation using pandas is highly sought after by employers.