A data scientist is an umbrella term used to describe people whose main responsibility is using data to help other people (or machines) make more informed decisions. The spectrum of data scientist roles is so broad that this discussion can be kept for the next post. The focus here is on the characteristics of a great data scientist.
There can be many criteria that distinguish a great data scientist from a good one. but following mentioned are some necessary:
- A great data scientist tries to properly understand the business problem that is being solved. Only understanding a problem is just half the answer. The key is to develop tailored business models.
- A great data scientist uses machine learning models if they solve the problem not because they are fancy.
- A great data scientist always has good communication skills, he knows how to describe the results and what related action to take.
- A great data scientist is someone who is constantly working to improve their skills.
- A great data scientist knows the terms ‘why he is doing, what he is doing’ and is not applying any algorithm because he read somewhere that it works.
- A great data scientist is someone who has the passion and motivation to be.
There are many data science programs available to become a great data scientist. You can get your data science certificate online by doing data science training that you could use as a stepping stone to a rewarding and profitable career.
If you want to be a great data scientist, you need to be adept in a wide array of skills from programming knowledge to be good in communication, and more. While the industry has varying metrics on what being a good data scientist is, here are the five key skills a great data scientist should have.
To become a great data scientist, you need to have a wide range of skills, from programming skills to good communication skills and more. Here are the five key skills that separates a great data scientist from a good one.
Most important skill for a great data scientist is an analytical mindset with a strong statistical background and a good understanding of data structures and machine learning algorithms. You need to be strong at Python or R and be familiar with large data sets. Almost 70% of a data scientist’s time is spent preparing data, cleaning data, and grouping and conditioning the data so that machine learning algorithms can be applied to that data. So it is important that you are familiar with 4V data – Volume, Velocity, Variety and Veracity.
A great data scientist always has a solid understanding of the domain. You need to understand the business problem and choose the appropriate data science model for the problem. You always need to have an eye for details. You also have to be good in communication skills as you need to explain your results in simple language that can be understood by a wider audience. You should be able to clearly document your approach so that others can easily build on it.
Problem Solving Skills
It is mandatory for a data scientist to work on problem solving. A data scientist is like a doctor, the more problems he solves and the more experience he has, the better his job is. This is why companies place much more value on experience than on educational qualifications. But basic data science training is important. A full-time course is valued higher than an executive course.
Statistical And Programming Skills
A great data scientist is expected to have proper knowledge of statistics, mathematics and algorithms, as well as good software engineering skills. You start with a basic course in statistics and mathematics with a focus on probability, set theory, algebra, functions and graphs. You also have to learn a programming language, preferably Python, as well as libraries like Pandas, Numpy, Scipy and Matplotlib or R. So you should study machine learning and possibly advanced deep learning topics. There are plenty of free and paid data science programs to help you to learn about these topics. After completing data science training, you will need to apply your knowledge in solving practical problems. You can do this by entering contests from various sites. You can even get your data science certificate online.
Solving Real-World Problems
If anyone wants to choose data science as a career, they should first focus on topics such as statistics, probability, algebra, set theory, data structures, and algorithms. Once they are familiar with the basic concepts, they can use the technological tools to their advantage to build extraordinary models.
Although a great deal of theoretical knowledge can be acquired in these courses, learning is only completed after applying to practical problems. Mentors from industry can play an important role in this. They will also help understand the practical difficulties involved in applying their knowledge to real-world problems. This will also help them build their domain knowledge which will help them become a great data scientist.
Data Science Bootcamps
Data Science Bootcamps are expensive programs designed to help you get started in your new career. Their syllabus covers the essential topics to get your foot in the door and potentially find a job. Bootcamps also offer a unique sense of community, informal events, and a great networking opportunity, all of which are important elements for people pursuing a career in data science. These data science programs also offer you a data science certificate online.
Data science is a rapidly evolving field. If you want to keep your business up to date (and prepare yourself well for your next job), you need to keep your skills up to date. For learning the latest skills you can take data science training from any data science programs. You need to keep in touch with the data science community to learn about new research and new tools that could improve your job performance.