Python is a powerful tool to perform data science. From cleaning null values to wrangling data for a detailed analysis to visualising algorithms, this tool is unlike any other. Let’s dive into the world of analysing numbers and writing algorithms with an introduction to data science in Python.
Data science was named the sexiest job of the 21st century by Harvard Business Review back in 2012, and the tool facilitating data scientists all over the world to perform this sexy job is Python. This object-oriented, high-level programming language has taken over the data world with its semantics, high-level built-in data structures, and dynamic binding. With the help of Python, data scientists use scientific methods, processes, and algorithms to extract knowledge and insights from many structural and unstructured datasets. After going through this blog post, you will have an understanding of data science in Python and will be able to use Python libraries for data science, get a hold of data science skills and see yourself in a data science career.
Introduction to Data Science in Python
Let’s start by getting a good hold of Python and understanding what it is – Python is primarily a programming language but is used in the field of data due to its versatility of functionalities from a mathematical and statistical perspective. Along with this, its processing speed, accessibility and syntax have made it super popular. It is not only a free software, but it also allows users to develop their own packages and libraries that others can reuse. And even if an individual has not coded in their entire life, they would be able to pick up Python in no time. The syntax used in this language is intuitive and easy to understand.
Don’t believe us? Try downloading and installing Python, and let’s take baby steps towards learning this super tool.
- Navigate to the Python downloads page and click on the link/button to download Python 3.8.x.
- Leaving all the details on default mode, proceed to installing Python as-is.
- Open your terminal again and type the command python. The Python interpreter should respond with the version number. If you’re on a Windows machine, you will likely have to navigate to the folder where Python is installed (for example, Python38, which is the default) for the python command to function.
How Does Python Help in Data Science
Python’s simplicity and easy learning curve have helped many data scientists. In the section covering ‘introduction to data science in python’, we learned that not having a coding background does not cause a hindrance in learning Python for data science. And this is only the beginning. We give you more reasons to understand how python can help in your data science career.
- Tailor-made data science libraries – You may not believe this, but there is a library for every possible data science task; and there are over 80,000 libraries for a data scientist to access. Some of them like NumPy and SciPy help with easy scientific calculations, whereas libraries like Pandas help manipulate and analyse the data.
- Python for data visualisation – One of the most important tasks for a data scientist is to create visual representations of the analysis that has been done. It helps understand and communicate the results better. Matplotlib package in Python is the genie for all data visualisations!
- Advantages in applications (machine learning and deep learning) – As a data science professional, you will eventually foray into artificial intelligence and work on solving machine learning and deep learning problems. Python will not only help during the analysis of these problems, but you would also be able to build products like virtual assistants/systems that are capable of predictive analytics.
Now isn’t that exciting, or do we need to convince you more?
Top 7 Python Libraries
As we recall, there are thousands of data science libraries that have been created by users for the ease of other scientists. While we have read that simple libraries like NumPy, SciPy help with scientific calculations, lets deep dive into some basic Python libraries for data science, and how they can help us:
1. Pandas is designed for quick and easy data manipulation, reading, aggregation, and visualisation. With this library, we can:
- Index, manipulate, rename, sort, merge data frames
- Update, Add, Delete columns from a data frame
- Impute missing files, handle missing data or NANs
- Plot data with histogram or box plot
- This makes Pandas a foundation library in learning Python for Data Science
2. NumPy is used to facilitate math operations on arrays and their vectorisation. This significantly enhances performance and speeds up the execution time on a dataset. With this library, we can:
- Perform basic array operations like add, multiply, slice, flatten, reshape, index arrays
- Perform advanced array operations like stack arrays, split into sections, broadcast arrays
- Work with DateTime or Linear Algebra
- Perform basic slicing and advanced indexing
3. SciPy is built on top of the NumPy array object and contains modules for efficient mathematical routines as linear algebra, interpolation, optimisation, integration, and statistics. With this library, we can perform common scientific programming tasks as linear algebra, integration, calculus, ordinary differential equations, and signal processing.
4. Matplotlib is to create stories with visualisations. Plotting 2D figures of Histogram, bar plots, scatter plots, area plot to pie plot, Matplotlib can depict a wide range of visualisations. This is what makes it a versatile library.
Seaborn is an extension of Matplotlib with advanced features. It is different from Matplotlib for its variety of visualisation patterns and lesser syntax. With this library, we can:
- Determine relationships between multiple variables
- Observe categorical variables for aggregate statistics
- Analyse univariate or bivariate distributions, plot regression models
- Provide high-level abstractions, multi-plot grids
5. Scikit Learn is a robust machine learning library featuring ML algorithms like SVMs, random forests, clustering (k-means, KNN). It supports both supervised and unsupervised learning algorithms, and focuses on modelling data rather than summarising or manipulating it. With this library we can:
- Classify and solve spam detection and image recognition
- Cluster to understand drug response and stock price
- Reduce dimensionality by visualisation, and increasing efficiency
6. TensorFlow is an AI library that helps developers to create large-scale neural networks with many layers using data flow graphs. With this library, we can perform:
- Voice/sound recognition
- Sentiment analysis on CRM or CX data
- Face Recognition
- Time series analysis on datasets from organisations like Amazon and Google
7. Keras, like TensorFlow, is for building and training deep neural network code; but here statistical modelling and working with images and text is a lot easier. Keras is exclusively for neural networks whereas TensorFlow is for various machine learning tasks.
Data Science Skills – Brushing Up On Python Syntax
For a successful career in data science, it is important to have programming skills, an understanding of mathematics/statistics/algebra, skills in machine learning, data wrangling, data visualisation and communication. Python helps with a majority of them, but it is not enough. Python libraries play a major role in applying Python to actual datasets and you could start by practising on datasets from Kaggle. Before you do that, let’s brush up on the basic syntaxes used in Python. Python syntax can be executed by writing directly in the Command Line or by creating a Python file on the server, using the .py file extension, and running it in the Command Line.
- Python Indentation refers to the spaces at the beginning of a code line. Where in other languages, the indentation in code is for ease of reading, the indentation in Python is used to indicate a block of code.
- Python Variables are created when a value is assigned to it. Python has no command for declaring a variable, although it has a commenting capability for the purpose of in-code documentation.
Line structure, comments, docstrings, indentations, quotations, identifiers, variables, string formatters all are significant parts of Python’s syntax.
Data Science as a Career Prospect
For years now, data scientist is the leading job in the USA, and this trend is now moving towards India as well. There is a dearth of professionals in this field, who have expertise in data science. Numbers in Harvard Business Review report that the demand for data science skills will drive a 27.9 percent rise in employment in the field through 2026.
A data science career is very fruitful and full of learning. You have already taken the first step by getting an introduction to data science in python. With all your additional data science skills, you should be confident in using python libraries for data science, and seeing yourself pursue a data science career. To learn the basics of python and apply it on linear regression, decision tree, and other algorithms, join Springboard’s Data Science Career Track program. With 1:1 mentoring-led sessions, detailed curriculum, and project-driven approach, you will be an expert in Python and Data Science within 6 months. And our career support services to help you prepare for Data Science interviews will give you the push that you need into a data science career. Apply Now!