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Joseph Santarcangelo
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Read more
Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!
Read more
Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!
LEARN TO ANALYZE DATA WITH PYTHON
Learn how to analyze data using Python in this introductory course. You will go from understanding the basics of Python to exploring many different types of data through lecture, hands-on labs, and assignments. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!
What you'll learn
- Import data sets, clean and prepare data for analysis, summarize data, and build data pipelines
- Use Pandas, DataFrames, Numpy multidimensional arrays, and SciPy libraries to work with various datasets
- Load, manipulate, analyze, and visualize dataset
- Build machine-learning models and make predictions with scikit-learn
Register for this course and see more details by visiting:
OpenCourser.com/course/zzrsl4/analyzing
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What's inside
Learning objectives
- Import data sets, clean and prepare data for analysis, summarize data, and build data pipelines
- Use pandas, dataframes, numpy multidimensional arrays, and scipy libraries to work with various datasets
- Load, manipulate, analyze, and visualize dataset
- Build machine-learning models and make predictions with scikit-learn
- Import data sets, clean and prepare data for analysis, summarize data, and build data pipelines
- Use pandas, dataframes, numpy multidimensional arrays, and scipy libraries to work with various datasets
- Load, manipulate, analyze, and visualize dataset
- Build machine-learning models and make predictions with scikit-learn
Syllabus
Module 1 - Importing Datasets
Learning Objectives
Understanding the Domain
Understanding the Dataset
Read more
Syllabus
Module 1 - Importing Datasets
Learning Objectives
Understanding the Domain
Understanding the Dataset
Read more
Python package for data science
Importing and Exporting Data in Python
Basic Insights from Datasets
Module 2 - Cleaning and Preparing the Data
Identify and Handle Missing Values
Data Formatting
Data Normalization Sets
Binning
Indicator variables
Module 3 - Summarizing the Data Frame
Descriptive Statistics
Basic of Grouping
ANOVA
Correlation
More on Correlation
Module 4 - Model Development
Simple and Multiple Linear Regression
Model EvaluationUsingVisualization
Polynomial Regression and Pipelines
R-squared and MSE for In-Sample Evaluation
Prediction and Decision Making
Module 5 - Model Evaluation
Model Evaluation
Over-fitting, Under-fitting and Model Selection
Ridge Regression
Grid Search
Model Refinement
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Geared towards beginners, this course provides a strong foundation in data analysis using Python
The course includes hands-on labs and interactive materials, making it engaging and practical
Covers essential data analysis topics such as data cleaning, visualization, and machine learning
Taught by experienced instructors, Joseph Santarcangelo
Requires prior programming experience, which may be a barrier for some learners
Save this course
Save Analyzing Data with Python to your list so you can find it easily later:
Save
Save
Reviews summary
Good python intro, limited interactivity
Learners say this course offers good and concise content for using Python basics and its libraries. However, this introduction course is somewhat limited in that it is just 6 hours long and does not offer deep knowledge. Also, the labs are not very interactive. One reviewer mentioned this course and its quizzes are available for free on Cognitive AI website.
Full course is available for free on Cognitive AI website.
"You can do the same course and learning path by IBM at https://cognitiveclass.ai/courses/data-analysis-python for free including the IBM certificate."
"Thank you to the previous reviewer for mentioning that the full course (including the graded quizzes) is available for free on the CognitiveAI website (not one I've heard of before)."
Offers good and concise content.
"Good concise content for using Python and its libraries to perform basic data analysis and machine learning."
Limited depth of knowledge due to short course length.
"However the course is quite short - about 6 hours should be enough to complete it."
"In consequence, the course does not offer deep knowledge."
Labs are not very interactive.
"This isn't a great interactive introduction to analysing data with Python."
"It is a decent lecture, I suppose, but the 'lab's are not interactive to any great extent, and the videos don't motivate you to actually go out and explore the dataset yourself."
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 Analyzing Data with Python with these
activities:
Organize Course Materials
Show steps
Establish an efficient learning environment by organizing course materials for easy access and review.
Show steps
-
Create a dedicated folder or space for course materials
-
Categorize and store materials logically
Review 'Python for Data Analysis' by Wes McKinney
Show steps
Gain foundational knowledge and best practices in Python for data analysis from an expert in the field.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
-
Read the book and highlight key concepts
-
Take notes and summarize the main ideas
Review Basic Python Syntax and Concepts
Show steps
Strengthen foundational knowledge of Python syntax and concepts to prepare for the course.
Browse courses on
Python
Show steps
-
Review online resources or tutorials
-
Practice writing basic Python code
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice Python Data Handling
Show steps
Practice data handling and manipulation skills to solidify foundational knowledge.
Browse courses on
Data Cleaning
Show steps
-
Import a dataset
-
Clean the data by handling missing values and outliers
-
Explore the data using statistical measures
-
Create visualizations to represent data insights
Follow Tutorials on Machine Learning with Scikit-Learn
Show steps
Explore advanced topics and enhance practical skills in machine learning using tutorials.
Browse courses on
Machine Learning
Show steps
-
Find tutorials on specific machine learning algorithms
-
Follow the tutorials and implement code examples
-
Test and evaluate the performance of models
Develop a Data Analysis Project
Show steps
Apply knowledge and skills by implementing a complete data analysis project.
Browse courses on
Data Analysis
Show steps
-
Define a problem statement and gather data
-
Analyze the data and draw insights
-
Develop a model or solution
-
Present and communicate the results
Organize Course Materials
Show steps
Establish an efficient learning environment by organizing course materials for easy access and review.
Show steps
Show steps
Show steps
- Create a dedicated folder or space for course materials
- Categorize and store materials logically
Review 'Python for Data Analysis' by Wes McKinney
Show steps
Gain foundational knowledge and best practices in Python for data analysis from an expert in the field.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
Show steps
Show steps
- Read the book and highlight key concepts
- Take notes and summarize the main ideas
Review Basic Python Syntax and Concepts
Show steps
Strengthen foundational knowledge of Python syntax and concepts to prepare for the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review online resources or tutorials
- Practice writing basic Python code
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice Python Data Handling
Show steps
Practice data handling and manipulation skills to solidify foundational knowledge.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Import a dataset
- Clean the data by handling missing values and outliers
- Explore the data using statistical measures
- Create visualizations to represent data insights
Follow Tutorials on Machine Learning with Scikit-Learn
Show steps
Explore advanced topics and enhance practical skills in machine learning using tutorials.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find tutorials on specific machine learning algorithms
- Follow the tutorials and implement code examples
- Test and evaluate the performance of models
Develop a Data Analysis Project
Show steps
Apply knowledge and skills by implementing a complete data analysis project.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Define a problem statement and gather data
- Analyze the data and draw insights
- Develop a model or solution
- Present and communicate the results
Career center
Learners who complete Analyzing Data with Python will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst converts raw data into a format that can be used by an organization to make better decisions. Using advanced techniques, Data Analysts transform complex data into a format that is easier to understand and use. This course can help you succeed as a Data Analyst by teaching you the basics of Python as well as how to explore many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!
Data Scientist
Data Scientist
A Data Scientist uses advanced statistical techniques to build models and analyze data. Their work helps organizations make better decisions. This course can help you succeed as a Data Scientist by providing a foundation in Python programming. You will also learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with various datasets. The course covers topics like data cleaning, data preparation, data visualization, and machine learning modeling.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds and maintains machine learning models. They use their expertise in data science and software engineering to create models that can solve complex problems. This course can help you succeed as a Machine Learning Engineer by providing a foundation in Python programming and machine learning. You will learn how to use Python libraries, like scikit-learn, to build and evaluate machine learning models.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data. They use their knowledge of statistics to help organizations make better decisions. This course can help you succeed as a Statistician by providing a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with various datasets.
Business Analyst
Business Analyst
A Business Analyst uses data to help organizations improve their performance. They use their knowledge of business and data analysis to identify problems and develop solutions. This course can help you succeed as a Business Analyst by providing a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with various datasets.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to help organizations make investment decisions. They use their knowledge of finance and data analysis to identify undervalued assets and develop investment strategies. This course can help you succeed as a Financial Analyst by providing a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with financial data.
Market Research Analyst
Market Research Analyst
A Market Research Analyst uses data to help organizations understand their customers. They use their knowledge of marketing and data analysis to identify trends and develop marketing strategies. This course can help you succeed as a Market Research Analyst by providing a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with market research data.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses data to help organizations improve their operations. They use their knowledge of operations research and data analysis to identify inefficiencies and develop solutions. This course can help you succeed as an Operations Research Analyst by providing a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with operational data.
Product Manager
Product Manager
A Product Manager uses data to help organizations develop and improve their products. They use their knowledge of product management and data analysis to identify customer needs and develop product strategies. This course can help you succeed as a Product Manager by providing a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with product data.
Software Engineer
Software Engineer
A Software Engineer uses data to help organizations develop and improve their software. They use their knowledge of software engineering and data analysis to identify bugs and develop software solutions. This course may be useful for you if you want to become a Software Engineer because it provides a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with software data.
Web Developer
Web Developer
A Web Developer uses data to help organizations develop and improve their websites. They use their knowledge of web development and data analysis to identify user needs and develop website strategies. This course may be useful for you if you want to become a Web Developer because it provides a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with web data.
Data Entry Clerk
Data Entry Clerk
A Data Entry Clerk enters data into a computer system. They use their knowledge of data entry and data processing to ensure that data is accurate and complete. This course may be useful for you if you want to become a Data Entry Clerk because it provides a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with data.
Customer Service Representative
Customer Service Representative
A Customer Service Representative provides customer service to customers. They use their knowledge of customer service and data analysis to resolve customer issues and improve customer satisfaction. This course may be useful for you if you want to become a Customer Service Representative because it provides a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with customer data.
Sales Representative
Sales Representative
A Sales Representative sells products or services to customers. They use their knowledge of sales and data analysis to identify customer needs and develop sales strategies. This course may be useful for you if you want to become a Sales Representative because it provides a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with sales data.
Marketing Manager
Marketing Manager
A Marketing Manager develops and executes marketing campaigns. They use their knowledge of marketing and data analysis to identify target markets and develop marketing strategies. This course may be useful for you if you want to become a Marketing Manager because it provides a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with marketing data.
For more career information including salaries, visit:
OpenCourser.com/course/zzrsl4/analyzing
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
Analyzing Data with Python.
Provides a comprehensive overview of data science with Python, covering topics such as data cleaning, analysis, visualization, and machine learning. It valuable resource for both beginners and experienced data scientists.
Teaches the fundamentals of data science without assuming any prior knowledge. It great resource for beginners who want to learn the basics of data science and Python.
Provides a comprehensive overview of machine learning algorithms and their implementation using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for learners interested in building and deploying machine learning models.
Comprehensive guide to Python for data analysis, covering topics such as data manipulation, data exploration, and data visualization. It valuable reference for learners who want to enhance their Python skills for data analysis.
Comprehensive guide to deep learning with Python, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for learners who want to dive deeper into the field of deep learning.
Comprehensive introduction to statistical learning, covering topics such as linear regression, logistic regression, and tree-based models. It valuable resource for learners who want to gain a deeper understanding of statistical methods and their applications in data analysis.
Save
Provides a critical perspective on data science, exploring the ethical and societal implications of data analysis and modeling. It valuable resource for learners who want to understand the broader context of data science and its impact on society.
Practical guide to data science for business professionals, covering topics such as data mining, predictive analytics, and decision making. It valuable resource for learners who want to apply data science techniques in a business context.
Comprehensive guide to machine learning with R, covering topics such as supervised learning, unsupervised learning, and ensemble methods. It valuable resource for learners who want to use R for data analysis and modeling.
Provides a Bayesian perspective on statistical modeling, covering topics such as probability theory, Bayesian inference, and hierarchical models. It valuable resource for learners who want to gain a deeper understanding of Bayesian statistics and its applications.
Save
Comprehensive introduction to causal inference, covering topics such as graphical models, structural equation models, and counterfactual reasoning. It valuable resource for learners who want to understand the principles of causal inference and their applications in data analysis.
Collection of recipes and examples for data science with Python, covering topics such as data manipulation, data exploration, and data visualization. It valuable resource for learners who want to apply Python for data analysis tasks.
Provides a practical introduction to machine learning for programmers, covering topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for learners who want to apply machine learning techniques in their own projects.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zzrsl4/analyzing
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Effort
2 to 4 hours per week for 5 weeks
Level
Elementary
Via
edX
Institution
IBM
Instructor
Joseph Santarcangelo
Session
January 31, 2024
- June 30, 2024
Language
English
Transcripts
English
Español
Français
Português
Deutsch
中文
العربية
हिंदी
Türkçe
తెలుగు
Bahasa Indonesia
Kiswahili
Good to know
Geared towards beginners, this course provides a strong foundation in data analysis using Python
The course includes hands-on labs and interactive materials, making it engaging and practical
Covers essential data analysis topics such as data cleaning, visualization, and machine learning
Taught by experienced instructors, Joseph Santarcangelo
Requires prior programming experience, which may be a barrier for some learners
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