揭秘 | 数据小白如何成为年薪10W的Data人才？

今日重磅活动一览

今日推荐：

澳洲Data实战班

适合人群：

重磅福利：

21世纪最好找工作的职业领域

就业率基本高达99%！

上升趋势快，未来空间大，积累性高

最容易给留学生Offer的职位之一

数据分析师因为稀缺，所以高薪

数据分析 = 未来必备技能 = 趁早抓住

对智慧型的中国留学生来说

求职Data相关工作才是更有优势和竞争力

你一定对它充满的疑问

这个网红爆款行业到底适不适合我？

哪些行业有Data岗位？

我如何成为一个年薪50W+ 的数据分析师？

金融投行对Data岗位需求与日俱增

截止目前，Data Scientist岗位的需求量在Glassdoor上就已达到了23K+。

图片来源：Glassdoor

Data Science行业领域的从业者已被誉为“今后十年IT行业最重要的人才”了。

根据Glassdoor的最新权威数据，数据科学家相关类岗位的平均年薪高达近13W刀！入门级Data Analyst岗位也有将近7W刀的薪资。

图片来源：Glassdoor

从事Data Science有哪些必备技能？

数据岗位不限制专业，但是确实更偏好于统计、精算、CS、数学等专业的学生。不过，数据岗位欢迎任何具备数据清理/数据挖掘/数据分析的同学，起步阶段不必会很多工具，用好其中一种就行：Python/SQL/R。

Excel为数据分析基本技能

数据分析能力不仅在BA, DA, DS的工作中用到，在四大, 咨询, 金融, 互联网等行业也是必备的，而Excel则是数据分析的基本技能。

真实Data职位JD

澳骄妹在以下列举了几个招聘Data相关职位的Job Requirement。

Deloitte - Data Scientist

Requirement：

Bachelor's degree in Computer Science, Engineering, Mathematics or other business-related field
Strong problem solving and troubleshooting skills with experience exercising mature judgment
Demonstrated ability to write clearly, succinctly, and in a manner that appeals to a wide audience
Proficiency in word processing, spreadsheet, and presentation creation tools, as well as Internet research tools
TS/SCI is required

Preferred:

Goldman Sachs - Data Team, Analyst

Basic Qualification：

Excellent technical skills (MS Access, Excel) required, basic understanding of SQL and computer language(s) a plus
Excellent time management and prioritization skills; detail-oriented
Knowledge of SQL, relational databases, program logic, file layouts preferred
Quick learner who excels with hands-on responsibilities
Strong communication skills
Exhibits sound judgment and integrity

Preferred Skills:

McKinsey - Data Analyst - Banking Analytics

Qualifications：

Hand-on experience in an analytics role in banking is strongly preferred;
Strong academic qualifications, including advanced understanding/coursework in Math, Statistics, Machine Learning and Data Management
Proficiency inPython, SQL, Rand/orSAS; additional knowledge ofother data mining software(Spark, Scala, etc.) is a plus
Advanced knowledge of machine learning /statistical techniques, data mining, (gradient boosting, random forest, deep learning/AI, regression, decision trees, text mining, etc.)
Exposure to credit bureau, online, mobile, and social data is helpful
Skills to communicate complex ideas effectively