Data Science Fundamentals for Data Analysts

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 we're going to guide you through the fundamental building blocks of data science, one of the fastest-growing fields in the world! 
With the help of our industry-leading data scientists, we’ve designed this course to build ready-to-apply data science skills in just 15 hours of learning. First, we’ll give you a quick introduction to data science - what it is and how it is used to solve real-world problems. For the rest of the course, we'll teach you the skills you need to apply foundational data science concepts and techniques to solve these real-world problems. 

By the end of this course, you'll be able to leverage your existing data analysis skills to design, execute, assess, and communicate the results of your very own data science projects.

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 2 of 3 in the

Data Science with Databricks for Data Analysts Specialization

Intermediate Level

Intermediate-level experience with SQL

Approx. 19 hours to complete

English

Subtitles: English

What you will learn

Apply foundational data science concepts and techniques to solve these real-world problems.

Design, execute, assess, and communicate the results of your very own data science projects.

Skills you will gain

Data ScienceData AnalysisMachine Learning

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 2 of 3 in the

Data Science with Databricks for Data Analysts Specialization

Intermediate Level

Intermediate-level experience with SQL

Approx. 19 hours to complete

English

Subtitles: English

Instructors

Emma Freeman

Technical Curriculum Developer

Databricks

2,493 Learners

2 Courses

Mark Roepke

Technical Curriculum Developer

Databricks

2,493 Learners

2 Courses

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

1 hour to complete

Welcome to the Course

Module description.

1 hour to complete

6 videos (Total 27 min), 2 readings, 1 quiz

Week

Week 2

3 hours to complete

An Introduction to Data Science

3 hours to complete

8 videos (Total 31 min)

Week

Week 3

3 hours to complete

Introductory Statistics for Data Science

3 hours to complete

19 videos (Total 53 min), 5 readings, 4 quizzes

19 videos

Module and Lesson Intro45s

An Introduction to Statistics4m

Descriptive Statistics5m

Descriptive Statistics Lab Intro18s

Inferential Statistics3m

An Introduction to Probability3m

Discrete Probability Lab Intro15s

Statistics and Probability Review1m

An Introduction to Probability Distributions4m

Discrete Probability Distributions4m

Discrete Probability Distributions Applications2m

Probability Distribution Lab Intro15s

Continuous Probability Distributions5m

Probability Distribution Review1m

An Introduction to Hypothesis Testing4m

Hypothesis Test Example2m

Types of Hypothesis Tests3m

Hypothesis Testing Lab Intro18s

Outlier Detection with Probability Distributions3m

5 readings

Descriptive Statistics Lab10m

Basic Rules of Discrete Probability10m

Discrete Probability Lab10m

Hypothesis Testing Lab10m

4 practice exercises

Statistics30m

Probability Distributions10m

Hypothesis Testing10m

Introductory Statistics for Data Science30m

Week

Week 4

3 hours to complete

Connecting Data Science to the Real World

3 hours to complete

9 videos (Total 32 min)

9 videos

Module and Lesson Intro1m

Why Good Questions Matter4m

Challenges with Solving Real-World Problems with Hypothesis Testing3m

Introduction to Machine Learning, Part 12m

Introduction to Machine Learning, Part 25m

Supervised and Unsupervised Learning5m

Regression and Classification4m

Clustering3m

Framing Real-World Questions Activity1m

2 practice exercises

Basics of Machine Learning30m

Classification, Regression and Clustering30m

Week

Week 5

6 hours to complete

Practical Machine Learning

6 hours to complete

28 videos (Total 85 min), 7 readings, 7 quizzes

28 videos

Module and Lesson Introduction39s

A Review of Supervised Learning and Regression1m

Linear Regression Assumptions4m

Applying Linear Regression2m

Accuracy and Interpretability5m

Regression Evaluation6m

Regression Interpretation3m

A Review of Regression Evaluation2m

An Introduction to In-sample and Out-of-sample Data2m

Evaluating on the Test Set2m

Overfitting and Underfitting3m

Overfitting and Underfitting Lab Intro23s

The Bias-Variance Tradeoff1m

The Bias-Variance Tradeoff and Model Generalization3m

A Review of Key Concepts2m

An Introduction to Logistic Regression3m

Applying Logistic Regression2m

Logistic Regression Lab 1 Intro15s

Assigning Classes Based on Probabilities3m

Classification Evaluation7m

Logistic Regression Lab 2 Intro16s

An Introduction to Decision Trees2m

The Decision Tree Training Algorithm7m

Decision Tree Hyperparameters3m

Applying Decision Trees2m

Decision Tree Lab Intro19s

Decision Trees for Regression2m

Extending Decision Trees3m

7 readings

An Introduction to Linear Regression10m

Linear Regression Lab 110m

Linear Regression Lab 210m

Overfitting and Underfitting Lab Activity10m

Logistic Regression Lab I10m

Logistic Regression Lab 210m

Decision Tree Lab10m

7 practice exercises

Linear Regression30m

Regression Evaluation30m

Bias-Variance Tradeoff 30m

Logistic Regression30m

Classification Evaluation30m

Decision Trees 130m

Decision Trees 230m

Week

Week 6

3 hours to complete

Completing Data Science Projects

3 hours to complete

13 videos (Total 27 min), 2 readings, 3 quizzes

13 videos

Module Introduction1m

Lesson Introduction51s

A Review of Problem Framing3m

Measurable Problem Objectives3m

Problem Constraints5m

Baseline Solutions3m

Baseline Solutions Lab Intro45s

Measuring Solutions in the Real-World3m

Machine Learning Solutions Discussion Intro1m

Lesson Introduction43s

A Review of the Data Science Process2m

Data Science Project Lab Intro51s

Data Science Project Summary Activity36s

2 readings

Baseline Solutions Lab10m

Data Science Project Lab10m

2 practice exercises

Measuring Success and Constraints30m

Machine Learning Solutions30m

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.
Will I earn university credit for completing the Course?
This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

More questions? Visit the Learner Help Center.

Data Science Fundamentals for Data Analysts