Python is a general-use high-level programming language that can be powerful, fast and relatively easy to learn. Python has gained popularity in data science and is used by a variety of industries.
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Here are five common steps to learn python for data analysis:
- Make sure that Python is the right language for your data science needs.
- Take the time to gain a firm understanding of Python rules and syntax.
- Try specific exercises to practice the skills you have already been taught through the process of basic syntax acquisition. Codewars or CodeAcademy may be useful for this type of targeted practice.
- Next, start working on small personal data science or data analysis projects so that you can practice your skills in an applied environment.
- Finally, join a hackathon or a team of friends so that you can take the opportunity to work on a real life project using your Python for data science skills.
Is Python Better Than R for Data Science?
Python’s use in data science applications has situated it in opposition to R, a programming language and software environment for graphics and statistical computing. What are the main features of each language when comparing Python vs R?
- Python is increasingly becoming the industry standard for data science and is used by many major companies.
- Python is a general programming language and is better suited for machine learning applications than R.
- Because of its programming capability, Python supports HTML, XML and other internet protocols.
- R was designed specifically for statistical analysis.
- It was not, however, designed for machine learning applications.
- Many data scientists who use R may also use Python.
How Long Will It Take to Learn Python?
The length of time it will take you to learn Python for data science varies greatly depending on your educational background and work experience. Prior experience may help you to reduce the amount of time it takes to learn Python for data science because Python shares a considerable amount of syntax and structure with other programming languages.
While learning speeds vary for different people, for those without prior experience, it may be helpful to devote at least three months in order to reach an intermediate to advanced level of skill. At this point your Python for data analysis skills may be sufficient to conduct analysis with confidence. With that in mind, remember that there is always more to know, and data scientists are lifelong learners.
How Is Python Used for Data Science?
Many companies ask their data science teams to use Python in order to build machine learning models that predict future trends for the company and the industry overall. Uber used Python as part of its foundation. A Python library was used in the development of mobile payment service Venmo. Facebook turns to the Python library Pandas for its data analysis and in its API because of its ability to use one programming language across multiple applications.
Seven Python Libraries for Data Science
Python libraries for data science are reusable functions and methods made readily available for you to use in your data analysis projects. While there are many libraries available to perform data analysis in Python, here are seven to get you started:
NumPy is fundamental for scientific computing with Python. It supports large, multidimensional arrays and matrices and includes an assortment of high-level mathematical functions to operate on these arrays.
Pandas, built on top of NumPy, offers data structures and operations for manipulating numerical tables and time series. It is a tool data scientists will use again and again.
SciPy works with NumPy arrays and is useful in statistics and programming tasks such as algebra and calculus.
Plotly is a library used for colorful, engaging data visualizations.
Matplotlib is a 2D plotting library that can also generate data visualizations, such as histograms, power spectra, bar charts and scatter plots.
Seaborn is a library that expands upon the functionality of Matplotlib, using enhanced graphics to make heatmaps and other dynamic visualizations.
Scikit-learn is a machine learning library built on NumPy, SciPy and Matplotlib that implements classification, regression and clustering algorithms including support vector machines, logistic regression, Naive Bayes, random forests and gradient boosting.
Want more? Here’s a longer list of Python libraries useful for data science applications.
Online Courses in Python for Data Science
In addition to general Python courses, there may be courses on data science and Python. Because Python can take different people different lengths of time to learn, Python data science courses may be helpful. There are a variety of bootcamps and online courses that can help you learn, if you prefer learning in a classroom setting versus self-teaching. One such course is:
CS109 Data Science from Harvard University: This Ivy-league introduction to data science uses Python for all programming assignments and projects. Slides and video lectures are available online free of charge and the IPython notebooks for the course are on GitHub.
Interested in a Career Shift? Check Out Our Bootcamps
One option for learning Python is to consider bootcamps. Bootcamps may focus on a whole topic like data science, or just focus on coding and different languages.
Here is our Data Science Bootcamp Guide. It provides a range of options in terms of cost length and price and offers a good sense of what to expect from your bootcamp experience.
Our Data Analytics Bootcamp Guide explains the different expectations that you should have going into your data analytics vs data science course of study.
Get a more general overview with our Coding Bootcamp Guide for those who want to focus first on learning coding languages prior to honing in on data science or data analysis.
Our FinTech Bootcamp Guide is for those who are planning to specialize in the financial technology sector.
Last updated: November 2020