Foundations of Data Analysis with Pandas and Python from Coursera

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.

What's inside

Syllabus

Installation and Setup

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.

Python Crash Course

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.

Exploring Pandas Series for Data Analysis

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides a Python crash course, making it accessible to learners without prior programming experience

Covers data importing from various sources like CSV, Excel, and SQL databases, which are commonly used in the field

Explores advanced analysis methods, including group-by operations and pivot tables, which are essential for sophisticated data analysis

Teaches how to set up Anaconda and Jupyter Lab on both macOS and Windows, which are standard tools for data analysis

Focuses on Pandas Series and DataFrames, which are fundamental data structures for data manipulation and analysis in Python

Uses pd.read_csv, which is a common method for importing data into Series, but may not cover more modern methods

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Foundations of Data Analysis with Pandas and Python with these activities:

Review Basic Python Syntax

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Reinforce your understanding of fundamental Python syntax to ensure a smooth start to the Python crash course and subsequent Pandas modules.

Browse courses on Python Syntax

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Review data types, operators, and control flow in Python.
Practice writing simple Python scripts.
Complete online Python tutorials or exercises.

Review 'Python for Data Analysis' by Wes McKinney

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Deepen your understanding of Pandas by studying the definitive guide written by its creator.

View Python for Data Analysis on Amazon

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Read the chapters on Pandas Series and DataFrames.
Work through the examples provided in the book.
Experiment with different Pandas functions and methods.

Review 'Data Science from Scratch' by Joel Grus

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Gain a deeper understanding of the underlying principles of data science to complement your Pandas skills.

View Data Science from Scratch: First Principles... on Amazon

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Read the chapters related to data manipulation and analysis.
Understand the mathematical concepts behind the algorithms.
Implement some of the algorithms from scratch.

Three other activities

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Pandas Data Manipulation Exercises

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Sharpen your Pandas skills by completing a series of data manipulation exercises focusing on filtering, sorting, and grouping data.

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Find a dataset online (e.g., Kaggle, UCI Machine Learning Repository).
Load the dataset into a Pandas DataFrame.
Perform various data manipulation tasks (filtering, sorting, grouping).
Analyze the results and draw conclusions.

Create a Data Analysis Blog Post

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Solidify your learning by writing a blog post explaining a specific data analysis technique covered in the course, such as pivot tables or merging datasets.

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Choose a data analysis technique from the course.
Find a relevant dataset to demonstrate the technique.
Write a clear and concise blog post explaining the technique and its application.
Include code examples and visualizations.

Analyze a Real-World Dataset

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Apply your Pandas skills to analyze a real-world dataset and extract meaningful insights, reinforcing your understanding of the entire data analysis workflow.

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Select a dataset of interest from a public repository.
Clean and preprocess the data using Pandas.
Perform exploratory data analysis (EDA) to identify patterns and trends.
Visualize the results using appropriate charts and graphs.
Write a report summarizing your findings and conclusions.

Career center

Learners who complete Foundations of Data Analysis with Pandas and Python will develop knowledge and skills that may be useful to these careers:

Data Analyst

A Data Analyst examines datasets to identify trends, create visualizations, and assist in data-driven decision-making. This course helps build a strong foundation in data manipulation using Python and Pandas, which are essential tools for a Data Analyst. The course's focus on creating and manipulating Pandas Series and DataFrames, along with importing data from various sources, directly prepares one to handle the daily tasks of a Data Analyst. The techniques of sorting, filtering and extracting data in this course will be key to success in the role, as will experience with group-by operations, merging and joining datasets, and creating pivot tables. This course may be particular useful to those with no prior programming experience because of the Python crash course.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

A Business Intelligence Analyst leverages data to extract information and insights to support business decision-making. This course may be useful to a Business Intelligence Analyst by providing a solid technical skill set in data manipulation and preparation using Pandas, a core library in the Python ecosystem for data handling. The skills of importing, cleaning, and transforming data using tools covered in the course are foundational for this role. In particular, learning to create pivot tables will help business intelligence analysts to summarize data in a way that is readable. The course's focus on merging and joining datasets, along with exploration of group-by operations, may also help the Business Intelligence Analyst to synthesize data from different sources.

See salaries and explore the career path for Business Intelligence Analyst

Market Research Analyst

A Market Research Analyst studies market conditions, including consumer behavior and competitor activity, to advise businesses on product development and marketing strategies. A Market Research Analyst can benefit from this course's focus on data manipulation with pandas, as they often need to analyze large datasets to draw conclusions. This course covers importing data from CSV, Excel, and SQL databases which may be a requirement. The skills of being able to sort, filter, and extract data, explored in the course curriculum, will help in the analysis of market survey data. Learning how to use group-by operations, merge and join datasets, and create pivot tables will help a market research analyst summarize their findings.

See salaries and explore the career path for Market Research Analyst

Financial Analyst

A Financial Analyst evaluates financial data to provide insights and recommendations for investment decisions. This course may be useful for a Financial Analyst, as they often need to manipulate financial statements and market data. The course lays a foundation in using Pandas for data manipulation, an essential skill for financial data analysis. Importing from various sources like CSV and Excel, as covered in the course, is common in the work of a Financial Analyst. Learning techniques to sort, filter, extract data, and create pivot tables, as taught in the course, will support financial analysis. The course's exploration of merging, joining and group-by operations will help financial analysts who need to compile information from different sources.

See salaries and explore the career path for Financial Analyst

Operations Analyst

An Operations Analyst works in supply chain, manufacturing, or logistics, improving the efficiency and effectiveness of daily workflows. This course may be helpful to an Operations Analyst, as it may help to build the data skills necessary to analyze operational data from various sources. The course's focus on data manipulation using pandas, such as importing data from various sources, and the ability to sort, filter and extract data, will help an operations analyst. Learning techniques to use group-by operations and create pivot tables may also help analysts organize and make sense of complex operational data. This course may be especially suited for those who have had limited programming experience, owing to the Python crash course.

See salaries and explore the career path for Operations Analyst

Research Assistant

A Research Assistant supports research activities, often involving data collection, management, and analysis. A Research Assistant will find this course helpful because it introduces a powerful tool for data analysis, the pandas library. This course covers the basics of data analysis, including how to import data from various sources, manipulate Series objects, and process data using a computer. The skills of sorting, filtering, extracting, and transforming data with Pandas will help any Research Assistant. Learning techniques for merging, joining, group-by operations, and creating pivot tables will help with data reporting and summarizing of research findings.

See salaries and explore the career path for Research Assistant

Quantitative Analyst

A Quantitative Analyst develops and implements mathematical models to analyze financial data and make predictions. This is a role that typically requires an advanced degree. A Quantitative Analyst would find this course may be useful, as it introduces pandas, core data handling tool in the ecosystem. This course may help because it introduces the foundations of data analysis, including data manipulation, using python and pandas. The course covers advanced operations like merging and joining datasets, and exploring group-by operations and pivot tables. These may be used in the work of a Quantitative Analyst. Because the course also includes a Python crash course, it may be particularly helpful for those who lack prior programming experience.

See salaries and explore the career path for Quantitative Analyst

Database Administrator

A Database Administrator is responsible for managing and maintaining databases, ensuring data integrity and accessibility. This course may be helpful to a database administrator as it covers data manipulation techniques using pandas, especially the ability to import data from SQL databases. Although it is not primarily a database course, the course's focus on data analysis using python and pandas may help Database Administrators who need to extract, manipulate, and report data. Learning to manipulate series and dataframe objects will help Database Administrators who work on data. The course's techniques for data sorting, filtering, and extraction may be particularly useful to them.

See salaries and explore the career path for Database Administrator

Data Reporting Specialist

A Data Reporting Specialist designs and generates reports using data analysis tools. A Data Reporting Specialist will find this course helpful, as it may help them gain skills in using the pandas library. The course covers the basics of data analysis, including how to import data from various sources, and the ability to work with Series objects and dataframes. The course also introduces practical skills such as sorting, filtering, and extracting data, which are essential for the work of a data reporting specialist. Learning techniques for merging, joining, group-by operations, and creating pivot tables may allow a data reporting specialist to summarize data in a readable format.

See salaries and explore the career path for Data Reporting Specialist

Risk Analyst

A Risk Analyst assesses the potential for risks in an organization, often concerning financial or operational risks. This course may be useful to a Risk Analyst because it may provide them with the skills in data manipulation that are necessary to identify risks in data. The course includes a detailed introduction to pandas, a key library for data analysis. The course's focus on importing data from various sources, and the ability to sort, filter and extract data, will be helpful for a Risk Analyst. Also, skills in using group-by operations, and merging and joining datasets, will be helpful in creating reports that summarize complex data effectively.

See salaries and explore the career path for Risk Analyst

Data Visualization Specialist

A Data Visualization Specialist turns complex data into visual formats that make it easier to understand. This course may be helpful to a Data Visualization Specialist, as the pandas library is foundational to data analysis, even before it is presented visually. The course's focus on learning to import data from various sources, manipulating and exploring datasets, can assist in the extraction of information. Pandas is an important library for preparing data prior to visualization. Learning to use group-by operations, merge and join datasets and create pivot tables are all ways of summarizing data prior to visualization. This course may be especially useful for those with no prior programming experience because of the Python crash course.

See salaries and explore the career path for Data Visualization Specialist

Research Scientist

A Research Scientist conducts scientific research and often uses data analysis to test hypotheses and draw conclusions. This is a role that typically requires an advanced degree. The course may be helpful for a Research Scientist because it introduces pandas, a key tool for data analysis, and also covers essential programming concepts with Python. Research scientists often need to manipulate and visualize data, and this course covers how to import from various sources, sort, filter, and extract data using pandas. The course may also be helpful because it also covers advanced techniques like merging and joining datasets, and using group-by operations, and creating pivot tables; these are all essential tools for any analyst.

See salaries and explore the career path for Research Scientist

Statistician

A Statistician applies statistical techniques to analyze data and draw insights. This is a role that typically requires an advanced degree. A statistician may find this course useful, as it deals with the foundations of data analysis using the pandas library. Although not primarily a statistics course, the foundations in data manipulation provided here may be useful to a statistician. The course's focus on importing from various sources, manipulating dataframes and series, and techniques for sorting, filtering, and extracting data may help a statistician to use their statistical skills. The course also covers merging and joining datasets, and the use of group-by operations and pivot tables, which can help with data reporting.

See salaries and explore the career path for Statistician

Actuary

An Actuary analyzes the financial costs of risk and uncertainty for insurance and financial institutions. This is a role that typically requires an advanced degree. While not directly related to actuarial science, an Actuary may find some of the data manipulation tools introduced in this course helpful. The course provides an introduction to pandas library, which is commonly used for data analysis, including the ability to import data from sources like CSV and Excel. The course's coverage of sorting, filtering, extracting, and transforming data, as well as merging, joining, and using group-by operations may be helpful in some actuarial workflows, such as organizing the dataset prior to analysis.

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Project Manager

A Project Manager plans, executes, and oversees projects, ensuring they are completed on time and within budget. This course may be useful to a Project Manager, as those in the field sometimes need to examine project-related data to draw conclusions. The course's focus on working with the pandas library can assist in preparing data for analysis. The course teaches data manipulation skills such as importing data from various sources, manipulating Series and DataFrames, and sorting, filtering, and extracting data. The skills of creating pivot tables and using group-by operations may help Project Managers organize and summarize project data so that it is easier to manage.

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Foundations of Data Analysis with Pandas and Python

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