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Ryan Ahmed
In this structured series of hands-on guided projects, we will master the fundamentals of data analysis and manipulation with Pandas and Python. Pandas is a super powerful, fast, flexible and easy to use open-source data analysis and manipulation tool. This guided project is the fourth of a series of multiple guided projects (learning path) that is designed for anyone who wants to master data analysis with pandas.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/k8lvtq/mastering
What's inside
Syllabus
Project Overview
In this structured series of hands-on guided projects, we will master the fundamentals of data analysis and manipulation with Pandas and Python. Pandas is a super powerful, fast, flexible and easy to use open-source data analysis and manipulation tool. This guided project is the fourth of a series of multiple guided projects (learning path) that is designed for anyone who wants to master data analysis with pandas.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Ryan Ahmed, who is recognized for their work in data analysis with Python and Pandas
Explores data analysis and manipulation, which are highly relevant to data science and analytics
Develops fundamental data analysis and manipulation skills using Python and Pandas
Part of a learning path series for mastering data analysis with Pandas
May require learners to have some experience with Python and data analysis
Save this course
Save Mastering Data Analysis with Pandas: Learning Path Part 4 to your list so you can find it easily later:
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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 Mastering Data Analysis with Pandas: Learning Path Part 4 with these
activities:
Review Current Data Analysis Trends
Show steps
Knowing the current trends in data science can give you a competitive advantage and help you think more critically about the data at hand.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
-
Find a reputable source for data science trends.
-
Identify the latest trends in data science.
-
Evaluate the potential impact of these trends on your field.
Attend Data Science Meetups
Show steps
Attending data science meetups can help you connect with other data scientists and learn about new trends.
Show steps
-
Find data science meetups in your area.
-
Attend a meetup.
-
Introduce yourself to other data scientists.
-
Ask questions and learn about what other data scientists are working on.
Form a Study Group
Show steps
Studying with a group can help you learn the material more effectively and improve your retention.
Show steps
-
Find other students who are taking the same course.
-
Form a study group.
-
Meet regularly to discuss the course material.
-
Quiz each other on the material.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Data Manipulation Skills
Show steps
Deepen your understanding of data manipulation techniques by practicing them.
Show steps
-
Find a set of data manipulation exercises.
-
Work through the exercises.
-
Check your answers against the provided solutions.
-
Identify areas where you need more practice.
Learn Advanced Data Analysis Techniques
Show steps
Gain knowledge of more advanced data analysis techniques that can help you solve complex problems.
Show steps
-
Identify an area of data analysis that you want to learn more about.
-
Find online tutorials or courses on that topic.
-
Work through the tutorials or courses.
-
Apply the new techniques to your own data analysis projects.
Build a Data Analysis Portfolio
Show steps
Creating a portfolio of your data analysis work can help you showcase your skills to potential employers or clients.
Show steps
-
Gather your best data analysis work.
-
Create a website or online portfolio to showcase your work.
-
Write case studies or blog posts about your projects.
-
Get feedback on your portfolio from other data scientists.
Mentor Junior Data Scientists
Show steps
Mentoring others can help you solidify your own learning and develop leadership skills.
Show steps
-
Identify a junior data scientist who you can mentor.
-
Meet with your mentee regularly to provide guidance and support.
-
Share your knowledge and experience with your mentee.
-
Help your mentee develop their data science skills.
Review Current Data Analysis Trends
Show steps
Knowing the current trends in data science can give you a competitive advantage and help you think more critically about the data at hand.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
Show steps
Show steps
- Find a reputable source for data science trends.
- Identify the latest trends in data science.
- Evaluate the potential impact of these trends on your field.
Attend Data Science Meetups
Show steps
Attending data science meetups can help you connect with other data scientists and learn about new trends.
Show steps
Show steps
Show steps
- Find data science meetups in your area.
- Attend a meetup.
- Introduce yourself to other data scientists.
- Ask questions and learn about what other data scientists are working on.
Form a Study Group
Show steps
Studying with a group can help you learn the material more effectively and improve your retention.
Show steps
Show steps
Show steps
- Find other students who are taking the same course.
- Form a study group.
- Meet regularly to discuss the course material.
- Quiz each other on the material.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Data Manipulation Skills
Show steps
Deepen your understanding of data manipulation techniques by practicing them.
Show steps
Show steps
Show steps
- Find a set of data manipulation exercises.
- Work through the exercises.
- Check your answers against the provided solutions.
- Identify areas where you need more practice.
Learn Advanced Data Analysis Techniques
Show steps
Gain knowledge of more advanced data analysis techniques that can help you solve complex problems.
Show steps
Show steps
Show steps
- Identify an area of data analysis that you want to learn more about.
- Find online tutorials or courses on that topic.
- Work through the tutorials or courses.
- Apply the new techniques to your own data analysis projects.
Build a Data Analysis Portfolio
Show steps
Creating a portfolio of your data analysis work can help you showcase your skills to potential employers or clients.
Show steps
Show steps
Show steps
- Gather your best data analysis work.
- Create a website or online portfolio to showcase your work.
- Write case studies or blog posts about your projects.
- Get feedback on your portfolio from other data scientists.
Mentor Junior Data Scientists
Show steps
Mentoring others can help you solidify your own learning and develop leadership skills.
Show steps
Show steps
Show steps
- Identify a junior data scientist who you can mentor.
- Meet with your mentee regularly to provide guidance and support.
- Share your knowledge and experience with your mentee.
- Help your mentee develop their data science skills.
Career center
Learners who complete Mastering Data Analysis with Pandas: Learning Path Part 4 will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst designs, deploys, and maintains systems that analyze data to gain knowledge and draw conclusions from it. This course would be extremely useful for this role, as it would help learners build a strong foundation in data analysis techniques and methodologies, specifically using Pandas and Python. Pandas, which is the focus of the course, is a robust and efficient data analysis library in Python, and proficiency in it is a highly sought-after skill for Data Analysts.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the causes and patterns of health and disease in populations. This course may be beneficial for aspiring Epidemiologists, as it would provide them with a strong foundation in data analysis, particularly using Pandas for data manipulation and statistical analysis, which are valuable skills for conducting epidemiological studies, analyzing health data, and identifying risk factors and trends.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical techniques to analyze data and make predictions for financial markets. This course would be highly relevant for aspiring Quantitative Analysts, as it would provide them with a strong foundation in data analysis and manipulation using Pandas, which is widely used in quantitative finance for data exploration, modeling, and risk assessment.
Biostatistician
Biostatistician
A Biostatistician applies statistical methods to analyze biological and medical data. This course may be helpful for those who want to become Biostatisticians, as it would provide them with a solid foundation in data analysis techniques and methodologies, especially using Pandas for data manipulation and statistical analysis, which are essential skills in biostatistics for data exploration, modeling, and hypothesis testing in clinical research and medical studies.
Data Manager
Data Manager
A Data Manager is responsible for managing the overall lifecycle of data within an organization. This course may be useful for aspiring Data Managers, as it would provide them with a comprehensive understanding of data analysis and management principles, including data organization, data quality management, and data security, with a focus on using Pandas for data manipulation and analysis, which are essential skills for efficiently managing large volumes of data and ensuring data integrity.
Data Governance Specialist
Data Governance Specialist
A Data Governance Specialist develops and implements policies and procedures to ensure the quality, consistency, and security of data within an organization. This course may be helpful for those who wish to become Data Governance Specialists, as it would equip them with a strong understanding of data analysis and management techniques, particularly using Pandas for data validation, data cleaning, and data quality assessment, which are essential skills for ensuring data integrity and compliance in data governance.
Data Visualization Specialist
Data Visualization Specialist
A Data Visualization Specialist designs and creates visual representations of data to make it easier to understand and communicate. This course may be useful for those who aspire to become Data Visualization Specialists, as it would equip them with the skills to manipulate and analyze data using Pandas, which is commonly used for data preparation and visualization tasks.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical techniques to improve efficiency and decision-making within an organization. This course may be useful for aspiring Operations Research Analysts, as it would provide them with a strong foundation in data analysis and optimization techniques, specifically using Pandas for data manipulation and analysis, which are valuable skills in this field.
Actuary
Actuary
An Actuary uses mathematical and statistical techniques to assess and manage risk. This course may be beneficial for those interested in becoming Actuaries, as it would provide them with a solid foundation in data analysis, particularly using Pandas for data manipulation and statistical analysis, which are essential skills for actuarial work in areas such as pricing, reserving, and risk modeling.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops and deploys machine learning models to solve complex problems. This course may be beneficial for those interested in becoming Machine Learning Engineers, as it would provide them with a foundation in data analysis using Pandas, which is often used for data preparation and feature engineering in machine learning.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains the infrastructure and systems used to store, process, and manage data. This course may be useful for aspiring Data Engineers, as it would strengthen their understanding of data analysis principles and techniques, particularly using Pandas for data manipulation and transformation, which are essential tasks in the data engineering process.
Financial Analyst
Financial Analyst
A Financial Analyst uses financial data and analysis to make investment recommendations and provide financial advice. This course may be beneficial for those interested in becoming Financial Analysts, as it would enhance their data analysis skills, which are crucial for evaluating financial data, identifying trends, and making sound investment decisions.
Business Analyst
Business Analyst
A Business Analyst uses data and analysis to identify and solve business problems. This course may be helpful for those interested in becoming Business Analysts, as it would equip them with valuable data analysis skills and knowledge of Pandas, which is often utilized in business analysis for data exploration and manipulation.
Market Researcher
Market Researcher
A Market Researcher conducts research on target markets, customer behavior, and industry trends. This course may be useful for aspiring Market Researchers, as it would provide them with a foundation in data analysis techniques and methodologies, which are essential for collecting, analyzing, and interpreting market data.
Data Scientist
Data Scientist
A Data Scientist uses advanced techniques and technologies to analyze and interpret complex data, often using statistical modeling and machine learning algorithms. This course may be helpful for aspiring Data Scientists, as it would provide them with a solid foundation in data analysis using Pandas, which is a popular and versatile library for data manipulation and analysis in Python.
For more career information including salaries, visit:
OpenCourser.com/course/k8lvtq/mastering
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Mastering Data Analysis with Pandas: Learning Path Part 4.
Comprehensive guide to data analysis with Python, covering topics such as data cleaning, data manipulation, data visualization, and machine learning. It great resource for anyone who wants to learn more about data analysis with Python.
Reference guide to data manipulation with Pandas, covering topics such as data cleaning, data manipulation, and data visualization. It great resource for anyone who wants to learn more about data manipulation with Pandas.
Comprehensive guide to data analysis with Python, covering topics such as data cleaning, data manipulation, data visualization, and machine learning. It great resource for anyone who wants to learn more about data analysis with Python.
Comprehensive guide to data analysis with R, covering topics such as data cleaning, data manipulation, data visualization, and machine learning. It great resource for anyone who wants to learn more about data analysis with R.
Save
Comprehensive guide to data analysis with R, covering topics such as data cleaning, data manipulation, data visualization, and machine learning. It great resource for anyone who wants to learn more about data analysis with R.
Comprehensive guide to machine learning, covering topics such as data cleaning, data manipulation, data visualization, and machine learning. It great resource for anyone who wants to learn more about machine learning.
Comprehensive guide to data science for business, covering topics such as data cleaning, data manipulation, data visualization, and machine learning. It great resource for anyone who wants to learn more about data science for business.
Save
Comprehensive guide to big data analytics, covering topics such as data cleaning, data manipulation, data visualization, and machine learning. It great resource for anyone who wants to learn more about big data analytics.
Comprehensive guide to data visualization, covering topics such as data cleaning, data manipulation, data visualization, and machine learning. It great resource for anyone who wants to learn more about data visualization.
Comprehensive guide to statistical thinking, covering topics such as data cleaning, data manipulation, data visualization, and machine learning. It great resource for anyone who wants to learn more about statistical thinking.
Comprehensive guide to causal inference, covering topics such as data cleaning, data manipulation, data visualization, and causal inference. It great resource for anyone who wants to learn more about causal inference.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/k8lvtq/mastering
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Taught by Ryan Ahmed, who is recognized for their work in data analysis with Python and Pandas
Explores data analysis and manipulation, which are highly relevant to data science and analytics
Develops fundamental data analysis and manipulation skills using Python and Pandas
Part of a learning path series for mastering data analysis with Pandas
May require learners to have some experience with Python and data analysis
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