Databricks - Data Science Fundamentals for Data Analysts
- Offered byCoursera
Data Science Fundamentals for Data Analysts at Coursera Overview
Duration | 19 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course |
Credential | Certificate |
Data Science Fundamentals for Data Analysts at Coursera Highlights
- Earn a shareable certificate upon completion.
- Flexible deadlines according to your schedule.
Data Science Fundamentals for Data Analysts at Coursera Course details
- 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.
Data Science Fundamentals for Data Analysts at Coursera Curriculum
Welcome to the Course
Course Introduction
Introduction to Databricks
Introduction to the Platform
Introduction to Apache Spark
Introduction to Delta Lake
Hands-on with Databricks
Before You Begin
Hands-on with Databricks Lab
About Data Science Fundamentals for Data Analysts
An Introduction to Data Science
Module and Lesson Introduction
The Scientific Method
Skills of Data Science
Defining the Skills of Data Science
Domain Knowledge
Defining Data Science
Examples of Data Science
Design a Data Science Process Activity
The Scientific Method
Defining Skills of Data Science
Introductory Statistics for Data Science
Module and Lesson Intro
An Introduction to Statistics
Descriptive Statistics
Descriptive Statistics Lab Intro
Inferential Statistics
An Introduction to Probability
Basic Rules of Discrete Probability
Discrete Probability Lab Intro
Statistics and Probability Review
An Introduction to Probability Distributions
Discrete Probability Distributions
Discrete Probability Distributions Applications
Probability Distribution Lab Intro
Continuous Probability Distributions
Probability Distribution Review
An Introduction to Hypothesis Testing
Hypothesis Test Example
Types of Hypothesis Tests
Hypothesis Testing Lab Intro
Outlier Detection with Probability Distributions
Descriptive Statistics Lab
Discrete Probability Lab
Discrete Probability Lab
Hypothesis Testing Lab
Statistics
Probability Distributions
Hypothesis Testing
Introductory Statistics for Data Science
Connecting Data Science to the Real World
Module and Lesson Intro
Why Good Questions Matter
Challenges with Solving Real-World Problems with Hypothesis Testing
Introduction to Machine Learning, Part 1
Introduction to Machine Learning, Part 2
Supervised and Unsupervised Learning
Regression and Classification
Clustering
Framing Real-World Questions Activity
Basics of Machine Learning
Classification, Regression and Clustering
Practical Machine Learning
Module and Lesson Introduction
A Review of Supervised Learning and Regression
An Introduction to Linear Regression
Linear Regression Assumptions
Applying Linear Regression
Accuracy and Interpretability
Regression Evaluation
Regression Interpretation
A Review of Regression Evaluation
An Introduction to In-sample and Out-of-sample Data
Evaluating on the Test Set
Overfitting and Underfitting
Overfitting and Underfitting Lab Intro
The Bias-Variance Tradeoff
The Bias-Variance Tradeoff and Model Generalization
A Review of Key Concepts
An Introduction to Logistic Regression
Applying Logistic Regression
Logistic Regression Lab 1 Intro
Assigning Classes Based on Probabilities
Classification Evaluation
Logistic Regression Lab 2 Intro
An Introduction to Decision Trees
The Decision Tree Training Algorithm
Decision Tree Hyperparameters
Applying Decision Trees
Decision Tree Lab Intro
Decision Trees for Regression
Extending Decision Trees
Linear Regression Lab 1
Linear Regression Lab 2
Overfitting and Underfitting Lab Activity
Logistic Regression Lab I
Logistic Regression Lab 2
Decision Tree Lab
Linear Regression
Regression Evaluation
Bias-Variance Tradeoff
Logistic Regression
Classification Evaluation
Decision Trees 1
Decision Trees 2
Completing Data Science Projects
Module Introduction
Lesson Introduction
A Review of Problem Framing
Measurable Problem Objectives
Problem Constraints
Baseline Solutions
Baseline Solutions Lab Intro
Measuring Solutions in the Real-World
Machine Learning Solutions Discussion Intro
Lesson Introduction
A Review of the Data Science Process
Data Science Project Lab Intro
Data Science Project Summary Activity
Baseline Solutions Lab
Data Science Project Lab
Measuring Success and Constraints
Machine Learning Solutions