系统培养数据分析师
技能+分析能力+项目经验
下一期训练营:2024年8月11日开班,快来晋升吧!早鸟价即将结束!
“数据分析(DA)职业发展与转行指南"
"Essential Statistics for Data Analysis: Mastering the Fundamentals"
"Mastering Regression and Time Series Modeling"
Simple Linear Regression:
Multiple Regression and Dummy Variables:
Multiple Regression in Machine Learning:
Time Series Modeling: Trend and Seasonality:
Time Series Modeling in Finance:
"让数据讲故事"
数据可视化简介与工具:
数据转化为故事:
利用图表辅助演讲:
提升演讲吸引力:
"编程基础与 Excel VBA 高效应用课程"
"Mastering Event Handling and Formula Integration"
Using Macro Recorder:
Advanced Formulas:
Pivot Table Basics:
Formula Manager: A Unique VBA Tool:
Events in VBA:
Practicing Advanced Skills:
Amazon Web Services - RDS Introduction:
SQL Components Deep Dive:
Assignments:
"Mastering Data Types, Operators, and Functions"
Common SQL Data Types:
SQL Operators:
SQL Aggregation Functions:
Common SQL Functions:
Advanced SQL Techniques:
" Mastering Joins and Window Functions"
SQL Joins:
Window Functions:
"Structure, Performance, and Efficiency"
Query Optimization: Structure & Layout:
Query Optimization: Performance:
"Comprehensive Guide to Microsoft PowerBI: From Desktop to Publishing"
PowerBI Desktop Introduction:
Microsoft PowerBI Publish Introduction:
Data Connectors in PowerBI:
Power Query and Editor Basics:
Table Transformation Techniques:
Connecting to a Database:
"Transforming Data Types and Optimizing Queries"
Check and Modify Data Types:
Creating Calendar Date Tables:
Index & Conditional Columns:
Grouping & Aggregating Data:
Merging & Appending Queries:
Refreshing Queries:
"From Data Modeling to Interactive Dashboards"
Data Model Construction:
Database Normalization Techniques:
Fact and Dimension Tables:
Primary and Foreign Key Concepts:
Creating Table Relationships:
Star and Snowflake Schemas:
Relationship Cardinality:
Connecting Multiple Fact Tables:
Data Formats and Categories:
Creating Hierarchies for Analysis:
"Advanced Design and Analysis Techniques"
Dashboard Design Framework:
Sketching the Dashboard Layout:
Adding Report Pages & Objects:
Diverse Types of Graphs and Their Uses:
Advanced Filtering and Data Representation:
Report Slicers, Drill Up & Drill Down:
Interactive Report Interactions:
Working with Parameters:
Designing Custom Tool Tips:
"Importing Themes, Mobile Layouts, and DAX Mastery"
Importing Report Themes:
Designing for Mobile Layouts:
Calculated Fields with DAX Code:
Introduction to DAX Calculated Columns and Measures:
Exploring DAX Syntax and Operations:
Basic Math and Statistical Functions in DAX:
Case Study of a Real-World Project:
"连接数据源与制作图表"
链接不同的数据源:
制作简单的 Tableau 图表:
交互式图表设计:
数据可视化的最佳实践:
"数据线、Dashboard、数据连接与计算技巧"
添加不同类型的线:
制作 Dashboard 和 Story:
连接不同的数据源:
基本及高级计算:
"打造动态和创新的数据视觉化"
Tableau 常用重要特性:
使用 Page 制作动态 Dashboard:
创建新颖的 Hex Map:
"实战演练与仪表盘制作"
综合应用之前学习的知识:
学习制作新型图表:
掌握 Tableau 中的 Actions 使用:
完成一个完整的 Tableau 仪表盘:
"策略、分析与职业规划"
市场营销策略基础:
市场营销分析概念:
数据分析岗位解析:
Google Analytics 的应用:
"数据分析行业洞察与求职简历制作指南"
行业分析和职业选择:
求职数据类工作简历:
"全面备战面试:敏捷角色、数据分析和行为问题解析"
敏捷角色面试环节:
数据分析职位面试问题:
情景面试(Behavioral Questions):
"Python 基础知识与实践应用"
Python 介绍、安装和第一个 Python 程序:
常用数据类型 - 文字类 (String):
"Python 数据类型深度解析:掌握核心编程基础"
数字类 (Integer, Float):
布尔值 (True or False):
数组类 (List, Tuple, Set):
字典 (Dictionary):
日期处理:
数据类型总结:
"Python 流程控制精讲:编写高效的循环与条件判断"
If 语句:
While 循环:
For 循环:
流程控制技巧:
"Python 编程深化:函数、类及实战项目"
函数(Function)的深入探索:
类(Class)的基础与应用:
Capstone 项目:停车场售票机的构建:
"Python 数据处理精粹:掌握 DataFrame 及高效数据操作"
数据结构(Data Structure)的概念:
DataFrame 的基础知识:
从 CSV 和其他格式创建 DataFrame:
应用统计方法于 DataFrame:
列表解析(List Comprehension)和字典查找:
过滤(Filter) DataFrame:
排序(Sort) DataFrame:
Map 与 Apply 的比较:
"时间序列分析与 Pandas:从数据处理到 ARIMA 预测"
使用 Pandas 处理时间序列(TimeSeries)数据:
时间序列模式识别(TimeSeries Pattern Recognition):
时间序列 ARIMA 模型(TimeSeries ARIMA Model):
"探索性数据分析案例研究:使用 Python 和 Pandas 库"
Pandas 库简介:
数据预处理与清洗:
探索性数据分析(EDA)技巧:
案例实战练习:
回归分析技巧:
机器学习实战应用:
"Matplotlib 可视化与机器学习基础:从图表到 KNN 模型"
使用 Matplotlib 进行探索性数据可视化:
Matplotlib 基础图表:
Matplotlib 高级图表:
机器学习入门:
KNN 模型实例讲解:
"回归模型深入解析:从基础到逻辑回归"
单神经元的基础理解:
从零开始编写单神经元分类器:
回归模型的关键概念:
逻辑回归中的对数函数作用:
逻辑回归的过拟合问题与正则化:
"Mastering Tree-Based and Ensemble Models in Machine Learning"
Basic Decision Tree Explained:
Key Concepts in Decision Tree Model:
Building Decision Trees with Sklearn:
Classification Model Performance Measurement:
Feature Importance and Tree Visualization Techniques:
Combatting Overfitting in Tree Models:
Parallel Tree Ensembles: Bagging and Random Forest:
Sequential Tree Ensembles: Boosting Tree Family:
In-Class Exercise: Income Prediction Using Different Tree Models:
Hybrid Ensemble Models:
"Building End-to-End Machine Learning Models: A Comprehensive Guide"
Categorical Data Encoding:
Scaling Techniques:
Saving Models with Pickle or Joblib:
The Curse of Dimensionality:
Dimension Reduction with PCA:
Building Machine Learning Pipelines with Sklearn:
Validation Explained:
Parameter Tuning with Grid Search:
Model Performance Measurement:
Practical Exercises:
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