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Final Blog

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After completion of all of the modules for this course, I realized that the modules are designed to support one main concept and that is to harness the proliferation of data in this Big Data era, to generate knowledge and to create value for organizations. Business Intelligence tools can be applied to different industries and domains to provide actionable insights and lead to better decision making. Examples of this can be for the medical field to understand the spread of cancer, forecasting trading opportunities for stocks, or to better enhance content on an e-commerce platform to retain the customer base. The types of information that one can derive from different sources of data are endless and organizations can tailor their data analysis based on questions they would like to find answers to. In order for a business to gain a competitive advantage in the industry, it must leverage Business Intelligence to gather and analyze the data to set up goals that align with the b
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Module 3 (Part II) Network Visualization

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For the last module of this course, we explored Gephi, an open-source network visualization tool to analyze networks. Gephi provides excellent visualization of all the nodes and edges in the network and is also able to quickly calculate the different metrics to get the full picture of the data. We also went over the two different case scenarios using network analysis, the Patent Network Applications between Apple and Google, and the Recipes Network Analysis. By comparing Apple and Google's network of inventors for their patents, one could tell right away from the network visualization that Apple has a more centralized set of core inventors that contribute to most of the patents while Google has a more decentralized approach where smaller groups of inventors work together to invent various types of patents. For the Recipes Network, a user has access to a large amount of data regarding the ratings, which ingredients are frequently used together, and as well as substitutes to dif
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Module 3 (Part I) Network Visualization

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As we progress into Module 3 of this course, we are introduced to Network, Network Visualization, and Network Properties. Network is a social structure made up of entities and relationships, which are represented as vertices and edges respectively, on a large social network graph. We want to learn about the network because it allows us to study complex relationships, whether it is the spreading of a disease, the propagation from a Reddit article, or the relationship between purchasers or a product. A network can be represented as a directed network or undirected network. By using Network Visualization, we are able to better understand the patterns of interaction and there are many network layouts including Force Directed Layout, Geographic Layout, Clustering Layout, and etc, which can be used for different scenarios depending on what the end-user is looking to analyze. There are many network properties that can be used to interpret the structure of social networks. Degree centrality
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Module 2 Web Analytics

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We are introduced to Web Analytics this week, which is the analysis of quantitative and qualitative data from the websites in order to continue to improve user experience and assist organizations in achieving their goals. Web Analytics also allow organizations to better understand their audiences, the ways that they behave, ways to improve product exposure, and as well as selection between different marketing channels to maximize profit. There are 5 W's to Web Analytics and these include who, what, when, where, and why. Web Analytics try to answer these 5 W's including what are users doing on your website, who are the audiences, when are they visiting, where are they coming from, and lastly, why are they doing what they are doing? One way to understanding Web Analytics is looking at the different types of web traffic. These include direct, organic, referral, and campaign. Another part of Web Analytics is looking at the different web metrics including number of visitors, bounc
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Module 1

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For Module 1, we started with Week 2, where we were introduced to the design process of data warehouses, the four step dimensional modeling process, and as well as the Balanced Scorecard. Data warehouses differ with databases in that it performs OLAP to analyze data over a period time, by performing complex joins, to try to make a business decision. Databases on the other hand, performs OLTP and is best used for transactional data for operational purposes. Data from databases are loaded into data warehouses by using ETL to extract data from the source database, transformed, and loaded into the data warehouse. The four steps of the dimensional modeling process includes selecting the business process to model, declaring the grain of the business case, choosing the dimension tables, and lastly identifying the attributes in a fact table. We also learned about the data cube as a representation of many attributes across its axis and the operations that can be performed including slice, dice,
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Introduction to Big Data and Business Intelligence Module 0

Three major characteristics that describe Big Data are Volume, Velocity, and Variety. Volume refers to the huge volume of data that is said to exceed a few Zettabytes by the year 2020. Velocity refers to the high velocity or speed of data that's ingested every second from all different types of sources. Lastly, Variety represents the high variety of sources of data ranging from applications, websites, to social media platforms, to QR codes. The three paradigm shift caused by Big Data are Datafication, Rich & Dynamic Content, and Large Population of Data. Datafication defines everything we do in the form of data. By evaluating trends and patterns, businesses can use these data to make recommendations, promote products or make other business innovations. The paradigm shift of Rich & Dynamic Content describe where there is a highly dynamic content containing reviews, locations, purchases, and etc. This will allow businesses to predict consumer behavior based on spatial locatio
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About Me

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Hello Everyone, Welcome to my blog! My name is Nanting Zheng (Aileen) and I've recently moved to San Francisco from my beautiful hometown Philadelphia. Growing up on the East Coast, I have never thought to live on the West Coast! I've decided to move to San Francisco in search of more opportunities for women in technology and to get out of my comfort zone. Overall, it has been both exciting and nervous for me at the same time. But I gotta say, the dog-friendly beach at Crissy Fields is my absolute favorite! I graduated with a bachelor's degree in Biology with and intent of becoming a doctor. It took me some time to realize that it is not of my true passion. I like connecting with people and I also enjoy analyzing data and looking for trends. This is the reason why I've started the MIS Master's Program with University of Arizona. My favorite classes so far are Data Mining for Business Intelligence and Enterprise Data Management. I enjoy using the various t
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