2023 CORE- Data Science and Machine Learning

2023 CORE: Data Science and Machine Learning, available at $79.99, has an average rating of 4.8, with 267 lectures, based on 334 reviews, and has 3166 subscribers.

You will learn about Learn all necessary core skills for Data Analysis, Data Science, and Machine Learning Understand the first principles of data science and why it is so popular and important Learn how to use, from scratch, Python, R, SQL, Tableau, and MS Excel for data science Learn about a broad range of data science and machine learning libraries and resources Build and host a personal resume and portfolio of data science projects using GitHub Pages Learn about key supporting skills like Git/version-control, Kaggle, Databases, Command Line tools, and much more! Learn how to setup development environments from scratch in R and Python Learn about important related technologies like cloud, docker, and web development, Learn to deploy a machine learning model using docker This course is ideal for individuals who are Those who feels like they dont know where to start with data science and machine learning or Those tired of courses that dont show the entire picture of data science and leave them asking now what? or Those interested in starting a journey into the data science and machine learning career field. or For those wanting to super-charge an existing skill set with the latest techniques and tools. It is particularly useful for Those who feels like they dont know where to start with data science and machine learning or Those tired of courses that dont show the entire picture of data science and leave them asking now what? or Those interested in starting a journey into the data science and machine learning career field. or For those wanting to super-charge an existing skill set with the latest techniques and tools.

Enroll now: 2023 CORE: Data Science and Machine Learning

Summary

Title: 2023 CORE: Data Science and Machine Learning

Price: $79.99

Average Rating: 4.8

Number of Lectures: 267

Number of Published Lectures: 267

Number of Curriculum Items: 273

Number of Published Curriculum Objects: 273

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will Learn

Learn all necessary core skills for Data Analysis, Data Science, and Machine Learning

Understand the first principles of data science and why it is so popular and important

Learn how to use, from scratch, Python, R, SQL, Tableau, and MS Excel for data science

Learn about a broad range of data science and machine learning libraries and resources

Build and host a personal resume and portfolio of data science projects using GitHub Pages

Learn about key supporting skills like Git/version-control, Kaggle, Databases, Command Line tools, and much more!

Learn how to setup development environments from scratch in R and Python

Learn about important related technologies like cloud, docker, and web development,

Learn to deploy a machine learning model using docker

Who Should Attend

Those who feels like they dont know where to start with data science and machine learning

Those tired of courses that dont show the entire picture of data science and leave them asking now what?

Those interested in starting a journey into the data science and machine learning career field.

For those wanting to super-charge an existing skill set with the latest techniques and tools.

Target Audiences

Those who feels like they dont know where to start with data science and machine learning

Those tired of courses that dont show the entire picture of data science and leave them asking now what?

Those interested in starting a journey into the data science and machine learning career field.

For those wanting to super-charge an existing skill set with the latest techniques and tools.

This is an ambitious course. The goal here is simple: Only teach what you need to know for day 1 of your first data science job. No fluff, nothing out of context, no topics that are not relevant to real world applications. We will cover EVERY core topic and tool required for those new to data science: Python, R, SQL, Useful Math/Stats/Algorithms, Tableau, and Excelin depth. The course will cover skills that align with three different job types:

– Data Analyst

– General Data Scientist

– Machine Learning Engineer

You can expect to learn from first principles the foundational topics and tools used in practice today. We will avoid topics that are not useful or are simply too advanced when starting out. Your journey will be guided by the Data Science Road Map, a collection of the best resources gathered through years of experience by the instructor.

In addition, we will survey every important technology required on the job including GitHub, Kaggle, the basics of cloud, web development and docker. With over 200 videos, readings, and assignments, you can be sure you will be well prepared to join the data community.

If you are just getting started or want to fill in some of your knowledge gaps this course is for you!

Course Curriculum

Chapter 1: Introduction – First Principals

Lecture 1: Introduction

Lecture 2: Course Overview

Lecture 3: Course Structure

Lecture 4: Course Philosophy

Lecture 5: First Principles – Who?

Lecture 6: First Principles – Why? 1/3

Lecture 7: First Principles – Why? 2/3

Lecture 8: First Principles – Why? 3/3

Lecture 9: Reading Assignment

Lecture 10: First Principles – What?

Lecture 11: First Principles – What? Data Analyst Example Product

Lecture 12: First Principles – What? Data Scientist Example Product

Lecture 13: First Principles – What? Machine Learning Engineer Example Product

Lecture 14: First Principles – What? Data & Sources

Lecture 15: First Principles – What? Kaggle Introduction

Lecture 16: First Principles – How?

Lecture 17: Data Science Battle Station

Lecture 18: Section Wrap Up

Lecture 19: Assignments

Chapter 2: Data Analyst – Case Study – Intro & Basic Spreadsheets

Lecture 1: Data Analyst Overview

Lecture 2: Spreadsheets Overview

Lecture 3: Introduction to MS Excel

Lecture 4: Overview of MS Excel

Lecture 5: Excel Templates

Lecture 6: Workbook Overview

Lecture 7: Protecting Workbooks

Lecture 8: Sharing Workbooks

Lecture 9: Operators

Lecture 10: Built-in Functions

Chapter 3: Data Analyst – Case Study – Intermediate Spreadsheets

Lecture 1: Math – Summary Statistics

Lecture 2: Calculating Summary Statistics from Scratch

Lecture 3: Import a Text File

Lecture 4: Data Tables

Lecture 5: Summary Statistics on Tables

Lecture 6: Assignment Review

Lecture 7: Importing Data – Intermediate

Lecture 8: Lookups and Matches

Lecture 9: Calculating Churn and Customer Lifetime Value

Lecture 10: Financial Forecasting (Time Series)

Lecture 11: Data Visualization Introduction

Lecture 12: Data Visualization Excel

Lecture 13: Dashboards Best Practices

Lecture 14: Assignment Solution

Chapter 4: Data Analyst – Case Study – Advanced Spreadsheets

Lecture 1: Importing Data – Power Query

Lecture 2: Pivot Tables

Lecture 3: Mathematical Modeling – Linear Programming

Lecture 4: Solver – Linear Programming in Excel

Lecture 5: Analysis Toolpack

Lecture 6: Visual Basic for Applications (VBA) – Introduction

Lecture 7: Spreadsheet Conclusion

Chapter 5: Data Analyst – Case Study – SQL Basics

Lecture 1: SSI – databases

Lecture 2: SQL Text Editor – Sublime

Lecture 3: SQL Syntax

Lecture 4: Introduction to SQLite Databases

Lecture 5: SQLite Install

Lecture 6: SQLite Database Creation

Lecture 7: Basic SQL – SELECT, FROM, WHERE statements

Lecture 8: Basic SQL – BETWEEN, LIKE statements

Lecture 9: Basic SQL – AND, OR, NOT, EXISTS, NULL statements

Lecture 10: Basic SQL – ORDER BY, DISTINCT statements

Chapter 6: Data Analyst – Case Study – SQL Intermediate and Advanced

Lecture 1: Intermediate SQL – Aggregate Functions

Lecture 2: Intermediate SQL – Joins

Lecture 3: Intermediate SQL – WITH and subqueries

Lecture 4: Advanced SQL – Inserting, Updating, and Deleting data

Lecture 5: Advanced SQL – Views

Lecture 6: Connecting SQLite to Excel

Chapter 7: Data Analyst – Case Study – Business Intelligence and Tableau Introduction

Lecture 1: Introduction to Business Intelligence (BI)

Lecture 2: Why Tableau?

Lecture 3: Installing Tableau Public

Lecture 4: Tableau Overview

Lecture 5: Tableau Data Types

Lecture 6: Tableau Basic Viz

Lecture 7: Tableau Filters

Lecture 8: Connecting Tableau to OData Sources

Lecture 9: Joining Data in Tableau

Chapter 8: Data Analyst – Case Study – Tableau Intermediate and Advanced Topics

Lecture 1: Tableau Intermediate Bar Charts

Lecture 2: Tableau Dates

Lecture 3: Tableau Visualizing Comparisons

Lecture 4: Tableau Visualizing Distributions

Lecture 5: Tableau Multiple Axis

Lecture 6: Tableau Formating

Lecture 7: Tableau Calculations and Parameters

Lecture 8: Tableau Dashboards and Stories

Lecture 9: Tableau Advanced Analysis

Lecture 10: Sharing with Tableau Public

Lecture 11: Tableau Desktop Pro Overview

Lecture 12: Assignment: Portfolio, and Resume Updates

Instructors

Dr. Isaac Faber
Chief Data Scientist

Rating Distribution

1 stars: 0 votes

2 stars: 3 votes

3 stars: 22 votes

4 stars: 102 votes

5 stars: 207 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!