Data Manipulation in Python: Master Python, Numpy & Pandas

When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find.

Like the Wall Street "quants" of the 1980s and 1990s, modern-day data scientists are expected to have a similar skill set. People with a background in physics and mathematics flocked to investment banks and hedge funds in those days because they could come up with novel algorithms and data methods. That being said, data science is becoming one of the most well-suited occupations for success in the twenty-first century.

It is computerized, programming-driven, and analytical in nature. Consequently, it comes as no surprise that the need for data scientists has been increasing in the employment market over the last several years. The supply, on the other hand, has been quite restricted.

It is challenging to get the knowledge and abilities required to be recruited as a data scientist. Lots of resources for learning Python are available online. Because of this, students frequently get overwhelmed by Python's high learning curve.