Data Science: 7 Key Factors You Need to Know
When people hear that I’m a data scientist, the next question is often, “What does that really mean?” Whether it’s friends, family, or people I’ve just met, explaining the key factors of data science can help make the field a little less mysterious. So, here’s a breakdown of the seven most important aspects of data science, the things that I work with every day to turn raw data into useful insights.
The first thing to know is that data cleansing is the foundation of any good data science project. It’s exactly what it sounds like—cleaning the data. Real-world data is messy. It’s full of errors, missing values, duplicates, or things that just don’t make sense. Before we can analyze anything, we have to make sure the data is accurate and reliable. Think of it like cleaning the kitchen before cooking a meal. If you don’t start with a clean space (or clean data), your results can be a disaster.
Data Cleansing
Once the data is clean, we need to make it understandable. That’s where data visualization comes in. This involves creating charts, graphs, and dashboards that make complex data easy to digest. If you’ve ever seen a chart that instantly made a trend obvious, that’s data visualization at work. It’s about making data not just accessible, but useful. In my job, I often use tools like Tableau or Python to build visualizations that help decision-makers see the big picture at a glance.
Data Visualization
Next up is exploratory data analysis (EDA), which is like getting to know your data. It’s the phase where we poke around, looking for patterns, testing theories, and figuring out what the data is really telling us. EDA helps us decide which data points are the most important and whether there are any surprising trends or anomalies that we didn’t expect. It’s a crucial step before we move on to more complex models. Think of it like exploring the ingredients before deciding what kind of dish to make.
Exploratory Data Analysis (EDA)
People are often most curious about machine learning, and for good reason—it’s one of the most exciting parts of data science. Machine learning is all about teaching computers to learn from data. Once we’ve done the EDA, we can build models that can make predictions or decisions without being explicitly programmed to do so. From recommendation systems (like Netflix suggesting what you should watch next) to more complex things like fraud detection, machine learning is used to identify patterns and forecast future outcomes.
Machine Learning
Behind a lot of what I do is probability theory. This is about understanding how likely something is to happen based on the data. In data science, nothing is ever 100% certain, so we use probability to make informed guesses. Whether I’m predicting customer behavior or assessing financial risk, probability helps us navigate uncertainty. It’s like playing the odds, but with a lot more math and a lot less guessing!
Probability Theory
What many people don’t realize is that communication skills are just as important as the technical work. It doesn’t matter how brilliant your analysis is if you can’t explain it to others. My job involves taking all the complex data and findings and translating them into something that non-technical people can understand and use to make decisions. Whether I’m talking to executives or writing reports, clear communication ensures that data science leads to actionable insights.
Communication Skills
Lastly, there’s data architecture, which is all about how we organize and store the data. If you imagine data as ingredients in a kitchen, data architecture is the pantry and fridge where everything is kept. Without a good system for storing, processing, and accessing data, nothing else works smoothly. It’s important to have the right frameworks in place to ensure the data is secure, scalable, and easy to work with. It’s the behind-the-scenes part of data science that keeps everything running smoothly.
Data Architecture
Hit me up on LinkedIn if you'd like to connect professionally. I also have another website that has nothing to do with data science also named Christopher Csiszar that's more geared toward my personal interests. I haven't built it out as much as I'd like to but if you're interested in food, check this page out: Christopher Csiszar Food Stories. I'm also on all the usual spots like Twitter/X, Instagram, Facebook, About Me, and Reddit. Here's an example of work that I've done in the field of data science. Christopher Csiszar Data Science Webscraping. Christopher Csiszar Github Profile.
In a nutshell, these are the seven key factors that shape my work as a data scientist: data cleansing, data visualization, exploratory data analysis, machine learning, probability theory, communication skills, and data architecture. Each of these plays an essential role in turning raw data into something meaningful. Whether it’s helping businesses make smarter decisions or improving processes in different industries, understanding these fundamentals is what makes data science such a powerful tool. So the next time someone asks, “What does a data scientist do?” you’ll know there’s a lot more to it than just crunching numbers!