Introduction: Data-Focused Fields
Zennemis – Data-Focused Fields. In the area of analytics, look up the most common words and phrases. Look into the duties of jobs in areas that focus on data. Learn about the different kinds of data, such as organized, unstructured, and semi-structured data. Find out more about how analytics work, such as predictive analytics. Compare terms used in data science and analytics with those used in business analytics and analysis. You could also learn how to use big data tools, computer languages, and analytics software.
Data-Focused Fields
Data Analyst
Data analysts are tech-savvy interpreters who gather, sort, and change data so that smart choices can be made. When it comes to analytics, they know a lot about the “Big 4”: data cleaning, data mining, data visualization, and medical analytics. Analytical tools are used to try ideas, find patterns and trends, make predictions, make plans, and gain useful insights. They might do some of the same things as a data scientist at a higher level.
Business Analytics Practitioners
People who work in business analytics fix problems for companies. They are committed to using analytics to make better business choices, create new products and services, run more effective campaigns, cut costs, or set up IT systems. They both describe and evaluate and make predictions. Also, they might know a lot about Business Intelligence (BI), Operations Research (OR), Requirements Engineering, and/or Management Information Systems (MIS).
Data Scientist
Data scientists are inventors whose job it is to come up with new ways to use and understand data. They know a lot about advanced analytics, using big data, making predictions, Artificial Intelligence (AI), and Machine Learning (ML). They write their own programs, make their own data visualization tools and dashboards, write their own algorithms and predictive models, and set up automation for analytics jobs. A lot of people who become data scientists started out as data researchers.
Data Engineer
Data engineers are infrastructure experts who build the systems that are needed to gather, store, and process large amounts of data. They come up with methods, make data platforms, data models, data warehouses, and database pipeline architectures that are built, tested, and kept up to date. Business Intelligence (BI) analysts, software engineers, database managers, and other jobs may be where data engineers start their careers because this is a mid-level job.
Data Architect
A data architect is a designer and visionary who creates the “blueprint” for an organization’s business data framework. They turn business needs into technical requirements, plan complicated technical architectures, work closely with data engineers to build safe and useful systems, and decide where data will be kept, how it will be accessed, and how it will be handled. Data architects are senior-level experts, just like architects who work on real-world projects.
Types of Data
Big Data
Big data generally means sets of structured, semi-structured, or unstructured data that are too big for normal data processing software and apps to handle. Because of the huge amount of data we have now, big data experts have to deal with 4 main problems:
- Size: Terabytes and petabytes of raw data are what you’re dealing with when you talk about “big data.”
- There are now a lot of different places to get data, like mobile devices, the Internet of Things (IoT), video, music, photos, and sensors.
- Velocity: Every day, the rate at which real-time data are being created grows at an exponential rate.
- Truth: Not all sets of facts can be trusted. Big data experts have to check the reliability and usefulness of data sources and take into account any bias that might be present.
Qualitative Data
You can’t organize qualitative facts; they are just descriptions. They talk about certain traits, qualities, and characteristics, but they are harder to measure and study precisely than quantitative data. Think of words that describe things, like “soft,” “quirky,” “happy,” “quickly,” and so on.
Individual answers to an open-ended questionnaire, notes taken during a focus group, copies of audio and video recordings, and descriptions of colors, sounds, textures, tastes, or smells are all types of qualitative data. Putting these ideas into groups and themes can help you figure out what the qualitative data means.
Quantitative Data
Sets of numbers that can be counted and measured are called quantitative data. They are usually organized and can be measured. The question “How many?” is a quick way to think about quantitative facts. How often or how much?
Numbers like sales and income, test scores, ages, customer reviews, website visits, distance measurements, and more are all examples of quantitative data. They can be gathered in a number of ways, such as through surveys, polls, headcounts, market reports, and scientific studies. And statistical research can be used to judge them.
Raw Data
You can collect both numeric and qualitative data that has not yet been processed, organized, cleaned, or mined. This is called “raw data.” If you only have raw data like transcripts, survey answers, product prices, performance data, and sales numbers, they don’t tell you much until you look at them.
Semi-Structured Data
There is a middle ground between organized and unstructured data, which is where semi-structured data lies. Even though these files can’t be put into databases in the form of rows and columns, they do have tags, information, and markers that can be used to sort them into groups and hierarchies. Think about emails: the text is probably not organized, but the names, times, dates, and category files are.
Structured Data
Structured data are organized and uniform so that they can fit into a model that has already been set up. It lives in relational databases and is saved in data warehouses. They can be put together in spreadsheets (like Excel) that have separate spaces for each piece of data and rows and columns to make sorting them easy. This is where you can find a lot of quantitative info.
Unstructured Data
There is no set model or design for organizing unstructured data. Audio and video files, notes, chats, texts, PDFs, and pictures are all types of unstructured data. Data lakes and NoSQL databases are where unstructured data live and are saved. Tools like Hadoop are making it much easier to look at unorganized data, which used to be hard to do.
Conclusion: Data-Focused Fields
Data-focused fields provide numerous career opportunities that cater to a wide range of interests and skills. For instance, data analysts focus on organizing and interpreting data to enable better decision-making, while data scientists drive innovation by creating new ways to process and understand data. Additionally, business analytics practitioners use data to solve organizational problems and improve efficiency. Meanwhile, data engineers design and maintain systems for managing large datasets, and data architects create blueprints for how business data should be structured and accessed. As data continues to grow in importance, professionals in these fields must adapt to new challenges, leveraging both technical expertise and analytical skills to unlock the full potential of data.
