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⇱ Python Course for Data Analysis - Become Data Analyst | Coursera

Python Course for Data Analysis - Become Data Analyst

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Python Course for Data Analysis - Become Data Analyst

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Master Python fundamentals, including syntax, variables, and functions for data analysis.

  • Gain proficiency in data manipulation using Pandas, from cleaning to merging data.

  • Develop powerful data visualizations with Matplotlib to present insights clearly.

  • Tackle real-world data problems with projects focused on job market analysis and skill trends.

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

10 assignments

Taught in English

There are 10 modules in this course

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. This course will take you through the fundamentals of Python programming and its application in data analysis. You’ll gain practical experience in using popular Python libraries like NumPy, Pandas, and Matplotlib to clean, manipulate, and visualize data. With a blend of interactive lessons and real-world exercises, you'll develop a deep understanding of Python as it applies to data analytics, allowing you to solve problems and analyze data with confidence. The course is structured to guide you from the basics to more advanced concepts. You will first familiarize yourself with Python syntax, variables, operators, and data types, moving on to hands-on skills such as loops, functions, and list comprehensions. The later sections focus on powerful data analysis tools like Pandas for data manipulation and Matplotlib for visualization, followed by advanced techniques in data cleaning and merging datasets. This course is perfect for anyone looking to enhance their skills in data analysis. Whether you are a beginner to Python or have some experience, you will find value in the practical exercises and projects. The course does not require prior knowledge of data science or Python, though a basic understanding of programming concepts will help. With a focus on real-world applications, the course is suitable for learners of all levels. By the end of the course, you will be able to write Python code for data analysis, clean and manipulate data using Pandas, visualize data using Matplotlib, and complete a comprehensive project that demonstrates your skills in a real-world context.

In this module, we will provide a detailed overview of the course, explaining the structure, objectives, and expected outcomes. This will help you understand the tools and concepts you will master and prepare you for a successful learning experience.

What's included

1 video1 reading

1 video•Total 1 minute
  • Introduction to Course•1 minute
1 reading•Total 10 minutes
  • Full Course Resources•10 minutes

In this module, we will explore the essentials of Python programming. You will gain a strong understanding of Python syntax, variables, and data types while learning to write efficient and readable code. By the end of this section, you will be able to build simple programs using core Python concepts.

What's included

22 videos1 assignment

22 videos•Total 347 minutes
  • Getting Started•18 minutes
  • Python Variables•9 minutes
  • Python Terms•17 minutes
  • Python Data Types•13 minutes
  • Strings•21 minutes
  • String Formatting•16 minutes
  • Arithmetic, Assignment & Comparison Operators•17 minutes
  • Conditional Statements•14 minutes
  • Lists•24 minutes
  • Dictionaries•13 minutes
  • Sets•8 minutes
  • Tuples•11 minutes
  • Logical Operators•15 minutes
  • Loops•26 minutes
  • List Comprehension•13 minutes
  • Exercise: Skill Investigation•11 minutes
  • Functions•13 minutes
  • Lambda Functions•13 minutes
  • Modules•18 minutes
  • Exercise: Cleaning Data•15 minutes
  • Libraries•18 minutes
  • Classes•25 minutes
1 assignment•Total 15 minutes
  • Basics of Python - Assessment•15 minutes

In this module, we will introduce the powerful NumPy library, a key tool for performing numerical computations. You’ll learn how to use NumPy arrays and perform operations that lay the foundation for more advanced data analysis.

What's included

1 video1 assignment

1 video•Total 15 minutes
  • Numpy: Introduction•15 minutes
1 assignment•Total 15 minutes
  • Introduction to Numpy - Assessment•15 minutes

In this module, we will dive into Pandas, one of the most essential libraries for data manipulation in Python. You will learn how to import, inspect, and clean data using Pandas, preparing you for deeper data analysis tasks.

What's included

5 videos1 assignment

5 videos•Total 77 minutes
  • Introduction to Pandas•14 minutes
  • Data Inspection•20 minutes
  • Data Cleaning•15 minutes
  • Data Analysis•14 minutes
  • Exercise: Pandas Basics•13 minutes
1 assignment•Total 15 minutes
  • Basics of Pandas - Assessment•15 minutes

In this module, we will introduce Matplotlib, the go-to library for creating data visualizations in Python. You’ll start by learning how to create basic plots and gradually progress to enhancing them with customizations to make your data more visually informative.

What's included

5 videos1 assignment

5 videos•Total 50 minutes
  • Introduction to Matplotlib•6 minutes
  • Plotting•21 minutes
  • Matplotlib: Labeling•8 minutes
  • Matplotlib: Pandas Plotting•8 minutes
  • Exercise: Matplotlib Basics•6 minutes
1 assignment•Total 15 minutes
  • Basics of Matplotlib - Assessment•15 minutes

In this module, we will explore advanced Pandas concepts, including data merging, concatenation, and pivot table creation. You will also learn how to manage large datasets effectively and efficiently handle missing or duplicate data.

What's included

15 videos1 assignment

15 videos•Total 231 minutes
  • Anaconda Installation•8 minutes
  • Visual Studio Code Installation•15 minutes
  • Virtual Environments•24 minutes
  • Accessing Data•14 minutes
  • Data Cleaning•13 minutes
  • Data Management•10 minutes
  • Pivot Tables•11 minutes
  • Index Management•12 minutes
  • Exercise: Job Demand•23 minutes
  • Merge DataFrames•18 minutes
  • Concat DataFrames•14 minutes
  • Exporting Data•9 minutes
  • Apply Function•27 minutes
  • Explode Function•20 minutes
  • Exercise: Trending Skills•14 minutes
1 assignment•Total 15 minutes
  • Pandas Advanced - Assessment•15 minutes

In this module, we will take a deep dive into advanced Matplotlib techniques, including customizing charts and creating scatter plots, histograms, and pie charts. These skills will help you present data in a visually appealing and professional manner.

What's included

7 videos1 assignment

7 videos•Total 112 minutes
  • Format Charts•19 minutes
  • Pie Plots•14 minutes
  • Scatter Plots•16 minutes
  • Advanced Customization•23 minutes
  • Histograms•8 minutes
  • Box Plots•17 minutes
  • Exercise: Skill Pay Analysis•14 minutes
1 assignment•Total 15 minutes
  • Matplotlib Advanced - Assessment•15 minutes

In this module, we will introduce Seaborn, a powerful library that simplifies the creation of visually rich plots. You’ll learn how to use Seaborn for quick and elegant data visualizations, enhancing your ability to communicate insights.

What's included

1 video1 assignment

1 video•Total 20 minutes
  • Seaborn: Introduction•20 minutes
1 assignment•Total 15 minutes
  • Introduction to Seaborn - Assessment•15 minutes

In this module, we will guide you through a comprehensive project where you’ll analyze real-world data. Using Python, Pandas, and Matplotlib, you’ll demonstrate your ability to extract meaningful insights from data while utilizing Git and GitHub for project management.

What's included

6 videos1 assignment

6 videos•Total 121 minutes
  • Project: Introduction•15 minutes
  • Git & GitHub Setup•18 minutes
  • Skill Demand•35 minutes
  • Skills Trend•21 minutes
  • Salary Analysis•14 minutes
  • Optimal Skills•20 minutes
1 assignment•Total 15 minutes
  • Project - Assessment•15 minutes

In this module, we will wrap up the course by showing you how to share your final project on GitHub. This will help you showcase your work to potential employers or collaborators and reflect on the progress you’ve made throughout the course.

What's included

1 video2 assignments

1 video•Total 4 minutes
  • Share on GitHub•4 minutes
2 assignments•Total 75 minutes
  • Full Course Assessment•60 minutes
  • Full Course Practice Assessment•15 minutes

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Frequently asked questions

Python for Data Analysis is a comprehensive course designed to teach you how to use Python programming and popular libraries such as Pandas, NumPy, and Matplotlib to handle, analyze, and visualize data. In today’s data-driven world, Python is an essential tool for data analysts, data scientists, and anyone interested in working with data. The course will enable you to process and analyze complex data sets, make data-driven decisions, and present insights in clear visual formats. Given the growing demand for data analysis skills, learning Python for data analysis is highly relevant to a variety of industries, including finance, healthcare, marketing, and technology.

This course is a complete guide to learning Python programming specifically for data analysis. It covers everything from the basics of Python programming to advanced techniques using libraries such as Pandas and Matplotlib. You'll start with foundational programming concepts, move on to essential data structures and functions, and explore libraries designed for working with large datasets, statistical analysis, and visualizations. By the end, you will have the skills to analyze, clean, visualize, and interpret data effectively using Python.

After completing this course, you will be proficient in Python programming and capable of applying your knowledge to real-world data analysis scenarios. You will know how to manipulate and clean data with Pandas, perform numerical operations with NumPy, create effective data visualizations using Matplotlib, and integrate these tools into powerful data analysis workflows. Additionally, you will be able to tackle data analysis projects, use Python libraries for exploration and cleaning, and create professional visualizations for reporting.

No prior programming experience is required to enroll in this course. The course starts with the basics of Python and gradually builds up to more advanced concepts. However, familiarity with basic mathematics and an interest in working with data will be beneficial. The course is designed for individuals who want to gain practical skills in data analysis and those looking to transition into a data-related role.

This course is ideal for beginners who want to learn Python programming for data analysis. It is designed for individuals aiming to pursue a career as a data analyst or those seeking to improve their data handling and analysis skills. Whether you are a student, a professional in a different field, or someone transitioning into data analytics, this course will equip you with the skills needed to succeed in the data-driven job market.

The course is designed to be completed in approximately 14 hours of video content. This timeline may vary depending on your pace and whether you decide to engage with additional exercises or practice outside of the course material. With consistent effort, you can complete it within a few weeks, or you can take your time to absorb each concept thoroughly. The hands-on exercises and project at the end will also help reinforce your learning.

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

This course is currently available only to learners who have paid or received financial aid, when available.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,