Data analytics and AI is the process of analyzing raw data to find trends and answer questions. It has a broad scope across the field. This process includes many different techniques and goals that can shift from industry to industry.
The data analytics and AI process has components that can help a variety of initiatives. By combining these components, a successful data analytics initiative can help answer business questions related to historical trends, future predictions and decision making. Artificial intelligence (AI) is a set of technologies that enable computers to perform a variety of advanced functions, including the ability to see, understand and translate spoken and written language, analyze data, make recommendations, and more.
What is data analytics and AI ?
Data analysis and AI is the process of gleaning insights from data to inform better business decisions. Analyzing data typically moves through five iterative phases:
- Identify the data you want to analyze
- Collect the data
- Clean the data in preparation for analysis
- Analyze the data
- Interpret the results of the analysis
Data analysis and AI can take different forms, depending on the question you’re trying to answer. Briefly, descriptive analysis tells us what happened; diagnostic analysis tells us why it happened; predictive analytics forms projections about the future; and prescriptive analysis creates AI actionable advice on what actions to take.
Hear from experts in the field about what data analysis means to them.
Steps in Data Analytics and AI :
The process of data analytics and AI involved in data analysis involves several steps:
- Determine the data requirement or how the data is grouped. Data may be separated age, demographic, income, or gender. Data values may be numerical or divided by category.
- Collect the data. This can be done through a variety of sources such as computers, online sources, cameras, environmental sources, or through personnel.
- Organize the data after collecting can be analyzed. This may take place on a spreadsheet or other form of software that can take statistical data.
- Clean up the data before it is analyzed. This is done by scrubbing it and ensuring there’s no duplication or error and that it is not incomplete. This step helps correct any errors before the data goes on to a data analyst to be analyzed.
What Does A Data Analytics and AI Do?
Data analysts make sense of information. They’ll gather, examine, and present data so leaders can make informed decisions.
Essentially, they’re telling stories with numbers by:
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Extracting, transforming, and loading (ETL) to find, clean, and combine data.
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Building databases to store and query information.
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Using computer programming to mine, process, and model data.
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Creating visualizations to tell stories using charts and graphs.
Tasks and responsibilities
A data analyst and AI is a person who gathers and interprets data to solve a specific problem. The role includes plenty of time spent with data but entails communicating findings too.
Here’s what many data analysts do on a day-to-day basis:
- Gather data: Analysts often collect data themselves. This could include conducting surveys, tracking visitor characteristics on a company website, or buying data sets from data collection specialists.
- Clean data: Raw data might contain duplicates, errors, or outliers. Cleaning the data means maintaining the data quality in a spreadsheet or through a programming language so that your interpretations won’t be wrong or skewed.
- Model data: This entails creating and designing the structures of a database. You might choose what data types to store and collect, establish how data categories are related, and work through how the data appears.
- Interpret data: Interpreting data will involve finding patterns or trends in data that can help you answer the question at hand.
- Present: Communicating the results of your findings will be a crucial part of your job. You create visualization like charts and graphs, write reports, and present information to interested parties.
What Education Do You Need in ?
Bachelor’s Degree in data analytics, computer science, math, or a similar subject. This provides a strong foundation in statistics, programming, and analytics.
Boot camps can be an alternative to earning a degree, or a quick way to upskill. They’re shorter than degree programs and teach on-the-job skills.
Certificates validate your skills and focus on specific technologies. Some options are:
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Amazon Web Services (AWS) Big Data Specialty Certification
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CompTIA Data+
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Google Data Analyst Certificate
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IBM Data Science Professional Certificate
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Microsoft Certified: Data Analyst Associate
Data Analytics FAQ
Data analytics and AI helps individuals and organizations make sense of data. Data analysts typically analyze raw data for insights and trends. They use various tools and techniques to help organizations make decisions and succeed.
There are various types of data analysis including descriptive, diagnostic, prescriptive and predictive analytics. Each type is used for specific purposes depending on the question a data analyst is trying to answer. For example, a data analyst would use diagnostic analytics to figure out why something happened.
There are various tools used in data analysis. Some data analysts use business intelligence software, such as Tableau. Others may use programming languages such as SQL or Python, which have various statistical and visualization libraries.
There is not much career data specific to data analysts, but they fall within the same category as data scientists. According to the U.S. Bureau of Labor Statistics, the projected growth for data scientists is 35% between 2022 and 2032External link:open_in_new. The same data shows that data scientists earned a median salary of $103,500 in 2022. However, salary compensation for data analysts varies depending on where they work and what industry they work in.
What is a Database?
The basic definition of a database is a system that helps collect and organize data. That data consists of various facts or other points that are related to the subject the database focuses on. For instance, the data contained by a customer service management database could consist of customer names, addresses, purchase histories and other relevant information.