商业数据分析项目班

Introduction and SQL components (1)

1. Introduction

- Data structures (Structured data, semi-structured data, unstructured data)

- RDBS and RDBMS concepts

(Relational databases and Relational database management system)

- Data analysis flow and example

- Data analyst job requirements

2. SQL components (1)

- SQL DDL (data manipulation language) - CREATE, DROP, TRUNCATE, ALTER, COMMENT, RENAME

- DB tools - DBeaver introduction and installation

Assignment:

1. Install DBeaver

2. sql_components_tutorial Q1 and Q2

3. Register an AWS account (if you don't have one)

AWS RDS and SQL components (2)

1. Amazon Web Services - RDS (Relational database service) Introduction

2. SQL components (2)

- DML (data manipulation language)

- DCL (data control language)

- TCL (transaction control language)

SQL operators and common used functions

SQL common data types, operators and functions:

Common data types:

- Text / Varchar

- Integer

- Date / Timestamp

- Serial

Operators:

- Arithmetic operators

- Comparison operators

- Logical operators

SQL aggregation functions:

- Sum

- Count

- Avg

SQL common functions:

- Min / Max

- Distinct

- Substring

- Length

- Upper / Lower

- Coalesce

- Extract

- Concat

- Case statement

- Cast / to_date

SQL Tutorial

SQL Tutorial exercise and case study 1

1. SQL Components - Tutorial (Lesson 2)

2. SQL和数据库相关基础概念 – DCL– Lab (Lesson 2)

3. SQL logical operators - Lab (Lesson 3)

4. SQL aggregation - Lab (2) (Lesson 3)

5. SQL常用功能 - Lab (Lesson 3)

6. Case study (Lesson 3)

SQL joins and window functions

SQL joins and window functions

Joins:

- No join

- Inner join

- Left join

- Right join

- Full join

- *Cross join

- *union

Window Functions:

- Row_number / Rank / Dense_rank

- Lead / Lag

- First_value / Last_value

SQL query optimization and performance tunning

Query optimization (Structure & Layout)

- with clause, views

Query optimization (Performance)

- materialized view

- indexes

- Table partitioning

SQL Tutorial练习

1. Case study - product spec & performance tuning

2. Leetcode - medium level challenge

Python 基本语法 - Data Structure

· Python 基本介绍和常用IDE各自的特点（学员已经在Tutor协助下完成安装的前提下）

· 基本编程概念 -Object, Class, Method, Attribute

· Python 最重要的三种数据结构（List, Tuple, Dictionary) 各自的基本特征和应用场景

· 数据类型之间的互相转换， (Int, String, Character, Float) 常用method进行数据处理

· Datetime 工具包 操作时间日期数据， 模拟批处理不统一格式下的日期时间数据

Python 基本语法- Control Flow

Python Control Flow:

条件语句 (if else elif; case)

基本循环 (for , while, range vs. xrange )

Error Handling (tyr except pass)

List Comprehension

函数与类的关系 (function, class & object)

基本函数的参数传导，继承 以及装饰器（decorator)

Lambda 匿名函数

多层循环 （利用 break/continue 优化多层嵌套循环）

Exercise:

6 different in class exercises to master python control flow

Pandas Dataframe Part 1

Use Pandas for data wrangling:

1. From Numpy to Pandas

2. Series, TimeSeries & Dataframe

3. Structural understanding of DataFrame object and its methods & attributes

4. Data exploration use DataFrame

5. Read/Write use DataFrame

6. Advanced DataFrame filtering & sorting

7. 3 different methods to Loop through Dataframe

8. Map & Apply with self defined function & annoymous function

Pandas DataFrame Advanced

Data Wrangling use Pandas part 2

1. Convert numerical data to categorical data use bin function

2. Merge & Concat equvalent of join & union in SQL (performance comparison and bottle neck for process data in SQL or Pandas)

3. Groupby and Pivot in Pandas

4. Advanced usage of Groupby with agg,apply & map

Exercise:

1 in class exercise and 5 take home exercises

Pandas Series Part 1

1. Basic concept of Pandas Series

2. Time Series explained and common usage of Time Series in real life data analysis

3. Advanced Datetime tool kit for populate time series,

4. Time Series Wrangling ( Resample, Offset, Freq....)

5 Time Series ploting and moving window function

Python Tutorial 语法

All Python Tutorial Exercise & Answer ( Recording after its available

Exercise Topics:

2. Loop, String, Datetime

5. Pandas exercise

Pandas Series Part 2 - Time Series modeling use ARIMA & DTW

常见的经典时间序列建模方式

Python Machine Learning Part1

1.Matplotlib for exploration plot

2. Matplotlib basic graph ( Line chart , Bar chart, Scatter chart)

3. Matplotlib advanced plot ( 3 D Scatter, Correlatin Heatmap, MSNO chart)

4. What is machine learning ( Knowledge Tree and machine learning life cycle)

5 Quick in class example of KNN model

Python Machine Learning Part2 - Regression Model

Regression Model:

1. From simple single Neuron to understand fundmental Regression Classifier model

2. Write your own single Neuron Classifier model from scratch

3. Key concepts for regression model : Loss Function; Gradient Descendent; Activation Function

4. how log function serve as Activation function in logistic regression

5. How Logistic regression fight overfitting problem ( L1,L2 Regularizer explained)

Python Machine Learning Part3 - Tree Model & Ensemble Model

Tree Based Models & Ensemble Models

1. Basic decision Tree explained

2. Key concept in decision Tree mdoel - information gain(Shannon Entrophy, Gini Inpurity)

3. Use Sklearn to build a basic decision tree

4. Classification model performance measure

5 Feature inportance & tree visualization technique

5 how tree model fight over fitting ( Prunning or go ensemble)

6. Ensemble tree models - Parallel trees - bagging, random forrest)

7. Ensemble tree models - Sequential trees -Boosting Tree family (GBDT, Ada boosting, XG Boost, Light GBM, Cat boost)

8. In class exercise : Income prediction use different tree model.

9. Hybrid Ensemble Model : Majority Vote Classifier & Stacking Classifier

Python Tutorial Pandas & ML

All Python Tutorial Exercise & Answer ( Recording after its available

Exercise Topics:

1. List& Dictionary

2. Loop, String, Datetime

3. Numpy & Matplotlib

5. Pandas ( no separate exercise, tutorial will be focuse on in class exercise inside corresponding lesson)

6. Machine Learning ( same as above)

Python Machine Learning Part 4 - Pre process - Validation - Parameter Tuning

Everything you need to know to build a end to end Machine Learning Model:

1. Categorical data encoding

2. Scaling technique ( when and how )

3. Save model & scaler use Pickle or Joblib

4 The curse of dimensionaly ( Dimension reduction vs. Dimension selection )

5. Dimension Reduction ( PCA explained and practise)

6. Machine Learning Pipe Line use Sklearn

7. Validation explained

8. Parameter Tuning - Grid Search technique

9. Model performance measure ( Confusion Matrics, ROC&AUC)

Exercise:

1. QLD Accident severity prediction

2. Income prediction

Python Tutorial Project

All Python Tutorial Exercise & Answer ( Recording after its available

Exercise Topics:

1. List& Dictionary

2. Loop, String, Datetime

Excel VBA Part 1 - VBA Syntax

· Variables, Constant and data type

· Excel Object hierarchy

· Loop & if statement

· Nested loop (using exit and goto statement)· String handling & datetime handling

· Arrays

· Error handling

· Forms & controls  

Excel VBA Part 2 - Event & formula integration

1. use Macro recorder (Pro & Con)

2. Advanced Formula ( Sumifs countifs, getpivotdata, arrayformula - more will be covered in tutorial time)

3. Pivot table basic, how to use pivot table as part of your Excel automation

4. Formula Manager, unique VBA tool to integrate VBA wiht Excel built in formula

5. Event - different way to fire your VBA code

6 How to further practise your skills to be advanced to expert Excel user

Machining Learning Project - Queensland Traffic

Machining Learning Project - Queensland Traffic

数据分析行业和简历

第一部分 行业分析和职业选择

澳洲数据岗位就业情况和求职方法

数据类职位的行业选择

数据类职位核心属性区别

数据分析岗位的核心技能要求和难点

第二部分 求职数据类工作简历

在清楚职业定位的基础上，结合过去的学术和工作经验，定制目标数据分析岗位简历。

如何优化内容分布

如何优化文字表述

如何加强学术和经验与目标工作的联系性

数据分析面试技巧

Data visualization 以及Tableau基础

- 介绍Data visualization以及不同的Data visualization tool

- 学习如何链接不同的数据源以及制作简单的Tableau图表，比如bar chart, line chart, text table, highlight table, map, tree map等等

Tableau的进阶

- 学习Tableau 添加不同的线（Reference line, trend line, forecast line）的使用

- 简单的Dashboard & Story的操作

- 学习如何在Tableau 中连接不同的Data Source（分别介绍 Union, Join, Blend 含义以及如何操作）

- Tableau的三种常用的基本及高级计算（Calculated field, Table calculation, 以及LOD表达式）

Tableau的进阶: 创建Dashboard

- Tableau常用重要的Features（Group & Set，Bin，Hierarchy，Parameter）

- 学习如何使用Page做动态的Tableau Dashboard

- 学习如何创建新颖的Hex Map

Tableau Case study

这一部分将会运用到之前所学到的知识，也会学习一些新的工作中常用的图表（Donut chart，waterfall chart）以及一些actions，最后完成一个完整的Tableau 仪表盘。

Marketing Analytics

在这一部分的学习中，会从Marketing Strategy入手，给大家介绍Marketing Analytics的一些概念，以及数据分析的应用和数据分析岗位的工作内容，通过比较Marketing Analyst和Data analyst的岗位不同点帮助学生更好的进行职业选择及规划。同时课程会覆盖Google Analytics的一些概念及操作，让学生对Marketing Analyst的工作有更清晰的认识。

Tableau Tutorial