In this article, we’re going to discuss Data Science with Python. The way to start your studies, the necessary stuff, and why you should learn Python.
1. Why Learn Python For Data Science?
I’m a big fan of the phrase “all knowledge is valid”. That means, everything you study can bring something to add to your life as well to your work.
Talking about the subject of this article, Python is one of the most easier programming languages to learn. Also, is a kind of new language, first released in 1991 (C was released in the 70s and it’s still largely used!).
And nowadays, we have a lot of data around the world. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from noisy, structured, and unstructured data.
Python has a lot of ready-to-use libraries and applications to work with Data Science models, and the important thing here is first to know how Python works and then go to learn more about Data Science. That’s the main reason why you should learn Python for Data Science!
2. How Long Will It Take To Learn Python?
I’m not an expert to say how long it takes to learn Python. It really depends on you! But, I can help to try figuring out how long it will take at your own pace.
Firstly, you need to know how many hours you will spread to learn how-to programming. There is no magic number, it’s what fits on your schedule.
Nowadays, we know that we have a busy life, so, don’t push yourself and don’t make sacrifices like avoiding do something joyful just to improve your skills. Just commit with yourself and do it!
Good sites to start your learning path in Python:
3. Where Can I Learn Python for Data Science?
Internet is the best choice of course. There is plenty of sites like YouTube, Udemy, Coursera, etc… with dozens of courses and tutorials about Data Science with Python.
Below, I’ve listed some courses based on reviews and content.
Udemy is always a good and safe option to find courses of any type. Moreover, I put in the link above a list of courses top-rated for beginners, where you can research and find one that fits you. Unfortunately, none of these top-rated is free.
This is a path program developed by Coursera. A great thing about Coursera is that all courses have a high curatorship, which means, you have the best-specialized people who created the course. This path program was developed by IBM for example.
As this kind, of course, is curated by Universities and Companies, the investment can be over your budget. So, you can start smoothly to get the basics and go for higher learning later.
4. Is Python Necessary in the Data Science Field?
Python is a great tool to use in the Data Science field. Furthermore, is an easy-to-learn programming language that makes your job smoother.
Exploring a little bit about how Data Science is categorized, we can see where Python fits.
- Data cleaning – when started to learn Data Science, Data Cleaning should be a important concept at the beginning. Python has libraries ready to do it like Pandas and Numpy.
- Data Visualization – self exaplanatory, this category is just to put data in graphs to a better picture to the final user. Here we can use Python libraries Seaborn, Datashader and many others.
- Big data – this a large data storage and use the usual approaching (as CRUD) is not enough to deal with it. Python also brings libraries to help us to collect and manipulates tons of data using other technologies like Apache Spark and Apache Hadoop.
5. Is Python Better than R for Data Science?
If you’re a starter developer, I say yes. R language is multiparadigm programming but was created over statistics analysis and maybe is a little more complicated to figure out in the beginning.
Python was created to be used all over fields and with a great contribution from the community, became portable to use in Data Science also, without thinking so much about statistics and other concepts.
A good exercise that I recommend is to start with Python in Data Science and when mastered, turn to R. Study more concepts about statistics and data cleaning with R and back to Python with more advanced data analysis skills and see which is better for you.
As much you have more tools to use, you can decide which fits into your challenge.
6. Conclusion
In conclusion, we saw how to start your learning path in Data Science with Python. Also, I introduce some aspects that are important to know how long it takes to learn a language programming.
Further, we’ve figured some courses to start Python and Data Science studies and make a comparison between Python and R in the data analysis field.
