Data Analysis with Python from Coursera

What's inside

Syllabus

Importing Data Sets

In this module, you will learn how to understand data and learn about how to use the libraries in Python to help you import data from multiple sources. You will then learn how to perform some basic tasks to start exploring and analyzing the imported data set.

Data Wrangling

In this module, you will learn how to perform some fundamental data wrangling tasks that, together, form the pre-processing phase of data analysis. These tasks include handling missing values in data, formatting data to standardize it and make it consistent, normalizing data, grouping data values into bins, and converting categorical variables into numerical quantitative variables.

Exploratory Data Analysis

In this module, you will learn what is meant by exploratory data analysis, and you will learn how to perform computations on the data to calculate basic descriptive statistical information, such as mean, median, mode, and quartile values, and use that information to better understand the distribution of the data. You will learn about putting your data into groups to help you visualize the data better, you will learn how to use the Pearson correlation method to compare two continuous numerical variables, and you will learn how to use the Chi-square test to find the association between two categorical variables and how to interpret them.

Model Development

In this module, you will learn how to define the explanatory variable and the response variable and understand the differences between the simple linear regression and multiple linear regression models. You will learn how to evaluate a model using visualization and learn about polynomial regression and pipelines. You will also learn how to interpret and use the R-squared and the mean square error measures to perform in-sample evaluations to numerically evaluate our model. And lastly, you will learn about prediction and decision making when determining if our model is correct.

Model Evaluation and Refinement

In this module, you will learn about the importance of model evaluation and discuss different data model refinement techniques. You will learn about model selection and how to identify overfitting and underfitting in a predictive model. You will also learn about using Ridge Regression to regularize and reduce standard errors to prevent overfitting a regression model and how to use the Grid Search method to tune the hyperparameters of an estimator.

Final Assignment

Congratulations! You have now completed all the modules for this course. In this last module, you will complete the final assignment that will be graded by your peers. In this final assignment, you will assume the role of a Data Analyst working at a real estate investment trust organization who wants to start investing in residential real estate. You will be given a dataset containing detailed information about house prices in the region based on a number of property features, and it will be your job to analyze and predict the market price of houses given that information.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Helps learners understand and use Pandas, Numpy, scikit-learn, and scipy to load, analyze, manipulate, and visualize data sets

Provides a strong foundation for learners interested in data analysis, machine learning, and data science

Taught by highly experienced Joseph Santarcangelo

Covers essential topics in data analysis, including data wrangling, exploratory data analysis, model development, and model evaluation

Includes hands-on labs and projects to help learners practice their skills

Offers an IBM digital badge upon completion

Reviews summary

Data analysis with python

learners say this course is a well-structured and comprehensive introduction to data analysis using Python. The course begins with the basics of Python and data analysis, and gradually introduces more advanced topics such as data visualization, machine learning, and statistical modeling. The course is taught by experienced instructors who are knowledgeable and passionate about data analysis. The hands-on labs are a great way to practice the concepts learned in the videos, and the quizzes and assignments help to reinforce the material. Overall, this course is a great choice for anyone who wants to learn more about data analysis using Python.

The hands-on exercises were really good and I learnt a lot of things from this wonderful course.

"The hands-on exercises were really good and I learnt a lot of things from this wonderful course."

I got a lot in this course, the teaching way is quite good with animation and real life based example.

"I got a lot in this course, the teaching way is quite good with animation and real life based example."

Assignments and exercises made us quite interactive and I really enjoyed that.

"Assignments and exercises made us quite interactive and I really enjoyed that."

This course covered a lot of great material.

"This course covered a lot of great material."

This course was quite good until Week 3 but after that it was poorly structured.

"This course was quite good until Week 3 but after that it was poorly structured."

There were some technical issues with the labs at times, but there's always the possibility to look up the blurry parts online.

"There were some technical issues with the labs at times, but there's always the possibility to look up the blurry parts online."

This course helped me a lot in solving my basics about data cleaning, Visualization, Techniques for getting better result and most important how we can judge whether a model is good or not.

"This course helped me a lot in solving my basics about data cleaning, Visualization, Techniques for getting better result and most important how we can judge whether a model is good or not."

The final project left out some higher cross-validation methods like Grid search and model comparison.

"The final project left out some higher cross-validation methods like Grid search and model comparison."

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 Data Analysis with Python with these activities:

Review Python basics

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Refreshes your understanding of Python's basic syntax and concepts, making it easier to follow along with the course material.

Browse courses on Python

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Review Python data types, variables, and operators.
Practice writing simple Python programs using loops and conditional statements.

Read 'Python for Data Analysis' by Wes McKinney

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Provides a comprehensive reference and guide to Python for data analysis, complementing the course material and deepening your understanding of the subject.

View Python for Data Analysis: Data Wrangling with... on Amazon

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Read through the book, focusing on topics relevant to the course.
Take notes and highlight important concepts.

Follow tutorials on data wrangling with Pandas

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Provides practical experience in cleaning and preparing data for analysis, which is essential for the data analysis tasks in the course.

Browse courses on Data Wrangling

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Find tutorials on Pandas data wrangling techniques.
Follow along with the tutorials, practicing the techniques on sample datasets.
Apply the techniques to clean and prepare a dataset of your own.

Five other activities

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Participate in peer study groups

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Encourages collaboration and sharing of knowledge, which can deepen your understanding of the course material and improve your problem-solving skills.

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Join or form a study group with classmates.
Meet regularly to discuss course topics, solve problems together, and quiz each other.

Solve practice problems on model evaluation metrics

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Strengthens your understanding of how to evaluate the performance of regression models, which is crucial for building and refining models in the course.

Browse courses on Model Evaluation

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Find practice problems or exercises on model evaluation metrics.
Solve the problems, calculating metrics such as R-squared, mean squared error, and MAE.
Interpret the results and draw conclusions about model performance.

Follow tutorials on machine learning with scikit-learn

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Provides practical experience in using scikit-learn for machine learning tasks, enhancing your understanding of the algorithms and techniques covered in the course.

Browse courses on Machine Learning

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Find tutorials on scikit-learn for regression and classification tasks.
Follow along with the tutorials, building and evaluating machine learning models.
Apply the techniques to solve real-world machine learning problems.

Build a data pipeline for a real-world dataset

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Provides hands-on experience in designing and implementing data pipelines, which is a key skill for data scientists and analysts.

Browse courses on Data Pipelines

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Choose a real-world dataset and define the data processing tasks.
Use Python libraries such as Pandas, NumPy, and scikit-learn to build the data pipeline.
Validate and test the pipeline to ensure it meets the desired requirements.
Deploy the pipeline to automate the data processing tasks.

Develop a data analysis project on a topic of your interest

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Provides an opportunity to apply the concepts and techniques learned in the course to a real-world problem, fostering your problem-solving and critical thinking skills.

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Identify a data analysis problem or topic that interests you.
Gather and clean the necessary data.
Analyze the data using techniques learned in the course.
Draw conclusions and present your findings.

Career center

Learners who complete Data Analysis with Python will develop knowledge and skills that may be useful to these careers:

Market Research Analyst

As a Market Research Analyst, you will use data to understand consumer behavior and trends. This course will help you build a foundation in data analysis and machine learning, which are essential skills for this role. You will learn how to import, clean, and analyze data, and how to build and evaluate machine learning models. This course will also help you develop the critical thinking and problem-solving skills that are necessary for success as a Market Research Analyst.

See salaries and explore the career path for Market Research Analyst

Machine Learning Engineer

As a Machine Learning Engineer, you will design and develop machine learning models. This course will help you build a foundation in data analysis and machine learning, which are essential skills for this role. You will learn how to import, clean, and analyze data, and how to build and evaluate machine learning models. This course will also help you develop the critical thinking and problem-solving skills that are necessary for success as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

As a Business Analyst, you will use data to make recommendations that improve business outcomes. This course will help you build a foundation in data analysis and machine learning, which are essential skills for this role. You will learn how to import, clean, and analyze data, and how to build and evaluate machine learning models. This course will also help you develop the critical thinking and problem-solving skills that are necessary for success as a Business Analyst.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

As an Operations Research Analyst, you will use data to improve the efficiency and effectiveness of operations. This course will help you build a foundation in data analysis and machine learning, which are essential skills for this role. You will learn how to import, clean, and analyze data, and how to build and evaluate machine learning models. This course will also help you develop the critical thinking and problem-solving skills that are necessary for success as an Operations Research Analyst.

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Statistician

As a Statistician, you will use data to make inferences about the world. This course will help you build a foundation in data analysis and machine learning, which are essential skills for this role. You will learn how to import, clean, and analyze data, and how to build and evaluate machine learning models. This course will also help you develop the critical thinking and problem-solving skills that are necessary for success as a Statistician.

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Data Engineer

As a Data Engineer, you will build and maintain the infrastructure that stores and processes data. This course will help you build a foundation in data analysis and machine learning, which are essential skills for this role. You will learn how to import, clean, and analyze data, and how to build and evaluate machine learning models. This course will also help you develop the critical thinking and problem-solving skills that are necessary for success as a Data Engineer.

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Data Visualization Specialist

As a Data Visualization Specialist, you will use data to create visual representations that communicate insights. This course will help you build a foundation in data analysis and machine learning, which are essential skills for this role. You will learn how to import, clean, and analyze data, and how to build and evaluate machine learning models. This course will also help you develop the critical thinking and problem-solving skills that are necessary for success as a Data Visualization Specialist.

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Data Scientist

As a Data Scientist, you will use scientific methods to extract knowledge and insights from data. This course will help you build a foundation in data analysis and machine learning, which are essential skills for this role. You will learn how to import, clean, and analyze data, and how to build and evaluate machine learning models. This course will also help you develop the critical thinking and problem-solving skills that are necessary for success as a Data Scientist.

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Data Analyst

As a Data Analyst, you will use data to solve problems and make informed decisions. This course will help you build a foundation in data analysis and machine learning, which are essential skills for this role. You will learn how to import, clean, and analyze data, and how to build and evaluate machine learning models. This course will also help you develop the critical thinking and problem-solving skills that are necessary for success as a Data Analyst.

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Database Administrator

As a Database Administrator, you will be responsible for the maintenance and performance of databases. This course will help you build a foundation in data analysis and machine learning, which are essential skills for this role. You will learn how to import, clean, and analyze data, and how to build and evaluate machine learning models. This course will also help you develop the critical thinking and problem-solving skills that are necessary for success as a Database Administrator.

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Financial Analyst

As a Financial Analyst, you will use data to make investment decisions. This course will help you build a foundation in data analysis and machine learning, which are essential skills for this role. You will learn how to import, clean, and analyze data, and how to build and evaluate machine learning models. This course will also help you develop the critical thinking and problem-solving skills that are necessary for success as a Financial Analyst.

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Risk Analyst

As a Risk Analyst, you will use data to identify and assess risks. This course will help you build a foundation in data analysis and machine learning, which are essential skills for this role. You will learn how to import, clean, and analyze data, and how to build and evaluate machine learning models. This course will also help you develop the critical thinking and problem-solving skills that are necessary for success as a Risk Analyst.

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Software Engineer

As a Software Engineer, you will be responsible for the design, development, and maintenance of software systems. This course will help you build a foundation in data analysis and machine learning, which are essential skills for this role. You will learn how to import, clean, and analyze data, and how to build and evaluate machine learning models. This course will also help you develop the critical thinking and problem-solving skills that are necessary for success as a Software Engineer.

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Quantitative Analyst

As a Quantitative Analyst, you will use data to make investment decisions. This course will help you build a foundation in data analysis and machine learning, which are essential skills for this role. You will learn how to import, clean, and analyze data, and how to build and evaluate machine learning models. This course will also help you develop the critical thinking and problem-solving skills that are necessary for success as a Quantitative Analyst.

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Actuary

As an Actuary, you will use data to assess risk and make financial decisions. This course will help you build a foundation in data analysis and machine learning, which are essential skills for this role. You will learn how to import, clean, and analyze data, and how to build and evaluate machine learning models. This course will also help you develop the critical thinking and problem-solving skills that are necessary for success as an Actuary.

See salaries and explore the career path for Actuary