Python for Data Science
Data Science is one of the booming careers. Many people are showing interest in this area and one of the preferred languages for this is Python.
In this article, we will get introduced to data science and also about Python as a powerful tool for data scientists. Let us start with the introduction to data science.
What is Data Science?
Data science is an interdisciplinary field of scientific methods, processes, algorithms, and systems. It involves extracting information or insights from various forms of data, either structured or unstructured. It employs a wide range of algorithms on the data to get the required details.
This is used in a wide range of areas including finance, healthcare, gaming, search on the internet and browsing, autonomous devices, education, and many more.
Why Python for Data Science?
Python is a preferred language by most data scientists and also by beginners learning data science. Some of the reasons for people being attracted towards Python for data science applications include:
1. Python is an open-source platform. It is a simple, readable, and user-friendly language. Also, its syntax is easy to learn and it helps beginners or experts concentrate on the concepts of data science rather than on the language used to implement them.
2. Python has a wide range of libraries and packages which are easy to use. These help the developers code less and implement more. One can do data manipulation, get insights into data, and also do visualization. They also have methods to employ different algorithms and these can take different arguments to make the program fit to the application.
Python vs R
Though there are a lot of tools available to implement data science projects or applications, there are two primary tools for data scientists. These are R and Python.
One of the main reasons for people to opt for Python over R is the large collection of libraries made available in Python. Some of these include Pandas, NumPy, SciPy, Scikit-Learn, etc. We will be discussing these in the next section. In addition, Python also has around 72,000 of these libraries in the Python Package Index (PyPI) and this count is growing constantly with the help of the active community.
Libraries for Data Science
As said before Python provides a great variety of libraries that can be used as tools for data science operations. Let us discuss some of the popular ones of these.
1. Pandas
It is an open-source library that can be used for easy analysis, manipulation, and visualization of large sets of data. This data can either be structured (tabular, multidimensional, potentially heterogeneous) or time-series data. We can deal with python data frames, time series and also import data from spreadsheets. And all these need just a few lines of code.
Pandas allow us to:
a. Rename, index, manipulate, sort, merge data frames
b. Update, Add, Delete columns and rows from a data frame
c. Handling missing data or NANs
d. Get statistical information and other details using conditionals
e. Plot the data
2. NumPy
NumPy package is a good choice when we want to perform mathematical operations. It provides tools to build multi-dimensional arrays and matrices and methods to perform calculations on these arrays. With this library, we can:
a. Perform basic array operations like addition, multiplication, slicing, reshaping arrays
b. Perform advanced operations like stacking arrays, splitting into sections, and broadcasting arrays
c. Work with DateTime values
d. Solve algebraic formulas, perform statistical operations, etc.
3. SciPy
Scipy is a tool used for scientific and technical computing. It is an extension to the NumPy module. It contains mathematical methods like linear algebra, interpolation, optimization, integration, and statistics. In addition, it has scientific programming operations like integration, calculus, ordinary differential equations, and signal processing.
4. Matplotlib
Matplotlib is a versatile library used for data visualization and analysis. It is used for the plotting of quality figures and sharing them in a variety of hard copy formats. This allows us to plot charts, graphs, pie charts, bar plots, scatterplots, histograms, etc. It can be used in all kinds of GUI toolkits such as python scripts, web applications, shell, etc.
5. Seaborn
It is another library used for visualization purposes and is an extension to matplotlib. They have many high-level interfaces, advanced features, and styles for adding graphics. Using seaborn we can:
a. Determine relationships between multiple variables, drawing multi plots
b. Analyze univariate or bivariate distributions plots
c. Provide high-level abstractions
6. Scikit-learn
It is a library including multiple supervised and unsupervised ML algorithms like SVMs, random forests, clustering, etc. It also provides tools for data analysis and data mining. We can implement all these algorithms by using the built-in functions allowing the data scientists to focus on the problem rather than the code. It is used for different applications like:
- Classification, spam detection, and image recognition
- Clustering the stock prices
- Increasing the efficiency
7. TensorFlow
It is an evolving open-source AI library that is used specifically for deep learning algorithms. Its name comes from its basic data structure named tensors. It also provides support for CPUs, GPUs, and TPUs. It also allows:
a. Voice and Face recognition
b. Sentiment analysis
c. Time series analysis on datasets
8. Keras
It is also a library for building and training deep neural networks. It allows us to work with the images and text a lot easier. Another difference from TensorFlow is that Keras is used mainly for neural networks and TensorFlow is also used for machine learning operations.
Python Community Support
Another advantage with Python is its active community updating the Python documentation adding more functionalities. In addition, they are also available to solve doubts and for discussions. A lot of datasets are also provided for the people to work on and test.
Conclusion
We are at the end of this article. We got knowledge about data science and various tools Python provides for data science. Hope this article helps you get some knowledge on data science and grow up as a future data scientist. Happy learning!