Congratulations! You’re a data scientist in the making. Or maybe you’re just an IT job, but that’s okay too.
If you can answer yes to all of the following questions, you may be a data scientist
If you can answer yes to all of the following questions, you may be a data scientist:
- You have a PhD in computer science, math, or a related field.
- You have experience working with data.
- You are comfortable with statistics and programming languages like Python or R (or both).
- You’ve used machine learning techniques like k-nearest neighbors or decision trees to build models that describe relationships between variables in your dataset–and it worked!
If any of these apply to you then congratulations–you might be well on your way toward becoming one of those rarefied individuals who can call themselves “data scientists.”
If you can answer yes to all of the following questions, you may be an IT job
If you can answer yes to all of the following questions, you may be an IT job:
- You have a computer science degree.
- You have worked in IT for a long time and feel like it’s your home.
- Your technical skill is high; you understand data and how it works and can process it easily.
- You have good problem-solving skills, because no matter what happens at work or in life outside of work (and sometimes even inside) there will always be some sort of issue that needs solving data science qualifications!
You need to know what sort of data scientist you are!
While you may think of yourself as a data scientist, it’s important to know what sort of data scientist you are. Data scientists come in many different flavors and each type has its own unique set of skills and challenges.
- The business-driven analyst: This type of data scientist works closely with business leaders to identify problems, define goals and metrics, collect relevant data sets and craft solutions that deliver value while meeting their needs. They often focus on generating insights from large datasets using statistical modeling techniques such as machine learning algorithms or deep learning models (e.g., neural networks).
- The technical innovator: These folks design new methods for analyzing complex problems by combining existing tools with innovative techniques such as distributed computing architectures like Spark/Hadoop clusters; graph databases like Neo4j; streaming analytics tools like Kafka; natural language processing engines such as Watson Question Answering System (QAS) NLP engine; advanced graphics cards like Nvidia Tesla GPUs…
Conclusion
The first step to figuring out where you fit in the data science ecosystem is to understand what kind of data scientist you are. Are you a business analyst who wants to learn more about data analysis? Or maybe an engineer looking for an opportunity to work with cutting-edge technology? Whatever it may be, we want our readers to know that there is a place for them in this growing field.
