We have seen how data science can be applied to different sectors. Python helps data scientists to do data analytics which transforms the raw data to information. As the process of analyzing raw data to find trends and answer questions, the definition of data analytics captures its broad scope of the field. However, it includes many techniques with many different goals.
The data analytics process has some components that can help a variety of initiatives. By combining these components, a successful data analytics initiative will provide a clear picture of where you are, where you have been and where you should go.
The Real Life Challenges
Some of the challenges Data Scientists face in the real world are listed here
- Data Quality – The quality of data is mostly not up to the set standards. You will usually come across data which is inconsistent, inaccurate, incomplete, not in the desirable format and with anomalies.
- Data integration – Data integration with several enterprise applications and systems is complex and a pain taking task.
- Unified Platform – Data is distributed to the Hadoop distributed file system (HDFS) from various sources to ingest, process, analyze and visualize huge data sets. The size of these Hadoop clusters can vary from a few nodes to a thousand nodes.
- But the challenge is to perform analytics on these large data sets efficiently and effectively. This is where python comes into play with its powerful set of libraries, functions, modules, packages and extensions.
Use of Python
Python deals with each stage of data analytics efficiently by applying different libraries and packages. Data Analytics stages includes: –
- Acquire (Data acquisition) – A Python library such as ‘scrapy’ comes handy here.
- Data Wrangling – Python data frames are very efficient in handling large data sets and makes data wrangling easier with its powerful functions.
- Explore – matplotlib library is very rich when it comes to exploration.
- Model – NumPy, scikit learns statistical and mathematical functions to help to build models for machine learning.
- Visualize – Modern libraries such as Bokeh create very intuitive and interactive visualizations. Its huge set of libraries and functions make big data analytics seem easy and hence solves the bigger problem.
Python applications and programs are portable and help in scaling out in any data platform.
Python Tools and Technologies
Python is a general-purpose, open-source, programming language that lets you work quickly and integrate systems more effectively.
- NumPy or Numerical Python is the fundamental package for Scientific computing.
- SciPy is the core of scientific computing libraries and provides many user-friendly and efficiently designed numerical routines.
- Matplotlib is a python 2D plotting library which produces publication quality figures and a variety of hard copy formats and interactive environments across platforms.
- Scikit learn is built on SciPy, NumPy and matplotlib for data mining and data analysis.
- Pandas is a library providing high performance, easy to use data structures and data analysis tools for python.
All these libraries, modules and packages are open source and hence using them is convenient and easy.
Benefits of Python
There are numerous factors which positions python well and makes it the tool for data science. It is
- Easy to learn, it’s a general purpose, function and object-oriented programming language.
- Open-source, it’s readily available, easy to install and get started.
- Big open-source community for software development and support.
- Efficient and multi-platform support
- Applications built with python Integrates well with enterprise apps and systems
- There are a lot of tools, products in the market from Great vendors and they provide great product support and services.
- Huge collections of libraries, functions, and modules. And it creates unique combinations for data science.
Python is supported by well-established data platforms and processing frameworks that help analyze data in a simple and an efficient way.