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Databricks - Data Science Fundamentals for Data Analysts 

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Data Science Fundamentals for Data Analysts
 at 
Coursera 
Overview

Duration

19 hours

Start from

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Total fee

Free

Mode of learning

Online

Difficulty level

Intermediate

Official Website

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Credential

Certificate

Data Science Fundamentals for Data Analysts
 at 
Coursera 
Highlights

  • Earn a shareable certificate upon completion.
  • Flexible deadlines according to your schedule.
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Data Science Fundamentals for Data Analysts
 at 
Coursera 
Course details

More about this course
  • 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

Data Science Fundamentals for Data Analysts
 at 
Coursera 
Admission Process

    Important Dates

    May 25, 2024
    Course Commencement Date

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