Learn Data Science And Analytics From Scratch

a wide coverage of analysis skills, statistics, modeling, ML, AB testing, SQL and python

What you'll 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

Requirements

No analytics/coding/stats basics are needed, you will learn everything from this course

Description

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: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.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:Nashville housing analysisTLDR: 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?Focus Area: Analytics and SQLSubscription business model analysisTLDR: 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.Focus Area: Analytics, Modeling, Python and SQLI hope this course can help set you ready for your future success. Please join us, If any of these interest you.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course outline

Lecture 3 What will we learn

Section 2: Statistics, Modeling and Machine Learning

Lecture 4 Stats Outline

Lecture 5 Hypothesis

Lecture 6 Sampling

Lecture 7 Sample Size Calculation

Lecture 8 Confusion Matrix

Lecture 9 ML 101

Lecture 10 Linear Regression 101

Lecture 11 Linear Regression 102

Lecture 12 Linear Regression 103

Lecture 13 Linear Regression 104

Lecture 14 Logistic Regression 101

Lecture 15 Logistic Regression 102

Lecture 16 Decision Tree 101

Lecture 17 Decision Tree 102

Lecture 18 Random Forest 101

Lecture 19 Random Forest 102

Lecture 20 GBDT 101

Lecture 21 GBDT 102

Lecture 22 Xgboost 101

Lecture 23 Xgboost 102

Lecture 24 Model Evaluation

Section 3: SQL

Lecture 25 How to run SQL in our class

Lecture 26 where our sql examples are and how to play with them

Lecture 27 Select

Lecture 28 Select distinct

Lecture 29 where clause

Lecture 30 Group by

Lecture 31 aggregate function

Lecture 32 Max/Min Function

Lecture 33 Having clause

Lecture 34 Join

Lecture 35 In operator

Lecture 36 Not equal operator

Lecture 37 date function

Lecture 38 case when statement

Lecture 39 Cast function

Lecture 40 Limit and offset function

Lecture 41 Window function

Lecture 42 subquery

Lecture 43 Complex Join

Lecture 44 Join and aggregate functions

Lecture 45 combine having and where

Lecture 46 Duplicates

Lecture 47 Nth number

Lecture 48 Previous Date/record

Lecture 49 Query Efficiency

Section 4: Analytic skills

Lecture 50 How to analyze a problem

Lecture 51 How to define success metrics

Lecture 52 A/B testing 101

Lecture 53 A/B testing 102

Lecture 54 Payment risk 101

Lecture 55 Payment risk 102

Section 5: Python

Lecture 56 Python input and output

Lecture 57 Python: Statement, Indentation and Comments

Lecture 58 Python: Data type

Lecture 59 Python: functions

Lecture 60 Python: operator

Lecture 61 Python: if else

Lecture 62 Python: for loop

Lecture 63 Python: while loop

Lecture 64 Python: List 101

Lecture 65 Python: List 102

Lecture 66 Python: Tuple 101

Lecture 67 Python: Tuple 102

Lecture 68 Python: Set 101

Lecture 69 Python: Set 102

Lecture 70 Python: Dictionary 101

Lecture 71 Python: Dictionary 102

Lecture 72 Python: numpy 101

Lecture 73 Python: Numpy 102

Lecture 74 Python: Numpy 103

Lecture 75 Python: Numpy 104

Lecture 76 Python: Pandas 101

Lecture 77 Python: Pandas 102

Lecture 78 Python: Pandas 103

Lecture 79 Python: Pandas 104

Lecture 80 Python: Pandas 105

Lecture 81 Python: matplotlib 101

Lecture 82 Python: matplotlib 102

Lecture 83 Python: scikit-learn 101

Section 6: Case Study

Lecture 84 First Case Study: Nashville Housing Overview

Lecture 85 Thinking process

Lecture 86 Nashville Housing Overall Trend

Lecture 87 Nashville Housing analysis

Lecture 88 Nashville Housing Summary

Lecture 89 Second Case Study: Subscription business model analysis

Lecture 90 Overall business performance

Lecture 91 Load Subscription business data into dataframe

Lecture 92 Build a decision tree model in Python to improve business performance

Lecture 93 Congratulations!

Students that are interested in data science and data analytics