Data Science for Beginners: How to Get Started Learning Data Science
From discussions on the same over at runrex.com, data science can be a very overwhelming field, particularly since most people will tell you to become a data scientist, you have to master statistics, linear algebra, calculus, databases, programming, machine learning, distributed computing, visualization, clustering, deep learning, natural language processing, and so forth. At the same time, you keep hearing how attractive a career in data science is, with data scientists being among the best paid and sort-after professionals out there as is covered over at guttulus.com. If you are wondering how you can get started learning data science and how you can launch a career in data science, then you are in the right place as this article will look to highlight some of the things you need to master.
Learn Python and R
As the subject matter experts over at runrex.com are quick to point out, R and Python are great choices as programming languages for data science. Even though Python tends to be more popular in industry while R is more popular in academia, both languages have a wealth of packages that support the data science workflow. There are lots of courses online that will help you learn about Python and R, an example of which is Google’s Python Class, or DataCamp’s short and interactive courses on both Python and R.
Learn data analysis, manipulation, and visualization
If you want to work with data in Python, you should make sure you learn how to use the Pandas library, which provides a high-performance data structure that is suitable for tabular data with columns of different types, just like an SQL table or Excel spreadsheet. Learning Pandas will significantly increase your efficiency when working with data given that it includes tools for reading and writing data, handling missing data, filtering data, cleaning messy data, merging datasets, and so much more as outlined over at guttulus.com.
Learn Machine Learning with scikit-learn
When it comes to Machine Learning in Python, you should learn how to use the scikit-learn library. This is because scikit-learn is the most popular library for Machine Learning in Python, and for good reason given that:
- It provides a clean and consistent interface to lots of different models.
- Its documentation is exceptional, helping you understand the models as well as how to properly use them.
- It offers many tuning parameters for each model, while still helping you by choosing sensible defaults.
Some numerous online courses and books should help you learn more about Machine Learning, with more information on this to be found over at the excellent runrex.com.
Go into depth as far as Machine Learning is concerned
Given how complex machine learning is, you shouldn’t stop at scikit-learn but should go into depth with it if you want to be successful as a data scientist. The good news is that, as pointed out by the gurus over at guttulus.com, at entry-level, ML doesn’t require much knowledge of mathematics or programming. However, you should make sure that at the end of it all, you are well-versed in Supervised Learning, Unsupervised Learning, and Reinforcement Learning as these three algorithms are at the very core of what Machine Learning is all about.
Learn about statistics and probability
Statistics and probability are the basis of data science according to discussions on the same over at runrex.com. While statistics and probability are separate and complex fields of mathematics, if you are getting started with data science, you can start with 5 basic statistic concepts: statistical features, probability distributions, dimensionality reduction, Over and Undersampling, and Bayesian statistics, with there being several online courses and resources that will help you learn about these concepts.
Practice
If you want to improve your data science skills, then you must regularly put what you learn into practice. This can be achieved through personal data science projects, attending meetups or conferences, Kaggle competitions, and so forth. Kaggle competitions are a great way to practice data science without having to come up with the problem yourself. You can also contribute to open-source projects which will help you practice collaborating with others. If you decide to create your own data science project, you should also share it on GitHub and make sure you include writeups as well as mentioned over at guttulus.com.
Finally, we are going to highlight some of the resources to check out if you are looking to get started with learning data science, and they include:
- White papers- such as the NITI AYOG National AI strategy discussion paper which will help you understand the AI landscape.
- Courses – like the Coursera course on Machine Learning by Andrew Ng
- Books- like Linear Algebra and its Applications by Gilbert Strang, Deep Learning by Goodfellow, and many others
- Online tutorials- like the Data Science Masterclass on GitHub, the Run Python3 Jupyter notebooks on AWS, just to mention a few, and many other resources.
Remember, you don’t have to master everything related to data science to launch your career in data science, you just have to get started, and you can uncover more information on this and other related topics by checking out the excellent runrex.com and guttulus.com.