CompTIA Data+ DA0-002

Hours: 150 / Access Length: 12 Months / Delivery: Online, Self-Paced
Online Hours: 150
Retail Price: $1,049.00

Course Overview:

The CompTIA Data+ exam will certify the successful candidate has the knowledge and skills required to transform business requirements in support of data-driven decisions through mining and manipulating data, applying basic statistical methods, and analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle.

Students will:

• Translate business requirements in support of data-driven decisions
• Acquire data
• Prepare data
• Transform data
• Create appropriate reports and visualizations
• Apply basic statistical methods and analyze complex data sets
• Adhere to governance and quality standards throughout the entire data life cycle

Course Outline:

Lesson 1: Summarizing Database Concepts

Data analysts work with data, and often that data is stored in a database. This is why it is of the utmost importance that you understand the basic foundations of databases. For example, the type of database you're working with can alter your decision-making as a data analyst, so it is crucial that you have a solid understanding of what differentiates relational from non-relational databases. Further, understanding how the tables in a relational database are designed and how the fields interact with each other will help you build necessary sets for analysis. Data can either be structured or unstructured, and how we interact with that data varies. We must also be able to approach the data at the most granular level, which is the field data type. It is important to understand that the way we work with data is typically dictated by the way the data was designed. It is equally important that you are able to identify when the data sets you are tasked to work with may not have followed the principles of good design.

Lesson 2: Comparing and Contrasting Different Data Systems

Learning about common types of database processing and storage systems will help you understand the different types of structures and schemas you will encounter in your work as a data analyst. You don't have to know everything about all of these data systems, as they are controlled by other data roles in an organization. However, you will be accessing data stored and/or processed with these technologies.

Lesson 3: Recognizing AI's Impact on Data Projects

In this module, you will explore the transformative role of artificial intelligence (AI) and automation in the data analyst's toolkit. You will gain insight into how cutting-edge generative AI models are shaping the way organizations extract value from large and complex datasets, and how natural language processing (NLP) techniques prepare real-world data so it can be analyzed using machine learning methods. You'll also learn about foundational models and how different methods of machine learning enhance data analysis, from uncovering trends to making predictions. As an analyst, you should also be familiar with how robotic process automation (RPA) works, as automating routine tasks empowers you to focus on high-value tasks and drive better-informed business decisions.

Lesson 4: Comparing Languages and Tools for Data

When coding is mentioned, people often think of programmers and software developers. However, coding is an important skill for data professionals as well. Using the syntax required to manage an object is known as scripting code. Data analysts who understand software development have the capacity to move deeper into data engineering roles. Which each company may utilize different tools for different jobs, all analysts should have a basic understanding of the different options out there, and what function each serves.

Lesson 5: Using Data Acquisition Methods

As a data analyst, one of your most important responsibilities is acquiring reliable and relevant data to drive analysis and support decision-making. This lesson will introduce you to a wide array of data acquisition methods and foundational concepts every analyst needs to know. You'll learn how to identify and work with different file formats, explore public and synthetic datasets, design and administer surveys, and collect and integrate data from a variety of sources. Additionally, you'll discover key technologies for obtaining data, such as APIs, web services, and web scraping, as well as how to handle machine-generated files and log data.

Understanding these topics will equip you with the practical skills to gather high-quality data from diverse sources, whether you're working with structured files, pulling data from the web, or designing a survey. You'll also learn essential data processing workflows like Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT). Mastering these techniques is crucial, as the quality, relevance, and structure of your data directly affect the accuracy and impact of your analyses.

Lesson 6: Applying Quality Control to Data

Have you ever heard the phrase "garbage in, garbage out"? In the world of data analysis, the use of proper data validation methods can turn some of that "garbage in" into "one person's trash is another person's treasure." Quality data leads to quality reporting. A report would be problematic if the data was inaccurate, incomplete, or inconsistent. In this module, we will describe the quality assurance process and explain the reasons we check for data quality. We will learn how to understand data quality metrics and walk through different methods to verify and validate the data that we provide for reporting.

Lesson 7: Profiling and Cleansing Data

Rapid changes in business practices and business requirements are the main reasons we encounter less-than-perfect data sets or data structures in our work, leading to the need for cleansing and profiling data. Data sets are often disparate, and the data analyst has to be able to combine data sets from various sources. Then there are data sets that have a poor design or are attempting to retrofit a process into an off-the-shelf software, which also often need to be cleaned or profiled. When a company designs a system or process for handling their data, they base it on what they know in that moment. If you have ever heard the saying, “flying the plane while building it,” that is a fair description of how many organizations approach building data-centric systems. Once you know the types of imperfections data systems may have, you can start handling these common issues.

Lesson 8: Executing Data Manipulation Techniques

Data analysts play a vital role in transforming raw information into meaningful insights, and the ability to manipulate data efficiently is at the heart of this process. Mastering data manipulation is crucial for extracting accurate, actionable results from data sets. The data does not always naturally come to you clean and workable. Filtering and sorting helps you to gain deeper insights, while replacing values with more readable information can improve meaningfulness. You will also apply functions to correct and enhance data quality, and use strategies like deriving variables when you encounter a need for data that is not in your data set. These skills are interconnected—together they enable you to clean, structure, and enrich data so you can confidently answer business questions and drive decision-making.

Lesson 9: Building Queries to Model Data

As a data analyst, when you grow in your skills, you might also gain greater system access, including back-end access to the tables and views. This means that instead of exporting data and doing VLOOKUPs to create a join between the data sets, you will actually query the database. A common misconception that early data professionals have is that they will be working with clean data of the appropriate data type, and that all of the data they need will exist in the data set that is provided. However, having data that is clean and ready for analysis rarely happens, especially when dealing with legacy systems (older technology). This is why the advent of data warehouses and the progression of reporting technology over the last decade is impactful to data workers.

Even when the ETL has been designed to provide data sets, there is always more that can be added to provide a broader context around the data itself. You can easily put a product invoice list into your tool of choice and get counts, totals, and groups. However, if you can query and add other tables, like product sentiment, you can then explore dimensions in that data. However, even when using modern software that provides comprehensive data sets, there will always be an opportunity to clean and manipulate data to better gain insight into the story it will tell.

Lesson 10: Preparing for Data Analysis

When preparing to perform data analysis, you must first identify whether you're working with qualitative or quantitative data, as this can affect the type of analysis you choose to perform. Then, you need to examine your options and determine the type of analysis that best suits your needs. Analysis can range from exploratory, which is performed at a high level and applicable to all data sets, to other types that are more specific to the goal and outcome of the research.

Lesson 11: Applying Descriptive Statistical Methods

The work of a data analyst involves summarizing overall findings and insights after analyzing the data. Data analysts describe data in conversations and presentations, and thus learning how to describe the data through various methods (e.g., averages of data, range of data) is a necessary part of the analyst's role. Data analysis also draws conclusions about the distribution of data and may show these findings visually in many workplace scenarios.

At the beginning of a project, descriptive statistical methods are invaluable for understanding your data and finding issues that need to be addressed. Understanding how to describe your data will help you to either be confident in the accuracy and quality of the data or speak to a lack of confidence in the data, depending on what your analysis reveals.

Lesson 12: Illustrating Different Statistical Methods

Inferential statistics involves reaching conclusions based on evidence and reasoning using data and includes methods such as confidence intervals, t-tests, and hypothesis testing. Using inferential statistics on both large and small sample sizes allows us to create findings for just about anything that we can gather data on and measure. With inferential statistics, we can analyze data gathered about programs, people, things, and even interventions. This differs from descriptive analysis, which just aims to describe the data we are using to draw conclusions. Inferential statistics also allow us to utilize smaller samples to represent a larger population. We use different statistical tests to determine the statistical significance of our findings, leveraging p-values.

Lesson 13: Summarizing Business Requirements in a Report Format

A key skill for the data analyst is the ability to translate information into business requirements, and translate business requirements into technical requirements. Developing business requirements in the data world means that, as an analyst, you understand the types of requirements needed to perform any business request that comes your way. You must then work through those requirements to prepare the appropriate deliverable, like a style of report or dashboard, at the right time for the right people. From the outset, a data analyst must develop a high-level understanding of the audience for the data, where the data came from, and how it will be delivered. This information will guide you in designing reports and dashboards that meet requirements and have the appropriate view filters and navigation. Understanding and outlining the requirements for data helps to ensure that reporting not only meets specification but is also usable by the people who need the information.

Lesson 14: Using the Appropriate Type of Visualization

Information will be more easily digested if you can present it visually versus showing endless lines of data in a spreadsheet or table. Data lends itself to visualization of all types, but just because a data set can be visualized a certain way doesn't mean that method is the right choice. When visualizing your data, one of the first steps you should take is determining which type of visualization is best suited for your data. Understanding a visual picture that represents thousands of lines of data is an art form.

The key to implementing effective data visualization is learning how to select the ideal method for your data. In some cases, basic visuals may be all you need to make a point, while you might require advanced visuals to bring other issues to light. Pivots are some of the most common visuals you will use, but they aren't always the best option. When deciding what visual you'll use, it's in your best interest to consider how a certain type of visualization supplements the story your data needs to tell.

Lesson 15: Designing Components for Reports and Dashboards

When developing reports and dashboards as a data analyst, you must consider not only how to make the data meaningful for what you are reporting on for the organization but also how to stay in accordance with the organization's style. Your audience will desire a great user experience, so reports and dashboards must be designed with this in mind. Using proper colors, knowing the needs of your audience, and following the company style guide are all important parts of the process. Everything must be considered right down to the font style and size used for the data. You also mustn't forget to include key elements, like the refresh date, and the narrative and key talking points, so you can be sure your report conveys what the data is truly saying. You should also provide answers for the questions your audience may have and include critical supporting information that not only helps them, but also helps you move on to the next task at hand.

Lesson 16: Preparing for the Delivery and Consumption of Reports

As an analyst, you will discover that there are differences in the types of reports you may create. Some will be static, and some will be dynamic. This designation refers to the way that the data refreshes, and what this means for the data analyst is either the audience will serve themselves from the dashboard you created, or you will update your reports and provide them a static copy. You will also receive one-time requests, which is referred to as ad-hoc reporting. You may find that these requests can sometimes lead to regular reporting for the organization. Whether fulfilling a one-time request or running a routine report, you will also need to consider the timing of the reporting, whether it covers a specified period of time or just a point in time. No matter the type or style of reporting, these are all important considerations for when people will be given access and their ability to consume information through the reports you provide.

Lesson 17: Summarizing the Importance of Data Governance

When a company adopts a data governance plan, it involves the people, processes, and technology needed to control data. Data governance aims to ensure that data maintains its quality and integrity by establishing definitions, rules, and standardization. Data governance also allows for the organization to adhere to regulations and helps maintain compliance. Data governance impacts the quality of data in a very positive way for the data analyst, as it sets the rules of the data from the top level to all levels of the organization.

Lesson 18: Explaining Data Management Concepts

Data management is key to data quality, as it enables the existence of a single source of truth at an organization. While data management is necessary in any organization, it is especially important for large organizations and regulated industries who must comply with regulations. Data management is traditionally supported through data governance and the use of dedicated software.

Lesson 19: Troubleshooting Issues and Measuring Performance

Troubleshooting not only requires a thorough knowledge of data concepts and tools, but also critical thinking. No matter what level your knowledge is overall, if you can look at troubleshooting as problem solving and use critical thinking, you'll be able to find answers.

All necessary course materials are included.

Certification(s):

This course prepares a student to take the CompTIA Data+ DA0-002 national certification exam.

System Requirements:

Internet Connectivity Requirements:

• Cable, Fiber, DSL, or LEO Satellite (i.e. Starlink) internet with speeds of at least 10mb/sec download and 5mb/sec upload are recommended for the best experience.

NOTE: While cellular hotspots may allow access to our courses, users may experience connectivity issues by trying to access our learning management system.  This is due to the potential high download and upload latency of cellular connections.   Therefore, it is not recommended that students use a cellular hotspot as their primary way of accessing their courses.

Hardware Requirements:

• CPU: 1 GHz or higher
• RAM: 4 GB or higher
• Resolution: 1280 x 720 or higher.  1920x1080 resolution is recommended for the best experience.
• Speakers / Headphones
• Microphone for Webinar or Live Online sessions.

Operating System Requirements:

• Windows 7 or higher.
• Mac OSX 10 or higher.
• Latest Chrome OS
• Latest Linux Distributions
  

NOTE: While we understand that our courses can be viewed on Android and iPhone devices, we do not recommend the use of these devices for our courses. The size of these devices do not provide a good learning environment for students taking online or live online based courses.

Web Browser Requirements:

• Latest Google Chrome is recommended for the best experience.
• Latest Mozilla FireFox
• Latest Microsoft Edge
• Latest Apple Safari

Basic Software Requirements (These are recommendations of software to use):

• Office suite software (Microsoft Office, OpenOffice, or LibreOffice)
• PDF reader program (Adobe Reader, FoxIt)
• Courses may require other software that is described in the above course outline.