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北美大公司高级专家授课 Python for Data Science

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发表于 2020-5-14 18:04:24 | 显示全部楼层 |阅读模式
本帖最后由 usalaotu 于 2020-5-16 16:57 编辑

只要你有颗求变之心并愿意行动,到这里来, 必使你得改变!
USALaotu 华人最大最久数据分析职业培训网校,十多年来北美业界专家授课, 培养学生能做会说,不用再修学位,不考证,很快高薪转行进大公司。

无论经济是好是坏,数据量一直都在增长,数据人才各个公司都缺。

Dr. Henry teach mew online live class:

Python for Data Science 第4期, 18:00 - 20:30 (USA PST), Mon, Thur  6/01 - 7/09/20  30 hour

课程内容介绍:https://vimeo.com/419424537

Click here register this online live class: http://www.usalaotu.org/student/register.php

1. What Is Data Science
Data Analysis Sequence
Data Acquisition Pipeline
Report Structure

2. Core Python for Data Science excerpt
Understanding Basic String Functions
Choosing the Right Data Structure
Comprehending Lists through List Comprehension
Counting with Counters
Working with Files
Reaching the Web
Pattern Matching with Regular Expressions
Globbing File Names and Other Strings
Pickling and Unpickling Data

3. Working with Text Data
Processing HTML Files
Handling CSV Files
Reading JSON Files
Processing Texts in Natural Languages

4. Working with Databases
Setting Up a MySQL Database
Using a MySQL Database: Command Line
Using a MySQL Database: PyMySQL
Taming Document Stores: MongoDB

5. Working with Tabular Numeric Data excerpt
Creating Arrays
Transposing and Reshaping
Indexing and Slicing
Broadcasting
Demystifying Universal Functions
Understanding Conditional Functions
Aggregating and Ordering Arrays
Treating Arrays as Sets
Saving and Reading Arrays
Generating a Synthetic Sine Wave

6. Working with Data Series and Frames
Getting Used to Pandas Data Structures
Reshaping Data
Handling Missing Data
Combining Data
Ordering and Describing Data
Transforming Data
Taming Pandas File I/O

7. Working with Network Data
Dissecting Graphs
Network Analysis Sequence
Harnessing Networkx

8. Plotting excerpt
Basic Plotting with PyPlot
Getting to Know Other Plot Types
Mastering Embellishments
Plotting with Pandas

9. Probability and Statistics
Reviewing Probability Distributions
Recollecting Statistical Measures
Doing Stats the Python Way

10. Machine Learning
Designing a Predictive Experiment
Fitting a Linear Regression
Grouping Data with k-Means Clustering
Surviving In Random Decision Forests
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