This course is part of the Data Science with Databricks for Data Analysts Specialization

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Data Science with Databricks for Data Analysts SpecializationDatabricks

About this Course

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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.

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Course 3 of 3 in the

Data Science with Databricks for Data Analysts Specialization

Intermediate Level

Completion of the first two courses in the Data Science with Databricks for Data Analysts Coursera specialization. This course is the final course.

Approx. 16 hours to complete

English

Subtitles: English

What you will learn

Explore data using unsupervised machine learning.

Solve complex supervised learning problems using tree-based models.

Apply hyperparameter tuning and cross-validation strategies to improve model performance.

Skills you will gain

Data ScienceMachine LearningDatabricks

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 3 of 3 in the

Data Science with Databricks for Data Analysts Specialization

Intermediate Level

Completion of the first two courses in the Data Science with Databricks for Data Analysts Coursera specialization. This course is the final course.

Approx. 16 hours to complete

English

Subtitles: English

Instructors

Instructor rating

5/5 (9 Ratings)

Offered by

Databricks

Databricks is the data and AI company. Founded by the creators of Apache Spark™, Delta Lake and MLflow, organizations like Comcast, Condé Nast, Nationwide and H&M rely on Databricks’ open and unified platform to enable data engineers, scientists and analysts to collaborate and innovate faster.

Syllabus - What you will learn from this course

Week

Week 1

2 hours to complete

Welcome to the Course

2 hours to complete

8 videos (Total 40 min), 2 readings, 1 quiz

8 videos

Course introduction3m

Review of Data Science4m

Review of Machine Learning5m

Data Science Process vs. Machine Learning Workflow3m

Introduction to Databricks (Optional)5m

Introduction to the Platform (Optional)7m

Introduction to Apache Spark (Optional)4m

Introduction to Delta Lake (Optional)6m

2 readings

Before you begin10m

Hands-on with Databricks Lab (Optional)30m

1 practice exercise

Course Introduction and Prerequisites5m

Week

Week 2

3 hours to complete

Applied Unsupervised Learning

3 hours to complete

15 videos (Total 69 min), 2 readings, 6 quizzes

15 videos

Lesson Introduction1m

Exploring Data4m

Visualizing Data11m

Introduction to K-means Clustering5m

Applied K-means Clustering7m

Identifying the Number of Clusters4m

Identifying the Number of Clusters Demo4m

Utilizing Clusters2m

Lesson Introduction1m

Feature Relationships4m

Correlation Matrix4m

Introduction to Principal Components Analysis3m

Applied Principal Components Analysis6m

PCA for Feature Relationships3m

PCA for Dimensionality Reduction3m

2 readings

K-means Clustering Lab30m

Principal Components Analysis Lab30m

6 practice exercises

Exploring and Visualizing Data5m

K-means Clustering5m

K-means Clustering Lab Results5m

Feature Correlation5m

Principal Components Analysis5m

PCA Lab Results10m

Week

Week 3

3 hours to complete

Feature Engineering and Selection

3 hours to complete

17 videos (Total 71 min), 2 readings, 6 quizzes

17 videos

Lesson Introduction4m

Introduction to Feature Engineering3m

Common Feature Improvements3m

Handling Missing Values3m

Imputing Missing Values11m

Feature Scaling3m

Converting Feature Types3m

Representing Categorical Features2m

One-hot Encoding7m

Lesson Introduction2m

Problems with High Dimensions and Dimensionality Reduction3m

A Review of Feature Importance4m

Linear Regression Coefficients and P-values6m

Introduction to Feature Selection2m

Regularization1m

Regularized Regression3m

Applied Regularized Regression5m

2 readings

Feature Engineering Lab30m

Feature Selection Lab30m

6 practice exercises

Feature Engineering Concepts5m

Missing Values5m

Feature Engineering Lab Results20m

Dimensionality and Feature Importance5m

Feature Selection in Linear Regression5m

Feature Selection Lab Results10m

Week

Week 4

6 hours to complete

Applied Tree-based Models

6 hours to complete

14 videos (Total 60 min), 10 readings, 9 quizzes

14 videos

Lesson Introduction56s

A Review of Decision Trees3m

Algorithm Selection2m

String Indexing Categorical Features8m

Decision Tree Pruning6m

Lesson Introduction32s

Introduction to Ensemble Modeling3m

Bootstrap Sampling Training Data2m

Applied Random Forest4m

Lesson Introduction56s

A Review of Classification Evaluation Metrics4m

A Review of Assigning Classes4m

Oversampling and Undersampling Classes4m

Weighting Classes in Random Forest11m

10 readings

Feature Engineering in Decision Trees5m

Preventing Overfitting5m

Applied Decision Trees Lab30m

Aggregating Bootstrapped Results7m

Random Forest Algorithm10m

Applied Random Forest Lab30m

Problems with Class Imbalance5m

Label-based Bootstrap Sampling4m

Label-based Evaluation Weighting6m

Label Imbalance Lab30m

9 practice exercises

Algorithm Selection and Decision Trees10m

Categorical Features5m

Applied Decision Trees Lab Results10m

Tree-based Ensemble Modeling30m

Bootstrap Aggregation30m

Applied Random Forest Lab Results5m

Classification Evaluation30m

Label Imbalance and Sampling30m

Label Imbalance Lab Results10m

About the Data Science with Databricks for Data Analysts Specialization

This specialization is intended for data analysts looking to expand their toolbox for working with data. Traditionally, data analysts have used tools like relational databases, CSV files, and SQL programming, among others, to perform their daily workflows. In this specialization, you will leverage existing skills to learn new ones that will allow you to utilize advanced technologies not traditionally linked to this role - technologies like Databricks and Apache Spark. By the end of this specialization, you'll be able to solve real-world business problems with Databricks and the most popular machine learning techniques.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

More questions? Visit the Learner Help Center.

Applied Data Science for Data Analysts

About this Course

What you will learn

Skills you will gain

Instructors

Kevin Coyle

Mark Roepke

Emma Freeman

Offered by

Databricks

Syllabus - What you will learn from this course

Week 1

Welcome to the Course

Week 2

Applied Unsupervised Learning

Week 3

Feature Engineering and Selection

Week 4

Applied Tree-based Models

Week 5

Model Optimization

About the Data Science with Databricks for Data Analysts Specialization

Frequently Asked Questions

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Browse popular topics

Upskill your team

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Applied Data Science for Data Analysts

About this Course

What you will learn

Skills you will gain

Instructors

Kevin Coyle

Mark Roepke

Emma Freeman

Offered by

Databricks

Syllabus - What you will learn from this course

Week 1

Welcome to the Course

Week 2

Applied Unsupervised Learning

Week 3

Feature Engineering and Selection

Week 4

Applied Tree-based Models

Week 5

Model Optimization

About the Data Science with Databricks for Data Analysts Specialization

Frequently Asked Questions

Coursera Footer

Start or advance your career

Browse popular topics

Upskill your team

Earn a degree or certificate online

Coursera

Community

More