10最好的数据分析教程推荐

Part 1: Introduction
The Field of Data Science – The Various Data Science Disciplines
The Field of Data Science – Connecting the Data Science Disciplines
The Field of Data Science – The Benefits of Each Discipline
The Field of Data Science – Popular Data Science Techniques
The Field of Data Science – Popular Data Science Tools
The Field of Data Science – Careers in Data Science
The Field of Data Science – Debunking Common Misconceptions
Part 2: Probability
Probability – Combinatorics
Probability – Bayesian Inference
Probability – Distributions
Probability – Probability in Other Fields
Part 3: Statistics
Statistics – Descriptive Statistics
Statistics – Practical Example: Descriptive Statistics
Statistics – Inferential Statistics Fundamentals
Statistics – Inferential Statistics: Confidence Intervals
Statistics – Practical Example: Inferential Statistics
Statistics – Hypothesis Testing
Statistics – Practical Example: Hypothesis Testing
Part 4: Introduction to Python
Python – Variables and Data Types
Python – Basic Python Syntax
Python – Other Python Operators
Python – Conditional Statements
Python – Python Functions
Python – Sequences
Python – Iterations
Python – Advanced Python Tools
Part 5: Advanced Statistical Methods in Python
Advanced Statistical Methods – Linear Regression with StatsModels
Advanced Statistical Methods – Multiple Linear Regression with StatsModels
Advanced Statistical Methods – Linear Regression with sklearn
Advanced Statistical Methods – Practical Example: Linear Regression
Advanced Statistical Methods – Logistic Regression
Advanced Statistical Methods – Cluster Analysis
Advanced Statistical Methods – K-Means Clustering
Advanced Statistical Methods – Other Types of Clustering
Part 6: Mathematics
Part 7: Deep Learning
Deep Learning – Introduction to Neural Networks
Deep Learning – How to Build a Neural Network from Scratch with NumPy
Deep Learning – TensorFlow 2.0: Introduction
Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks
Deep Learning – Overfitting
Deep Learning – Initialization
Deep Learning – Digging into Gradient Descent and Learning Rate Schedules
Deep Learning – Preprocessing
Deep Learning – Classifying on the MNIST Dataset
Deep Learning – Business Case Example
Deep Learning – Conclusion
Appendix: Deep Learning – TensorFlow 1: Introduction
Appendix: Deep Learning – TensorFlow 1: Classifying on the MNIST Dataset
Appendix: Deep Learning – TensorFlow 1: Business Case
Software Integration
Case Study – What’s Next in the Course?
Case Study – Preprocessing the ‘Absenteeism_data’
Case Study – Applying Machine Learning to Create the ‘absenteeism_module’
Case Study – Loading the ‘absenteeism_module’
Case Study – Analyzing the Predicted Outputs in Tableau
Appendix – Additional Python Tools
Appendix – pandas Fundamentals
Appendix – Working with Text Files in Python
Bonus Lecture

Getting Started
Excel Formulas 101
Conditional Statements & Logical Operators
Common Excel Statistical Functions
Lookup & Reference Functions
Text Functions
Date & Time Functions
Formula-Based Formatting
Dynamic Array Formulas
Bonus Functions
Wrapping Up

Intro to Course and Python
Setup
Learning Numpy
Intro to Pandas
Working with Data: Part 1
Working with Data: Part 2
Working with Data: Part 3
Data Visualization
Example Projects.
Machine Learning
Appendix: Statistics Overview
Appendix: SQL and Python
Appendix: Web Scraping with Python
Appendix: Python Special Offers
BONUS SECTION: THANK YOU!

“Installation and Setup
BONUS: Python Crash Course
Series
DataFrames I: Introduction
DataFrames II: Filtering Data
DataFrames III: Data Extraction
Working with Text Data
MultiIndex
The GroupBy Object
Merging, Joining, and Concatenating DataFrames
Working with Dates and Times in Datasets
Input and Output in pandas
Visualization
Options and Settings in pandas
Conclusion”

Welcome!
Intro to MySQL
Basic Queries
Level 2 Concepts

“Watch This First
Jumpstart
Getting Acclimated with EXCEL – Foundations
Basic EXCEL Formulas & Functions – Foundations
Text, Times, and Dates Data Formats – Foundations
Introduction to Statistics in EXCEL – Foundations
Handling Data in EXCEL
Pivot Tables
Introduction to Charts in Excel
Case Study: Crytocurrency Data
Case Study: Survey Data
Case Study: The Startup Market
Case Study: Data Preparation (for Startup Market Data)
Appendix: Bonus Topics
New to Udemy?”

Introduction
Preparing Data for Analysis
Data Analysis – Key Metrics
Comparison Analysis
Trend Analysis
Ranking Analysis
Interactive Dashboard Creation
Variance Analysis
Contribution Analysis
Frequency Analysis
Correlation Analysis
Pareto Analysis
Conclusion

Statistics Introduction and Definitions
Basics of Statistics and Microsoft Excel
Descriptive Statistics
Data Visualization
Basic Probability Concepts
Probability Distributions
Hypothesis Testing
ANOVA – Analysis of Variance
Goodness of Fit and Contingency Table
Correlation and Linear Regression
Conclusion

Introduction
Tables
Methods of Aggregation
Pivot Tables and Card Visualizations
Slicers and Visualization Filters
Custom Calculations
Case Study
Time Series Graphs
Other Visualizations
Publishing and Sharing Interactive Dashboards
Cases Studies
Conclusion

Setup and Jupyter Environment (Python 3)
Data Manipulation with Numpy (Python 3)
Data Manipulation with Pandas (Python 3)
Starting with File Operations (Python 3)
Data Analysis and Methodologies – Learn to perform Operations on datasets (Py 3)
Data Visualization