Python Project for Data Science from Coursera

This mini-course is intended to for you to demonstrate foundational Python skills for working with data. This course primarily involves completing a project in which you will assume the role of a Data Scientist or a Data Analyst and be provided with a real-world data set and a real-world inspired scenario to identify patterns and trends.

You will perform specific data science and data analytics tasks such as extracting data, web scraping, visualizing data and creating a dashboard. This project will showcase your proficiency with Python and using libraries such as Pandas and Beautiful Soup within a Jupyter Notebook. Upon completion you will have an impressive project to add to your job portfolio.

PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data.

NOTE: This course is not intended to teach you Python and does not have too much instructional content. It is intended for you to apply prior Python knowledge.

What's inside

Syllabus

Crowdsourcing Short squeeze Dashboard

In this module, you will demonstrate your skills in Python - the language of choice for Data Science and Data Analysis. You will apply Python fundamentals, Python data structures, and work with data in Python. By working on a real project, you will model a Data Scientist or Data Analyst's role, and build a dashboard using Python and popular Python libraries using Jupyter notebook.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Assumes proficiency with Python and data, as it requires prior knowledge from a prerequisite course

Involves working on a hands-on project, which allows learners to apply their skills in a practical setting

Focuses on building a dashboard using Python and libraries like Pandas and Beautiful Soup, which are widely used in data science

Designed for learners who want to showcase their Python skills for roles in data science or data analytics

Lacks instructional content and is intended for learners to apply existing Python knowledge

May require additional resources or support for learners who need more guidance on Python fundamentals

Reviews summary

Web scraping for data science

Learners say "this project course is a practical and rewarding way to apply some Python basics. It teaches very well how to web scrape and opens up the path to data visualization with realistic use cases."

The course emphasizes practical application and allows learners to work on real-world projects involving data scraping, analysis, and visualization.

"this project course is a practical and rewarding way to apply some Python basics."

The course provides a hands-on introduction to web scraping using Python libraries such as BeautifulSoup and Pandas. It teaches students how to extract data from websites, clean it, and analyze it.

"It teaches very well how to web scrape and opens up the path to data visualization with realistic use cases."

Some learners have reported experiencing technical issues with the IBM Cloud platform or Watson Studio, which can impact their ability to complete the project.

"Some learners have reported experiencing technical issues with the IBM Cloud platform or Watson Studio, which can impact their ability to complete the project."

The course assumes prior knowledge of Python and data science concepts, and learners may need to do additional research or seek help from the discussion forums to complete the project.

"The course assumes prior knowledge of Python and data science concepts, and learners may need to do additional research or seek help from the discussion forums to complete the project."

The course has a limited time frame of only one week, which may not be enough for some learners to complete the project and receive feedback.

"it is only available for one week, which is not enough time to complete the project and receive feedback."

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 Python Project for Data Science with these activities:

Create a Python resource repository

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Creating a resource repository will help you organize and track your Python resources and notes.

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Create a folder or notebook to store your Python resources.
Add notes, tutorials, code snippets, and other useful materials to the repository.

Review Python basics

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This course assumes proficiency in Python. Refreshing Python basics will improve your understanding of course content.

Browse courses on Python Basics

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Review variables, data types, control flow, and functions.
Complete a few practice exercises on a coding platform like HackerRank or LeetCode.

Follow Python tutorials

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Following Python tutorials will supplement your understanding of course material and expose you to additional resources.

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Find Python tutorials online or on platforms like YouTube.
Watch or read the tutorials.
Complete any exercises or practice problems provided.

One other activity

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Join a Python study group

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Participating in a study group will provide you with opportunities to discuss course material, ask questions, and learn from others.

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Find a study group online or through your university.
Attend regular study sessions.
Contribute to discussions and help other group members.

Career center

Learners who complete Python Project for Data Science will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models. They need Python for data preprocessing, feature engineering, and model training. This course teaches Python, NumPy, Pandas, and Matplotlib, skills essential to a Machine Learning Engineer's success.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

A Data Analyst gathers, analyzes, interprets, and presents data. They use Python to extract, clean, and explore data. This course teaches Python, NumPy, Pandas, and Matplotlib, skills crucial to a Data Analyst's success.

See salaries and explore the career path for Data Analyst

Data Scientist

A Data Scientist builds machine learning models, analyzes data, and communicates insights to stakeholders. They need Python to wrangle, analyze, and visualize data. This course teaches Python, NumPy, Pandas, and Matplotlib, skills foundational to a Data Scientist's success.

See salaries and explore the career path for Data Scientist

Data Engineer

A Data Engineer designs, builds, and maintains data pipelines and infrastructure. They need Python for data extraction, transformation, and loading. This course teaches Python, NumPy, Pandas, and Matplotlib, skills important to a Data Engineer's success.

See salaries and explore the career path for Data Engineer

Business Analyst

A Business Analyst analyzes business processes and identifies opportunities for improvement. They use Python to automate tasks, analyze data, and create visualizations. This course teaches Python, NumPy, Pandas, and Matplotlib, skills beneficial to a Business Analyst's success.

See salaries and explore the career path for Business Analyst

Financial Analyst

A Financial Analyst analyzes financial data and makes investment recommendations. They need Python for data analysis, financial modeling, and risk assessment. This course teaches Python, NumPy, Pandas, and Matplotlib, skills helpful to a Financial Analyst's success.

See salaries and explore the career path for Financial Analyst

Operations Research Analyst

An Operations Research Analyst applies mathematical and analytical methods to improve business operations. They need Python for data analysis, optimization, and simulation. This course teaches Python, NumPy, Pandas, and Matplotlib, skills helpful to an Operations Research Analyst's success.

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Statistician

A Statistician collects, analyzes, interprets, and presents data. They need Python for data analysis, modeling, and visualization. This course teaches Python, NumPy, Pandas, and Matplotlib, skills essential to a Statistician's success.

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Software Engineer

A Software Engineer designs, develops, and tests software applications. They need Python for web development, data analysis, and machine learning. This course teaches Python, NumPy, Pandas, and Matplotlib, skills helpful to a Software Engineer's success.

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Data Visualization Specialist

A Data Visualization Specialist designs and creates data visualizations. They need Python for data analysis, visualization, and storytelling. This course teaches Python, NumPy, Pandas, and Matplotlib, skills essential to a Data Visualization Specialist's success.

See salaries and explore the career path for Data Visualization Specialist

Quantitative Analyst

A Quantitative Analyst applies mathematical and statistical methods to financial data. They need Python for data analysis, modeling, and risk assessment. This course teaches Python, NumPy, Pandas, and Matplotlib, skills beneficial to a Quantitative Analyst's success.

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Actuary

An Actuary analyzes and manages financial risks. They need Python for data analysis, modeling, and forecasting. This course teaches Python, NumPy, Pandas, and Matplotlib, skills beneficial to an Actuary's success.

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Consultant

A Consultant provides advice to organizations on a variety of topics. They need Python for data analysis, problem solving, and communication. This course teaches Python, NumPy, Pandas, and Matplotlib, skills helpful to a Consultant's success.

See salaries and explore the career path for Consultant

Product Manager

A Product Manager manages the development and launch of new products. They need Python for data analysis, market research, and customer segmentation. This course teaches Python, NumPy, Pandas, and Matplotlib, skills beneficial to a Product Manager's success.

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Data Science Manager

A Data Science Manager leads and manages data science teams. They need Python for understanding data science projects and communicating with technical staff. This course teaches Python, NumPy, Pandas, and Matplotlib, skills helpful to a Data Science Manager's success.

See salaries and explore the career path for Data Science Manager