The Data Science Toolkit — A Guide
“The world is one big data problem”
- Andrew McAfee
One of the biggest, if not the biggest boom of the 21st century is the dependence of human beings on data for their daily needs.
Be it your fitness tracker, online streaming data or sports — data is everywhere.
Thus, it is not surprising to see data science as a go-to job for many people across the world. According to sources, USA, Europe, UK, China and India hold the greatest opportunities for data scientists.
Want to know more about data science and the basic tools and software used in the data science world? Well, you have come to the right place. So, let’s begin!
What is Data Science?
The meaning comes from the word itself — Data + Science. That is, the science of data is exactly what we call data science.
You might be wondering — what exactly is this science?
Science here means developing methods to analyze, clean, store and record data to effectively gather information out of it.
The main objective of data science is to gain deep insight into and gather knowledge from a given data — whether the data be structured or unstructured.
This raises another question — how exactly is the data processed?
Read on! The answer is in the next section!
Programming Languages and Packages
Before we talk about packages, it is a must that we know which programming languages can be used for writing our codes. Well, to be completely honest — ALL basic programming languages can be used. I repeat ALL!
Well, it might come across as a surprise to some, but this is the truth.
However, there is a small catch here.
Though all the programming languages can be used, the efficiency of one type of programming language differs from the other.
The types of programming languages which I referred to here are — low-level and high-level programming language.
Defining both these languages in short — low-level languages are non-human readable and is cryptic, while high-level languages are easy to read, non-cryptic and have a comparatively simple syntax.
Data Scientists usually prefer high-level programming language over low-level programming language. This is because high-level programming language has a strong focus on detail and has the ability to create code which is independent of the type of computer, which is not the case for low-level programming language.
Some of the most commonly used programming languages in data science are — Python, JavaScript, Scala, R, SQL and Julia — all of which are undoubtedly high-level programming languages.
If you want a personal opinion as to which programming language you should use as an aspiring Data Scientist, I would suggest Python. The main reason for this is the simplicity of the programming language, which I feel is not present in other languages.
It’s just my opinion though. You can choose which one is best for you!
Looks like the programming language hurdle has been cleared.
Next comes the packages.
Just like how we need a pen or pencil to write on a piece of paper, we need the fundamental data science packages to make our first dive into the world of data science.
Some of the basic data science packages are:
1. NumPy
This is the first package I came across when I explored data science for the first time and I have got to say that it has remained my favorite package to date.
NumPy basically works like a scientific calculator which you might have come across. It performs the basic computing operations in Python. It is also known as the most fundamental package needed for scientific computing with Python.
2. Matplotlib
This is the basic Python 2D plotting library. It is mainly used to produce bar graphs, histograms and other similar 2D plots. The field of data science requires a lot of plotting and drawing graphs; hence you will come across this package a lot when you code.
3. Pandas
Though not strictly a machine learning package, it is well-suited for data analysis and manipulation, and is extremely useful especially when there are large datasets to handle. It forms a lethal combination with NumPy, and you have got to see both of them in action together — it’s a sight to behold!
4. Seaborn
We saw Matplotlib, which can be used for 2D visualizations. But, the problem with Matplotlib is that the visualization might not be as attractive as one thought. This is where Seaborn comes in. Seaborn is based on Matplotlib and provides a high-level interface for drawing attractive and informative statistical graphics. This package is preferred over Matplotlib whenever there is a need for a more detailed statistical inference.
5. SciPy
While NumPy can help in performing fundamental mathematical and scientific operations, there are times when complex operations are required. Here, SciPy is used. Operations such as signal processing, optimizing algorithms, Fourier Analysis etc. are performed using SciPy.
6. Scikit-Learn
This beginner-friendly package is built on top of SciPy and NumPy. It contains a lot of tools for classification, regression, clustering and dimensionality reduction. It is mainly used for predictive data analysis.
7. PyTorch
A performance-oriented package, it is mainly used in place of NumPy. This package has better performance and more importantly, is very fast and efficient. PyTorch provides two main features — Tensor computing with strong acceleration via graphics processing units (GPUs) and Deep Neural Networks built on a type-based automatic differentiation system.
8. TensorFlow
A popular package, it specializes in numerical computation using dataflow graphs. One of the features which I feel makes TensorFlow stand out from other packages is the fact that it can train and run deep neural networks for simulations like handwritten digit classification, image recognition, word embeddings etc.
9. Keras
This package is usually built on top of TensorFlow. It is mainly used for easy and fast prototyping. It can also run on both CPU and GPU, making it another unique package.
10. BeautifulSoup
This package is completely different from the ones we have seen so far. While the packages discussed above focus on how to manipulate and process data, BeautifulSoup is different. It is a data mining package, from where we can extract data to perform various tasks. Though the data may not be in the standard csv format, BeautifulSoup can help one to arrange the required data in the format which is needed.
These are the most popular packages used in the field of data science. However, when you take your first step into the data science world you will surely understand that it is not a pond, but an ocean, with a huge variety of packages coming into play.
Though I am not a data scientist yet, trust me — if you are an aspiring data scientist, an amazing journey awaits you — with lots of data and datasets to play with!
