无论是求职还是寻求晋升,项目经验的准备都是重中之重的一环。
真实的好项目长什么样?面试官怎么考查项目经验的真实性?不同行业、不同类型的项目该怎么有针对性地准备?怎么让你的项目经验发挥出“万能”效果?面试中项目细节该准备到什么程度?有哪些地雷千万不能踩?…… 

Real Projects for Data Analysis: Powers Your Project Experiences and Accelerates Your Career As An Intermediate Analyst 

本课程正在参加:DQ数据分析五周年·五重福利之项目实战营免费体验日活动:

  • 体验活动日:2018年2月11日(项目营首日)
  • 体验活动报名开始时间:2018年1月20日
  • 体验活动报名方式:微信扫描Q堂主二维码,登记名字、微信ID、Email报名。成功获得免费体验机会的学员将收到Email通知。
  • 上课方式:现场、远程、视频录播3种教学方式供选择。免费体验活动仅限现场参与。
  • 限时活动:在微信朋友圈成功分享本课程页面,报名时可获得$ 100学费减免。

 

 ♞ 课程简介:

作为DQ数据分析培训的进阶必修经典课,本课程通过不一样的项目实战方法,帮助对数据分析已有初步认识或经验的初/中级分析师,在短期内通过训练实现质的飞跃,获取真正实用、有效的项目工作经验,并成功扫除求职面试和晋升过程中由项目经验匮乏带来的各类障碍。

通过 10周 的培训,本课程创造了以“两奇”著称的效果:时间奇短、效果奇强2017年为例:

  • 在职学员经过培训后,年薪(税前)提升比率平均为27.6%
  • 中级职位面试的成功比例平均提升了61.9%
开课时间: 2018年2月11日~4月17日(共10周,每周8小时)
上课时间:
周日 1:00pm ~ 5:00pm
周一 6:00pm ~ 8:00pm
周二 6:00pm ~ 8:00pm
咨询电话:     905-361-8881       
其他形式: 提供网络课堂和课程视频
授课老师: DQ
限时优惠
2018.02.02前报名,可获赠两门技术课程:
1.专为数据分析设计的编程基础课
– Python Basics for Analyst(10周)
 
2.2018年新课:
 IT Toolbox for Analyst(5周)

 ♞ 课程适合对象:   

本课程适合于转行,或者是对数据分析有一定了解的刚毕业的同学。具体来讲,适合于以下群体

  • 晋升群体:想增强在加拿大市场金融、保险、零售、咨询等行业的数据分析项目工作经验的初/中级分析师;
  • 求职群体:对数据分析有一定了解和基础,比如有EXCEL / VBA / SQL等工具的使用经验;
  • 对数据分析感兴趣的自学人士:比如在自学EXCEL / VBA /Access,SQL,SAS,Tableau, Python, R等软件,但欠缺实际项目经验的人群。

 ♞ 课程特点:   

1. 以一敌百的“有用”项目:拒绝“高大全”“假大空”

目前市场上各种项目培训班参差不齐,其中不乏许多以次充好、还往往看起来特别有诱惑力的。学员本来就是因为缺乏经验所以需要训练,因而,即便那些所谓Real Project项目事实上脱离工作实际、只有教学价值,报名者们也很难判断。只有学员的真实成长,能够检验项目培训的真实价值。好的项目,尤其是值得花钱来学的好项目,应该具有以下特征:

  • 有真用:不管是面试时聊项目经验,还是升职后第一次接手全新的数据分析项目挑战,都能从项目训练获得的经验上得到启发;
  • 以一敌百:除了项目本身有价值,还要有衍生的无限可能,参与一个项目能学到一大类项目的处理经验;
  • 传授“关键”:不仅训练学员成功完成任务,更要训练解决问题的思路与应变方法,从获取“鱼”真正过渡到“渔”;
  • 让你简历中的每一个技能点都得到项目经验的佐证和升华。
✱2. 项目培训的实质:不是项目,而是“有价值的项目工作经验”!

绝大部分学员,并不知道,其实自己要的“经验”, 并非一般意义之经验, 是除了项目本身的知识,其实还包括了工作内容、工作流程、工作环境、现实工作和沟通中可能遇到的问题、各类工具和模型的选择标准等等全方位的知识。因为,不管你是求职还是晋升,其实都是同一项工程——获取新的工作机会,而它,是一个系统工程,并不是单个要素就能左右的。

这也是为什么DQ项目培训能收效明显的原因,因为DQ项目强化营不同于其他的培训项目,它是从整个系统上对学员进行提升和改造

⇲ 每一个知识点都要MAP到技术流程上
⇲ 每一个知识点都是以面试问题的形式提出来
⇲ 每一个ASSIGNMENT,都是在真实工作环境下完成的
⇲ 每一个ASSIGNMENT,都要探讨用不同工具完成,让工具回归到应用级别
⇲ 每一个项目都是流程完整的项目,从客户背景,到技术选型,效能评测,。。。
⇲ 每一个项目都是文档齐备的项目,并非只是把CODE写完就完事了
⇲ …
 
✱3. “真相帝”与“潜规则”

和DQ其他课程一样,上完这个项目营,你会知道DQ老师“真相帝”的名号非虚。关于行业、关于高薪、关于工作的现实、关于发展路径和预期,请听听DQ老师的大实话,再合理做判断 – 至于行业中的行业规范、“潜规则”、套路等,更是其他培训中少有涉及的

✱4. 浓缩高效

有过经验的人都知道,课程的价值并不取决于持续时间的长短,而是内容。DQ全系列培训都遵从一条原则:用最短的时间、最高效的方式和最有用的内容,让就业直接见到效果。时间短,不仅降低培训费用,减少时间精力的耗损,也能有效对抗大家身上普遍存在的“拖延症”

✱5. 学习效果保障及后续支持
  • 课程视频:DQ数据独家提供全程课程录像,供学员一年内无限次观看、复习
  • 免费repeat:一年内可无限次重回课堂;
  • 咨询和交流:针对在学习、求职、跳槽过程中遇到的任何问题及时获得支持和指导;免费加入学习群、交流群,免费入场各类讲座或分享活动等。

 ♞ 课程引论:DQ谈项目准备   

✱ Manager为啥喜欢问项目?他要知道什么?

技术过硬就能搞定面试? 就能拿到OFFER? 如果是这样,就不用面试了,直接考试算了, 大家谁的分数高,谁有工作!面试和考试最大区别是,Manager会问项目, 会根据你的项目延展出其他问题,在根据你的回答问新的问题。这样做当然是有原因的 ,你不理解是因为你不在他的位置上【 世界上的事情多是屁股决定脑袋】

❶ Analyst 不同于Programmer, 不是IT的职位,Hiring Manager 也不是搞IT的,他们更加看重的是Candidate解决问题的能力,是否有能力协调各方面的需求来完成Manager的要求, 用什么工具是次要的 ,了解Business 比Technology 更加重要。
❷ Manager 不见得了解技术细节, 也不认为技术细节重要,他们只关心你能否整体上把握项目,配合他们的需求,有什么解决不了的问题,知道找谁解决。
❸ 真正的工作中,Analyst 的核心技能是Project Management, Requirement Analysis, SQL, PYTHON, and Reporting Generation其他工具都可能发生变化。在这种情况下Hiring Manager 当然不会在意个别工具的运用,而是要看你是否在项目中能够把你的故事讲清楚。

✱ 我们如何谈项目?谈什么manager才满意?

面试中项目谈什么内容,可以商榷,一个原则是要谈对方听得懂的话,把对方一步步领进你的场景,让对方问出你设计好的问题,了解项目的尺度,要解决的问题,带来的价值,以及你在项目中的角色。最应该避免的是在对方还没有搞清楚状况的情况下,谈论过多的技术,因为你不知道你了解的技术对方是否感兴趣。

❶ 开场要谈项目背景,客户是谁,在哪个行业,SIZE有多大,目前OPERATION如何!
❷ 客户为什么要做这个项目,客户自己做了哪些准备工作,为什么要找我们来完成项目
❸ 客户的需求到底是什么?如何来评估项目实施是否成功?
❹我们提出的解决方案是什么,有哪些准备工作要先做,项目实施计划如何,团队都有哪些人, 用了多少时间,最后的提交产品是什么, 客户是否满意?

这些问题谈清楚之后,才能谈技术,问对方是否关心技术细节。对方可能不了解你用的技术,但对于你谈的Business, 对方可以完全了解, 进而对你的水平有一个全面的评估。

✱ 我们必须准备的典型项目

项目准备真的是个技术活,到底应该准备什么类型项目,细节准备到什么程度,技术选型有什么考量,如何与其他部门协调,数据从哪里来,报表送到哪里去,项目的流程如何管控,哪些部分自动,哪些部分手动….., 不是我怀疑大家能力高低,而是你根本没有这个能力准备,这里面细节太多,下面我给大家提些建议,供参考:

❶ 自己准备项目也好,参加培训也好,项目的文档一定要齐全,没有文档,学生根本没有能力复述项目,讲的内容都是七零八落的。文档至少要包含覆盖如下要点:
– Requirements and Design documents
– Project planning and control documents
– Technical Architecture Design documents
– Project Deployment and Integration documents

❷ 你可能认为理想的项目应该面面俱到,覆盖完整流程;但事实上,只有十分,十分,十分小的项目你才有机会从头做到尾,大部分情况我们只能做其中的一部分。为了节省精力,可以与不同的业务背景结合起来准备若干项目,每个有各自的重点:
– Data Source identification
– Exploratory Data Analysis and Data Visualization / Distribution
– Prepare Model set [Training, Validation and Test ]
– Design report template and Generate Report
– Integrate result into existing decisions support system

❸ 大部分培训出来的学生,除了数据分析,产生报表,对其他的工作要求一无所知。要知道,我们的工作不是孤立的,我们需要和IT交流去拿到数据,分析产生的结果为其他部门提供Support, 这些都需要做System Integration:
– Integration by Files | Flat and Delimited
– Integration by Databases
– Integration with SOA / Web Services

✱ 选择项目行业
在所有的因素中,重中之重是reasonably 选择项目。目前来讲的热点是银行,保险,零售方面的业务。在设计项目的时候,要结合上面的第二点, 同时要确保数据量要足够,至少要有几十万条数据,否则无法考量Performance. 下面是一些例子供大家参考:

❶ Insurance 方向是就业的一个主要方向,业务方面要了解Claim process, Loss evaluation…..有可能的话,项目应该包含EDA, Evaluate different Models, and make a prediction. 技术选型可以用Python, SAS, or other Analytic tools
❷ Finance方向的项目不大好做,小的公司做的比较多的是integration. 建议考虑建立System Integration 的项目,技术选型采用Web Service / SOA 的技术框架,业务方面可以考虑提供比较成型的服务如 Principle Component Analysis, or Portfolio Optimization
❸ Retail / CRM这一方向职位最多的. 有两个比较重要的方面需要注意,一个是对于客户消费行为的预测,可以考虑用RFM Model,另一方面比较大的需求是STORE级别的销售预测。技术方面,由于分析的任务不是特别难,要把精力放在REPORTING上, 比如Template的设计,Report Deployment, 以及Automate the reporting process

 ♞ 课程内容   

Customer Relationship Management | Project 1(12 hours):
It is well known that Retail CRM has become an underlying platform supporting proven business development strategies — such as channel communications, customer experience (CX) management, social media engagement, loyalty programs, precision marketing and mobility

Objective
– Store Level EDA
– Customer Lead Generation
– Pre/Post Campaign analysis
Technical Activities
– Python for ETL
– IO with Python
– Data Migration
– RFM to score customers


Insurance Loss Prediction | Project 2(12 hours):
To improve loss ratios through more effective pricing and marketing, insurance providers need a solution that gathers relevant claims data from every corner of the enterprise as well as third-party data sources to provide both claims propensity predictions and claim size predictions.
Objective
– understand the current state of the insurance landscape
– dealing with rising claim rates and high loss ratios, the need have never been greater – establish and maintain a complete view of insurers’ current and prospective policyholders.
Technical Activities
– EDA Analysis to know, visualize data
– Various Algorithms Performance Evaluation
– Python as the analytic tool
– Prepare data
– Reporting in PDF, and HTML formats

Buildup Recommendation Engine | Project 3(12 hours):
One of the secrets for amazon to protect its winning position is an effective recommendation system, which is the so-called Collaborative filtering system, a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from (collaborating). In this project, we will iron the idea, and implement it with Python
Objective
– Prepare data in JSON format
– Build up recommendation engine for CSV online customers
Technical Activities
– Training pipeline
– Computing Similarity
– Collaborative filtering
– Using RE make recommendations

Digital Marketing with AWS and Spark | Project 4(12 hours):
Different user has its own journey with a company’s digital ads (from search, display, social etc. channels). Based on the user’s journey pattern which has already led them to become company A’s client through online ads, use Machine Learning model to predict other users who haven’t converted to company A’s client will get converted in the future or not.
Objective
– Build up ML model for digital marketing
– Introduction to Google Cloud env.
– Put Spark, ML, and Cloud together
Topics to be covered
– Extract small amount of data from the whole dataset to build ML model
– Google Cloud Platform, the service and the product useD in the course
– Use Google BigQuery to query dataset for building ML model
 
Data Visualization and Prediction with Python (20 hours):
Today’s data visualization tools go beyond the standard charts and graphs used in Excel spreadsheets, displaying data in more sophisticated ways such as infographics, dials and gauges, geographic maps, sparklines, heat maps, and detailed bar, pie and fever charts. The images may include interactive capabilities, enabling users to manipulate them or drill into the data for querying and analysis.
Objective
– Familiar with development env
– Typical graphic presentation with Python
– Prediction and simulation with Python
Topics to be covered
– Setup Linux development Env.
– Data structure with Numpy, Pandas, Dataframe
– Graphic package with Matplotlib
– Simulations
 
Predictive Models with Python (12 hours):
Build forecasting models with Python programming language using training data. Learning forecasting methods and models is indispensable for business or financial analysts in areas such as sales and financial forecasting, inventory optimization, demand and operations planning, and cash flow management. It is also essential in data science, applied statistics, operations research, economics, econometrics and quantitative finance. And it is necessary for any business forecasting related decision.

Objective
– Directed Models
– Undirected Models
Topics to be covered
– Logistic regression
– Confusion matrix
– Support vector machines
– Naïve bayes
– Decision Trees
– Random Forests
– K mean
– Affinity groups


 ♞ 课程时间表   
 
Data Visualization with Python
Week 1                     
[Sun] 1:00-5:00PM [Mon] 6:00-8:00PM
– Course Introduction
– Env. Setup
– Data analysis process
– SQL Review / Test
– Installing Linux
– Setup python dev. env.
[Tue] 6:00-8:00PM
– Linux commands
– Linux scripts
Week 2                     
[Sun] 1:00-5:00PM [Mon] 6:00PM-8:00PM
– Review on Python
– Numpy
– Regular Expression
– Vectorization
– Data visualization
– Matplotlib basics
[Tue] 6:00-8:00PM
– Components for Graphic plot area
– Real time data plot
Week 3                     
[Sun] 1:00-5:00PM
– Dataframe
– Time series
– Structured data
– I/O with files


Project 1- CRM in Retails Industry (12 hours):
Week 3         
[Mon] 6:00-8:00PM [Tue] 6:00-8:00PM
– CVS Project Intro.
– Setup Dev. Env
– SDLC
– Business Requirement
  Analysis
[Sun] 1:00-5:00PM
– Identify data sources
– Data Migration
– Data cleansing & merging
– Build up Customer Signature
Week 4         
[Mon] 6:00-8:00PM [Tue] 6:00-8:00PM
– Campaign
– CRM
– RFM with Excel
– RFM with SQL
Predictive Modeling with Python (12 hours):
Week 5         
[Sun] 1:00-5:00PM [Mon] 6:00-8:00PM
– EDA with Python
– EDA with Excel
– Seaborn
– Vincent
– Logistic regression
– Confusion matrix
[Tue] 6:00-8:00PM
– Support vector  machines
– Naïve bayes
Week 6         
[Sun] 1:00-5:00PM
– Decision Trees
– Random Forests
– K mean
– Affinity groups

Project 2 – Insurance Loss Prediction (12 hours):
Week 6                     
[Mon] 6:00-8:00PM [Tue] 6:00-8:00PM
– Folium for mapping
– Heat map
– EDA for house pricing
– EDA result interpretation
Week 7                     
[Sun] 1:00-5:00PM [Mon] 6:00-8:00PM
– Insurance Lost project
– Insurance claim process
– EDA for insurance lost
– EDA result interpretation
– Data preprocessing
– Data wrangling
[Tue] 6:00-8:00PM
– Build-up prediction model
– Training different models
Project 4 – Digital Marketing with Google Cloud (12 hours):
W8 [Sun] 1:00-5:00PM W9[Sun] 1:00-5:00PM
– Intro digital Marketing
– Setup project
  development Env.
– EDA and Resampling
– Model preliminary
  assessment
– Google Cloud
  Platform
– Google BigQuery
– Google Dataproc
W10[Sun] 1:00-5:00PM
– GCP compute engine, PySpark Setup
– Use Google BigQuery to query data set
– Accessing data in Pyspark
– Use SparkML to run model
Recommendation engine Project 3(12 hours):
Week 8         
[Mon] 6:00-8:00PM
– Concept of recommendation engine
– Preparing data
[Tue] 6:00-8:00PM
– EDA for Customer data
– Build up collaborative filtering RE
Week 9          
[Mon] 6:00-8:00PM
[Tue] 6:00-8:00PM
– Report / Summary
– Dashboard
– Python Html report
– Python Pdf report
– Python Excel report
Week 10       
[Mon] 6:00-8:00PM
– Web application with flask
– Jinja2 for report template
[Tue] 6:00-8:00PM
– Web services
– Deployment and Integration
 
 
Project 4 – Digital Marketing with Google Cloud (12 hours):
W8 [Sun] 1:00-5:00PM
– Intro digital Marketing
– Setup project development Env.
– EDA and Resampling 
– Model preliminary assessment
W9[Sun] 1:00-5:00PM
– Google Cloud Platform
– Google BigQuery
– Google Dataproc
W10[Sun] 1:00-5:00PM
– GCP compute engine, PySpark Setup
– Use Google BigQuery to query dataset
– Accessing data in Pyspark
– Use SparkML to run ML model
 
     
 
Business Analyst已经成为大多商科大学生毕业后的最佳就业方向
 
众所周知,Python目前是打开这一类BA就业大门的必备钥匙

维多利亚隆重推出初级分析师Python项目实践白天班
 
大师级的数据分析就业专家DQ曲达雍老师亲自授课
他将毫无保留分享20年的BA资深行业经验,无阻的北美 
BA就业之路从见到DQ老师开始!

应届毕业生Business Analyst 求职要点
 
很多商科方向的大学生毕业后想找Business Analyst的工作。个人认为找BA工作的想法起码比找CSR工作的想法靠谱的多(实在无法理解辛苦学习4年后去找一份高中必业就可以胜任的工作)。那么成功找到BA工作的CASE有多少?很低!究其原因,有两点
不知道,不了解Business Analyst的工作性质及日常工作职责,盲目的认为商科方向毕业后BA是对口就业方向
缺乏对Business Analyst工作中必要工具的了解及使用经验
 
Business Analysis的范围覆盖面很广,一两篇文章无法阐述清楚,要求从业人员对有Domain knowledge及Technology有比较深入的了解, 同时具备较强的Communication能力。可以说,BA是对综合素质要求最高的工作。在这里我们谈谈适合商科应届毕业生的Junior Business Analyst工作。
 
1. What does Business Analystdo?                                                                  
BA的工作集中于Mid Office, 处理公司的核心业务。由于各个行业业务的多样性导致了工作的多样性。总体来讲有两种Business Analyst - 业务分析员和系统分析员( 这两个其实区别很大 ), 其工作从业务分析,系统验收,提供报表,到需求收集.....零零总总,不一而同。
业务分析员,主要关注于公司某项业务,某项产品,某个业务,各个KPI指标是否达到预期,是否赚钱,和其他同类业务比较的Benchmark. 这工作对于刚毕业的学生有一定挑战 - 需要补充业务知识及掌握一些分析工具来开展工作。
 
系统分析员,OPENING很多,但能申请的人却不多。其工作需要和IT团队打交道,这一点成为许多人的软肋,这也是我们中国背景的大学生比较容易打开的就业大门(很可惜,大部分商科毕业并不知道这一点,不知道可以申请系统分析员)。开门你需要钥匙  - 打开这一类BA就业大门的钥匙是IT知识
 
2. How is the Career Future for being a Business Analyst?
由于现在已经没有那个行业不需要IT的系统支持,对于既了解业务,又有IT背景,并有较强沟通能力的从业人员的需求居高不下。
See details below from IT World Canada
http://www.itworldcanada.com/article/the-rise-of-the-it-business-analyst/46611
"IT business analyst” was also rated one of the country’s top 12 jobs to pursue last year by Money Magazine, which listed median pay for that position at $83,100. Computerworld’s Salary Survey 2012 listed an average total compensation for IT technology/business system analysts at $84,376, up 1.4 per cent from 2011.
 
3. "Wow, IT, I CANNOT do IT!" - What Skills BA need to have?
大部分同学咨询的问题是相类似的,我这里总结一下
问:各方面听起来都不错,一个问题,BA需要编程吗?
答:日常工作不用编程,但要了解IT及编程的基础知识,在这一基础上进行系统方案评估
问:我学了4年商科,看起来白学了,申请Business Analyst工作没有任何优势?
答:BUSINESS Analyst, 重点在商务逻辑,IT 是敲门砖,是药引子,主菜还是BUSINESS
问:我不想再学什么了,已经学了4年了!想尽快找到一份BA工做如何做?
答:很难,找其他工作还有可能,找BA希望很小。BA的沟通有其自己的一套流程,不培训,基本不可能无师自通。至少要看见过别人写的文档,你才有可能写出差不多的吧!
 
4. What should I do to be a BA?
根据我的经验为你提供一个关于Jr. Business Analyst 的Road Map 供你参考(不适用于Sr的职位),在紧紧抓住Business Analyst 相关知识以及简历面试的同时,对于不同类型的BA有如下建议
Business Analyst紧密相关的知识,包括
IIBA Certificate  / UML - 了解BA沟通的流程
Programming Basic  - 了解IT 相关知识
Web Application - 大部分程序是网上的程序,是否应该学,你懂的
UAT Testing - BA的一个主要工作职责是主持验收测试
 
对于业务分析员 - 学习Business Major 工作相关的一些SKILLS和工具( EXCEL, VBA, ACCESS, SQL,  DATABASE, SEO, SEM, CRM, CMS......), 同时REVIEW上学时学的ACCOUNTING / FINANCE的知识,没有准确就业定位的同学请考虑定位Marketing  方向,分析类工作。
对于系统分析员 - 补充Software Testing 工作相关的一些SKILLS和工具( ISTQB Certificate, Web Services, SQL Server, Automation Test........) 同时进一步加强IT方面的知识,做到能够和软件测试人员有效沟通
 
课程独特优势:
 
1)    短平快的课程, 白天集中强化, 着重技术实战
 
2)    培训所对应的数据分析师职位工作稳定,年薪4万以上,职位符合留学生办移民要求。
 
3)    课程时间灵活,随时入学训练,每个季度一期,全年大约四次。
 
4)    课上包含配套projects,真实项目活学活用, 文档正规背景完善, 应对面试游刃有余。
 
Python Basics for Business Analyst  

Objective: As one of the most important programming language in Data Analysis paradigm, Python becomes to a necessary tool for data analyzing, presentation, and integration given its abundance algorithms, libraries, and supported packages.  “Petite de petite, la soeur fait son ne” – let us start from the fundamentals – from Programming basics, Object Oriented Programming to GUI; eventually, we will have 3 labs to implement the concepts covered above.

 Instructor: Dayong Qu, who has 20-year extensive practice in Project Management, Business Intelligence, and Software outsourcing with companies from government agencies, financial institutions and public firms, currently takes a role of senior Director of Resource Manager in the company that provides sourcing solution for major financial institutions.

 Course Outline:

Session 1(5 hours):

·        1.1 - Why use Python and where is the best fit for it?

o   - How does it compare to SAS

o   - How does it compare to R

o   - How does it compare to SQL

·         1.2 Prepare your Python Development Environment

o   - Interpreted vs Compiled languages

o   - Which version should I use?

o   - Prepare multiple development envs.

·         1.3 The lifecycle for Analytical project

·         1.4 Quick Demos with basic Data types

 

Session 2(5 hours):

·         Various Data Type

o   List

§  Multi-Dimensional List

o   Tuple

o   Set

o   Dictionary

o   Class

·         Functions

o   Call By Value

o   Call By Ref

·         Conditionals

·         Loop

o   For loop / While loop

o   Continue and Exit

·         String Functions and Formatting

·         Lambda

 

Session 3(5 hours):

·         Object Oriented Programming

o   Inheritance

§  Function Overload

§  Function Override

o   Abstract

o   Encapsulation

o   Polymorphism

·         Lab 1 – Victoria Student Mgt. System

 

Session 4(5 hours):

·         GUI with  TK

o   How GUI works

§  Event Driven

§  Layout

o   GUI Components

§  Button

§  Label

§  Input box

§  Radio Buttons

o   Demo – Loan Calculator

·         Lab 2 -  Color Picker

o   Passing inline parameters

o   Using different data structures

·         Lab 3 -  Bubble Sort

o   Create a standalone library

o   Configuration with GUI

 

Session 5(5 hours):

·         Python Ecosystem for Data Analysis

·         Numpy Data Structure

o   Quickly review Python List

o   Demo on numpy

o   Structured array

·         Vectorization

o   zeros | ones

o   zeros_like | ones_like

o   transpose

·         Regular Expression

o   Search

o   Match

o   Substitut

 

Session 6(5 hours):

·         Data Visualization

·         Python plot packages

o   Matplotlib package

o   Basic components

§  Multi axis’s plotting

§  Multi plot areaas

·         2D Plotting

o   Line chart

o   Bar chart

o   Boxplot chart

o   Scatter chart

o   Histogram chart

·         Plot real-time data

·         3D Plotting

 

Session 7(5 hours):

·         Handling Time Series Data

·         Introduction to DataFrame

o   Basics of DataFrame

o   Construct time series with DataFrame

o   DataFrame supports directly plot

·         Structure Financial Data with DataFrame

o   Retrieve real time financial data

o   Calculate returns and moving average and Moving Historical Volatility

·         Regression Analysis with Financial Data

·         Data Resampling  for  High frequency data

 

Session 8(4.5 hours):

·         Input / Output for variables Sources

·         Basic I/O

o   Data Serialization

o   With txt files

o   With csv files

·         I/O with Pandas

o   With csv

o   With excel

o   With XML

·         I/O with PyTables

·         Working with Database

 

Python Real Project for Business Analyst

Objective: Only knowing basic business analysis knowledge as well as a programming language may give you the opportunity to step into the area of Business Analysis.  To further strengthen your professional portfolio, you need more knowledge of experience, process, and real practice. In the following sections, we will cover 2 certificates (IIBA and ISTQB) and implementation – the practice drilling down from requirement design, solution design, coding to testing.

 Instructor: Dayong Qu, who has 20-year extensive practice in Project Management, Business Intelligence, and Software outsourcing with companies from government agencies, financial institutions and public firms, currently takes a role of senior Director of Resource Manager in the company that provides sourcing solution for major financial institutions.

Duration: 39.5 Hours

Course Outline:

Session 1(5 hours):

·         Introduction

o   What is Business Analysis?

o   Who is a Business Analyst?

·         Business Analysis Key Concepts

o   The Business Analysis Core Concept

o   Key Terms

o   Requirements Classification Schema

o   Requirements and Designs

·         Business Analysis Planning and Monitoring

o   Plan Business Analysis Approach

o   Plan Stakeholder Engagement

o   Plan Business Analysis Governance

 

Session 2(5 hours):

·         Elicitation and  Collaboration

o   Conduct / Confirm Elicitation

o   Communicate Business Analysis Information

·         Requirement Life Cycle Management

o   Trace Requirements

o   Maintain Requirements

o   Prioritize Requirements

·         Strategy Analysis

o   Analyze Current State

o   Define Future State

o   Assess Risks

o   Define Change Strategy

 

Session 3(5 hours):

·         Requirement Analysis and Design Definition

o   Specify and Modeling

o   Requirements structuring

o   Requirements packaging

·         Solution Evaluation

o   Upfront Criteria /KPI / Metric

o   Testing and Evaluation

·         Use Case

·         UML

o   Static Diagram

o   Dynamic Diagram

·         ER Diagram

 

Session 4(5 hours):

·         Hands-on Practice in Requirement Analysis

o   Interview Business User

o   Operate requirement Workshop to establish requirement basis

·         Document business requirements

·         Review meetings

o   Walkthrough

o   Inspection

o   Checklist

·         Requirement management

o   Version

o   Update

Session 5(5 hours):

·         Implement Use Case of Login

o   UI Specification

§  Html Page

§  Field validation

o   Business logic implementation

o   Database design

·         Unit Test on Use Case Login

 

Session 6(5 hours):

·         Implement a selected Use Case

o   UI Specification

§  Html Page

§  Field validation

o   Business logic implementation

o   Database design

·         Unit Test on a selected Use Case

Session 7(5 hours):

·         Static Testing

o   Review Process

o   Walkthrough vs Inspection

·         Dynamic Testing

o   BVA/EP

o   Decision Table

o   State Transition Testing

o   Use case testing

 

Session 8(4.5 hours):

·         Hands-on Practice in UAT / BAT

o   Development Test Plan

o   Generate Test scenarios

o   Develop Test cases

o   Prepare Test data

o   Execute test cases

 

o   Report test outcomes

 



 

 

 

学员成功就业故事

Crystal我于20136月毕业于多伦多大学,主修数学与经济,副修统计。当初选择这个专业,是觉得就业面比较广,找工作会容易一些。但是毕业后却发现自己处于一个很尴尬的位置,既没有commerce同学accountingfinance的背景,也没有computer science同学programming的知识。大学四年,作为一名数学专业的同学,学的知识太过于理论化,多数时间都用于证明各种定理及题目,而当毕业了开始找工作的时候,却发现自己无法将所学的知识用于实践,为企业服务。因此,由于缺乏相关技能和工作经验,投了很多份简历都石沉大海,杳无音讯。Read More...

 
Helen四的时候,对于找工作,面试我都相当怀疑自己的能力,所以一直打算毕业后找一家移民中介,让他们给找份administration的工作,然后熬一年办移民。可是没想到去年11月份的移民政策突然砍掉了六个职位,当时一下子不知道该怎么办。对于简历我都不知道写什么,统计毕业的我知道,假如不是研究生肯定很难找到关于统计的工作,所以对于找什么样的,往哪里找工作自己都很纠结。那段时间的我像个无头苍蝇,找了家写简历的公司写了份简历,然后在网上乱投,都没有音讯。每天都过的比较痛苦。Read More...
 
Mary我是Mary – 多大金融专业本科毕业的留学生,毕业后好几个月找不到工作,心理很焦虑,有时候真想买张机票回国算了,省得在这里经受煎熬。听相同背景的朋友说他参加了 FA/BA培就业培训班后很快就找到了工作,抱着试试看的态度,报了FA/BA就业培训班,心想就算是找不到工作,学点实用的东西再回国也行啊。Read More...
 

【更多大学生就业故事】请点击:   http://www.vicmiss.com/category/?id=257


Reporting & Analysis with Excel
The course is designed for individual, who processes a basic understanding on how business case is created and operated, and knowledge in Accounting, Financial Planning and Investment Decision. Technically, student will use MS Excel to realize profitability studies, financial planning and various analysis by changing different drivers. The course materials are organized with several hands-on case studies to realize the concepts terms mentioned above. After taking the course, the student is expected to handle creating and maintaining daily business related reports and analysis.
VBA & Access / Databasefor Data Modeling
Data and Database; MS ACCESS,MS VBA; MS VBA with Sales & Marketing; MS VBA Projects
CRM / CMS / SEO for Business Processing
Digital Marketing can be defined as promoting of brands or products and services using all forms of digital advertising. The core is online marketing and all web related technologies; and Digital marketing uses Internet, mobile and any form of digital media to reach customers in a timely, relevant, personal and cost-effective manner.
Resume & Interview Workshop
Job Hunting in a systematical way; How the hiring process goes?; Stratagy on preparation.; Resume preparation; Working with agencies; Promote yourself to the market; Phone interview; Interview Anatomy –Prequel; Interview Anatomy –Non Technical Session; Interview Anatomy –Technical Session;Interview Anatomy –Yose
 

 

 

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