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Data Management for Analytics Part 2

Xuemin Jin

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What's inside

Syllabus

Structured Query Language, Part 1

This module first presents an overview of the structured query language (SQL) Data Definition Language (SQL DDL) to define a relational data model. It examines the schema creation, table creation, drop command, and alter command. Various syntaxes are illustrated with explicit examples. This module also discusses the SQL Data Manipulation Language (SQL DML) used to retrieve data, update data, insert new data, and delete existing data. The focus is on SQL INSERT statements for inserting data into tables and some simple SQL SELECT statements. More complex SQL SELECT statements will be discussed in later modules along with SQL DELETE and SQL UPDATE statements.

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Career center

Learners who complete Data Management for Analytics Part 2 will develop knowledge and skills that may be useful to these careers:

SQL Developer

A SQL Developer specializes in designing, developing, and optimizing databases and Structured Query Language queries for various applications. This role is central to many data-driven operations, ensuring efficient data storage, retrieval, and manipulation. The "Data Management for Analytics Part 2" course is nearly a perfect fit for an aspiring SQL Developer, offering an in-depth exploration of SQL from data definition to complex manipulation. Modules covering SQL DDL, DML, aggregate functions, group clauses, join queries, nested queries, and extensions like triggers, stored procedures, and recursive queries provide comprehensive practical skills. This detailed curriculum ensures you gain a robust understanding of advanced SQL constructs, crucial for building and maintaining high-performance database solutions and upholding data governance standards.

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Database Administrator

A Database Administrator is responsible for the performance, integrity, and security of a database. This involves designing, implementing, maintaining, and repairing organizational databases. The "Data Management for Analytics Part 2" course provides a comprehensive foundation for a Database Administrator, deeply exploring Structured Query Language for schema and table management, data manipulation, and query optimization. Furthermore, the course's coverage of extensions to Relational Database Management Systems, such as triggers and stored procedures, is directly applicable to advanced database functionality. Expertise in NoSQL databases like MongoDB and Neo4j, also covered, broadens your capabilities to manage diverse data storage solutions. A strong grasp of data governance principles, a core theme of this course, is vital for ensuring compliance and data security in this role.

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NoSQL Database Specialist

A NoSQL Database Specialist focuses on designing, implementing, and managing non-relational database systems, chosen for their flexibility, scalability, and performance in specific use cases. The "Data Management for Analytics Part 2" course is exceptionally tailored for a NoSQL Database Specialist, dedicating significant modules to two prominent NoSQL databases: MongoDB and Neo4j. You will learn about MongoDB for document storage and Neo4j for graph data, understanding their respective applications in areas like social networks. This direct exposure to the theory and practical application of these NoSQL technologies, alongside a foundational understanding of data storage and data governance, provides a strong starting point for specializing in these cutting-edge database solutions. This course truly offers specific, hands-on knowledge for this role.

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Database Developer

A Database Developer focuses on the design, implementation, and maintenance of databases, writing complex queries, stored procedures, and functions to support applications. The "Data Management for Analytics Part 2" course provides a robust foundation for a Database Developer, with its extensive coverage of Structured Query Language, including schema creation, table management, data manipulation, and advanced querying techniques. The modules on extensions to Relational Database Management Systems, such as triggers and stored procedures, are directly applicable to building sophisticated database logic. Furthermore, understanding NoSQL databases like MongoDB and Neo4j expands your toolkit for designing solutions that may require flexible or specialized data models. The course also enhances your understanding of data storage principles and data governance, critical for robust database development.

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Cloud Data Engineer

A Cloud Data Engineer designs, implements, and maintains data infrastructure and pipelines within cloud environments, leveraging cloud-specific services for data storage, processing, and analytics. The "Data Management for Analytics Part 2" course is highly beneficial for a Cloud Data Engineer. The comprehensive skills in Structured Query Language are universally applicable across cloud database services. The introduction to big data management, including Hadoop and Apache Spark, provides a foundational understanding of technologies often deployed and managed in the cloud for large-scale data processing. Knowledge of NoSQL databases like MongoDB and Neo4j is also relevant, as cloud providers offer managed services for these and other non-relational databases. This course helps build the essential data management principles crucial for designing efficient and well-governed cloud data solutions.

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Data Engineer

A Data Engineer designs, builds, and maintains the infrastructure for data generation and analysis, ensuring data is available and reliable for various applications, including analytics and machine learning. The "Data Management for Analytics Part 2" course is highly relevant for a Data Engineer, providing robust skills in Structured Query Language, essential for building efficient data pipelines and transformations. The introduction to big data management tools like Hadoop, MapReduce, and Apache Spark directly aligns with the technologies used to process large datasets. Furthermore, understanding NoSQL databases such as MongoDB and Neo4j equips you to work with diverse data models. This course builds a foundational understanding of data storage and governance, critical for constructing scalable and well-governed data ecosystems.

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Data Architect

A Data Architect designs an organization's data strategy, including database systems, data flows, and overall data infrastructure to meet business needs. This role requires a holistic understanding of data technologies and governance principles. The "Data Management for Analytics Part 2" course offers an excellent foundational understanding for a Data Architect. Its balanced focus on both theoretical concepts and practical applications of data storage and data governance is critical for making informed design decisions. The course covers relational databases through Structured Query Language and explores various NoSQL databases like MongoDB and Neo4j, equipping you to evaluate and select appropriate technologies. The introduction to big data management tools also provides insight into scalable solutions. While significant experience is often pursued for this role, the course provides essential insights into managing diverse data assets.

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Data Governance Analyst

A Data Governance Analyst establishes and enforces policies and procedures for data use, security, and quality within an organization, ensuring compliance and data integrity. The "Data Management for Analytics Part 2" course is highly relevant for a Data Governance Analyst, as it specifically covers fundamental concepts in data storage and data governance. Understanding the intricacies of Structured Query Language and how data is defined, manipulated, and secured in relational databases is crucial. Furthermore, familiarity with different data models, including NoSQL databases like MongoDB and Neo4j, allows for comprehensive governance strategies across diverse data assets. This course provides a solid theoretical and practical basis for understanding how data is managed, which directly supports the implementation and monitoring of effective data governance frameworks.

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Data Quality Analyst

A Data Quality Analyst is dedicated to ensuring the accuracy, completeness, and consistency of an organization's data. This role involves identifying data discrepancies, implementing cleansing processes, and setting standards for data integrity. The "Data Management for Analytics Part 2" course provides exceptional foundational knowledge for a Data Quality Analyst. Its deep dive into Structured Query Language, particularly features like DDL for schema definition and DML for data manipulation and updates, is critical for inspecting and correcting data. Furthermore, the course's explicit focus on data storage and robust data governance principles directly supports developing strategies for maintaining high data quality. Understanding various database types, including NoSQL databases like MongoDB, also enables you to address quality issues across diverse data landscapes.

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ETL Developer

An ETL Developer designs and implements processes to Extract, Transform, and Load data from source systems into data warehouses or other data repositories. This role is critical for ensuring data quality and availability for analytics and reporting. The "Data Management for Analytics Part 2" course provides highly relevant skills for an ETL Developer. Its comprehensive coverage of Structured Query Language, including complex joins, aggregate functions, and data manipulation statements, is fundamental for transforming and loading data efficiently. Understanding various data storage concepts, including both relational and NoSQL databases like MongoDB and Neo4j, is valuable when working with diverse source systems. The brief introduction to big data management tools like Hadoop and Spark also helps build awareness of scalable data processing, which is key for modern ETL pipelines.

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Analytics Engineer

An Analytics Engineer bridges the gap between data engineering and data analysis, focusing on building robust and accessible data models that empower analysts to derive insights efficiently. This role requires strong data transformation skills and a deep understanding of data warehousing principles. The "Data Management for Analytics Part 2" course is an excellent fit for an Analytics Engineer. Its comprehensive coverage of Structured Query Language, including complex queries, joins, and aggregate functions, is paramount for building data models and transformations. Understanding different data storage paradigms, including relational and NoSQL databases like MongoDB and Neo4j, helps in sourcing and processing data effectively. The course's focus on data governance also helps build reliable and trustworthy data assets, which is critical for accurate analytical outputs.

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Data Analyst

A Data Analyst interprets complex datasets to help organizations make informed decisions. This role involves collecting, cleaning, and analyzing data, often translating findings into reports and visualizations. The "Data Management for Analytics Part 2" course is foundational for aspiring Data Analysts, as it thoroughly covers Structured Query Language, an indispensable tool for data extraction and manipulation. Understanding various database types, including relational and NoSQL databases like MongoDB and Neo4j, ensures that you can access and process data from diverse sources. The course's emphasis on data storage and data governance also helps build a solid understanding of data quality and reliability, which is crucial for delivering accurate and trustworthy analyses.

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Business Intelligence Developer

A Business Intelligence Developer creates dashboards, reports, and data visualizations that help businesses understand their performance and make data-driven decisions. This role heavily relies on extracting, transforming, and loading data from various sources into data warehouses or reporting tools. The "Data Management for Analytics Part 2" course is exceptionally well-suited for a Business Intelligence Developer. Its extensive modules on Structured Query Language, including complex queries, aggregate functions, and joins, are fundamental for preparing and aggregating data for reporting. A strong grasp of data storage concepts and data governance, as taught in this course, helps ensure the accuracy and reliability of the data presented in BI solutions. Understanding both relational and NoSQL databases provides versatility in sourcing data from different systems.

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Big Data Engineer

A Big Data Engineer builds and maintains large-scale data processing systems that can handle vast volumes of data. This role involves working with distributed computing frameworks and ensuring data pipelines are efficient and scalable. The "Data Management for Analytics Part 2" course includes a crucial brief introduction to big data management, encompassing Hadoop, MapReduce, and Apache Spark. These are core technologies for a Big Data Engineer. While this course provides a foundational understanding rather than an exhaustive deep dive into these ecosystems, it initiates your learning journey into distributed data processing. Additionally, the comprehensive Structured Query Language skills and knowledge of NoSQL databases like MongoDB and Neo4j are highly beneficial for managing and querying the diverse and complex datasets often found in big data environments.

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Machine Learning Engineer

A Machine Learning Engineer designs, builds, and deploys machine learning models and the infrastructure to support them. While this role is heavily algorithm-focused, strong data management skills are absolutely essential for data collection, cleaning, and feature engineering. The "Data Management for Analytics Part 2" course provides a foundational understanding of theory and applications of database management to support machine learning. Expertise in Structured Query Language allows for efficient data extraction and preparation, while knowledge of NoSQL databases like MongoDB can be crucial when working with unstructured data. The brief introduction to big data management tools like Apache Spark is also beneficial for processing large datasets often used in machine learning. This course helps ensure data quality and accessibility, which are paramount for model performance. An advanced degree, such as a master's or doctorate, is often required for this role.

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Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Data Management for Analytics Part 2.

Designing Data-Intensive Applications

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Is highly relevant for understanding contemporary database topics, particularly in the context of distributed systems, scalability, and various data storage technologies like NoSQL. It is excellent for professionals and advanced students looking to deepen their understanding of modern data systems and their trade-offs.

Data Management for Analytics Part 2

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