Data Science Tutorial for Beginners
To start off with anything else would be a futile task. What we need to understand first and foremost is what dealing with data and what is the hype around it lately. If the answer to this is clear, everything else falls into place itself. Data Science is a field that caters to a large amount of data, drawing algorithms and then coming up with useful business insights.
Introducing the Term Data Science
The data being talked about here will be in large numbers and individuals need to use methods to clean data and then convert it into a format that can be used by the company for gaining insights. This field is not just restricted to engineers and anyone interested in the domain can take up the course.
In today’s technologically advanced world the field is gaining immense popularity across different sectors. As a result of large chunks of data being created in every nook and corner there is a need to draw valuable insights from the same. To put it in a simpler manner, data is what is driving the present generation. Using the right kind of tools and techniques, businesses get the leverage of drawing meaningful insights.
Job Options After Studying Data Science
Now that we have understood what data science caters to, we will see the various job profiles one can opt for after studying data science.
- Data Engineer: A data engineer is a person who has the responsibility of taking care of huge chunks of data. He is the one who needs to clean it, extract it and prepare it so that others can understand it too.
- Data Analyst: If you are a data analyst your sole responsibility lies in mining the data. You will have to look for patterns, trends, and relationship and then come with your inferences.
- Data Scientist: A data scientist is someone who uses various tools and techniques and come up with compelling data insights.
- Machine Learning Expert: If you a machine learning expert you will have to work on different machine learning algorithms like clustering, classification, regression, random forest etc.
Where is Data Science Applied?
Data Science is majorly applied in:
- Image recognition
- Stock market analysis
- Internet search
- Personalized recommender systems
- Fraud detection
- Pathological diagnosis
- Optimization techniques
What are the qualities of a data scientist?
Dwelling into the field of data science is not so easy as it seems to be. Before understanding the complex processes involved in the field it is essential to understand what does it simply by a data scientist and what are the qualities that a data scientist needs to possess. Some of the skills include analytical mind, statistical thinking and a problem-solving approach to things to name a few. There are others as well. They are:
- A data scientist needs to be aware of the real-world data problems. This is essential to get a clearer picture of the processes involved in the field. If a data scientist is unaware of the real-world problems, he won’t be able to solve the ones in the virtual world as well.
- If you are a data scientist, you need to be well versed with the happenings in the same field otherwise your knowledge will be simply rendered useless. As of late, there are a lot of meetups and competitions about data science that takes place throughout the country. As a data scientist, you need to attend these and take part in knowledge transfer sessions.
- If you are someone who wishes to become an accomplished data scientist someday, you need to understand the fact that it is a mandate to have a collaborative approach towards your work. You need to talk and interact with your peers as much as possible and keep reading about the latest advancements in the field of technology. It is essential to act like a team member instead of simply being an individual contributor.
- In order to excel in the field of data science, it is imperative that you practice on a daily basis. This needs to be done so that you can upgrade your skill set continuously. Without practice, one stay updated and loses touch with everything.
Before jumping on to the complex technical processes that data science entails, make a thorough note of the points metioned above so that you do not face any hurdles.