What are the best tools for performing data science tasks? And which tool should you pick up as a newcomer in data science?
I’m sure you’ve asked (or searched for) these questions at some point in your own data science journey. These are valid questions! There is no shortage of data science tools in the industry. Picking one for your journey and career can be a tricky decision.
Let’s face it – data science is a vast spectrum and each of its domains requires handling of data in a unique way that leads many analysts/data scientists into confusion. And if you’re a business leader, you would come across crucial questions regarding the tools you and your company choose as it might have a long term impact.
So again, the question is which data science tool should you choose?
In this article, I will be attempting to clear this confusion by listing down widely used tools used in the data science space broken down by their usage and strong points. So let us get started!
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To truly grasp the meaning behind Big Data, it is important that we understand the basic principles that define the data as big data. These are known as the 3 V’s of big data:
As the name suggests, volume refers to the scale and the amount of data. To understand the scale of the data I’m talking about, you need to know that over 90% of the data in the world was created in just the last two years!
Over the decade, with the increase in the amount of data, the technology has also become better. The decrease in computational and storage costs has made collecting and storing huge amounts of data far easier.
The volume of the data defines whether it qualifies as big data or not.
When we have data ranging from 1Gb to around 10Gb, the traditional data science tools tend to work well in these cases. So what are these tools?
We have covered some of the basic tools so far. It is time to unleash the big guns now! If your data is greater than 10Gb all the way up to storage greater than 1Tb+, then you need to implement the tools I’ve mentioned below:
Variety refers to the different types of data that are out there. The data type may be one of these – Structured and Unstructured data.
Let us go through the examples falling under the umbrella of these different data types:
Take a moment to observe these examples and correlate them with your real-world data.
As you might have observed in the case of Structured data, there is a certain order and structure to these data types whereas in the case of unstructured data, the examples do not follow any trend or pattern. For example, customer feedback may vary in length, sentiments, and other factors. Moreover, these types of data are huge and diverse.
It can be very challenging to tackle this type of data, so what are the different data science tools available in the market for managing and handling these different data types?
The two most common databases are SQL and NoSQL. SQL has been the market-dominant players for a number of years before NoSQL emerged.
Some examples for SQL are Oracle, MySQL, SQLite, whereas NoSQL consists of popular databases like MongoDB, Cassandra, etc. These NoSQL databases are seeing huge adoption numbers because of their ability to scale and handle dynamic data.
The third and final V represents the velocity. This is the speed at which the data is captured. This includes both real-time and non-real-time data. We’ll be talking mainly about the real-time data here.
We have a lot of examples around us that capture and process real-time data. The most complex one is the sensor data collected by self-driving cars. Imagine being in a self-driving car – the car has to dynamically collect and process data regarding its lane, distance from other vehicles, etc. all at the same time!
Some other examples of real-time data being collected are:
Did you know?
More than 1Tb of data is generated during each trade session at the New York stock exchange!
Now, let’s head on to some of the commonly used data science tools to handle real-time data:
Now that we have a solid grasp on the different tools commonly being used for working with Big Data, let’s move to the segment where you can take advantage of the data by applying advanced machine learning techniques and algorithms.
If you’re setting up a brand new data science project, you’ll have a ton of questions in mind. This is true regardless of your level – whether you’re a data scientist, a data analyst, a project manager, or a senior data science executive.
Some of the questions you’ll face are:
In this section, we will be discussing some of the popular data science tools used in the industry according to different domains.
Data Science is a broad term in itself and it consists of a variety of different domains and each domain has its own business importance and complexity which is beautifully captured in the below image:
The data science spectrum consists of various domains and these domains are represented by their relative complexity and the business value that they provide. Let us take up each one of the points I’ve shown in the above spectrum.
Let’s begin with the lower end of the spectrum. It enables an organization to identify trends and patterns so as to make crucial strategic decisions. The types of analysis range from MIS, data analytics, all the way over to dashboarding.
The commonly used tools in these domains are:
Moving further up the ladder, the stakes just got high in terms of complexity as well as the business value! This is the domain where the bread and butter of most data scientists come from. Some of the types of problems you’ll solve are statistical modeling, forecasting, neural networks, and deep learning.
Let us understand the commonly used tools in this domain:
The tools we have discussed so far are true open-source tools. You don’t require to pay for them or buy any extra licenses. They have thriving and active communities that maintain and release updates on a regular basis.
Now, we will check out some premium tools that are recognized as industry leaders:
Deep Learning requires high computational resources and needs special frameworks to utilize those resources effectively. Due to this, you would most likely require a GPU or a TPU.
Let us look at some of the frameworks used for Deep Learning in this section.
The era of AutoML is here. If you haven’t heard of these tools, then it is a good time to educate yourself! This could well be what you as a data scientist will be working with in the near future.
Some of the most popular AutoML tools are AutoKeras, Google Cloud AutoML, IBM Watson, DataRobot, H20’s Driverless AI, and Amazon’s Lex. AutoML is expected to be the next big thing in the AI/ML community. It aims to eliminate or reduce the technical side of things so that business leaders can use it to make strategic decisions.
These tools will be able to automate the complete pipeline!
We have discussed the data collection engine and the tools required to accomplish the pipeline for retrieval, processing, and storage of data. Data Science consists of a large spectrum of domain and each domain has its own set of tools and frameworks.
Picking your data science tool will often come down to your personal choice, your domain or project, and of course, your organization.
Let me know in the comments about your favorite data science tool or framework that you love to work with!
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Hi, great article. Thank you. Where would you put KNIME and Alteryx in your Data Science Spectrum?
Sir ! I really enjoyed and learn a lot about different tools used for different domains . It is really helpful to me for choosing the right one according to my domain.
Interesting and informative post. Very cleanly devided.
Highly appreciate the way your have articulated various sections in data science. I'm a follower of analytics vidhya blogs and articles. All the best.
This is A very Effective information for us
Ok
Shell scripting is light weight and effective tool for data processing.
Great article. Good to know more about each one of these tools.
Thanks for the great tips! I know there's a lot of information around getting technology on your side as a business owner, and a lot of information about using data to influence your decisions, so it's great to get information about both.
Awesome !
I miss tools like Orange, Rapidminer and KNIME, that allow you to build data-processing workflows with input-ouput blocks that perform operations on the data (e.g. remove outliers, do sequence mining, perform linear regression, pivot table and a lot more). I guess they belong in the category "Predictive Analytics and Machine Learning Tools".
very useful post.try to share such post in future
I was always lost in all these tools! Thanks for throwing the light!
This is a super post that gives a good amount of knowledge about tools used when working with BIG data
Pls give : More details abt powers of matlab -its features, capabilities, and compatibilities, in ML, and most importantly in hardware implementation (e.g. FPGA, or any controller boards). So as to make a device product.
Hi, Thanks for sharing wonderful stuff...
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