Databases are the backbone of modern life, powering everything from online shopping to social media to memberships and countless other activities. They enable us to store, manage, and retrieve vast amounts of information quickly and efficiently. Understanding databases is the very first step in mastering data analytics. In this course, you will explore databases, beginning with the basic concepts of data analytics, databases, including relational and non-relational databases, and common roles in the field of data science. Then you will examine structured query language (SQL) including examples of SQL operations. Finally, you will investigate the purpose of databases in applications, database management systems (DBMS), how databases are implemented in everyday business environments, and common database tasks. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.

Databases are used for creating and storing virtually any type of data. Data drives business in the twenty-first century, and IT professionals interested in mastering data analytics must understand the key concepts surrounding databases and their uses in almost every facet of business. In this course, you will discover database concepts, beginning with challenges associated with databases, self-driving databases, data warehouses, data marts, and data lakes and lakehouses. Then you will explore the concepts of Online Transactional Processing (OLTP) and Online Analytical Processing (OLAP). You will learn about database schemas and look closely at star and snowflake schemas, which are common in data warehouses. Finally, you will explore slowly changing dimensions that shape the methods analysts use to keep historical and current data. This course can be used to prepare for the DA0-001: CompTIA Data+ exam.

Databases cannot perform at all without data - it is that simple. Data is the lifeblood of databases, and once a database is populated with data, the things a data analyst can do with it are truly remarkable. By harnessing the power of data, modern life has become more efficient in virtually every way. In this course, you will explore the basics of data, beginning with an introduction to data types, structured data, defined rows and columns, and key-value pairs. You will then proceed to explore unstructured data, undefined fields, machine data, and discrete and continuous data. Next, you will dig into categorical data, numerical data, text data, multimedia data. Finally, you will examine text files, HTML files, XML files, and JSON files. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.

Data that lives in a database is only part of the equation when considering data analytics. Data needs to be accessed and processed in order to be useful. The importance of data in the modern world can easily be observed by considering the sheer number of data analytics tools. Without these tools, data loses some of its usefulness. In this course, you will explore popular data analytics tools, beginning with Structured Query Language (SQL), and Python. Next, you will dig into data science styling recommendations in Python, data science reporting best practices, Microsoft Excel, and the R programming language. Then you will discover tools like RapidMiner, IBM Cognos, IBM SPSS Modeler, SPSS, SAS, Tableau, and Power BI. Finally, you will focus on the purposes and roles of tools such as Qlik, MicroStrategy, BusinessObjects, APEX, Amazon QuickSight, Stata, and Minitab. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.

Data, when coming in from a source en masse, is rarely structured the way that data analysts would like it to be. When you consider the multitude of sources that data comes from, it would be highly unrealistic to assume that you could take a tranche of data and begin working with it without some sort of processing to make it more useful. In this course, you will explore data acquisition and cleansing, beginning with data integration and data integration tools, focusing on the roles and characteristics of the extract, transform, load (ETL) and extract, load, transform (ELT) processes. Then you will examine tools and methods such as delta load and data acquisition application programming interfaces (APIs). Next, you will learn how to clean datasets and investigate common data issues, including data redundancy, missing values, non-parametric data, and outliers. Finally, you will take a look at key characteristics of data type validation. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.

Data is rarely received in perfect form and often requires some sort of manipulation to make it sing. That is why the world needs data analysts. They can squeeze every bit of usefulness from datasets, and they also know how to prep datasets to extract meaning from them. In this course, you will explore key concepts of data manipulation, beginning with data manipulation tools. Then you will learn organization techniques like filtering and sorting data to make it easier to interpret. Next, you will focus on date functions, logical functions, aggregate functions, and system functions. Finally, you will investigate the best practices associated with data manipulation. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.

Data rarely comes in perfect form and often requires manipulation in order to make it truly meaningful. Without the ability to manipulate data, you often cannot extract full meaning from it. In this course, you will explore techniques for manipulating data, beginning with recoding data, derived variables, and data merging and blending. Then you will perform a data merge and a data blend and dig into concatenation and appending. Next, you will focus on imputation, reduction, and aggregation. Finally, you will learn how and why to transpose datasets, how to achieve standardized data formats using data normalization, and how to parse and manipulate strings. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.

You have received a large dataset, spent the time and requisite care cleansing and manipulating it to make it the best possible dataset so that you can extract useful information from it. Now what? Well, now you can begin querying the data. But queries can get complicated fast, especially when dealing with massive amounts of data. That's where query optimization comes in. Queries can be optimized to make your results faster and more meaningful. In this course, you will explore the key concepts of query optimization, beginning with query optimization and typical tools used in query optimization. Then you will conduct parameterized queries, perform index scans, use temporary tables, and use record subsets. Finally, you will dig into execution plans and common best practices for query optimization. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.

Descriptive statistics are used to describe the characteristics of datasets. They are leveraged by data analysts to find answers or characteristics of data that aren't immediately or directly answered by analyzing the data alone. In other words, descriptive statistics are used to summarize characteristics of data that are not actually contained or explicitly described by the data. In this course, you will explore descriptive statistical methods, beginning with the purpose and role of descriptive statistics. Then you will dig into measures of central tendency, measures of dispersion, and frequency distribution. Finally, you'll examine percent change, percent difference, and confidence intervals. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.

Unlike descriptive statistics, which describe characteristics of datasets, inferential statistics are used to make inferences or predictions about data. Information not readily evident by analyzing datasets can be gleaned by talented data analysts in order to form conclusions, and while it may sound like educated guessing, descriptive statistics are far more sophisticated than simply guessing. In this course, you will explore inferential statistical methods, identifying the purpose of inferential statistics and comparing them to descriptive statistics. Then you will investigate and perform inferential statistical methods such as t-tests, z-score, p-values, and chi-square. Finally, you will focus on hypothesis testing, simple linear regression, and correlation testing. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.

Databases are places where information resides. What that information looks like is up to the creators of a database, but one thing is for certain: analyzing data is one of the main points of collecting and storing data. Analysis types and techniques are plentiful, and how a data analyst chooses to analyze data really depends on a number of factors. In this course, you will explore data analysis types and techniques, beginning with the data analysis process. Next, you will review and refine business questions and determine data needs and sources. Then you will discover scoping and gap analysis, analysis types, trend analysis, and performance analysis. Finally, you will examine exploratory data analysis and link analysis. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.

Data is meaningful only when information is extracted from it. That information can tell a story, and the best data analysts are magnificent storytellers. But no matter how accomplished a data analyst, a story can't be told compellingly without visualizing what the data says and a key part of a data analyst's role is in reporting on what the data is saying. In this course, you will explore data visualization reports, beginning with data visualization tools and best practices. Then you will focus on examples of data visualization translating requirements for reports, key report components, report best practices, corporate standardization, and style guides. Next, you will discover how to create a report and examine the differences between static and dynamic reports, ad-hoc and self-service reports, and recurring vs. tactical reports. Finally, you will learn how to implement various design and documentation elements in reports, including using charts and graphs to enhance your report. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.

Data visualization prepares data for presentation to make it easier for non-data analysts to understand what the data is saying. Often, non-data analysts can't see relationships in data without visual aids, but effective data presentation allows them to change parameters and understand and view the data in a similar manner to a data scientist. In this course, explore the purpose and key considerations for data visualization dashboards in data analytics, dashboard utilization best practices, and elements of dashboard development. Next, learn about dashboard delivery and how to create a dashboard. Finally, discover how to utilize saved searches, implement filters, optimize dashboards, and use access permissions. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.

Some accomplished data scientists may be able to look at raw data and easily gather some findings from it. Everyone else may need to see visuals such as charts and graphs to help them understand the relationships expressed by data. In this course, explore how to create charts and graphs for data analytics, beginning with line charts, pie charts, bubble charts, and scatter plots. Next, discover how to use datasets and chart generation software to construct bar charts, histograms, waterfalls, heatmaps, and geographic maps. Finally, learn how to make treemaps, stacked charts, infographics, and word clouds to visualize data. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.

Data collection and how that data is used is highly regulated and for good reason. The world operates on the circulation of data, whether that's personally identifying information, health information, financial information, or even something as seemingly innocuous as someone's web browsing or purchasing habits. The regulations that exist are there to protect users, and in some cases, the people collecting the data. In this course, you will explore basic data governance concepts, beginning with access requirements, and role-based access control (RBAC). Then, you will delve into users and groups, data use, and the release process. Next, you will focus on data protection, data de-identification and masking, and storage environment requirements. Finally, you will learn about use requirements, entity relationship requirements, data classification, regulatory requirements, and data breach reporting. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.

Ask any data analyst and they will tell you in no uncertain terms how important it is to work with quality data. Quality must be paramount to properly analyze data and glean important information from it. Likewise, master data management (MDM) is a key activity required of data analysts to ensure that information and business work in concert to ensure the completeness and proper care of important data. In this course, you will explore the importance of data quality, including common issues, best practices, and data quality tools. Then you will dig into examples of data quality, data validation, and types of data validation. Next, you will focus on data quality dimensions, data quality metrics, and validation methods. Finally, you'll learn about Master Data Management (MDM), MDM processes, and the importance of MDM. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.