CompTIA Data+ Certification Training

Compare ETL and ELT approaches for loading data into a data warehouse, and explain delta load, which pulls only new data to speed processing and reduce costs.

Learn how APIs enable app-to-app integration with clear documentation, defined terms and conditions, and the ability to pull data from back-end systems.

Learn to pull data from an API using Python and the Requests library, with the Cat API as a practical example; check status codes, parse JSON, and extract image URL.

For CompTIA Data+ certification training, explore web scraping basics by examining an HTML page and learning how tags, attributes, id, and class guide data extraction.

Learn how to apply web scraping in Python using Selenium, Scrapy, and Beautiful Soup, focusing on extracting HTML elements, tags, and course cards with find and find_all.

Learn to scrape a real eBay page with Python, requests, and Beautiful Soup to extract book names from h3 tags with class s item title, using soup.text.

Explore probabilistic sampling and how to select samples that reflect the population. Learn four main probabilistic techniques: simple random, systematic, stratified, and cluster sampling, and their impact on data analysis.

Explore non probabilistic sampling and its four main types—convenience, voluntary response, purposive, and snowball—and note how the absence of randomness compares with probabilistic sampling.

Identify and remove duplicate and redundant data to improve data quality, save memory, and enable accurate analysis of datasets. Handle invalid data carefully to ensure trustworthy results.

Explore handling missing values and outliers in data sets, using mean/median/mode imputation, last observation carried forward, next observation carried backward, linear interpolation, and K nearest neighbors to preserve data integrity.

Validate data from multiple sources by addressing specification mismatches and data type validation, converting formats when needed, and ensuring accurate analytics.

Learn to manipulate data with filtering and sorting in Excel and SQL queries, extracting high grade records and ordering results to reveal key insights from student datasets.

Master logical operators and data aggregation in SQL and NoSQL contexts, filtering by grade and completed tasks, using and, or, not, between, in, and group by with count.

Explore query optimization for big data, mastering parameterization, indexing, temporary tables, subset selection, and a clear execution plan to reduce size and speed up queries.

Explore the three measures of central tendency: mean, median, and mode, and learn practical calculations, including weighted mean, floor and ceiling medians, and how these concepts apply to data analysis.

Explore probability distributions by contrasting discrete and continuous random variables, and learn how distribution functions like probability mass functions and cumulative distribution functions quantify outcomes from coins to dice.

Explore measures of dispersion, including min, max, range, variance, and standard deviation, to complement the mean and reveal data spread across populations.

Explore confidence intervals and error bars to gauge how likely data points fall within a range around the mean, and relate this to standard deviation and the three sigma rule.

Form the null and alternate hypotheses in hypothesis testing, then test with data to determine if boys are taller than girls. Compare within- and between-group variance with error bars.

Explore how to use a t test to compare two group means, calculate the t value, and interpret p values with one-tailed and two-tailed options in CompTIA data+ certification training.

Compare z tests and t tests, noting when to use a z test (n>30, known sigma) and one-tailed and two-tailed tests, p-values, and type I and II errors guide decisions.

Apply the chi-squared (G squared) test to compare observed and expected frequencies, and examine goodness of fit, tests of independence, and homogeneity, along with their advantages and limitations.

Explains correlation as the coordinated movement between two variables, showing positive and negative relationships. Demonstrates how Pearson's correlation coefficient measures this relationship and how a correlation matrix summarizes connections.

Explore linear regression to understand data relationships and make predictions using a regression line, least squares, and residuals across simple and multiple variables.

Choose among the four data analysis types—trend analysis, performance analysis, exploratory data analysis, and link analysis. Apply the selected method to time series, performance metrics, big data, or network graphs.

Explore common data analytics tools like Excel, SQL, SAS, Python with pandas, R, Rapid Minor, Cognex Analytics, and SPSS Software and SPSS Modeler to manipulate and analyze diverse data sets.

Compare popular data analytics tools like Tableau, Power BI, and Click for dashboards and visualizations. Assess alternatives such as Domo, MicroStrategy, SAP Business Objects, and Minitab for different data tasks.

Learn how scatter plots reveal data relationships, illustrated with car mpg (highway vs city), grouping by drive train, labeling, and axis scaling with logarithms.

Explore line plots, defined by intercept and slope, and learn how they reveal trends and seasonality in time series data, while cautioning against overuse in noninformative cases.

Explore pie charts as a plot type for data analysis, using an example of metals won by U.S. athletes at the Olympics to illustrate distribution, starting angles, and exploded charts.

Learn how bar charts and histograms visualize data, explain their construction for categorical data and distribution, and adjust bins, orientation, and coloring to reveal skewness, density, and most frequent ranges.

Explore advanced bar charts, including side-by-side and stacked forms, and the normalized stacked charts and Nightingale chart to compare categories, proportions, and counts in data analytics.

Explore heat maps, geographic maps, and treemaps to visualize data with color bars and size, quickly identify high and low values, and interpret correlations.

Explore waterfall charts to track value progress and final values, infographic dashboards with stacked plots and tables, and word clouds that highlight the most important words for clear messaging.

Explore the rgb color model, where red, green, and blue render all computer colors. Distinguish categorical from continuous data and choose diverging or sequential palettes for clarity.

Explore overplotting challenges in data visualization and learn practical solutions, adjust transparency, apply jittering for discrete values, and use rugs to reveal point density, ensuring accurate interpretation of scatter plots.

Eliminate chart junk and apply key chart elements, such as clear titles, axis labels, and units, to produce accurate, easy-to-interpret graphs; avoid misleading plots and improper time ranges.

Develop high-quality dashboards by identifying users and needs, defining topics and terminology, using color and hierarchy, selecting dashboard types (operational, analytical, strategic), and applying the 5/2 rule.

Explore the key report types: informational, analytical, operational, product, industry, department, progress, internal vs external, and vertical or lateral, to visualize and analyze business data for informed decisions.

Explore data governance concepts and its six components: data architecture, quality, data management, security, software and tools, and compliance, to transform data into high-quality, compliant assets.

Explore data quality dimensions essential to data governance, including completeness, accuracy, consistency, validity, uniqueness, and integrity, to ensure reliable analysis and regulatory compliance.

Explore data quality validation methods, including source system loop back, source to source verification, issue tracking, certification, statistics collection, and workflow management, to ensure accurate data and support governance.

Learn to append and merge data tables, using vertical and horizontal consolidation based on common columns, while handling missing values across 1-to-1, 1-to-many, and many-to-many relationships.

Explore how to consolidate data sets using inner, outer, left, right, and full joins on common IDs, handling missing values, and choosing the right join for data analysis.

Explore data standardization to create a common, understandable format that supports system integration, and learn how a data dictionary documents data objects, definitions, missing values, and usage rules.

Identify sensitive data and align with GDPR, CCPA, Prisma, and ISO 27000 to implement a data use compliance plan, perform regular security checks, and update protections to prevent leaks.

Centralize data to streamline access and improve data quality, reducing migrations and duplication. Consolidated databases enable faster lookups, integrated formats, easier updates, and clearer collaboration.