By Hari Haran
In order excel in the data science insdustry, you need to be good to statistics,Statistics is a critical aspect of data science. It enables data scientists to make sense of the vast amounts of data they work with and extract valuable insights that can help businesses make informed decisions. Python is one of the most popular programming languages in the data science community, and it offers a range of powerful packages for statistical analysis. In this blog post, we will explore the top 3 Python packages to learn statistics for data scientists.
1. NumPy
NumPy is a popular Python package for scientific computing that offers support for multidimensional arrays and matrices, along with an extensive library of mathematical functions.
NumPy is widely used in data science for performing mathematical operations on arrays and matrices, and it provides an intuitive interface to work with statistical functions. Some of the statistical functions available in NumPy include mean, median, variance, standard deviation, correlation, and covariance.
One of the key features of NumPy is its ability to handle large datasets efficiently. It can perform operations on arrays and matrices much faster than traditional Python code. Additionally, NumPy is an open-source package, which means that it is continuously evolving as a result of the contributions of the vast community of developers.
To use NumPy in your project, you first need to install it. You can use the following command in your terminal:
Copy codepip install numpy
2. Pandas
Pandas is another popular Python package for data analysis that provides data structures and functions for working with structured data. Pandas is built on top of NumPy and provides a powerful interface for manipulating and analyzing data. It can handle data in various formats, including CSV, Excel, and SQL databases.
Pandas offers a range of statistical functions for data analysis, such as mean, median, mode, standard deviation, correlation, and regression. It also provides tools for data visualization, which can help you gain insights from your data. Pandas is an open-source package, and its community of developers is continuously working to improve it.
To use Pandas in your project, you first need to install it. You can use the following command in your terminal:
Copy codepip install pandas
3. SciPy
SciPy is a collection of Python packages that provides a range of functions for scientific computing, including statistics, optimization, signal processing, and machine learning. It is built on top of NumPy and provides an interface for working with arrays and matrices, along with a wide range of statistical functions.
Some of the statistical functions available in SciPy include descriptive statistics, hypothesis testing, probability distributions, and correlation.
SciPy also provides functions for machine learning, such as clustering, classification, and regression. It is an open-source package, and its community of developers is continuously working to improve it.
To use SciPy in your project, you first need to install it. You can use the following command in your terminal:
Copy codepip install scipy
Verdict
I just made as short as possible order to understand the basic steps involved in data science,Statistics is a critical aspect of data science, and Python provides a range of powerful packages for statistical analysis. NumPy, Pandas, and SciPy are three of the most popular Python packages for statistical analysis, and they provide an intuitive interface to work with statistical functions. By learning these packages, you can improve your data analysis skills and gain valuable insights from your data.