Data Science Fundamentals for Data Analysts
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Approx. 19 hours to complete
Course Summary
This course is designed for data analysts who want to expand their knowledge in data science. It covers the fundamental concepts and techniques used in data science and how they can be applied to real-world problems.Key Learning Points
- Learn the basics of data science and how to use it to solve real-world problems
- Get hands-on experience with popular tools and techniques used in data science
- Learn how to analyze and interpret data using statistical methods
- Explore data visualization techniques to present your findings in a clear and concise way
- Understand the ethical considerations involved in working with data
Related Topics for further study
Learning Outcomes
- Apply fundamental concepts and techniques of data science to real-world problems
- Use popular data science tools and techniques to analyze and interpret data
- Communicate findings effectively through data visualization and storytelling
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with a programming language (Python or R preferred)
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
- Video lectures
- Hands-on exercises
- Quizzes
Similar Courses
- Applied Data Science with Python
- Data Science Essentials
Related Education Paths
Notable People in This Field
- Hadley Wickham
- Cassie Kozyrkov
Related Books
Description
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!
Knowledge
- 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.
Outline
- 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
Summary of User Reviews
Read reviews of Data Science Fundamentals for Data Analysts course on Coursera. Users have rated this course highly for its comprehensive coverage of data science concepts and practical exercises. One key aspect that many users thought was good is the course's focus on real-world applications.Pros from User Reviews
- Comprehensive coverage of data science concepts
- Practical exercises to apply learned concepts
- Focus on real-world applications
- Expert instructors with industry experience
- Flexible schedule allows for self-paced learning
Cons from User Reviews
- Some users found the difficulty level to be too basic
- Lack of interaction with instructors
- Limited feedback on assignments
- Course material can be dry at times
- Course may not be suitable for advanced data analysts
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