Install C Compiler untuk Windows

Compiler merupakan program spesial yang berfungsi untuk menerjemahkan code dari bahasa pemprograman menjadi bahasa mesin, komputer hanya mengerti bahasa mesin yang merupakan kumpulan angka 0 dan 1…


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If you are a data scientist, the answer is no! You can have both; as long as your data-driven stories are built on solid research ground. A data scientist utilizes techniques and tools found in the context of many disciplines (computer science, statistics, math, information theory) to find meaningful interpretation of data. This interdisciplinary nature of data science attracts people from all sort of background; economist, physicists, mathematicians, computer scientists, engineers, statisticians and many more.

Today we will be interviewing three budding data scientists about their data science journey. All our data scientists have successfully gone through the phase of settling in. They have faced the same questions and hurdles many newcomers in the field are going to meet. We hope this fusion of opinions from our data scientists would provide an aspiring data scientist with a clear picture of the current state of data science in the industry.

Meet our data scientists:

Passion is the name of the game: Data scientists are some of the most passionate people out there. For all of our data scientists it was a conscious decision to move into this field. We wanted to know why.

Learning to learn: As with most things in life getting started often poses the hardest challenge. All our data scientists took the route of completing a graduate degree to get into the industry. They all felt that their graduate degree was instrumental to their success.

What happens in a co-op stays in.. :Getting some industry experience in the form of a Co-op/Internship remains an effective way to get into the industry. All of our data scientists had 4–8 months Coop work experience before starting their current job.

Skills Needed: The field of data science is ever evolving. The tools and techniques you learned today might not be relevant tomorrow. The field requires a strong commitment towards learning.

Languages and tools: We asked our data scientists about their choice of programming languages and tools.

The work they do: We asked them about the kind of projects they work on a day to day basis.

Everyone has an opinion: Industry is often different than what people expect.

Avoiding known pitfalls: We asked them about the way they’d choose to do the course again if they have a choice to do so.

A window into the future: We asked our data scientists about the future of data science.

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