Business Analytics

10-weeks training to transform you into a business analyst role with skill set on industry domain knowledge, basic statistics knowledge, programming skills, reporting skills and Critical thinking thought.

Syllabus

START:
November 10, 2018
DURATION:
10 Weeks
PRICE:
$3200
PRICE
$3,200.00

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

Machine Learning 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, Python 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. 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.

 


Course Information(详细课程内容)

在商业分析师第13期课程中,七位来自不同行业的有10年工作经验的导师,将带着学员完成以下内容:

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)、消费(时尚、旅游)、体育、游戏、医疗保险、电商等
  • 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

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

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
  • 全面带领学生巩固r基础,包括各种常用functions与packages与应用

TA Session

  • 利用我们涵盖的所有语言辅助同学并解答作业问题,case与课程包含的projects

导师背景介绍:

 

NoSQL 大数据实战)

  1. 国际数据工程与数据科学协会 主席(ideassn.org)
  2. Symantec 高级全栈数据科学家
  3. USC Marshall 商学院 客座教授

 

 

(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

 

 

(电商数据分析课)

  1. 现在硅谷 Tresl Data Science公司创始人兼CEO
  2. 斯坦福大学统计硕士
  3. 前LinkedIn Data Scientist/ Analytics Manager
  4. 全面讲解电商领域如何做一套完整的数据分析

 

 

(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

 

 

(医疗保险行业 Readmission 建模与数据分析实战课)

  1. 南加大商学院Business Administration博士
  2. 曾就职于汇丰银行,担任项目经理
  3. 具有多年建模、统计分析研究经验

     

 

 

 

(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. 曾任杜兰大学Research and Teaching Assistant和南加州大学Reasearch Programmer
  3. 美国杜兰大学生物统计Master,擅长SQL、R、统计模型等深入浅出讲解

 

七位导师也将根据自己丰富的业界经验,总结最新的业界就业趋势和面试要点,结合业界最新要求,为学员提供个性化、进阶式的求职辅导。

 

课程时长: 10周

Open Date:2018/11/10

Language:中英文

Effort:每周7小时课程 + 1小时ta答疑

Average Spend time for students: 3-5小时课后练习

 

每周上课安排

  • 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。偏向商业知识点,通过刷案例了解各个行业基础知识与常见问题,熟练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,偏向软件学习,老师带学生们练习sql语句,建模与tableau等。

 

  • Technical 技术课(3hours/week):解析SQL高频题型和R必考题型,做好面试题,老师手把手更加针对性的细化作为ba应该掌握的技能比如r语言中的分析手段与公式,解析sql高频面试题与r必考题型,教会学生如何正确应用tableau等分析软件。

 

  • TA答疑(1hours/week) :实战讲解,提供更贴近工业界case与project,讲解作业疏通思路。

 

For detailed course information, please see the full Syllabus.

So, are you ready to become a Business Analyst?

Join us at this 10-week training to transform you into an AWESOME business analyst!

Apply directly at the lefthand sidebar.

 

课程更新概况

 

BA 1710 : Version 8

  • 新增课堂笔记整理
  • 新增电商数据分析内容
  • 加入Business Report 部分两个案例分析(洛杉矶犯罪报告,费城房价报告)
  • 新增sql 面试真题

 

BA 1802 : Version 9

  • 新增游戏数据分析项目
  • 新增游戏数据分析课程
  • 新增healthcare数据分析内容

 

BA 1804 : Version 10

  • 加入excel面试真题
  • 更新lead generateration中的小项目为用户变动预测
  • 更新project I 项目报告(Tableau)
  • 更新logistic regression的小项目为titanic Machine Learning

 

BA 1806 : Version 11

  • 加入NoSQL内容
  • 更新数据整合以及可视化包‘kableextra’
  • 更新project II 项目报告 (Tableau)

 

BA 1809 : Version 12

  • 新增Basic Statistic knowledge for Machine Learning
  • 更新供应链管理分析内容

 

BA 1811 : Version 13

  • Coming Soon…

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.

 

 Capstone Projects

 

I. Logistics and demand forecasting Strategy Project

A supply chain analyst is focusing on improving the performance of an operation by figuring out what is needed for a certain project and coordinating with other employees – such as engineers, business development and quality assurance professionals – to implement and test their new supply chain methods. Some sample projects a supply chain analyst could be demand forecasting related to warehouse capacity management or tasked with are improving the management structure of a warehouse stocking program,  and helping expand on a company’s relationships with suppliers and carriers (New Business). Supply chain analysts must also be able to translate business problems into a solution for information technology (IT) technicians and working with SQL analysis tools to find causes to problems.

 

 

II.  User Portrait 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, homestay 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 function, summary function, table function
  • using R with visualization – ggplot package, dplyr package
  • using R with EDA – prop.table function, dplyr package, ifelse function, str_split_fixed funciton, as.data.frame function
  • Build up random forest model – random forest function, importance function, varimpplot function, predict function

 

 

III. Mobile Game Revenue optimization and Prediction Project

With the developing of smartphone, it could help us to handle more and more tasks in our daily life, and it became one of most effective way to chill and gain a lot fun. Mobile Game is one of the main function in the smartphone has that could provide users pleasant experience. Therefore, mobile game industry is one of the most profitable all over the world, by that, analysis on mobile game data become a significant part in the industry, a lot of data analyst positions are needed recently. In this project, we will analyze a simulation game data. The majority of our users never generate any revenue (i.e., ‘convert’, in marketing parlance). We want to offer a sale to some of these ‘non-payers’, in hopes of generating incremental revenue. In order to design a sale that maximizes incremental revenue, manipulating and cleaning up data in R firstly are necessary, then we have to visualize data in Tableau to get specific ‘non-payer’s pattern, moreover, conducting a test and evaluation protocol could help us to verify if our sale plan work for those ‘non-payers’, at last, build a model in R o help us identify users who are likely to convert on their own.

Project Proposal

  1. Business Requirements:
  • Exploratory analysis through data pre-processing which include data manipulation and data cleaning using R for your later analysis
  • Conduct data visualization to identify a target group of users(‘Non-payers’) and specify one or more offers for target users. Please include details about how the offer should be presented, and on how many occasions.
  • Specify a test and evaluation protocol. How long should the test run? How will you know if you have successfully increased revenue?
  • Build a model in R to help us identify users who are likely to convert on their own.
  1. Data Description:

We’ve provided sample data for a group of users who installed during the first quarter of 2016. The data contains 4 tables with over 500,000 observations:

    • User data, including user ID and install date
    • Session history, including date and session number
    • Purchase history including date and amount
    • Spending history including data, currency, and amount

     

  1. What kind of tools we use:
  • R, Tableau
  1. What you can learn from this project:
  • using R with exploration – str function, summary function, table function
  • using R with visualization – ggplot package, dplyr package
  • using R with EDA – prop.table function, dplyr package, ifelse function, str_split_fixed funciton, as.data.frame function
  • Build up random forest model – random forest function, importance function, varimpplot function, predict function

 

IV. Analytics and Featured Prediction Project (Optional)

 After you had finished with the ‘Analytics and Marketing Strategy’ project, you would be familiar with B2C business. In addition, in this project, we are going to deal with a C2B2B model company. This is a supermarket ordering and delivery app, aims to make it easy to fill your refrigerator with your personal favorites and home-stay convenient experience. For example, you child is going to be nursed, you call this platform to help you buy a bucket of milk in a supermarket and deliver to your home in short period of time. In fact, there are many domestic C2B2B model companies. In this case, we are going to use this anonymized data on customer orders over time to predict which previously purchased products will be in a user’s next order. The process of this project should contain exploratory analysis and statistic model to make predictions and also consider if there are any addition information you need to make decisions. Through this project, you can choose whatever tools you want to use, either using SQL to manipulate data, data cleaning using R or visualization using Tableau.

 

Project Proposal

  1. Business Requirements:
  • Exploratory analysis through information about 3.4 million orders distributed across 6 csv files, you can join each table to see conduct more information.
  • Contain metric(s)/KPI to support business needs such as ‘each users’ frequency of same items’, ‘most often reordered’, ‘correlation between probability of reorder and number of orders or last time order’
  • Provide recommendation to the marketing team in order to increase company’s profitability
  • Fit a model to predict customer’s destination for the first time, choice is on yours for which model to use
  • 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 based on customers’ orders over time. The goal of the project is to predict which products will be in a user’s next order, the dataset is anonymized and contains a sample of over 3 million supermarket orders from more than 200,000 active or precious users. Each user has the number between 4 and 100 of their orders. There are also additional data of the week and hour of day the order was placed, and a relative measure of time between orders.

 

  1. What kind of tools we use:
  • R, SQL, Tableau, Excel

 

  1. What you can learn from this project:
  • Using R with exploration – str function, summary function, table function, dim function, etc.
  • Using R with visualization – ggplot package, dplyr package, etc.
  • Using R with EDA – prop.table function, dplyr package, ifelse function, str_split_fixed function, as.data.frame function ,etc.
  • Using R with modeling – random forest function, importance function, varimpplot function, predict function, etc.
  • Using SQL with data manipulation – Join, Subqueries, Date & Time Function, etc.
  • Using Excel with data wrangling – PivotTable, forecasting, trend line, etc.

 

Exploratory Analysis and General Machine Learning Homework practices:

 

V. 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 together and export final table
  1. Data Description:
  • This project is based 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 contain 104 observations and 16 variables.

 

  • What kind of tools we use:
  • R
  1. What you can learn from this project:
  • using R with exploration – str function, summary function, table function
  • using R with visualization – cor function, grep function, plot function, for loop function, while function
  • Build up linear regression – lm function

 

 

VI. E-Commerce

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()

 

VII. 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 and AUROC
  • Predict loan status on test data and save the output as csv file
  1. Data Description:
  • This project is based 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 function
  • Using R with visualization – ggplot2 package
  • Using R with EDA– dplyr package
  • Using R with model – glm function, anova function, coef function, exp(coef()) function
  • Using R with model evaluation – package ROCR, performance function, slot function
  • Using R with decision tree model – rpart function, plotcp function, prune.rpart function

 

VIII. Hospital Readmission Prediction

Project Proposal

  1. Business Requirements:
  • Conduct data exploration and divide features into several categories for further analysis
  • Conduct data cleaning to create binary response to see whether a patient has the readmission or not
  • Conduct data pre-processing including: drop useless variables and unnecessary patient id, convert data types in data frame to right format
  • Use xgboost to make prediction on hospital readmission rate and evaluate model with misclassification rate and auc
  • Predict readmission rate on test data and save the output as csv file
  1. Data Description:
  • This project is based on healthcare analytics, it requires you to understand domain knowledge in big data in health care and model building procedure which includes data collection, data preparation, profiling, modeling and implementation, and it asks you to determine whether a patient will readmit in the next 30 days. In doing so we are building a xgboost because our target outcome is binary. This dataset is from ‘The Nationwide Readmissions Database’ by federal-State-Industry partnership.
  1. What kind of tools we use:
  • R
  1. What you can learn from this project:
  • Using R with exploration – colsums function, apply functions
  • Using R with visualization – ggplot2 package
  • Using R with EDA– dplyr package
  • Using R with model – package FeatureHshing, package xgboost, xgboost function, hashed.model.matrix function, hash.mapping function
  • Using R with model evaluation – package glmnet, package ROCR, predict function, performance function

 

 

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This 10 weeks 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 hands-on project and best industry practices with reporting tools such as Excel and Tableau, Statistics language like R and relational database skills with MySQL and NoSQL querying skills such as MongoDB.