Data Science For Dummies. Lillian Pierson
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an obstacle to timely decision-making. The capabilities of data-handling and data-processing technologies often limit data velocities.
Tools that intake data into a system — otherwise known as data ingestion tools — come in a variety of flavors. Some of the more popular ones are described in the following list:
Apache Sqoop: You can use this data transference tool to quickly transfer data back-and-forth between a relational data system and the Hadoop distributed file system (HDFS) — it uses clusters of commodity servers to store big data. HDFS makes big data handling and storage financially feasible by distributing storage tasks across clusters of inexpensive commodity servers.
Apache Kafka: This distributed messaging system acts as a message broker whereby messages can quickly be pushed onto, and pulled from, HDFS. You can use Kafka to consolidate and facilitate the data calls and pushes that consumers make to and from the HDFS.
Apache Flume: This distributed system primarily handles log and event data. You can use it to transfer massive quantities of unstructured data to and from the HDFS.
Dealing with data variety
Big data gets even more complicated when you add unstructured and semistructured data to structured data sources. This high-variety data comes from a multitude of sources. The most salient point about it is that it’s composed of a combination of datasets with differing underlying structures (structured, unstructured, or semistructured). Heterogeneous, high-variety data is often composed of any combination of graph data, JSON files, XML files, social media data, structured tabular data, weblog data, and data that’s generated from user clicks on a web page — otherwise known as click-streams.
Structured data can be stored, processed, and manipulated in a traditional relational database management system (RDBMS) — an example of this would be a PostgreSQL database that uses a tabular schema of rows and columns, making it easier to identify specific values within data that’s stored within the database. This data, which can be generated by humans or machines, is derived from all sorts of sources — from click-streams and web-based forms to point-of-sale transactions and sensors. Unstructured data comes completely unstructured — it’s commonly generated from human activities and doesn’t fit into a structured database format. Such data can be derived from blog posts, emails, and Word documents. Semistructured data doesn’t fit into a structured database system, but is nonetheless structured, by tags that are useful for creating a form of order and hierarchy in the data. Semistructured data is commonly found in databases and file systems. It can be stored as log files, XML files, or JSON data files.
Identifying Important Data Sources
Vast volumes of data are continually generated by humans, machines, and sensors everywhere. Typical sources include data from social media, financial transactions, health records, click-streams, log files, and the Internet of things — a web of digital connections that joins together the ever-expanding array of electronic devices that consumers use in their everyday lives. Figure 2-1 shows a variety of popular big data sources.
FIGURE 2-1: Popular sources of big data.
Grasping the Differences among Data Approaches
Data science, machine learning engineering, and data engineering cover different functions within the big data paradigm — an approach wherein huge velocities, varieties, and volumes of structured, unstructured, and semistructured data are being captured, processed, stored, and analyzed using a set of techniques and technologies that are completely novel compared to those that were used in decades past.
All these functions are useful for deriving knowledge and actionable insights from raw data. All are essential elements for any comprehensive decision-support system, and all are extremely helpful when formulating robust strategies for future business growth. Although the terms data science and data engineering are often used interchangeably, they’re distinct domains of expertise. Over the past five years, the role of machine learning engineer has risen up to bridge a gap that exists between data science and data engineering. In the following sections, I introduce concepts that are fundamental to data science and data engineering, as well as the hybrid machine learning engineering role, and then I show you the differences in how these roles function in an organization’s data team.
Defining data science
If science is a systematic method by which people study and explain domain-specific phenomena that occur in the natural world, you can think of data science as the scientific domain that’s dedicated to knowledge discovery via data analysis.
Data scientists use mathematical techniques and algorithmic approaches to derive solutions to complex business and scientific problems. Data science practitioners use its predictive methods to derive insights that are otherwise unattainable. In business and in science, data science methods can provide more robust decision-making capabilities:
In business, the purpose of data science is to empower businesses and organizations with the data insights they need in order to optimize organizational processes for maximum efficiency and revenue generation.
In science, data science methods are used to derive results and develop protocols for achieving the specific scientific goal at hand.
Data science is a vast and multidisciplinary field. To call yourself a true data scientist, you need to have expertise in math and statistics, computer programming, and your own domain-specific subject matter.
Using data science skills, you can do cool things like the following:
Use machine learning to optimize energy usage and lower corporate carbon footprints.
Optimize tactical strategies to achieve goals in business and science.
Predict for unknown contaminant levels from sparse environmental datasets.
Design automated theft- and fraud-prevention systems to detect