Category: agile Data Science

NoSQL and Dataflow programming ”; Previous Next There are times when the data is unavailable in relational format and we need to keep it transactional with the help of NoSQL databases. In this chapter, we will focus on the dataflow of NoSQL. We will also learn how it is operational with a combination of agile and data science. One of the major reasons to use NoSQL with agile is to increase the speed with market competition. The following reasons show how NoSQL is a best fit to agile software methodology − Fewer Barriers Changing the model, which at present is going through mid-stream has some real costs even in case of agile development. With NoSQL, the users work with aggregate data instead of wasting time in normalizing data. The main point is to get something done and working with the goal of making model perfect data. Increased Scalability Whenever an organization is creating product, it lays more focus on its scalability. NoSQL is always known for its scalability but it works better when it is designed with horizontal scalability. Ability to leverage data NoSQL is a schema-less data model that allows the user to readily use volumes of data, which includes several parameters of variability and velocity. When considering a choice of technology, you should always consider the one, which leverages the data to a greater scale. Dataflow of NoSQL Let us consider the following example wherein, we have shown how a data model is focused on creating the RDBMS schema. Following are the different requirements of schema − User Identification should be listed. Every user should have mandatory at least one skill. The details of every user’s experience should be maintained properly. The user table is normalized with 3 separate tables − Users User skills User experience The complexity increases while querying the database and time consumption is noted with increased normalization which is not good for Agile methodology. The same schema can be designed with the NoSQL database as mentioned below − NoSQL maintains the structure in JSON format, which is light- weight in structure. With JSON, applications can store objects with nested data as single documents. Print Page Previous Next Advertisements ”;

Agile Data Science – Data Science Process ”; Previous Next In this chapter, we will understand the data science process and terminologies required to understand the process. “Data science is the blend of data interface, algorithm development and technology in order to solve analytical complex problems”. Data science is an interdisciplinary field encompassing scientific methods, processes and systems with categories included in it as Machine learning, math and statistics knowledge with traditional research. It also includes a combination of hacking skills with substantive expertise. Data science draws principles from mathematics, statistics, information science, and computer science, data mining and predictive analysis. The different roles that form part of the data science team are mentioned below − Customers Customers are the people who use the product. Their interest determines the success of project and their feedback is very valuable in data science. Business Development This team of data science signs in early customers, either firsthand or through creation of landing pages and promotions. Business development team delivers the value of product. Product Managers Product managers take in the importance to create best product, which is valuable in market. Interaction designers They focus on design interactions around data models so that users find appropriate value. Data scientists Data scientists explore and transform the data in new ways to create and publish new features. These scientists also combine data from diverse sources to create a new value. They play an important role in creating visualizations with researchers, engineers and web developers. Researchers As the name specifies researchers are involved in research activities. They solve complicated problems, which data scientists cannot do. These problems involve intense focus and time of machine learning and statistics module. Adapting to Change All the team members of data science are required to adapt to new changes and work on the basis of requirements. Several changes should be made for adopting agile methodology with data science, which are mentioned as follows − Choosing generalists over specialists. Preference of small teams over large teams. Using high-level tools and platforms. Continuous and iterative sharing of intermediate work. Note In the Agile data science team, a small team of generalists uses high-level tools that are scalable and refine data through iterations into increasingly higher states of value. Consider the following examples related to the work of data science team members − Designers deliver CSS. Web developers build entire applications, understand the user experience, and interface design. Data scientists should work on both research and building web services including web applications. Researchers work in code base, which shows results explaining intermediate results. Product managers try identifying and understanding the flaws in all the related areas. Print Page Previous Next Advertisements ”;

Deploying a predictive system ”; Previous Next In this example, we will learn how to create and deploy predictive model which helps in the prediction of house prices using python script. The important framework used for deployment of predictive system includes Anaconda and “Jupyter Notebook”. Follow these steps to deploy a predictive system − Step 1 − Implement the following code to convert values from csv files to associated values. import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import mpl_toolkits %matplotlib inline data = pd.read_csv(“kc_house_data.csv”) data.head() The above code generates the following output − Step 2 − Execute the describe function to get the data types included in attributed of csv files. data.describe() Step 3 − We can drop the associated values based on the deployment of the predictive model that we created. train1 = data.drop([”id”, ”price”],axis=1) train1.head() Step 4 − You can visualize the data as per the records. The data can be used for data science analysis and output of white papers. data.floors.value_counts().plot(kind=”bar”) Print Page Previous Next Advertisements ”;