Image Generation

buatkan summary berupa infografik berdasarkan informasi berikut: Introduction to Data What is Data Oftentimes, I get asked, just what is data anyways? Hey, ChatGPT, give me a name of a fictional character I can use for an upcoming example. Here's a suggestion, Alex Grayson. All right, Alex wakes up and checks how much sleep he got. Alex then make some breakfast while tracking his macros. Like many of us, Alex has to commute and checks the traffic before leaving. At work, Alex uses emails and productivity tools and he even listens to music. After work, Alex heads to the gym, making sure to track his workouts. Later, Alex coaches youth sports and ensures that kids get equal playing time. After a long day, Alex and his family relax by watching a funny show or two. Getting back to being asked what is data anyways, in our world, we might as well tweak that question from what is data to something more like what data isn't. But if you need more of a traditional answer, I'd say data is simply information. It can be anything from numbers to words, pictures, even choices you make, tracking your steps, choosing a song. Heck, you're watching this video right now. Up to this point, I have said 205 words, that's data. Every time a customer interacts with your products, orders from your website, and even contacts your customer service, all data. It's the raw details that are collected and organized to be analyzed or used as a basis for making decisions. Data in Modern Context So what is data in modern context? Is data merely just a collections of ones and zeros stored on a server? Is it defined as a role within an organization or perhaps the organization itself? Or is there more to it than that? Here's a simple way to look at it. Data in its simplest form is information that's collected and organized to be analyzed or used as a basis for making decisions. It can be anything from numbers to texts to images to sounds recorded and stored in various ways. Data can also be qualitative, meaning descriptive or quantitative, meaning numeric. In a modern world, data is considered one of the biggest assets for almost every industry. It contributes to economic growth, public policy, and scientific advancements, which makes data literacy critical for individuals and organizations alike. So what about all those data buzzwords and fancy titles you see on LinkedIn or Indeed? Well, from a trending perspective, a lot of these titles, they come and they go and I can see how that can be confusing. Should I work in big data? Is big data still a thing? Should I be an analyst? And if so, what kind, a business analyst or business intelligence analyst? And are those even the same role? Roles aside, just look at the data landscape of data tooling from a few years ago. Look at what it is today. In 2012, we had 139 data companies to today where we have over 1500. So where does that leave us? I'm glad you asked. The role of data in the modern world isn't just a passing trend. It's constantly evolving and becoming more embedded in everything we do. Whether it's driving advancements in healthcare, shaping business strategies, or even curating our daily digital experiences, data is everywhere. Come here. No matter your industry or role, understanding and working with data is no longer optional, it is essential. The more we understand the data around us, the better we can leverage it to make informed decisions, improve processes, and shape the future. Data may start as numbers and words, but its real value comes from what we do with it. And that's why in today's world, data literacy is more than just skill. It's a mindset and a practice for success. Data Journey: Creation to Storage Did you catch that? I'm not talking about liking that post, but did you see what happened? When I clicked that heart, a new piece of information was born. That very action just created a brand‑new data point. This may just look like a dot, but it's more than a dot. It's information pertaining to what just happened. I interacted with a post, which triggers all sorts of information. Check it out. You can see a Like ID, User ID, Host ID, a Timestamp, even the Post Type, and which hashtags the post contained. This dot is considered to be a database entry. The data points Like ID and User ID are recorded in a database alongside other similar interactions. We can actually take this data point and place it into a larger dataset. Apps and products tend to have 30 plus features being tracked, so it doesn't make sense to have our liked data included with other features. This is where aggregation comes in. Aggregation is taking all the data and organizing it in a way that makes it easier to understand. It's like taking all the data inside of a database and separating it into smaller groups based on things like counting, adding up, or finding the average. When looking at our data point, what data type do you think it is? There's a few different types we use frequently. I'll explain it like this, my kid's laundry. You have the first child, we'll call him structured data. This type of data is very organized. It looks like a table you might see in a spreadsheet or a database where information is neatly arranged in rows and columns, much like my son's dresser. My middle child, well, we'll just call him semi‑structured data. This type of data is somewhat unorganized, but not as neatly as structured data. It has some labels or tags to separate different pieces of information, but it doesn't fit perfectly into rows and columns. And then there's the third child, unstructured data. This type of data has no specific organization. It's like a messy room where information is scattered around and there's no specific order. Regardless of the data type, we can store it in a number of places, relational databases, data warehouses, data lakes, NoSQL databases, cloud storage, you name it. So the next time you scroll through an app or click like, remember you're not just interacting with a post, you're creating data. That tiny action is just one of millions happening every second across the globe, all contributing to the ever‑growing pool of information. Even having a basic understanding of how this data is structured, stored, and processed can open up a whole new world of possibilities, whether you're analyzing trends, improving user experiences, or building the next big app. Data Journey: Cleaning to Visualization Believe it or not, data is not always perfect. It can be quite messy. This is where validation comes in or in other words, ensuring that the data is accurate and consistent. Messy data can be data that has missing values, duplicate entries, inconsistent data formats, trailing or leading spaces, even inconsistent categorical values to name a few. That's not even the best part, saying that sarcastically, of course. Some sets of data contain multiple errors. Sometimes the fixes are easy and other times it's very time consuming, depending on the data and volume of data we're working with. Once cleaned and polished, it's time to hand off the data over to be analyzed. Now there's a lot of ways this can be achieved. See for yourself in the course called Introduction to Data Literacy. You see, we have a lot of different analysis techniques to choose from. Let's look at two common ones. Qualitative analysis, analyzing non‑numerical data to understand concepts, opinions, or experiences. An example of this could be maybe that like triggered a survey asking for your feedback on that feature or the product as a whole. Quantitative analysis, analyzing numerical data to quantify variables and uncover patterns. An example of this is how many likes does this post have today, for the month, quarter, even a year or years? Let's say you are an analyst. A manager might ask a question like this. Can you analyze the number of likes on our recent posts to see if there's any correlation between the time we post and a type of content, whether it's images, videos, or text? That's quantitative analysis. Or your manager may ask, in a survey, can you ask respondents how that post made them feel? We're looking to understand the emotional impact of the content and gather feedback on what aspects of the post resonated with them on a personal level. That's qualitative analysis. Or like most of us, we're often on the receiving end of data. It's already been created, stored, cleaned, and a visualization or dashboard has been created for us to interpret. Now it's up to us to make sense of it, which brings us to data‑driven insights. For example, our manager has a question, so we turn to the data for answers and report back. In other words, we're making decisions based on the analysis and interpretation of data. Why is this important? Well, instead of guessing or relying on personal opinions, data‑driven insights help people make choices based on facts and evidence. Being data‑driven means making decisions informed by data rather than relying on intuition or simple observations alone.