Choosing the right analytics certificate program as a Federal employee will depend on what type of career as a data analyst you want to have in the federal government.
There is a slew of Federal employee training courses out there which include analytics certificate programs and professional development training for those who want to pursue or move up the proverbial ladder in their federal careers. But how does one choose the right professional training that can give their efforts that much-needed boost? Let’s find out.
Certificate vs. Certification
Before we move further, let’s talk about the difference between certificates and certification. While these two terms sound similar, their definitions are very different. For instance, while a data analytics certificate shows that an individual has received some education in the field, a data analytics certification is proof that one has passed an examination, test, or assessment that is required for a given task.
When it comes to data analytics, certifications are more desirable than a certificate program, mainly because the latter does not prove that you have expertise in the specific field, just that you’ve completed a course. A data analytics certification on the other hand proves that one has the required skills needed to complete a task, which in this case is that of a data analyst.
Characteristics of a Good Analytics Certificate Program
A primary duty of a business analyst is to assist in making decisions based on market information. The success of a firm or organization is primarily reliant on making solid and timely judgments, that are backed by hard data.
This is why data analytics has long played a significant role in improving the viability of business decisions. However, with recent advancements in information technology, the relevance of data analytics in decision-making has grown exponentially. This has resulted in more and more business analysts being employed in private and government organizations.
According to research by the National Association of State CIOs, the top 10 priorities list includes, data management and analytics; with government organizations searching for professionals with expertise in data architecture, data governance, predictive analytics, strategic business intelligence, and Big Data. This comes as no surprise since local and state governments are always looking for ways to overcome managerial and technical challenges to improve their services for the people.
The field of data analytics can be broken down into four sub-categories, which should give those who are looking for federal careers in data analytics a good idea of what type of data analysis they want to work with.
Prescriptive Data Analytics
Prescriptive analytics, a subset of data analytics seeks to provide answers to questions such as, "What needs to be done to achieve this outcome?" It entails using various technologies to help companies make better decisions. The data used in prescriptive analytics, includes data relating to potential situations, available resources, as well as present performance prior to recommending a possible course of action. It may be used to make judgments on any time horizon, from the short to the long term.
In this way, prescriptive data analytics is the inverse of descriptive analytics, which looks at decisions and results after they have occurred.
Predictive Data Analytics
Predictive analytics forecasts prospective situations based on data collected, which helps in the decision-making process.
These predictions can cover possible events in the near future, such as forecasting particular machinery to fail later that week—or predicting occurrences in the distant future, such as projecting a company's cash flows for the following year.
Manual or machine-learning techniques can be used to do predictive analysis with the help of previously collected data to make future predictions.
A great example of predictive data analytics is regression analysis that’s used to determine the relationship between multiple variables. Predictive data analytics helps by cutting through the noise and ever-changing scenarios.
Diagnostic Data Analytics
Diagnostic data analytics answers the “Why” when things happen. For instance, Diagnostic analytics gives critical information on why a trend occurred and is essential for professionals who want to utilize data to back up their judgments.
In short, the technique of analyzing data to discover the origin and connection between trends is known as diagnostic analytics. Manual diagnostic analysis can also be done using algorithms or statistical software such as Microsoft Excel.
Diagnostic analytics may be used to determine why something occurred and the links between various components. After you've mastered the fundamentals, investigate these four instances of diagnostic analytics in action and how they could apply to your business.
Descriptive Data Analytics
Descriptive analytics is a sort of data analytics that examines historical data to provide a narrative of what occurred. Results are often displayed in readily understandable reports and various other visualizations.
Data analytics is classified into four categories such as prescriptive, descriptive, predictive, and diagnostic analytics. Real-time analytics, the fifth category, examines data as it is created, gathered, or updated.
In short, it is a statistical interpretation that is used to detect patterns and correlations in historical data. The goal of descriptive analytics is to characterize an event, occurrence, or consequence. It aids in understanding what has occurred in the past and provides firms with a solid foundation for tracking trends.
Descriptive analytics employs a variety of statistical analysis approaches to slice and dice raw data into a format that allows users to spot patterns, uncover anomalies, and enhance and compare planning. Descriptive analytics is most impressive when enterprises use it to compare variables over time or against each other. For instance, the finance department in a government agency may analyze data month over month or against similar categories, such as age, gender, professional, ethnicity, or zip code in order to improve services.
Ending Note
The best possible analytics programs for federal employees would be the ones designed for them since they would have taken into consideration the data sets federal employees have access to and the analytics goals various federal departments/agencies might have.
Choosing the Right Analytics Certificate Program as a Federal Employee
Client:
Choosing the right analytics certificate program as a Federal employee will depend on what type of career as a data analyst you want to have in the federal government.
There is a slew of Federal employee training courses out there which include analytics certificate programs and professional development training for those who want to pursue or move up the proverbial ladder in their federal careers. But how does one choose the right professional training that can give their efforts that much-needed boost? Let’s find out.
Certificate vs. Certification
Before we move further, let’s talk about the difference between certificates and certification. While these two terms sound similar, their definitions are very different. For instance, while a data analytics certificate shows that an individual has received some education in the field, a data analytics certification is proof that one has passed an examination, test, or assessment that is required for a given task.
When it comes to data analytics, certifications are more desirable than a certificate program, mainly because the latter does not prove that you have expertise in the specific field, just that you’ve completed a course. A data analytics certification on the other hand proves that one has the required skills needed to complete a task, which in this case is that of a data analyst.
Characteristics of a Good Analytics Certificate Program
A primary duty of a business analyst is to assist in making decisions based on market information. The success of a firm or organization is primarily reliant on making solid and timely judgments, that are backed by hard data.
This is why data analytics has long played a significant role in improving the viability of business decisions. However, with recent advancements in information technology, the relevance of data analytics in decision-making has grown exponentially. This has resulted in more and more business analysts being employed in private and government organizations.
According to research by the National Association of State CIOs, the top 10 priorities list includes, data management and analytics; with government organizations searching for professionals with expertise in data architecture, data governance, predictive analytics, strategic business intelligence, and Big Data. This comes as no surprise since local and state governments are always looking for ways to overcome managerial and technical challenges to improve their services for the people.
The field of data analytics can be broken down into four sub-categories, which should give those who are looking for federal careers in data analytics a good idea of what type of data analysis they want to work with.
Prescriptive Data Analytics
Prescriptive analytics, a subset of data analytics seeks to provide answers to questions such as, "What needs to be done to achieve this outcome?" It entails using various technologies to help companies make better decisions. The data used in prescriptive analytics, includes data relating to potential situations, available resources, as well as present performance prior to recommending a possible course of action. It may be used to make judgments on any time horizon, from the short to the long term.
In this way, prescriptive data analytics is the inverse of descriptive analytics, which looks at decisions and results after they have occurred.
Predictive Data Analytics
Predictive analytics forecasts prospective situations based on data collected, which helps in the decision-making process.
These predictions can cover possible events in the near future, such as forecasting particular machinery to fail later that week—or predicting occurrences in the distant future, such as projecting a company's cash flows for the following year.
Manual or machine-learning techniques can be used to do predictive analysis with the help of previously collected data to make future predictions.
A great example of predictive data analytics is regression analysis that’s used to determine the relationship between multiple variables. Predictive data analytics helps by cutting through the noise and ever-changing scenarios.
Diagnostic Data Analytics
Diagnostic data analytics answers the “Why” when things happen. For instance, Diagnostic analytics gives critical information on why a trend occurred and is essential for professionals who want to utilize data to back up their judgments.
In short, the technique of analyzing data to discover the origin and connection between trends is known as diagnostic analytics. Manual diagnostic analysis can also be done using algorithms or statistical software such as Microsoft Excel.
Diagnostic analytics may be used to determine why something occurred and the links between various components. After you've mastered the fundamentals, investigate these four instances of diagnostic analytics in action and how they could apply to your business.
Descriptive Data Analytics
Descriptive analytics is a sort of data analytics that examines historical data to provide a narrative of what occurred. Results are often displayed in readily understandable reports and various other visualizations.
Data analytics is classified into four categories such as prescriptive, descriptive, predictive, and diagnostic analytics. Real-time analytics, the fifth category, examines data as it is created, gathered, or updated.
In short, it is a statistical interpretation that is used to detect patterns and correlations in historical data. The goal of descriptive analytics is to characterize an event, occurrence, or consequence. It aids in understanding what has occurred in the past and provides firms with a solid foundation for tracking trends.
Descriptive analytics employs a variety of statistical analysis approaches to slice and dice raw data into a format that allows users to spot patterns, uncover anomalies, and enhance and compare planning. Descriptive analytics is most impressive when enterprises use it to compare variables over time or against each other. For instance, the finance department in a government agency may analyze data month over month or against similar categories, such as age, gender, professional, ethnicity, or zip code in order to improve services.
Ending Note
The best possible analytics programs for federal employees would be the ones designed for them since they would have taken into consideration the data sets federal employees have access to and the analytics goals various federal departments/agencies might have.
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