Business Analyst

9-week training to transform you into a business analyst with skill set on Advanced Excel, R, MySQL, Tableau, Google Analytics, and PowerBI skills.


February 3, 2018
9 Weeks

1.      Job Market of Business Analyst


Information Technology tremendous changes the world we live. How to collect data is no longer the problem for people doing business, but the issue is that how can a company “create value from data”.



American employers will need 876,000 business analysis related professionals by 2020.

Source: U.S. Bureau of Labor Statistics, Employment Projections Program



More and more companies are willing to hire Business Analysts to figure out strategies or recommendations for development and growth, and there are great demands in different industries: High Tech, Health Care, Finance, Retails…etc. In addition, the tools Business Analysts use are pretty various, including Excel, SAS, R, Tableau, Python and more. Business Analysts are also the bridges between IT and clients since they need to understand customer’s demands and boost potential growth for company. That is to say, Business Analyst is the person “who understand IT and business”, familiar with industry trends and analyze data to provide strategies—create value from numbers.

Available positions:

Business Analyst

Data Analyst

Management consultant

Marketing Analyst/Digital Marketing Analyst/Statistical Analyst in marketing department

Business Systems Analyst/ Systems Analyst/Process analyst

Product Manager/ Enterprise analyst/ Business Architect/ Business Intelligence Analyst等


2.      How can we help you? 

Data Application Lab will help students learn the frameworks, theories and practices that have given rise to business analysis. Our instructors will go through essential business knowledge including Marketing strategies, Segmentation and Targeting, Business Development and Sales, Customer Experience and Pricing, Fraud Detection,  A/B Testing, Key Metrics, Funnel Analysis and others. 
The courses combine theories and practices that allow students to figure out how tools can support Business Analyst finish comprehensive tasks. Instructors will introduce the most popular analysis tools such as Excel, MySQL,R and Tableau depends on course design and real situations. All of our course schedules help students get the idea, sharpen computer skills and strength critical thinking. The most important thing is that students will have confidence to communicate with experienced workers using business and technical language, and build the ability to solve problems in real business. So, are you ready to become a Business Analyst?

What you will learn and master from the program through our project cases?

  1. As one of the core business of Internet industry, business analytical skills set is highly desirable in e-commerce companies like Amazon, Groupon, Alibaba, Revolve and others.
  • How to collect and process large number of customer data, what is the key metrics for analysis?
  • What is funnel analysis and how to improve the conversion rate? How to improve customer acquisition, retention, loyalty and satisfaction?
  • How to collect user behavior data through A/B testing to drive company’s decision making in product iteration and development?
  • How to compare the effect of marketing multichannel marketing campaign and ROI calculation?

2.  For most companies like Facebook, Linked, Amazon, Google and others, solid SQL skills are essential in order to pass the first round of business analyst interview as well as tackling data challenge.

  • What is RDBMS (relational database management system) and data schema?
  • How to handle data cleaning, data quality control, and exploratory data analysis?
  • How to improve the efficiency of your SQL query?

3.  For students with finance background or would like to enter finance industry, business analyst could play a significant role in financial areas include fraud detection, loan granting, credit card transactions, customer acquisition.

  •   How to identify high risk users and fraudulent activities based on users economic situation, geographical distribution, user habits, amount of consumption, social media data and other information?
  • How to utilize the data to establish and optimize the classification/clustering model?

4.  Sales prediction

  • how to analyze the sales data for each quarter, different categories of goos?
  • how to calculate sales growth or decline proportions?
  • how to predict the next quarters’ sales performance?
  • how to improve and optimize the customer segmentation?

5. Sentiment analysis on Twitter

  • how to analysis customer feedback and preference based on sentiment analysis using twitter’ API?

6. Data visualization

  • How to build eye-catching and easy-to-communicate dashboard to present your findings and daily report?


3. Who should take this course?

Business Analyst Training is the course designed for people interested in career path as Business Analyst, Marketing Analyst, Data Analyst, Operation Analyst, Project Manager, Business Strategy Analyst and Business Consultant. Students and young professionals in statistics, applied match, social science background, economics, finance, communication management, MBA in marketing track, industrial engineering, engineering management background who are interested in Business Analytics or seek the chance to switch to this position are welcome to join.


4.  Program Design-“  frameworks, theories and practices ”

You will start from scratch to gain hands-on experience in R programming, MySQL, database management, data visualization with R and Tableau, and other statistical modeling cases. Ideally students understand basic business concepts or have business academic background will help you get with this training better. By partnering with emerging startup companies, we offer business consulting project experience and internship opportunity for you to work though the whole business analysis process under guidance for decision-making that drives positive impact.

Sample project # 1

Hmazing Marketing Analytics Project


Hmazing is an emerging company featuring monthly painting rentals. The word “Hmazing” is a blending word by handmade + amazing. Every artwork is unique piece 100% hand-painted using environmental friendly oil paints. It normally takes around 2 weeks for an artist to complete one painting. A professional craftsman will mount the canvas on stretcher bars and the then mount the artwork on a frame carefully selected by us. I.e., the artworks are created the same way the original paintings were created.

Technavio’s analysts forecast the wall décor market in the US to grow at a CAGR of 8.41% during the period 2016-2020.

Challenges: Start-up companies typically find it hard to increase their popularity in the beginning stage.

In this project, you will apply analytical thinking approach and data analytical tools such as Google Analytics, SQL, Tableau, R to explore customer insights, marketing campaign performance, and make significant impacts in a real business setting. In addition, you’ll also be able to get hands-on experience on marketing analytics approaches, how start-up companies use various marketing tools to expand and growth.



After finishing the program, you will be familiar with Business Analytics Project Lifecycle and Cross industry Standard Process for Problem Solving through real world case studies.



18小时Domain Knowledge Lecture

  • 横向实战多领域业界前沿的商业分析项目:互联网(电商和产品)、金融(信贷和风控)、消费(时尚、旅游)、体育等
  • 熟练掌握数据收集、数据清洗、数据分析、建模、数据可视化、汇报等商业分析全周期
  • 掌握常见商业分析案例:客户获取和留存、Segmentation analysis、A/B testing产品迭代、定价策略、时间序列分析-销售预测、网络爬虫-获取数据、信贷评估和欺诈监测

18小时Analytical Modeling Lab

  • 熟练掌握Relational Database, SQL, R(dplyr, ggplot2, tidyr), Tableau等必备核心技术
  • 掌握Statistical Modeling和Machine Learning basics等建模分析方法

27小时Technical Class

  • 全面复习Statistics, Machine Learning Modeling, Product Metrics技术知识点
  • Technical Interview中SQL、R、统计概率高频真题训练



  1. 现任洛杉矶某电商公司Lead Data Scientist
  2. 5年+数据分析经验,曾任KPMG的Senior Data Scientist Consultant,在多个行业数据咨询有丰富经验,对电商有深厚了解;曾任Revolve( 美国最火fashion brand的)的Lead Data Scientist,负责整个团队的数据模型
  3. 华大PhD



(数据库+SQL应用课+A/B testing实战课)

  1. 现任Capital One的Business Analytics Manager
  2. 曾在洛杉矶多家科技和Top Digital Marketing公司任职Senior Data Analyst, Manager of Data Analysis
  3. 擅长数据库、SQL应用,市场营销数据分析;
  4. UIUC-Master of Technology Management



(统计模型+R 应用实战课)

  1. 现在硅谷某数据上市公司任Data Scientist,曾在硅谷多家科技公司任Data Scientist
  2. UC Irvine统计硕士
  3. 擅长深入浅出讲清楚统计模型和R的应用




(Business Process 理论+Excel实战课)

  1. 现任AT&T 大数据组的资深商业数据分析经理,Senior Business Analytics Manager,曾任职于罗氏诊断Roche的数据分析顾问;
  2. 10年商业数据分析和多年招聘经验,曾为AT&T、South West等多家公司提供企业员工培训
  3. Purdue大学化学PhD,MIT EMBA



美国老师,全英语教学 (Tableau实战课)

  1. 现任Datastorm公司 Managing Director和洛杉矶市政府Data Analytical Manager,
  2. 曾任UK 亚马逊公司 Program Manager, 洛杉矶明星E-learning公司Senior Data Scientist,Business Intelligence Manager等职位
  3. 10年+工作经验,丰富的项目管理和数据分析经验,擅长Tableau,SQL、Python等
  4. 英国University of Edinburh MBA



1.现任Amazon的Senior Business Intelligent Analyst,曾在PwC任Business Analyst

2. 5年+数据分析工作经验




(金融行业Fraud Detection 建模和数据分析实战课)

  1. 现任Discover的Data Scientist,负责金融风控与大数据建模工作
  2. 在如何利用统计回归和机器学习技术对消费信贷产品进行模型建立和数据分析方面具有丰富经验
  3. Purdue大学PhD



(Technical 求职课,解析SQL高频题型和R必考题型,case讲解)

  1. 现任Data Application Lab全职Business Analyst老师
  2. 曾任Age of Learning(美国明星教育公司)senior business analyst,曾在阿里巴巴、Top Marketing Agency任职business analyst
  3. 3年+工作经验, USC-统计Master,擅长SQL、R、统计模型等深入浅出讲解



课程时长: 9周

Open Date:2018/02/03




  • Domain Knowledge商业课(2 hours/week):通过刷案例,熟悉掌握常见商业问题的解决思路
  • Analytical & Modeling实战课(2 hours/week):老师手把手教学员实战项目
  • Technical 求职课(3 hours/week):解析SQL高频题型和R必考题型,做好面试题
  • TA答疑(1.5 hours/week) :实战演练,讲解作业解答思路


For detailed course information, please download the Syllabus.

So, are you ready to become a Business Analyst?

Join us at this 9-week training to transform you into an AWESOME business analyst!

Apply directly at the lefthand sidebar.


Build Your Portfolio with Practical Projects

The practical experiences are the most important and convincing parts in Business Analytics resume. Real data experiences will distinguish you from others who only take courses online, and make you outstanding and attractive for hiring managers.

So in order to provide our students the hands-on experiences, besides the courses we also provide with practical projects. Our students will create the solutions throughout the process including identifying market needs through data exploration, data pre-processing, data visualization, model selection, and presentation. In this way, students can better master different ways of the data cleaning methods and apply domain knowledge into projects, which would be beneficial for the preparation of job interview.

Our practical projects are supported by start-up companies, who will provide the real data operation.


 Practical Project


Analytics and Marketing Strategy Project


Typically, Online Rental Service Company is an online marketplace services, enabling people to lease or rent short-term lodging including vacation rentals, apartment rentals, homestays or hotel rooms. In our Marketing strategy project, we will use the business analytics knowledge we’ve learned in the courses to allocate $500,000 marketing campaign budget on international market and domestic market (USA) and to determine the optimal provider to invest in, we also needs to helping Online Rental Service Company to identify the correlation between next country destination and user segmentation factors, such as age, gender, nationality, and others. We will determine which is the significant features in different groups and give marketing campaign strategy recommendations. At last, we will use Tableau to create dashboards to find business insights and consider if there are any additional information we need to make those insights more comprehensive.


Project Proposal

  1. Business Requirements:
  • New users on Online Rental Service Company can book a place to stay in 34,000+ cities across 190+ countries
  • Contain metric(s)/KPI (Key Performance Indicators) to measure Online Rental Service Company’s business with these metrics
  • Provide recommendation to the marketing team of Online Rental Service Company (in terms of targeting, segmentation, resource allocation, etc.)
  • Fit a random forest model to predict customer’s destination for the first time
  • Provide your own opinion if you have access to any data you need, what are some other business questions you might be interested?
  1. Data Description:
  • This project is more base on the field of marketing business analytics dataset, it asks to distribute $500,000 marketing campaign budget on international market and domestic market, provide you recommendations base on your insights. The dataset contains about 213451 observations and 16 variables with target variable ‘country destinations’.
  1. What kind of tools we use:
  • R (Rstudio), SQL, Tableau
  1. What you can learn from this project:
  • using R with exploration – str(), names(), summary(), table(, dim()
  • using R with visualization – ggplot(), plot(), dplyr()
  • using R with EDA (Exploratory Data Analysis) – prop.table(), dplyr(), ifelse(), str_split_fixed(),
  • Build up random forest model – randomforest(), importance(), varimpplot(),predict()


Mini Project

Marketing Mix Model

Project Proposal

  1. Business Requirements:
  • Check the correlation between impressions and spends
  • Run a linear regression model and Summary model, compute contribution and recreate contribution result table with for loop
  • Compute return on investment and combine contribution and ROI (Return on Investment) together and export final table
  1. Data Description:
  • This project is more base on marketing business analytics which requires domain knowledge on marketing mix modeling which includes product, pricing, promotion and place which are some target segment we need to focus on in order to estimate the impact of various marketing tactics on sales, it asks to calculated contributions and ROI for each marketing channel using coefficients run by a linear regression. The datasets contains 104 observations and 16 variables.
  1. What kind of tools we use:
  • R
  1. What you can learn from this project:
  • using R with exploration – str(), names(), summary(), table(
  • using R with visualization – cor(), grep(), plot(), for(), while()
  • Build up linear regression – lm()


 Sales Forecasting

Project Proposal

  1. Business Requirements:
  • Conduct data exploration and visualization, use different variables to compare with item outlet sales and see which attributes has more significant impact on target
  • Conduct data pre-processing by combine train and test datasets and impute missing values and deal with mis-matched level issue
  • Run a linear regression model, check regression plot and deal with heteroskedasticity
  • Predict sales for test dataset and save result as csv file
  1. Data Description:
  • This project is more base on retailer store business analytics which needs you to understand domain knowledge of retailer and marketing strategy and then forecast the impact of future sets of tactics, and it asks to make a predict sales base on running a linear regression. And check the performance of the model and deal with heteroskedasticity. The train datasets contains 8523 observations and 12 variables, with target variable ‘Item Outlet Sales’. The test dataset contains 5681 observations and 11 variables.
  1. What kind of tools we use:
  • R
  1. What you can learn from this project:
  • using R with visualization – ggplot2()
  • using R with EDA (Exploratory Data Analysis) – mean(), median(), ifelse(), plyr(), revalue(),
  • using R with model – lm(log()), rmse(), predict()



Project Proposal

  1. Business Requirements:
  • Conduct data exploration and visualization to find insights as much as possible
  • Detect correlations between econv_rate with other attributes
  • Fit a linear regression to find out which attributes has a significant impact to econv_rate
  1. Data Description:
  • This project is base on E-Commerce industry, which a business model enabling a form or individual to conduct business over an electronic network, typically the internet. In data analytics full cycle for e-commerce, you have to look out for potential issues, problems and potential for optimization to boost business performance. The dataset contains 3809 observations and 16 variables, with the target variable ‘econv_rate’. In doing so we are building a linear regression and check the correlation between target variable with other attributes.
  1. What kind of tools we use:
  • R
  1. What you can learn from this project:
  • Using R with visualization: ggplot2(), dplyr()
  • Using R check correlation: cor(), corrplot()
  • Using R with model: lm(), summary()


Loan prediction

Project Proposal

  1. Business Requirements:
  • Conduct data exploration and visualization, check the correlations between target variable (Loan Status) and different attributes
  • Conduct data pre-processing dealing with missing values for different features like gender, married, dependents, loan amount and credit history using dplyr package
  • Run a logistic regression & decision tree on train dataset and evaluate the model by check misclassification rate, ROC (receiver operating characteristic curve) and AUROC (area under receiver operating characteristic curve)
  • Predict loan status on test data and save the output as csv file
  1. Data Description:
  • This project is base on financial business analytics, it requires you to understand domain knowledge on customer lending and model building procedure which includes data collection, data preparation, profiling, modeling and implementation, and it asks you to determine whether a loan will default. In doing so we are building a logistic regression because our target outcome is binary. The train dataset contains 614 observations and 14 variables, with target variable ‘Loan Status’. The test dataset contains 367 observations and 13 variables.
  1. What kind of tools we use:
  • R
  1. What you can learn from this project:
  • Using R with exploration – colsums(
  • Using R with visualization – ggplot2()
  • Using R with EDA (Exploratory Data Analysis)– dplyr()
  • Using R with model – glm(), anova(), coef(), exp(coef())
  • Using R with model evaluation – package ROCR, performance(), slot()
  • Using R with decision tree model – rpart(), plotcp(), prune.rpart()



  1. Business Analyst在互联网FLAG级别公司的岗位需求分析
  2. Business Analyst在消费金融里的应用
  3. Business Analyst在中美两国的就业机会和求职建议
  4. 不是科班出身,如何奠定优秀商业分析师的基础
  5. 62页诚意满满,DAL与Amazon、Facebook等BA老师携手完成:BA求职白皮书-“The Ultimate Guide-Getting the first job as Business Analyst ” (报名即可获得)

This 9 week program will provide essential business domain knowledge, sufficient skill set training, hands-on practice in lab session, and case studies on Customer Segmentation, Marketing Targeting, Business Development and Product Promotion and Pricing Strategy. Candidate who complete this program could perform comprehensive business analytical tasks including business requirement elicitation, data manipulation, statistic modeling implementation and business insight interpretation based on Advanced Excel, R, MySQL, Tableau, Google Analytics, PowerBI skills.