Overview of Data Analysis

Question 1

Descriptive analytics

Answer 1

Descriptive analytics help answer questions about what has happened based on historical data. Descriptive analytics techniques summarize large datasets to describe outcomes to stakeholders.

Question 2

Diagnostic analytics

Answer 2

Diagnostic analytics help answer questions about why events happened. Diagnostic analytics techniques supplement basic descriptive analytics, and they use the findings from descriptive analytics to discover the cause of these events.

Question 3

Predictive analytics

Answer 3

Predictive analytics help answer questions about what will happen in the future. Predictive analytics techniques use historical data to identify trends and determine if they’re likely to recur.

Question 4

Prescriptive analytics

Answer 4

Prescriptive analytics help answer questions about which actions should be taken to achieve a goal or target. Prescriptive analytics techniques rely on machine learning as one of the strategies to find patterns in large datasets.

Question 5

Cognitive analytics

Answer 5

Cognitive analytics attempt to draw inferences from existing data and patterns, derive conclusions based on existing knowledge bases, and then add these findings back into the knowledge base for future inferences, a self-learning feedback loop. Effective cognitive analytics depend on machine learning algorithms, and will use several natural language processing concepts to make sense of previously untapped data sources, such as call center conversation logs and product reviews.

Question 6

Business analyst

Answer 6

A business analyst is closer to the business and is a specialist in interpreting the data that comes from the visualization.

Question 7

Data analyst

Answer 7

A data analyst enables businesses to maximize the value of their data assets through visualization and reporting tools such as Microsoft Power BI. Data analysts are responsible for profiling, cleaning, and transforming data. Their responsibilities also include designing and building scalable and effective data models, and enabling and implementing the advanced analytics capabilities into reports for analysis.

Question 8

Data engineer

Answer 8

They manage and secure the flow of structured and unstructured data from multiple sources. Primary responsibilities of data engineers include the use of on-premises and cloud data services and tools to ingest, egress, and transform data from multiple sources.

Question 9

Data scientist

Answer 9

Data scientists perform advanced analytics to extract value from data. Their work can vary from descriptive analytics to predictive analytics. Descriptive analytics evaluate data through a process known as exploratory data analysis (EDA). Predictive analytics are used in machine learning to apply modeling techniques that can detect anomalies or patterns. These analytics are important parts of forecast models.

Question 10

Database administrator

Answer 10

A database administrator implements and manages the operational aspects of cloud-native and hybrid data platform solutions that are built on Microsoft Azure data services and Microsoft SQL Server. A database administrator is responsible for the overall availability and consistent performance and optimizations of the database solutions.

Question 11

the data analysis process: Prepare

Answer 11

Data preparation is the process of profiling, cleaning, and transforming your data to get it ready to model and visualize.

• It involves ensuring the integrity of the data, correcting wrong or inaccurate data, identifying missing data, converting data from one structure to another or from one type to another, or even a task as simple as making data more readable.
• Data preparation also involves understanding how you’re going to get and connect to the data and the performance implications of the decisions.
• Privacy and security assurances are also important.

Question 12

the data analysis process: Model

Answer 12

Data modeling is the process of determining how your tables are related to each other. This process is done by defining and creating relationships between the tables. The model is another critical component that has a direct effect on the performance of your report and overall data analysis. The process of preparing data and modeling data is an iterative process.

Question 13

the data analysis process: Visualize

Answer 13

The ultimate goal of the visualize task is to solve business problems. A well-designed report should tell a compelling story about that data, which will enable business decision makers to quickly gain needed insights. An important aspect of visualizing data is designing and creating reports for accessibility.

Question 14

the data analysis process: Analyze

Answer 14

you should understand the analytical capabilities of Power BI and use those capabilities to find insights, identify patterns and trends, predict outcomes, and then communicate those insights in a way that everyone can understand.

Question 15

the data analysis process: Manage

Answer 15

Power BI consists of many components, including reports, dashboards, workspaces, datasets, and more. As a data analyst, you are responsible for the management of these Power BI assets, overseeing the sharing and distribution of items, such as reports and dashboards, and ensuring the security of Power BI assets. The management of Power BI assets helps reduce the duplication of efforts and helps ensure security of the data.