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David Dalsveen
In this project, you'll serve as a data analyst at a marketing firm specializing in social media brand promotion. Your task is to use Python to extract, clean, and analyze tweets in specific categories (health, family, food, etc.) and generate visualizations.
Your analysis will provide valuable insights to help clients enhance their social media performance and allow the firm to deliver tweets on time and within budget, leading to faster results.
There isn’t just one right approach or solution in this scenario, which means you can create a truly unique project that helps you stand out to employers.
Read more
In this project, you'll serve as a data analyst at a marketing firm specializing in social media brand promotion. Your task is to use Python to extract, clean, and analyze tweets in specific categories (health, family, food, etc.) and generate visualizations.
Your analysis will provide valuable insights to help clients enhance their social media performance and allow the firm to deliver tweets on time and within budget, leading to faster results.
There isn’t just one right approach or solution in this scenario, which means you can create a truly unique project that helps you stand out to employers.
Read more
In this project, you'll serve as a data analyst at a marketing firm specializing in social media brand promotion. Your task is to use Python to extract, clean, and analyze tweets in specific categories (health, family, food, etc.) and generate visualizations.
Your analysis will provide valuable insights to help clients enhance their social media performance and allow the firm to deliver tweets on time and within budget, leading to faster results.
There isn’t just one right approach or solution in this scenario, which means you can create a truly unique project that helps you stand out to employers.
ROLE: Data Analyst
SKILLS: Python
PREREQUISITES:
Python, Numpy, Matplotlib or Seaborn, Git, Jupyter Notebook
Register for this course and see more details by visiting:
OpenCourser.com/course/e5zqof/clean
What's inside
Syllabus
Project
In this 6-8 hour project, you'll conduct a clean & analysis of Tweets using Python and upload your findings to your Coursera profile to showcase to potential employers.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by highly experienced data analysts, this course provides hands-on experience with relevant tools
Helps fill the digital marketing skills gap for entry-level data analysts
By providing insights into social media performance, it can help brands enhance their reach and engagement
The practical project allows for the application and demonstration of skills, making it appealing to potential employers
Students can showcase their data analysis and visualization abilities through the project
Save this course
Save Clean and analyze social media usage data with Python 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 Clean and analyze social media usage data with Python with these
activities:
Review Python modules
Show steps
Warming up on Python syntax and modules will enhance your Python skills and prepare you to follow through with the course materials.
Browse courses on
NumPy
Show steps
-
Review the Python documentation for Numpy, Matplotlib, and Seaborn.
-
Complete a few practice exercises using these modules.
Compile resources on Tweet analysis
Show steps
Collecting and organizing resources on Tweet analysis will enhance your understanding of the field and provide valuable references for future projects.
Show steps
-
Search for articles, tutorials, and documentation on Tweet analysis.
-
Create a list or folder to store the collected resources.
Join a study group
Show steps
Collaborating with peers will provide you with diverse perspectives, enhance your understanding, and improve your problem-solving skills.
Show steps
-
Find a study group or online forum for Tweet analysis.
-
Participate in discussions, ask questions, and share your insights.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Extract and clean Tweets
Show steps
Practicing data extraction and cleaning techniques will help you understand the practical applications of the course concepts.
Show steps
-
Find a dataset of Tweets.
-
Write a Python script to extract the text, author, and date from each Tweet.
-
Write a Python script to clean the extracted data by removing noise and inconsistencies.
Follow tutorials on advanced Tweet analysis techniques
Show steps
Exploring advanced Tweet analysis techniques will extend your knowledge and prepare you for more complex projects.
Show steps
-
Search for tutorials on specific Tweet analysis techniques.
-
Follow the tutorials to learn how to apply the techniques.
Visualize Tweet data
Show steps
Creating visualizations of your analyzed data will solidify your understanding of the course concepts and provide valuable insights.
Show steps
-
Choose a visualization technique that suits your data.
-
Use Matplotlib or Seaborn to create visualizations of the extracted and cleaned Tweet data.
-
Include labels, titles, and legends to make your visualizations clear and informative.
Participate in a Tweet analysis competition
Show steps
Participating in a competition will test your skills, provide real-world experience, and showcase your abilities to potential employers.
Show steps
-
Research and find a Tweet analysis competition that aligns with your interests.
-
Register for the competition and form a team or participate individually.
-
Apply the skills and knowledge you've gained in this course to solve the competition's challenges.
Review Python modules
Show steps
Warming up on Python syntax and modules will enhance your Python skills and prepare you to follow through with the course materials.
Browse courses on
NumPy
Show steps
Show steps
Show steps
- Review the Python documentation for Numpy, Matplotlib, and Seaborn.
- Complete a few practice exercises using these modules.
Compile resources on Tweet analysis
Show steps
Collecting and organizing resources on Tweet analysis will enhance your understanding of the field and provide valuable references for future projects.
Show steps
Show steps
Show steps
- Search for articles, tutorials, and documentation on Tweet analysis.
- Create a list or folder to store the collected resources.
Join a study group
Show steps
Collaborating with peers will provide you with diverse perspectives, enhance your understanding, and improve your problem-solving skills.
Show steps
Show steps
Show steps
- Find a study group or online forum for Tweet analysis.
- Participate in discussions, ask questions, and share your insights.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Extract and clean Tweets
Show steps
Practicing data extraction and cleaning techniques will help you understand the practical applications of the course concepts.
Show steps
Show steps
Show steps
- Find a dataset of Tweets.
- Write a Python script to extract the text, author, and date from each Tweet.
- Write a Python script to clean the extracted data by removing noise and inconsistencies.
Follow tutorials on advanced Tweet analysis techniques
Show steps
Exploring advanced Tweet analysis techniques will extend your knowledge and prepare you for more complex projects.
Show steps
Show steps
Show steps
- Search for tutorials on specific Tweet analysis techniques.
- Follow the tutorials to learn how to apply the techniques.
Visualize Tweet data
Show steps
Creating visualizations of your analyzed data will solidify your understanding of the course concepts and provide valuable insights.
Show steps
Show steps
Show steps
- Choose a visualization technique that suits your data.
- Use Matplotlib or Seaborn to create visualizations of the extracted and cleaned Tweet data.
- Include labels, titles, and legends to make your visualizations clear and informative.
Participate in a Tweet analysis competition
Show steps
Participating in a competition will test your skills, provide real-world experience, and showcase your abilities to potential employers.
Show steps
Show steps
Show steps
- Research and find a Tweet analysis competition that aligns with your interests.
- Register for the competition and form a team or participate individually.
- Apply the skills and knowledge you've gained in this course to solve the competition's challenges.
Career center
Learners who complete Clean and analyze social media usage data with Python will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
As a Data Analyst, you will use your skills in Python, Numpy, Matplotlib or Seaborn, Git, and Jupyter Notebook to extract, clean, and analyze data. This course will help you build a foundation in these skills and provide you with the experience you need to be successful in this role. This course is particularly relevant to Data Analysts who want to specialize in social media data analysis, as it will provide you with the skills and knowledge you need to succeed in this field.
Data Scientist
Data Scientist
Data Scientists use their skills in Python, Numpy, Matplotlib or Seaborn, Git, and Jupyter Notebook to extract, clean, and analyze data. This course will help you build a foundation in these skills and provide you with the experience you need to be successful in this role. This course is particularly relevant to Data Scientists who want to specialize in social media data analysis, as it will provide you with the skills and knowledge you need to succeed in this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use their skills in Python, Numpy, Matplotlib or Seaborn, Git, and Jupyter Notebook to build and deploy machine learning models. This course will help you build a foundation in these skills and provide you with the experience you need to be successful in this role. This course is particularly relevant to Machine Learning Engineers who want to specialize in social media data analysis, as it will provide you with the skills and knowledge you need to succeed in this field.
Software Engineer
Software Engineer
Software Engineers use their skills in Python, Numpy, Matplotlib or Seaborn, Git, and Jupyter Notebook to design, develop, and maintain software applications. This course will help you build a foundation in these skills and provide you with the experience you need to be successful in this role. This course is particularly relevant to Software Engineers who want to specialize in social media data analysis, as it will provide you with the skills and knowledge you need to succeed in this field.
Data Engineer
Data Engineer
Data Engineers use their skills in Python, Numpy, Matplotlib or Seaborn, Git, and Jupyter Notebook to build and maintain data pipelines. This course will help you build a foundation in these skills and provide you with the experience you need to be successful in this role. This course is particularly relevant to Data Engineers who want to specialize in social media data analysis, as it will provide you with the skills and knowledge you need to succeed in this field.
Marketing Analyst
Marketing Analyst
Marketing Analysts use their skills in Python, Numpy, Matplotlib or Seaborn, Git, and Jupyter Notebook to analyze data and make recommendations to businesses. This course will help you build a foundation in these skills and provide you with the experience you need to be successful in this role. This course is particularly relevant to Marketing Analysts who want to specialize in social media data analysis, as it will provide you with the skills and knowledge you need to succeed in this field.
Business Analyst
Business Analyst
Business Analysts use their skills in Python, Numpy, Matplotlib or Seaborn, Git, and Jupyter Notebook to analyze data and make recommendations to businesses. This course will help you build a foundation in these skills and provide you with the experience you need to be successful in this role. This course is particularly relevant to Business Analysts who want to specialize in social media data analysis, as it will provide you with the skills and knowledge you need to succeed in this field.
Product Manager
Product Manager
Product Managers use their skills in Python, Numpy, Matplotlib or Seaborn, Git, and Jupyter Notebook to analyze data and make recommendations to businesses. This course will help you build a foundation in these skills and provide you with the experience you need to be successful in this role. This course is particularly relevant to Product Managers who want to specialize in social media data analysis, as it will provide you with the skills and knowledge you need to succeed in this field.
Risk Analyst
Risk Analyst
Risk Analysts use their skills in Python, Numpy, Matplotlib or Seaborn, Git, and Jupyter Notebook to analyze data and make recommendations to businesses. This course will help you build a foundation in these skills and provide you with the experience you need to be successful in this role. This course is particularly relevant to Risk Analysts who want to specialize in social media data analysis, as it will provide you with the skills and knowledge you need to succeed in this field.
Data Visualization Analyst
Data Visualization Analyst
Data Visualization Analysts use their skills in Python, Numpy, Matplotlib or Seaborn, Git, and Jupyter Notebook to analyze data and make recommendations to businesses. This course will help you build a foundation in these skills and provide you with the experience you need to be successful in this role. This course is particularly relevant to Data Visualization Analysts who want to specialize in social media data analysis, as it will provide you with the skills and knowledge you need to succeed in this field.
Consultant
Consultant
Consultants use their skills in Python, Numpy, Matplotlib or Seaborn, Git, and Jupyter Notebook to analyze data and make recommendations to businesses. This course will help you build a foundation in these skills and provide you with the experience you need to be successful in this role. This course is particularly relevant to Consultants who want to specialize in social media data analysis, as it will provide you with the skills and knowledge you need to succeed in this field.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their skills in Python, Numpy, Matplotlib or Seaborn, Git, and Jupyter Notebook to analyze data and make recommendations to businesses. This course will help you build a foundation in these skills and provide you with the experience you need to be successful in this role. This course is particularly relevant to Operations Research Analysts who want to specialize in social media data analysis, as it will provide you with the skills and knowledge you need to succeed in this field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their skills in Python, Numpy, Matplotlib or Seaborn, Git, and Jupyter Notebook to analyze data and make recommendations to businesses. This course will help you build a foundation in these skills and provide you with the experience you need to be successful in this role. This course is particularly relevant to Quantitative Analysts who want to specialize in social media data analysis, as it will provide you with the skills and knowledge you need to succeed in this field.
Market Researcher
Market Researcher
Market Researchers use their skills in Python, Numpy, Matplotlib or Seaborn, Git, and Jupyter Notebook to analyze data and make recommendations to businesses. This course will help you build a foundation in these skills and provide you with the experience you need to be successful in this role. This course is particularly relevant to Market Researchers who want to specialize in social media data analysis, as it will provide you with the skills and knowledge you need to succeed in this field.
Financial Analyst
Financial Analyst
Financial Analysts use their skills in Python, Numpy, Matplotlib or Seaborn, Git, and Jupyter Notebook to analyze data and make recommendations to businesses. This course will help you build a foundation in these skills and provide you with the experience you need to be successful in this role. This course is particularly relevant to Financial Analysts who want to specialize in social media data analysis, as it will provide you with the skills and knowledge you need to succeed in this field.
For more career information including salaries, visit:
OpenCourser.com/course/e5zqof/clean
Reading list
We've selected 13 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
Clean and analyze social media usage data with Python.
Provides a comprehensive overview of social media data mining techniques and tools, including how to collect, clean, and analyze social media data.
Provides a comprehensive guide to sentiment analysis using Python, including how to build and evaluate sentiment analysis models.
Comprehensive guide to natural language processing (NLP) using Python, covering a wide range of NLP tasks, including text classification, sentiment analysis, and machine translation.
Provides a comprehensive guide to data analysis using Python, including how to load, clean, and analyze data.
Provides a comprehensive overview of data science for business, including how to use data to solve business problems.
Provides a gentle introduction to machine learning, making it accessible to even beginners.
Save
Provides a comprehensive overview of deep learning, covering a wide range of deep learning topics, including convolutional neural networks and recurrent neural networks.
Provides a comprehensive overview of reinforcement learning, covering a wide range of reinforcement learning topics, including Markov decision processes and deep reinforcement learning.
Provides a comprehensive overview of convex optimization, covering a wide range of convex optimization topics, including linear programming and semidefinite programming.
Provides a comprehensive overview of information theory and coding, covering a wide range of information theory and coding topics, including entropy and mutual information.
Provides a comprehensive overview of statistical learning theory, covering a wide range of statistical learning theory topics, including supervised learning and unsupervised learning.
Provides a comprehensive overview of statistical learning, covering a wide range of statistical learning topics, including linear regression and generalized linear models.
Provides a comprehensive overview of machine learning algorithms, covering a wide range of machine learning algorithms, including decision trees and support vector machines.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/e5zqof/clean
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Effort
8 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
David Dalsveen
Language
English
Good to know
Taught by highly experienced data analysts, this course provides hands-on experience with relevant tools
Helps fill the digital marketing skills gap for entry-level data analysts
By providing insights into social media performance, it can help brands enhance their reach and engagement
The practical project allows for the application and demonstration of skills, making it appealing to potential employers
Students can showcase their data analysis and visualization abilities through the project
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