course overview
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Overview
CompTIA's Data+ Certification is an early-career data analytics certification for professionals tasked with developing and promoting data-driven business decision-making.
The exam will certify the successful candidate has the knowledge and skills required to transform business requirements in support of data-driven decisions by:
Mining data
Manipulating data
Applying basic statistical methods
Analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle
Audience
Analytics professionals responsible for collecting and analyzing data in order to provide an accurate picture of business operations or performance for a company. The analyst may specialize in a core business function such as marketing and sales, finance and accounting, HR, or operations or may be aspiring to a more general data analyst role. Roles for which this course would be ideal are: Data Analysts, Report Analysts, Business Intellignece Analysts, Market Research Analysts or Operations Analysts.
Skills Gained
This course can benefit you in two ways. If you intend to pass the CompTIA Data+(Exam DA0-001) certification examination, this course can be a significant part of your preparation. But certification is not the only key to professional success in the field of data analysis. Today's job market demands individuals with demonstrable skills, and the information and activities in this course can help you build your data skill set so that you can confidently perform your duties in any entry-level data analysis role.
On course completion, you will be able to do the following:
Identify basic concepts of data schemas
Understand different data systems
Understand types and characteristics of data
Compare and contrast different data structures, formats, and markup languages
Explain data integration and collection methods
Identify common reasons for cleansing and profiling data
Execute different data manipulation techniques
Explain common techniques for data manipulation and optimization
Apply descriptive statistical methods
Describe key analysis techniques
Understand the use of different statistical methods
Use the appropriate type of visualization
Express business requirements in a report format
Design components for reports and dashboards
Distinguish different report types
Summarize the importance of data governance
Apply quality control to data
Explain master data management concepts
Identify common data analytics tools
Prerequisites
To ensure your success in this course, you should have 18–24 months of hands-on experience working in a business intelligence, report/data analyst job role.
You should have a working knowledge of Microsoft Excel or a spreadsheet program.
You should understand how to build basic math calculations, like add, subtract, divide, and multiply (basic arithmetic).
You should know how to build basic functions like Sums, Average, and Count.
You should understand the basics of sorting and filtering data sets in Excel or a similar spreadsheet program.
You should have a working knowledge of how to build very basic pivot tables.
You should have some understanding of databases and all knowledge toward understanding how databases designed will be helpful.
You should have a basic understanding of how to build simple charts in using data.
Outline
Lesson 1: Identifying Basic Concepts of Data Schemas
Topic 1A: Identify Relational and Non-Relational Databases
Topic 1B: Understand the Way We Use Tables, Primary Keys, and Normalization
Lesson 2: Understanding Different Data Systems
Topic 2A: Describe Types of Data Processing and Storage Systems
Topic 2B: Explain How Data Changes
Lesson 3: Understanding Types and Characteristics of Data
Topic 3A: Understand Types of Data
Topic 3B: Break Down the Field Data Types
Lesson 4: Comparing and Contrasting Different Data Structures, Formats, and Markup Languages
Topic 4A: Differentiate Between Structured Data and Unstructured Data
Topic 4B: Recognize Different File Formats
Topic 4C: Understand the Different Code Languages Used for Data
Lesson 5: Explaining Data Integration and Collection Methods
Topic 5A: Understand the Processes of Extracting, Transforming and Loading Data
Topic 5B: Explain API/Web Scraping and Other Collection Methods
Topic 5C: Collect and Use Public and Publicly Available Data
Topic 5D: Use and Collect Survey Data
Lesson 6: Identifying Common Reasons for Cleansing and Profiling Data
Topic 6A: Learn to Profile Data
Topic 6B: Address Redundant, Duplicated, and Unnecessary Data
Topic 6C: Work with Missing Values
Topic 6D: Address Invalid Data
Topic 6E: Convert Data to Meet Specifications
Lesson 7: Executing Different Data Manipulation Techniques
Topic 7A: Manipulate Field Data and Create Variables
Topic 7B: Transpose and Append Data
Topic 7C: Query Data
Lesson 8: Explaining Common Techniques for Data Manipulation
and Optimization
Topic 8A: Use Functions to Manipulate Data
Topic 8B: Use Common Techniques for Query Optimization
Lesson 9: Applying Descriptive Statistical Methods
Topic 9A: Use Measures of Central Tendency
Topic 9B: Use Measures of Dispersion
Topic 9C: Use Frequency and Percentages
Lesson 10: Describing Key Analysis Techniques
Topic 10A: Get Started with Analysis
Topic 10B: Recognize Types of Analysis
Lesson 11: Understanding the Use of Different Statistical Methods
Topic 11A: Understand the Importance of Statistical Tests
Topic 11B: Break Down the Hypothesis Test
Topic 11C: Understand Tests and Methods to Determine Relationships Between Variables
Lesson 12: Using the Appropriate Type of Visualization
Topic 12A: Use Basic Visuals
Topic 12B: Build Advanced Visuals
Topic 12C: Build Maps with Geographical Data
Topic 12D: Use Visuals to Tell a Story
Lesson 13: Expressing Business Requirements in a Report Format
Topic 13A: Consider Audience Needs When Developing a Report
Topic 13B: Describe Data Source Considerations for Reporting
Topic 13C: Describe Considerations for Delivering Reports and Dashboards
Topic 13D: Develop Reports or Dashboards
Topic 13E: Understand Ways to Sort and Filter Data
Lesson 14: Designing Components for Reports and Dashboards
Topic 14A: Choose Design Elements for Reports/Dashboards
Topic 14B: Utilize Standard Elements for Reports/Dashboards
Topic 14C: Create a Narrative and Other Written Elements
Topic 14D: Understand Deployment Considerations
Lesson 15: Distinguishing Different Report Types
Topic 15A: Understand How Updates and Timing Affect Reporting
Topic 15B: Differentiate Between Types of Reports
Lesson 16: Summarizing the Importance of Data Governance
Topic 16A: Define Data Governance
Topic 16B: Understand Access Requirements and Policies
Topic 16C: Understand Security Requirements
Topic 16D: Understand Entity Relationship Requirements
Lesson 17: Applying Quality Control to Data
Topic 17A: Describe Characteristics, Rules, and Metrics of Data Quality
Topic 17B: Identify Reasons to Quality Check Data and Methods of Data Validation
Lesson 18: Explaining Master Data Management Concepts
Topic 18A: Explain the Basics of Master Data Management
Topic 18B: Describe Master Data Management Processes
Appendix A: Identifying Common Data Analytics Tools
Appendix B: Mapping Course Content to CompTIA Data+ Certification (DA0-001)
If you need training for 3 or more people, you should ask us about onsite training. Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. Its a cost effective option. One on one training can be delivered too, at reasonable rates.
Submit an enquiry from any page on this site and let us know you are interested in the requirements box, or simply mention it when we contact you.
All $ prices are in USD unless it’s a NZ or AU date
SPVC = Self Paced Virtual Class
LVC = Live Virtual Class
Our clients have included prestigious national organisations such as Oxford University Press, multi-national private corporations such as JP Morgan and HSBC, as well as public sector institutions such as the Department of Defence and the Department of Health.