The quest for a solution starts with the problem. Similarly, every data science project starts with the problem that enthusiasts like you aim to solve. Data Science is not all rainbows and unicorns, as it needs you to walk through the real-world task and use cases. Too often, Data scientists look around to solve their problems with Python. Python for Data Science is an unbeatable combination for today’s data scientists.
So in the following blog, we will see how python has made Data Science tasks easier and faster.
- What is Data Science?
- Python as a Language
- Data Science with Python
- Why Python for Data Science?
- Python Libraries for Data Science
- Conclusion
What is Data Science?
One of the hottest tech fields to be in right now! Agree? If you don’t believe let me show you some stats. Glassdoor ranks Data Science as the top job in demand, and even the U.S. Bureau of Labor Statistics says that data science will create around 11 million jobs by 2026. And if you look at the salary figure, Data scientists earn $113,000 per annum on average in US, and in India, it goes to Rs. 907,000 per annum on average.
Data Science is the field where data is at supremacy. One needs to deal with the vast amount of data, modern techniques and algorithms to deliver valuable insights and information. The significant component of Data Science tasks uses Machine Learning for predictive models.
Data Science is loaded with opportunities and major career prospects. Look at the various field where Data Science aid its contribution; you will not find any field that is not used: banking, logistics, manufacturing, airlines, technology, and healthcare.
Python as a Language
Python is a free, object-oriented, functional programming language that came in 1989 by Guido Van Rossum. The language got its name just for Rossum’s affinity towards the “Monty Python’s Flying Circus”. The motto that makes python one of my favourite languages emphasises the “Don’t Repeat Yourself (DRY)” principle.
Who uses Python majorly? The short answer: Data Scientist. And as of April 2021, 70% of data scientists reported using Python for their task. And it can be simply put that demand for python experts is rising and will continue to rise.
And Do you know, companies like Google, Youtube, Facebook, Instagram, Netflix, and NASA use python for many purposes like research, server-side, data analysis, forecasting, etc. Python can handle every job, from data cleaning to data visualisation to website development to executing embedded systems, all under unified language.
Data Science with Python
It is rightly said, data is an asset in today’s world and can take you to greater heights; if you know how to extract relevant information. And hence comes Python in the frame to help us to extract insights and visualise data.
- Data exploration & analysis.
- Pandas, NumPy, SciPy act as a helping hand from Python’s Standard Library.
- Data visualisation. A pretty self-explanatory name that helps in converting data into colourful visualisation in the form of graphs, charts and so on
- Matplotlib, Seaborn, is very helpful for this visualisation task.
Why is Python preferred for Data Science?
Python has grabbed attention as an attractive language due to Dynamic Typing, Self-sufficient libraries, powerful frameworks, and excellent community support.
Python is preferred for advanced data work under the umbrella of Machine Learning. Almost anything related to AI is the implementation of Machine Learning, and not to mention, a lot of Machine Learning tasks are done with python.
Python has other advantages too that put it ahead in the race; its capabilities to integrate with PaaS providers. This dynamic language is easy to learn and enables quick improvement; see the example below.
Here, rather than writing three lines, we can execute our task in a single line of code. So, imagine the overall time it saves.
Python Libraries for Data Science
Python is a perfect fit for data science due to its full-fledged libraries rooted in many data science tasks like data cleaning, data analysis and varied data visualisation options.
I think it will be fair to say that “The Libraries make the Python language“: as its 72,000 libraries and still growing number attribute to its success.
NumPy
NumPy = Numerical Python
NumPy is a Python library for numerical computing. It provides high-level math functions along with data manipulations on large arrays and matrices. The library helps in enhancing computation speed and performance. The different use-cases of NumPy are shown in the image below.
Pandas
The Pandas library is known for its simplicity. It is preferred for data wrangling and data manipulation as it allows a user to read data in, change it, look for missing values. Not only does it help in manipulating structured data, but it is considered a go-to library to perform data analysis. Panda has two data structures.
Series – Store and Handle one-dimensional data.
d = {'a': 1, 'b': 2, 'c': 3}
>>> ser = pd.Series(data=d, index=['a', 'b', 'c'])
>>> ser
a 1
b 2
c 3
dtype: int64
Data Frame – Store and Handle two-dimensional data.
d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df
col1 col2
0 1 3
1 2 4
Matplotlib
Matplotlib are known to simplify data visualisation task. It provides a solid foundation that provides a good sense of data and helps in creating graphical charts and interactive plots. Various plots or graphs that can be created with it are – bar graphs, Scatter plots, Pie charts, histograms, line graphs, area plots and so on.
Seaborn
Another library for data visualisation is built on top of Matplotlib. It helps in creating appealing statistical graphs. Seaborn is dataset-oriented, and declarative API helps in understanding data and different elements of the plot. The plotting function performs semantic mapping and statistical aggregation to build an informative plot.
SciPy
SciPy = Scientific Python
This open-source library is related to scientific and technical computing. SciPy is based on NumPy and provides user-friendly numerical integration, linear algebra, and statistics. SciPy is used for data optimisation and modification, algebra, special functions, etc. It is also preferred for ML tasks because it has all the algorithms you’ll want to use for regression, classification, and unsupervised learning.
Scikit-learn
It can be considered as a one-stop solution for the thriving needs of all the machine learning tasks. To give you insight, it helps you with supervised, unsupervised, SVM, k-means, logistic regression, DBSCAN, gradient boosting. It is built on top of NumPy, SciPy and Matplotlib and hence help in implementing data mining and data analysis task quickly.
Conclusion
Data Science is a vast field, and python has emerged as a versatile language that helps in various applications and use-cases of data science. What we have seen in this blog is just the tip of the iceberg, Python has more to offer, and the faith of tech giants like Google has secured Python top spot in the Data Science field.
Related References
- Introduction to Recurrent Neural Networks (RNN)
- Introduction To Data Science and Machine Learning
- Deep Learning Vs Machine Learning
- Introduction to Artificial Neural Network in Python
- Natural Language Processing with Python
Next Task For You
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