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Kevin Coyle,
Mark Roepke,
and
Emma Freeman
In this course, you will develop your data science skills while solving real-world problems. You'll work through the data science process to and use unsupervised learning to explore data, engineer and select meaningful features, and solve complex supervised learning problems using tree-based models. You will also learn to apply hyperparameter tuning and cross-validation strategies to improve model performance.
Read more
In this course, you will develop your data science skills while solving real-world problems. You'll work through the data science process to and use unsupervised learning to explore data, engineer and select meaningful features, and solve complex supervised learning problems using tree-based models. You will also learn to apply hyperparameter tuning and cross-validation strategies to improve model performance.
Read more
In this course, you will develop your data science skills while solving real-world problems. You'll work through the data science process to and use unsupervised learning to explore data, engineer and select meaningful features, and solve complex supervised learning problems using tree-based models. You will also learn to apply hyperparameter tuning and cross-validation strategies to improve model performance.
NOTE: This is the third and final course in the Data Science with Databricks for Data Analysts Coursera specialization. To be successful in this course we highly recommend taking the first two courses in that specialization prior to taking this course. These courses are: Apache Spark for Data Analysts and Data Science Fundamentals for Data Analysts.
Register for this course and see more details by visiting:
OpenCourser.com/course/zzx65u/applied
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What's inside
Syllabus
Welcome to the Course
Applied Unsupervised Learning
Feature Engineering and Selection
Read more
Syllabus
Welcome to the Course
Applied Unsupervised Learning
Feature Engineering and Selection
Read more
Applied Tree-based Models
Model Optimization
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines unsupervised learning, a technique used to explore data in industries like finance, healthcare, manufacturing, and retail
Develops supervised learning models using tree-based algorithms, a popular approach in industries like finance and healthcare
Enhances model performance through hyperparameter tuning and cross-validation, techniques valued in industries like finance, healthcare, and manufacturing
Emphasizes feature engineering and selection, essential skills in industries where data preparation is crucial, such as finance and healthcare
Requires prior knowledge in data science fundamentals and Apache Spark, which may limit accessibility for beginners
Taught by recognized instructors in the field of data science, enhancing the course's credibility
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Reviews summary
Applied data science exercises
Learners say this course is a well-suited option for those looking for engaging assignments in Data Science because it has straightforward, comprehensive, and practical exercises that utilize PySpark. However, it may be a better option for those with some background in the field as others find it to be too high level or lacking in the details of the principles.
Easy to understand
"It was interesting how they managed to talk about machine learning, data science and all the technical stuff without making it heavy to understand."
Uses PySpark in Databricks
"Well defined exercise with usage of Pyspark in Databricks"
"Nice course, its approach is the right mix between theory and hands-on exercise on the databricks platform..."
Straightforward and comprehensive exercises
"Straightforward and comprehensive - exactly what I needed!"
"Perfect and objective content. Simple and practical exercises."
Some labs are not smooth
"Good one. Some labs are not smooth but still it is great."
Lacking in detail
"Great course for an overview but with a high level of abstraction (usage of existing libraries but very little coding of algorithms that show the details of the principles)"
"An excellent and comprehensive course, however would have been even better if a little more of the SparkML machine learning APIs were exposed during the course."
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 Applied Data Science for Data Analysts with these
activities:
Review (or just browse!) Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
Show steps
Expand your understanding of the fundamentals of data science and how it applies to business.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
-
Read the preface and Chapter 1.
-
Read the chapter summaries and skim the rest of the chapters.
Practice using Apache Spark for Data Analysts.
Show steps
Develop your proficiency in using Apache Spark for data analysis.
Browse courses on
Apache Spark
Show steps
-
Read the Apache Spark documentation.
-
Complete the Apache Spark for Data Analysts Coursera course.
-
Work through the Apache Spark tutorial.
-
Build a data analysis project using Apache Spark.
Follow a tutorial on machine learning using Python.
Show steps
Gain hands-on experience with machine learning by following a guided tutorial.
Browse courses on
Machine Learning
Show steps
-
Choose a machine learning tutorial that aligns with your interests.
-
Follow the tutorial step-by-step.
-
Implement the machine learning model in your own project.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group with other students in this course.
Show steps
Enhance your understanding by discussing course material and collaborating with peers.
Browse courses on
Data Science
Show steps
-
Find other students who are taking this course.
-
Meet regularly to discuss the course material.
-
Work together on assignments and projects.
Create a data visualization of your favorite dataset.
Show steps
Solidify your understanding of data visualization techniques by creating your own visualization.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset that interests you.
-
Explore the data and identify key insights.
-
Select an appropriate visualization technique.
-
Create your visualization.
-
Share your visualization with others.
Volunteer with a local data science organization.
Show steps
Gain practical experience and make connections within the data science community.
Browse courses on
Data Science
Show steps
-
Research local data science organizations.
-
Contact the organization and inquire about volunteer opportunities.
-
Attend volunteer events and contribute your skills.
Create a data science portfolio.
Show steps
Showcase your data science skills and knowledge by creating a portfolio.
Browse courses on
Data Science
Show steps
-
Gather your best data science work.
-
Create a website or online portfolio to showcase your work.
-
Share your portfolio with potential employers and clients.
Review (or just browse!) Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
Show steps
Expand your understanding of the fundamentals of data science and how it applies to business.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
Show steps
Show steps
- Read the preface and Chapter 1.
- Read the chapter summaries and skim the rest of the chapters.
Practice using Apache Spark for Data Analysts.
Show steps
Develop your proficiency in using Apache Spark for data analysis.
Browse courses on
Apache Spark
Show steps
Show steps
Show steps
- Read the Apache Spark documentation.
- Complete the Apache Spark for Data Analysts Coursera course.
- Work through the Apache Spark tutorial.
- Build a data analysis project using Apache Spark.
Follow a tutorial on machine learning using Python.
Show steps
Gain hands-on experience with machine learning by following a guided tutorial.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a machine learning tutorial that aligns with your interests.
- Follow the tutorial step-by-step.
- Implement the machine learning model in your own project.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group with other students in this course.
Show steps
Enhance your understanding by discussing course material and collaborating with peers.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find other students who are taking this course.
- Meet regularly to discuss the course material.
- Work together on assignments and projects.
Create a data visualization of your favorite dataset.
Show steps
Solidify your understanding of data visualization techniques by creating your own visualization.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset that interests you.
- Explore the data and identify key insights.
- Select an appropriate visualization technique.
- Create your visualization.
- Share your visualization with others.
Volunteer with a local data science organization.
Show steps
Gain practical experience and make connections within the data science community.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Research local data science organizations.
- Contact the organization and inquire about volunteer opportunities.
- Attend volunteer events and contribute your skills.
Create a data science portfolio.
Show steps
Showcase your data science skills and knowledge by creating a portfolio.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Gather your best data science work.
- Create a website or online portfolio to showcase your work.
- Share your portfolio with potential employers and clients.
Career center
Learners who complete Applied Data Science for Data Analysts will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist may use the knowledge gained in this course to understand how to approach a problem within the context of data science, develop data-driven solutions, and understand the nuances of unsupervised learning. Furthermore, this course may help build a foundation for applying machine learning algorithms like tree-based models, feature engineering, and feature selection to improve model performance and outcomes.
Machine Learning Engineer
Machine Learning Engineer
This course may help a Machine Learning Engineer by providing an understanding of how to approach problems within the context of data science, as well as develop data-driven solutions. Furthermore, the course can help build a foundation for applying machine learning algorithms like tree-based models, feature engineering, and feature selection to improve model performance and outcomes.
Data Analyst
Data Analyst
This course can be an excellent resource for a Data Analyst by providing an understanding of how to approach problems within the context of data science, as well as develop data-driven solutions. Furthermore, the course can help build a foundation for applying machine learning algorithms like tree-based models, feature engineering, and feature selection to improve model performance and outcomes.
Business Analyst
Business Analyst
A Business Analyst may find this course helpful as it provides the foundational understanding needed to approach problems within the context of data science. Additionally, the course may help develop data-driven solutions, which can be essential for a Business Analyst.
Quantitative Analyst
Quantitative Analyst
This course can be a good starting point for a Quantitative Analyst, as it provides an introduction to data science and machine learning concepts. The course may help build a foundation for applying machine learning algorithms like tree-based models, feature engineering, and feature selection to improve model performance and outcomes.
Software Engineer
Software Engineer
As a Software Engineer, this course can provide an introduction to data science and machine learning that may be useful for developing data-driven software solutions.
Data Engineer
Data Engineer
This course may be useful for a Data Engineer as it provides an introduction to data science and machine learning, which can be helpful for understanding data and building data pipelines.
Product Manager
Product Manager
This course can be helpful for a Product Manager as it provides an understanding of data science and machine learning, which can be helpful for developing data-driven product roadmaps.
Consultant
Consultant
This course may be useful for a Consultant as it provides an introduction to data science and machine learning, which can be helpful for understanding client needs and developing data-driven solutions.
Researcher
Researcher
As a Researcher, this course may be helpful as it provides an introduction to data science and machine learning, which can be helpful for conducting research and developing data-driven insights.
Statistician
Statistician
This course may be useful for a Statistician as it provides an introduction to data science and machine learning, which can be helpful for developing statistical models and analyzing data.
Teacher
Teacher
This course can be helpful for a Teacher as it provides an introduction to data science and machine learning, which can be helpful for developing data-driven lesson plans and engaging students.
Writer
Writer
As a Writer, this course may be useful as it provides an introduction to data science and machine learning, which can be helpful for understanding data and writing data-driven articles and reports.
Marketer
Marketer
This course may be useful for a Marketer as it provides an introduction to data science and machine learning, which can be helpful for understanding customer behavior and developing data-driven marketing campaigns.
Salesperson
Salesperson
As a Salesperson, this course may be useful as it provides an introduction to data science and machine learning, which can be helpful for understanding customer needs and developing data-driven sales strategies.
For more career information including salaries, visit:
OpenCourser.com/course/zzx65u/applied
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
Applied Data Science for Data Analysts.
Save
A practical guide to Apache Spark, covering core concepts, advanced techniques, and real-world applications.
A hands-on guide to machine learning using Python, covering supervised and unsupervised learning algorithms, feature engineering, and model evaluation.
A comprehensive textbook on statistical learning, covering linear regression, logistic regression, tree-based models, and support vector machines.
A non-technical introduction to data science, covering data mining techniques, data visualization, and business applications.
A practical guide to using Python for data analysis, covering data manipulation, data cleaning, and data visualization.
A comprehensive guide to feature engineering, covering data transformation, feature selection, and feature creation.
Save
A practical guide to building predictive models using R, covering linear regression, logistic regression, and tree-based models.
A comprehensive textbook on decision tree learning, covering theory, algorithms, and applications in data mining.
A comprehensive textbook on unsupervised learning, covering clustering, principal component analysis, and other dimensionality reduction techniques.
A textbook on statistical methods for data analysis, covering probability, statistics, and hypothesis testing.
A beginner-friendly introduction to data science using Python, covering data manipulation, data visualization, and machine learning.
Save
A comprehensive textbook on deep learning, covering neural networks, convolutional neural networks, and recurrent neural networks.
A comprehensive textbook on reinforcement learning, covering Markov decision processes, value functions, and reinforcement learning algorithms.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zzx65u/applied
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Effort
1.5 to 2 hours a week
Level
Intermediate
Via
Coursera
Institution
Databricks
Instructors
Kevin Coyle
Mark Roepke
Emma Freeman
Language
English
Transcripts
Español
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آذربایجان دیلی
This course belongs to a
collection
called
Data Science with Databricks for Data Analysts. It's comprised of three courses:
- Applied Data Science for Data Analysts (viewing)
- Apache Spark (TM) SQL for Data Analysts
- Data Science Fundamentals for Data Analysts
Good to know
Examines unsupervised learning, a technique used to explore data in industries like finance, healthcare, manufacturing, and retail
Develops supervised learning models using tree-based algorithms, a popular approach in industries like finance and healthcare
Enhances model performance through hyperparameter tuning and cross-validation, techniques valued in industries like finance, healthcare, and manufacturing
Emphasizes feature engineering and selection, essential skills in industries where data preparation is crucial, such as finance and healthcare
Requires prior knowledge in data science fundamentals and Apache Spark, which may limit accessibility for beginners
Taught by recognized instructors in the field of data science, enhancing the course's credibility
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