Learn data science and analytics from scratch

Name: Learn data science and analytics from scratch
Rating: 4.8 (10 reviews)

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

Created bySean Li

Last updated 5/2024

English

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

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

6 sections • 93 lectures • 7h 37m total length

Introduction1:33
Course Introduction: Welcome to the class
Course outline2:29
In this lecture, I will show what knowledge and skillsets we will learn and share the course outline
What will we learn1:15
We talk about what we will learn in this entire process.

Stats Outline0:49
Outline of the stats section
Hypothesis1:21
In this class, let’s talk about the hypothesis. This is the foundation for confusion matrix and A/B testing.
Sampling6:56
In this class, let’s talk about the sampling. This is the foundation for confusion matrix and A/B testing.
Sample Size Calculation2:00
In this class, we will cover how to calculate the sample size
Confusion Matrix9:56
In this lesson, let’s go through everything about the confusion matrix. We are going to talk about things like False positives, True negatives, and Precision rate.
ML 1014:10
Introduction for ML modelings.
Linear Regression 1013:51
introduction for linear regression
Linear Regression 1024:33
R squared and p value
Linear Regression 1031:29
How to build a linear regression
Linear Regression 1044:34
let's build a simple linear regression in excel.
Logistic Regression 1016:12
Introduction for Logistic Regression
Logistic Regression 1026:50
In this class, we go through how to Get the parameters, R squared and p value for the logistic regression.
Decision Tree 1011:41
Decision tree introduction
Decision Tree 10210:11
How to build a decision tree and evaluate
Random Forest 1011:59
Random forest introduction
Random Forest 1025:34
How to build a random forest
GBDT 1011:19
Gradient Boosting introduction
GBDT 1026:43
how to build a gradient boosting tree
Xgboost 1011:46
Xgboost introduction.
Xgboost 1028:14
How to build xgboost
Model Evaluation3:05
How to do model testing and evaluation like cross validation.
Statistic and ML Quiz

How to run SQL in our class1:22
We use this online SQL editor to run our queries, you can also use the one that Udemy provides, whatever fits you the best
where our sql examples are and how to play with them3:20
All our SQL data and queries can be found in the attached excel
Select1:52
SQL: Select
Select distinct2:22
SQL: Select distinct
where clause1:49
SQL: Where
Group by2:32
SQL: group by
aggregate function2:21
SQL: Aggregate function
Max/Min Function2:00
SQL: Max/min function
Having clause2:46
SQL: Having
Join9:56
SQL: Join
In operator2:23
SQL: In
Not equal operator2:42
SQL: not equal
date function2:55
case when statement2:52
SQL: Case when
Cast function2:03
SQL: Cast
Limit and offset function3:37
SQL: Limit and offset
Window function8:31
SQL: Window Function
subquery7:38
SQL: Subquery
Complex Join5:00
SQL: Complex Join
Join and aggregate functions4:01
SQL: Join and aggregate functions
combine having and where5:54
SQL: Combine having and where
Duplicates5:56
SQL: Duplicates
Nth number7:46
SQL: Nth number
Previous Date/record7:25
SQL: Previous record
Query Efficiency2:37

Python input and output1:32
We can google anaconda, we can either download the Python or use the code in the cloud service, both are free.
Python: Statement, Indentation and Comments4:30
Python: Data type5:21
Python: functions1:58
Python: Functions
Python: operator2:50
Python: Operator
Python: if else3:03
Python: if else
Python: for loop5:57
Python: for loop
Python: while loop3:35
Python: While Loop
Python: List 1017:39
Python: List 101
Python: List 10210:02
Python: List 102
Python: Tuple 1016:06
Python: Tuple 101
Python: Tuple 1025:38
Python: Tuple 102
Python: Set 1015:41
Python: Set 101
Python: Set 1028:16
Python: Set 102
Python: Dictionary 1013:39
Python: Dictionary 101
Python: Dictionary 1025:02
Python: Dictionary 102
Python: numpy 1016:42
Python: Numpy 101
Python: Numpy 2017:16
Python: Numpy 201
Python: Numpy 3018:46
Python: Numpy 301
Python: Numpy 4015:42
Python: Numpy 401
Python: Pandas 1013:12
Python: Pandas 101
Python: Pandas 20110:23
Python: Pandas 201
Python: Pandas 30110:16
Python: Pandas 301
Python: Pandas 40112:17
Python: Pandas 401
Python: Pandas 50110:41
Python: Pandas 501
Python: matplotlib 1011:13
Python: matplotlib 2014:11
Python: Matplotlib
Python: scikit-learn 1011:25

First Case Study: Nashville Housing Overview1:51
Thinking process1:31
Nashville Housing Overall Trend7:05
Nashville Housing analysis17:22
Nashville Housing Summary1:56
Case Study: Nashville
Second Case Study: Subscription business model analysis1:51
Overall business performance9:28
Case Study: subscription SQL
Load Subscription business data into dataframe6:54
Build a decision tree model in Python to improve business performance15:04
Congratulations!1:07

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 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
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.

Who this course is for:

Students that are interested in data science and data analytics

Learn data science and analytics from scratch

What you'll learn

Explore related topics

Coding Exercises

Course content

Introduction3 lectures • 5min

Statistics, Modeling and Machine Learning21 lectures • 1hr 33min

SQL25 lectures • 1hr 42min

Analytic skills6 lectures • 30min

Python28 lectures • 2hr 43min

Case Study10 lectures • 1hr 4min

Requirements

Description

Who this course is for: