Python and Statistics for Financial Analysis Training

R is a good place to start with statistics. It was developed for statistical computing and graphics, so it offers a ton of statistical packages to its users. Python, on the other hand, is a general-purpose language that has many applications. However, you can also use Python for statistics.

Python is widely used in quantitative finance - solutions that process and analyze large datasets, big financial data. Libraries such as Pandas simplify the process of data visualization and allow carrying out sophisticated statistical calculations.

The future of Python is promising. Up to now, Python has been in demand in areas such as web development, software development, and system administration. With the rise of data science and Python scientific packages such as NumPy, pandas, and Matplotlib, Python popularity is growing.

We are happy and proud to say that we have strong relationship with over 700+ small, mid-sized and MNCs. Many of these companies have openings in Python. Moreover, we have a very active placement cell that provides 100% placement assistance to our students. The cell also contributes by training students in mock interviews and discussions even after the course completion.

For pure data science R still has a slight edge over Python, although the gap has closed significantly. Nevertheless, the wider applications of Python make it the better all-round choice. If you're at the start of your career then learning Python will also give you more options in the future.

Java and Python, both are widely used programming languages, but Java is better to learn first than Python because of reasons below: ... It's because everything is shown up at runtime in Python, which also makes it difficult to debug and analyze the code in Python, as compared to Java.

Due to python's simplicity and high readability, it is gaining its importance in the financial industry. The course combines both python coding and statistical concepts and applies into analyzing financial data, such as stock data.

Python is an incredibly versatile language with a very simple syntax and great readability. It is used for building highly scalable platforms and web-based applications, and is extremely useful in a burdened industry such as finance. Spending an hour a day on Python is more than enough to become proficient in its use.

You may not realize it now, but programming is one of the most important financial skills a person can have. Programming can help you automate tasks that would otherwise require manual data entry, repeat those tasks indefinitely, and manage and process data more seamlessly than any human brain can.

Financial analysts use spreadsheet and statistical software packages to analyze financial data, spot trends, and develop forecasts. On the basis of their results, they write reports and make presentations, usually making recommendations to buy or sell a particular investment or security.

• Python Is Easy to Understand.
• Python Has a Wide Range of Uses.
• Python Has a Supportive Developer Community.
• Demand for Python Developers Is Growing.
• Python Developers Are Well Paid.
• Python Resources Are Everywhere.