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Topic:

The Market Sector Covered in this Lecture is Retail

Coursework Instructions:

Please respond to the lecture on Data Science in Retail (Lecture 7: Anurag Bhardwaj) answering the base questions asked in the course information document.
prepare a short (~2-3 pages, 12 point single space) report that addresses as many of the following questions as are relevant:
• Describe the market sector or sub-space covered in this lecture.
• What data science related skills and technologies are commonly used in this sector?
• How are data and computing related methods used in typical workflows in this sector? Illustrate with an example.
• What are the data science related challenges one might encounter in this domain?
• What do you find interesting about the nature of data science opportunities in this
domain?
In addition,
(i) Describe some of the data science problems relevant to Manufacturing and Warehousing in the Retail Product Lifecycle. (10 pts of the 80 C+R points in the rubric) )
(ii) Describe some of the data science problems and techniques that would be useful in Inventory Management and Pricing Optimization . (10 pts of the 80 C+R points in the rubric)
(iii) Also, answer the following multiple-choice questions: You can list the question number and the letter corresponding to the correct choice as Answer in your report, (2x5 = 10 pts of the 80 C+R points in the rubric)
Q1: The lecture describes the Retail space as having different technology categories, and certain categories having the most growth in terms of number of companies in that space. Select the top technology category that has the most companies in that space as illustrated in the lecture.
A. Retail Payments
B. Deals and Rewards
C. In-Store Experience
D. Marketing Platforms
Q2: Based on the lecture, funding for different Retail technology categories varies. Select the category that has the largest Retail funding support as illustrated in the lecture.
A. Product Recommendations
B. Advertising Technology
C. Last Mile Logistics
D. Deals and Rewards

Q3: Based on the lecture, some Retail technologies are more mature than the others (based on median age of the companies in the space). Select the top 4 most mature Retail Technology Categories as illustrated in the lecture.
1. Product Recommendations
2. Price Comparison
3. IOT
4. Advertising Technology
5. Personalization Platforms
6. Made-to-Measure
A. 1,2,3,4 B. 1,3,4,6 C. 1,4,5,6 D. 2,4,5,6

Q4. Based on the lecture, Manufacturing and Warehousing are important components of the Retail Product Lifecycle. Select the INCORRECT statement about these Retail Lifecycle components as mentioned in the lecture
A. Deep Learning and Computer Vision play a big role in Robotic Automation.
B. Motion Planning and Reinforcement Learning are technologies useful in Warehouse Planning.
C. Warehouse Operations encompass the entire process from facilitating ordering an item to shipping an item using automation.
D. Planning on what type of items to stock in a warehouse is an import part of Warehouse Planning as well as Inventory Management

Q5. Based on the lecture, Select the INCORRECT statement about Price Optimization
A. Production cost and shipping cost are both factors in price optimization.
B. Matching products using Machine Leaning is an important task in Competitive Pricing.
C. Competitive Pricing is always the best pricing strategy when competing with giant companies such as Amazon.
D. Omni-channel pricing strategy is often applied to reduce storage cost in physical stores.

Coursework Sample Content Preview:
The Market Sector Covered in this Lecture is Retail
The market sector covered in this lecture is retail. Some of the most important problems faced by the sector are optimizing inventory, managing supply chains, and understanding customer behavior. Data science plays a pivotal role in solving these problems by providing insights that can be used to make better decisions. Some of the most important data sets in the sector are sales, customer, and product data. Some of the most important data science methods used in the sector are predictive analytics, machine learning, and data mining. Demand forecasting, pricing, and personalization are some of the most important application areas of data science in the sector.
Data science-related skills and technologies are commonly used in this sector.
Data science-related skills and technologies that are commonly used in this sector include data mining, predictive modeling, and prescriptive analytics. Data mining involves extracting valuable information from large data sets, and it is often used in marketing in order to identify customer trends and target marketing campaigns. Predictive modeling is a type of data analysis that is used to make predictions about future events. It is often used in marketing in order to identify potential customers and target them with specific marketing campaigns. Prescriptive analytics is a type of data analysis that is used to recommend actions that should be taken in order to achieve the desired goal. It is often used in marketing in order to optimize pricing and target marketing campaigns.
One important thing to note is that data science methods used in a sector are often very different in other sectors. This is because the problems that data science methods solve in one sector may not be present in other sectors. Data scientists need to be familiar with various properties when it comes to interpreting the results of data science methods. Some of these properties are statistical significance, descriptive power, predictive power, and explanatory power. If a data scientist is able to characterize the properties of the data in their sector of interest, it becomes easier to interpret the results of the data science methods. Apart from the properties of the data itself, the properties of the data science methods that are used also need to be interpreted. Some of the most important properties of data science methods are generalizations and assumptions. Suppose you can interpret the results produced by a data science method and identify the properties of the data and the methods themselves. In that case, it becomes much easier to understand the data science results.
How are data and computing-related methods used in typical workflows in this sector? Illustrate with an example
Data and computing-related methods are used in typical workflows in this sector in order to improve customer segmentation, target marketing campaigns, and optimize pricing. In addition, the data and computing-related methods are used in retail workflows to track inventory, manage customer data, and track sales. Typical data analyzed in the retail sector include customer, sales, and inventory data. While the analytical tasks performed in the retail sector include customer segmentation, ...
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