20 Jul 2022
A Comprehensive Guide For Data Science With Python From Scratch | Ekeeda
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Python is a high-level, general-purpose programming language with an efficient syntax that will allow programmers to focus more on problem-solving than on syntactical errors. Python developers have kept the language quite fun to use. Python has gained massive buzz in the field of modern software development, infrastructure management, and, especially in, data science AI. Python has risen to the top three list of the TIOBE index of language popularity.
Why Is Python Required for Data Science?
Python Programming comes as the first preferred language when we think of data science. Python has rapidly gained popularity in the IT community as a simple yet feature-rich language that powers anything from simple web applications to IoT, gaming, and even AI. Big Data and Data Analytics are other sectors under which Python is inroads. Python Data Science courses, let us find why Python is used in BIG data. But it is data that offers a peep into these programming languages that make the way into the amazing world of data science. Nothing can be as compelling as data science itself to unveil the results of comparison between different data science tools.
Python Spearheads The Language Kit
Python is one of the easiest and fastest-growing languages on the globe, and it is quite easy to learn it. Being a high-level programming language, Python is popularly used in mobile app development, Software development, web development, software development, analysis, and computing of numeric and scientific data. Python can run on various platforms such as - Windows, Linux, Macintosh, etc.
Why Do People Prefer Python Over Other Languages?
Python Codes are written in a very natural style, that is why it’s easy to learn, read and understand. Some of the features that make Python so popular language in Data Science applications include:
Python is for the ones who aspire to learn because it's easy to learn and understand: Python is a popular data science tool with over 35% of data analysts making efficient use of it. It also follows R in popularity and is ahead of SQL and SAS.
Python is known to be an extremely scalable language when compared to other languages such as R. Python is also faster to use than MATLAB or Stata. Python’s scalable nature lies in its flexibility during problem-solving situations because of which even YouTube has migrated to Python. Python has come to be good for different usages in industries as many data scientists use this language to develop various types of applications successfully.
Python is compatible with Data science due to the availability of various libraries like pandas, stats models, NumPy, SciPy, and scikit-learn. The hurdles that developers faced a year ago are addressed by the Python community which helps people address problems of a specific nature through robust solutions.
One of the main reasons for the efficient use of Python in the industry is the ecosystem. Many volunteers are developing python libraries for data science and machine learning because Python has extended its hands to the data science community. It has led the way to create the most modern tools and processing in Python. The community helps these Python aspirants with relevant solutions to their coding problems.
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Graphics and Visualizations
Python offers various graphic & visualization options, which are very helpful to generate insights into the data available. Matplotlib is a plotting library in Python that offers a solid base around which other libraries, such as Seaborn, pandas, and ggplot, have been successfully built. These packages help in extracting the intense and good sense of data, creating charts, graphical plots, web-ready interactive plots, and much more.
Data Science With Python vs R
For nearly a decade, R&D has debated which is the best programming language Python Or R For Data Science? With the adoption of open-source technologies taking over traditional, closed-source commercial technologies, Python and R have become in-demand languages for data scientists and analysts. Python share rose by 51% in 2015 thereby showcasing its influence as a popular Data Science tool.
Steps To Install Python
There are two ways to install Python:
You can either download Python directly from its website and install the required individual components and libraries.
You may download and install a package, which comes with preinstalled libraries such as downloading Anaconda or Enthought Canopy Express
Well! The second method is easy to install and is ideal for beginners. But one has to wait for the entire package to upgrade, even if they want the latest version of just a single library. Unless there is cutting-edge statistical research, there should not be a problem. The next is to choose a development environment. Once Python gets installed, there are many options to choose an environment. The following three are feasible options: IDLE (default environment), Terminal/Shell-based, and IPython Notebook. Now that we know what is Python, why it’s used, and how to install it, let’s get ahead in this python data science program and learn the concepts of Python libraries for Data Science.
Python Libraries for Data Science And Machine Learning
Python has gained a lot of popularity as a general purpose and high-level back-end programming language to create prototypes and develop applications. Python’s readability, flexibility, scalability, and suitability for data science have made it the most preferred language amongst developers.
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Python is extensively used by developers in gaming, standalone PCs, mobile applications, and other enterprise software and applications. Python libraries simplify complex tasks and make data integration much easier with fewer codes in lesser time. Python has more than 137,000 libraries, which are very powerful and are vastly used to meet the needs of customers & businesses. These libraries have helped scientists and developers to analyze BIG data, generate insights, engage in critical decision-making, and much more. Following are a few Python libraries that are popularly used in the fields of data science:
NumPy is an extensive Python library that is used for scientific computations. It leverages your usage of sophisticated functions, N-dimensional array objects, tools for integrating C/C++ and Fortran code, mathematical concepts, such as linear algebra, random number capabilities, etc. You can use it as a multidimensional container for treating generic data. It allows you to load data into Python and export data from the same.
Pandas is the most powerful open-source library of Python for data manipulation. It is known as the Python Data Analysis Library. It is developed over the NumPy package. DataFrames are considered the most used data structures in Python that help in handling and storing data from tables by performing manipulations over rows and columns. pandas is very useful in merging, reshaping, aggregating, splitting, and selecting data.
Matplotlib is a popular plotting library of Python that is extensively used by data scientists for designing numerous figures in multiple formats depending on their compatibility across their respective platforms. For example, with Matplotlib, you can create your own scatter plots, histograms, bar charts, and so on. It provides good quality 2D plotting and basic 3D plotting with limited usage.
Scikit-Learn is a collection of tools to perform mining-related tasks and data analysis. Its foundation is built over SciPy, NumPy, and Matplotlib. It consists of classification models, regression analysis, image recognition, data reduction methods, model selection and tuning, and many other things.
SciPy is another important library of Python for developers, researchers, and data scientists out there. It includes optimizations, statistics, linear algebra, and integration packages for computation. It can be of great help for someone who has just started their career in data science, to guide them for numerical computations.
Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.
Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. Based on Matplotlib, Seaborn aims to make visualization a central part of exploring and understanding data.
Bokeh for creating interactive plots, dashboards, and data applications on modern web browsers. Bokeh empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.
Blaze to extend the capability of Numpy and pandas to distributed and streaming datasets. Blaze can be used to access data from a multitude of sources including bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on large chunks of data.
Scrapy for web crawling. Scrapy is a very useful framework for getting specific patterns of data. It has the capability to start at a website's home URL and then dig through web pages on the website to gather information.
SymPy for symbolic computation. SymPy has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics, and quantum physics. Another useful feature of SymPy is its capability of formatting the result of the computations as LaTeX code.
Requests to access the web. It works similar to the standard Python library urllib2 but is much easier to code. You will find subtle differences between Requests and urllib2, but for beginners, Requests may be more convenient. Some additional python libraries for data science and machine learning include:
Data Science with Python
You’re a data scientist in a telecom company whose customers are switching to its competitors. You need to analyze the data of your company and find insights and stop your customers to switch over to other cellular companies.
Tasks To Be Achieved:
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Top Companies That Practice Data Science With Python
Instagram has about 400 million daily active users who share more than 95 million photos and videos. It's like billions of data. Instagram has recently moved to Python 3, and the main reason why Instagram chose Python was its simplicity and popularity. Instagram claims to have considered different languages over Python but did not get any significant performance improvement.
Spotify trusts Python and uses it for back-end services, as well as for data analysis. Spotify claims that the speed of development is its priority, and that is the reason why Spotify uses Python to build its music streaming service as Python meets Spotify’s development speed expectations. For data analysis, Spotify uses Hadoop with Python to process large amounts of data in order to polish its services.
Amazon analyzes customers’ buying habits and searches patterns to provide them with accurate recommendations. It is possible due to their Python ML engine, which interacts with Hadoop, the company’s database. They work in conjunction to achieve maximum efficiency and accuracy in providing recommendations to customers. Amazon prefers Python language because it is popular, scalable, and appropriate to deal with Big data sets.
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Facebook deals with large amounts of data, including tons of images, and it uses Python to process the images. Facebook decided to use Python for its back-end applications connected with image processing, such as image resizing, because of its simplicity and ease of development. If you have any doubts or requirements related to Data Science, join Data Science Community.
On A Concluding Note -
Python is a great tool and has been a widely popular language among data scientists as it’s easy to learn and integrates well with other databases and tools like Spark and Hadoop. Python also has great computational intensity and powerful data analytics libraries. Experts emphasize learning Python to carry out the full life cycle of any data science project. It includes reading, analyzing, visualizing, and make predictions. From this data science with python training blog, you might have understood why Python is preferred over other languages and python libraries for data science.
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