Learn data science and analytics from scratch

Learn data science and analytics from scratch, available at $19.99, has an average rating of 5, with 94 lectures, 47 quizzes, based on 6 reviews, and has 19 subscribers.

You will learn about Set a solid foundation for data analytics and data science Master the statistic basics such as hypothesis testing and confusion matrix, and modeling basics such as regression model and ML model Master the analytic basics like AB testing and coding basics for SQL and Python Complete 2 case studies from end to end with the skillsets we learned This course is ideal for individuals who are Students that are interested in data science and data analytics It is particularly useful for Students that are interested in data science and data analytics.

Enroll now: Learn data science and analytics from scratch

Summary

Title: Learn data science and analytics from scratch

Price: $19.99

Average Rating: 5

Number of Lectures: 94

Number of Quizzes: 47

Number of Published Lectures: 93

Number of Published Quizzes: 47

Number of Curriculum Items: 141

Number of Published Curriculum Objects: 140

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will Learn

Set a solid foundation for data analytics and data science

Master the statistic basics such as hypothesis testing and confusion matrix, and modeling basics such as regression model and ML model

Master the analytic basics like AB testing and coding basics for SQL and Python

Complete 2 case studies from end to end with the skillsets we learned

Who Should Attend

Students that are interested in data science and data analytics

Target Audiences

Students that are interested in data science and data analytics

Hi, this is Kangxiao, I have many years working experience from industry leaders like Paypal, Google and Chime. Throughout my entire career, I use data to do analysis, build models and solve key business problems.

When I learn online, I often ran into two issues:

1. The course offers in-depth knowledge, but it doesn’t have very broad coverage. In reality, we don’t need to be experts for everything. But it will give us a huge advantage if we know the basics for a lot of things.

2. The course focuses too much on the technical side. I find a lot of the courses focus entirely on either coding like how to write python codes, or stats like the math behind different kinds of ML models. And there are very few courses that link data analysis, modeling and coding together to solve real world problems.

In this course, I want to fulfill these gaps by offering a very broad coverage of data science, statistics, modeling and coding, and using case studies to connect data, coding, and stats together. That’s exactly what we do in the real world, in our day to day work. The best talents I observe in Paypal, Google and Chime are the ones who are really good at connecting these dots together to solve complicated problems.

At the end of this course, we will go through two major projects together with different focus areas. We will apply the knowledge we learned before (statistics, analytics, SQL, Python and modeling) to solve these two cases. The details of these two cases are shown below:

1. Nashville housing analysis

   1. TLDR: Nashville housing is booming, we have some data about the house prices, house details and seller information. How can we use these to perform analysis and give business advice?

   2. Focus Area: Analytics and SQL

2. Subscription business model analysis

   1. TLDR: We launched the subscription service 2 years ago. As the VP of analytics, we want to provide an update to our CEO including the business performance, where the opportunities and next step suggestions. We will use data to support our story.

   2. Focus Area: Analytics, Modeling, Python and SQL

I hope this course can help set you ready for your future success. Please join us, If any of these interest you.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Course outline

Lecture 3: What will we learn

Chapter 2: Statistics, Modeling and Machine Learning

Lecture 1: Stats Outline

Lecture 2: Hypothesis

Lecture 3: Sampling

Lecture 4: Sample Size Calculation

Lecture 5: Confusion Matrix

Lecture 6: ML 101

Lecture 7: Linear Regression 101

Lecture 8: Linear Regression 102

Lecture 9: Linear Regression 103

Lecture 10: Linear Regression 104

Lecture 11: Logistic Regression 101

Lecture 12: Logistic Regression 102

Lecture 13: Decision Tree 101

Lecture 14: Decision Tree 102

Lecture 15: Random Forest 101

Lecture 16: Random Forest 102

Lecture 17: GBDT 101

Lecture 18: GBDT 102

Lecture 19: Xgboost 101

Lecture 20: Xgboost 102

Lecture 21: Model Evaluation

Chapter 3: SQL

Lecture 1: How to run SQL in our class

Lecture 2: where our sql examples are and how to play with them

Lecture 3: Select

Lecture 4: Select distinct

Lecture 5: where clause

Lecture 6: Group by

Lecture 7: aggregate function

Lecture 8: Max/Min Function

Lecture 9: Having clause

Lecture 10: Join

Lecture 11: In operator

Lecture 12: Not equal operator

Lecture 13: date function

Lecture 14: case when statement

Lecture 15: Cast function

Lecture 16: Limit and offset function

Lecture 17: Window function

Lecture 18: subquery

Lecture 19: Complex Join

Lecture 20: Join and aggregate functions

Lecture 21: combine having and where

Lecture 22: Duplicates

Lecture 23: Nth number

Lecture 24: Previous Date/record

Lecture 25: Query Efficiency

Chapter 4: Analytic skills

Lecture 1: How to analyze a problem

Lecture 2: How to define success metrics

Lecture 3: A/B testing 101

Lecture 4: A/B testing 102

Lecture 5: Payment risk 101

Lecture 6: Payment risk 102

Chapter 5: Python

Lecture 1: Python input and output

Lecture 2: Python: Statement, Indentation and Comments

Lecture 3: Python: Data type

Lecture 4: Python: functions

Lecture 5: Python: operator

Lecture 6: Python: if else

Lecture 7: Python: for loop

Lecture 8: Python: while loop

Lecture 9: Python: List 101

Lecture 10: Python: List 102

Lecture 11: Python: Tuple 101

Instructors

Sean Li
Certified Fraud Examiner, risk expert

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5 stars: 6 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!