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Muhammad Saad uddin
In this 3 and half hour long project-based course, you will learn how to perform imputations, feature engineering, statistical analysis, train & evaluate models and exploratory analysis and visualization on your data.
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OpenCourser.com/course/vuka3e/python
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possible dealbreakers
Develops data analysis skills, which are core for data scientists and analysts
Taught by Muhammad Saad uddin, who are recognized for their work in data analysis
Teaches data analysis tools and techniques, which are widely used in industry
Provides practical experience through hands-on labs and interactive materials
Requires some prior knowledge in statistics and programming
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Save Python: Imputations, Feature Creation & Statistical Analysis to your list so you can find it easily later:
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Reviews summary
Clear python data analysis
This 3.5 hour long project-based course covers concepts of data imputation, feature engineering, statistical analysis, model training, and visualization through a practical lens. Reviews of this course are generally favorable, highlighting the instructor's clear explanations and provision of multiple examples. The course is particularly well-received by those seeking an introduction to data analysis from scratch.
Course is good for beginners in data analysis.
"Good for someone who wants to know how data is analysed from scratch."
Instructor provides clear explanations.
"The instructor has given multiple examples for visualization & data analysis. Good explanation."
Course lacks detailed explanations of commands and parameters.
"there is no details for the typed commands and parameters"
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: Imputations, Feature Creation & Statistical Analysis with these
activities:
Review Exploratory Data Analysis Concepts
Show steps
Revisit the fundamental concepts of exploratory data analysis to strengthen your understanding of data exploration and visualization techniques.
Browse courses on
Exploratory Data Analysis
Show steps
-
Review lecture notes or textbooks on exploratory data analysis
-
Complete practice exercises on data exploration and visualization
Review SQL
Show steps
Familiarize yourself with the basics of SQL to better understand the syntax and concepts used in the course.
Browse courses on
SQL
Show steps
-
Read a tutorial on SQL basics
-
Complete practice exercises on SQL queries
Join a Study Group
Show steps
Engage with fellow learners by joining a study group to discuss course concepts, share insights, and support each other's progress.
Show steps
-
Find or create a study group with other course participants
-
Establish regular meeting times and topics for discussion
-
Prepare for and actively participate in group discussions
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Python Data Manipulation
Show steps
Sharpen your Python skills by working through guided tutorials on data manipulation techniques.
Browse courses on
Python
Show steps
-
Follow an online tutorial on Python data manipulation
-
Complete coding challenges related to data manipulation
Solve Statistics Practice Problems
Show steps
Enhance your understanding of statistical concepts by solving practice problems, reinforcing formulas and techniques.
Browse courses on
Statistics
Show steps
-
Find practice problem sets on statistics
-
Solve problems covering various statistical topics
-
Review solutions and identify areas for improvement
Create a Resource Collection
Show steps
Organize and compile useful resources, such as articles, videos, and tutorials, to supplement your learning and provide easy access to relevant materials.
Show steps
-
Identify and gather relevant resources related to course topics
-
Organize the resources into a structured format
-
Share your resource collection with other learners
Build a Data Visualization Dashboard
Show steps
Apply your data manipulation and visualization skills to create an interactive dashboard, showcasing your ability to present insights effectively.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset and define the scope of your dashboard
-
Design the layout and visualizations for your dashboard
-
Develop the dashboard using a data visualization tool
-
Share your dashboard and gather feedback
Attend a Workshop on Machine Learning Algorithms
Show steps
Deepen your understanding of machine learning algorithms and their applications by participating in a hands-on workshop.
Browse courses on
Machine Learning
Show steps
-
Find and register for a relevant workshop
-
Attend the workshop and actively participate in exercises
-
Apply the knowledge gained to your course projects
Review Exploratory Data Analysis Concepts
Show steps
Revisit the fundamental concepts of exploratory data analysis to strengthen your understanding of data exploration and visualization techniques.
Browse courses on
Exploratory Data Analysis
Show steps
Show steps
Show steps
- Review lecture notes or textbooks on exploratory data analysis
- Complete practice exercises on data exploration and visualization
Review SQL
Show steps
Familiarize yourself with the basics of SQL to better understand the syntax and concepts used in the course.
Browse courses on
SQL
Show steps
Show steps
Show steps
- Read a tutorial on SQL basics
- Complete practice exercises on SQL queries
Join a Study Group
Show steps
Engage with fellow learners by joining a study group to discuss course concepts, share insights, and support each other's progress.
Show steps
Show steps
Show steps
- Find or create a study group with other course participants
- Establish regular meeting times and topics for discussion
- Prepare for and actively participate in group discussions
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Python Data Manipulation
Show steps
Sharpen your Python skills by working through guided tutorials on data manipulation techniques.
Browse courses on
Python
Show steps
Show steps
Show steps
- Follow an online tutorial on Python data manipulation
- Complete coding challenges related to data manipulation
Solve Statistics Practice Problems
Show steps
Enhance your understanding of statistical concepts by solving practice problems, reinforcing formulas and techniques.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Find practice problem sets on statistics
- Solve problems covering various statistical topics
- Review solutions and identify areas for improvement
Create a Resource Collection
Show steps
Organize and compile useful resources, such as articles, videos, and tutorials, to supplement your learning and provide easy access to relevant materials.
Show steps
Show steps
Show steps
- Identify and gather relevant resources related to course topics
- Organize the resources into a structured format
- Share your resource collection with other learners
Build a Data Visualization Dashboard
Show steps
Apply your data manipulation and visualization skills to create an interactive dashboard, showcasing your ability to present insights effectively.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset and define the scope of your dashboard
- Design the layout and visualizations for your dashboard
- Develop the dashboard using a data visualization tool
- Share your dashboard and gather feedback
Attend a Workshop on Machine Learning Algorithms
Show steps
Deepen your understanding of machine learning algorithms and their applications by participating in a hands-on workshop.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find and register for a relevant workshop
- Attend the workshop and actively participate in exercises
- Apply the knowledge gained to your course projects
Career center
Learners who complete Python: Imputations, Feature Creation & Statistical Analysis will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians can benefit from this course as it covers advanced statistical analysis techniques, including hypothesis testing and regression analysis. The course also provides practical experience with statistical software, which is essential for statisticians working with large datasets.
Actuary
Actuary
Actuaries can use the knowledge gained from this course to analyze data and assess risks in the insurance industry. The course covers data imputation and feature creation techniques, which are important for working with incomplete and noisy insurance data. Furthermore, the course provides insights into statistical analysis and model evaluation, which are critical for pricing insurance products and assessing financial risks.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts can apply the skills learned in this course to analyze financial data and make investment decisions. The course covers data imputation and feature creation techniques, which are important for working with incomplete and noisy financial data. Furthermore, the course provides insights into statistical analysis and model evaluation, which are critical for assessing the risk and return of investments.
Financial Analyst
Financial Analyst
Financial Analysts can benefit from this course as it provides a foundation in data analysis and statistical modeling. The course covers data imputation and feature creation techniques, which are important for working with incomplete and noisy financial data. Furthermore, the course provides insights into statistical analysis and model evaluation, which can be applied to financial forecasting and investment analysis.
Risk Analyst
Risk Analyst
Risk Analysts can use the knowledge gained from this course to assess and mitigate risks in various domains. The course provides a foundation in data analysis and statistical modeling, which are essential for identifying and quantifying risks. Furthermore, the course covers model evaluation techniques, which are crucial for assessing the effectiveness of risk management strategies.
Data Scientist
Data Scientist
Data Scientists can benefit from this course as it covers key aspects of data science, including data preprocessing, feature engineering, statistical analysis, and model evaluation. The course provides hands-on experience with real-world datasets, allowing learners to develop practical skills in data science.
Healthcare Analyst
Healthcare Analyst
Healthcare Analysts can use the knowledge gained from this course to analyze healthcare data and identify trends and patterns. The course covers data imputation and feature creation techniques, which are important for working with incomplete and noisy healthcare data. Furthermore, the course provides insights into statistical analysis and model evaluation, which can be applied to healthcare forecasting and quality improvement.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers can use the knowledge gained from this course to perform data imputation, feature engineering, and statistical analysis on data used for machine learning models. The course also covers model evaluation techniques, which are crucial for assessing the performance of machine learning models.
Public Policy Analyst
Public Policy Analyst
Public Policy Analysts can benefit from this course as it provides a foundation in data analysis and statistical modeling. The course covers data imputation and feature creation techniques, which are important for working with incomplete and noisy public policy data. Furthermore, the course provides insights into statistical analysis and model evaluation, which can be applied to policy evaluation and forecasting.
Market Researcher
Market Researcher
Market Researchers can leverage the skills learned in this course to analyze data and identify market trends. The course covers data imputation and feature creation techniques, which are important for working with incomplete and unstructured market data. Furthermore, the course provides insights into statistical analysis and model evaluation, which can be applied to market segmentation and forecasting.
Data Engineer
Data Engineer
Data Engineers can benefit from this course as it provides a foundation in data preprocessing techniques, including data imputation and feature creation. The course also covers statistical analysis and model evaluation, which are important for understanding data quality and preparing data for machine learning models.
Data Analyst
Data Analyst
Data Analysts can leverage the skills learned in this course to perform data analysis and visualization on large datasets. The course provides a foundation in data imputation and feature creation, which are essential skills for data analysts working with incomplete and unstructured data. Furthermore, the course provides insights into statistical analysis and model evaluation, which are critical for drawing meaningful conclusions from data.
Auditor
Auditor
Auditors can use the skills learned in this course to analyze data and identify financial risks. The course covers data imputation and feature creation techniques, which are important for working with incomplete and unstructured financial data. Furthermore, the course provides insights into statistical analysis and model evaluation, which are crucial for assessing the accuracy of financial statements.
Data Journalist
Data Journalist
Data Journalists can leverage the skills learned in this course to analyze data and tell compelling stories. The course covers data imputation and feature creation techniques, which are important for working with incomplete and unstructured data. Furthermore, the course provides insights into statistical analysis and model evaluation, which can be applied to identifying trends and patterns in data.
Business Analyst
Business Analyst
Business Analysts can use the skills learned in this course to analyze data and identify trends and patterns. The course covers data imputation and feature creation techniques, which are important for working with incomplete and unstructured data. Furthermore, the course provides insights into statistical analysis and model evaluation, which can be applied to business decision-making.
For more career information including salaries, visit:
OpenCourser.com/course/vuka3e/python
Reading list
We've selected 12 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
Python: Imputations, Feature Creation & Statistical Analysis.
Provides a practical guide to machine learning with Python, covering topics such as data preprocessing, feature engineering, model training, and model evaluation. It valuable resource for those who want to learn more about how to use Python for machine learning tasks.
Provides a comprehensive guide to using Python for data analysis, covering topics such as data wrangling, data visualization, and statistical modeling. It valuable resource for those who want to learn more about the Python data analysis ecosystem and how to use it effectively.
Provides a comprehensive overview of pattern recognition and machine learning, covering topics such as supervised learning, unsupervised learning, and model evaluation. It valuable resource for those who want to learn more about the fundamentals of pattern recognition and machine learning.
Provides a comprehensive overview of statistical learning, covering topics such as supervised learning, unsupervised learning, and model evaluation. It valuable resource for those who want to learn more about the fundamentals of statistical learning.
Provides a practical guide to machine learning, covering topics such as supervised learning, unsupervised learning, and model evaluation. It valuable resource for those who want to learn more about how to use Python for machine learning tasks.
Provides a hands-on introduction to data science, covering topics such as data wrangling, data visualization, and machine learning. It valuable resource for those who want to learn more about the fundamentals of data science and how to use Python for data science tasks.
Provides a comprehensive overview of feature engineering, covering topics such as feature selection, transformation, and creation. It valuable resource for those who want to learn more about how to engineer features effectively for machine learning models.
Provides a comprehensive guide to machine learning with Python, covering topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for those who want to learn more about how to use Python for machine learning tasks.
Provides a comprehensive guide to data science with Python, covering topics such as data wrangling, data visualization, and machine learning. It valuable resource for those who want to learn more about the Python data science ecosystem and how to use it effectively.
Provides a comprehensive overview of machine learning from a probabilistic perspective, covering topics such as supervised learning, unsupervised learning, and model evaluation. It valuable resource for those who want to learn more about the probabilistic foundations of machine learning.
Provides a gentle introduction to machine learning, covering topics such as supervised learning, unsupervised learning, and model evaluation. It valuable resource for those who want to learn more about the basics of machine learning without getting too technical.
Save
Provides a comprehensive overview of deep learning, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for those who want to learn more about the fundamentals of deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/vuka3e/python
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Time
3 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Muhammad Saad uddin
Good to know
Develops data analysis skills, which are core for data scientists and analysts
Taught by Muhammad Saad uddin, who are recognized for their work in data analysis
Teaches data analysis tools and techniques, which are widely used in industry
Provides practical experience through hands-on labs and interactive materials
Requires some prior knowledge in statistics and programming
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