Top 5 Tools For Data Scientists And How To Ace Them
Is it overwhelming for you to deal with all the different tools referenced in data science discussion forums? You may have thought MATLAB would be sufficient, yet you are flooded with advice of how to begin. Some may advise you to start with Python or R. Then you should learn SQL or SPSS. Some may say you should excel at MS Excel and be rapid enough to learn Rapid Miner. And then…the learning process doesn’t stop.
You are in dire need to find a data science line of work and play with data throughout the day. You love math, however you don’t think academic qualification is the best option for you. So you've perused a few articles and a few papers and end up absurdly amped up for AI and about how to apply it in a business setting. Presently data science is viewed as something which will continue to grow. So you need to gain proficiency with the tools that will make you hirable as a data scientist, however, you aren't exactly certain where to start. So bid your worries a goodbye because we are here to guide you.
Top 5 Tools For Data Scientists
The top 5 data scientists’ tools are as follows:
1- SAS
One number one, we have SAS which has been explicitly designed and intended for statistical tasks. SAS is a closed source proprietary software used by giant associations for data analysis and statistical modeling. It is generally used by experts and organizations taking a shot at solid business software. SAS offers various libraries and tools that can be used by data scientists for displaying and sorting out their data. SAS is costly and is only preferred by big organizations. Despite being only used by industry giants, the functionalities SAS offers are extraordinarily amazing.
2- R and Python
Python and R – both are customized for data analysis – versatile and flexible. R is best at statistical analysis, for instance, normal distribution, regression analysis, cluster classification algorithms, etc. While Python can deal with text processing without hardly lifting a finger. Python has countless libraries for almost everything you could imagine, a few of them are Keras, Tensorflow, viz. Scikitlearn, and Theano. We have listed Python and R together on the same spot because both these tools are equally important when it comes to data science.
3- H2O.ai
On number three we have H20 AI. If you are hoping to accelerate your end-to-end data science processes, H2O is a decent tool to look at. H2O is a semi-open source AI platform for making models and deploying them into production. H2O provides support for different statistical & machine learning algorithms, it has an AutoML functionality too. This tool lets businesses extract useful insights without tuning machine learning models.
4- BigML
In the fourth position, we have the BigML tool. BigML offers a cloud-based GUI environment which can be used for dealing with Machine Learning Algorithms. Through it, organizations can use Machine Learning algorithms across different pieces of their organization. For instance, it can be used for sales prediction, product innovation, and risk analytics. Using Rest APIs, BigML offers a simple to use web-interface and you can make a free account or premium account dependent on your data needs.
5- Talend
Talend is an open-source data integration tool that was launched in 2005. The tool is known to yield solutions for data planning, joining, and application mix. There are so many advantages of this tool, some of them are real-time statistics, early cleansing, easy scalability, faster designing, efficient management, better collaboration, and native code. Talend is constantly introducing new features to keep with the developments in the field of data science, it not going to go obsolete anytime sooner. Like Python, it has a huge community that is ever ready to help out naïve users.
We have listed the top 5 data science tools for different fields that can be used for, regardless of anything. Use of these tools will vary with the nature of your work, for many of you learning Excel might seem to be the best option and for some of you, getting hands-on on Python is favorable. No matter which tool you’ll learn, you should know how to ace it. Moving forward, we will tell you how to ace these tools.
How To Ace These Tools?
To ace these tools, you need to be open to learning new things. Many experts say that individuals panic at the thought of learning a new tool, new programming language or a framework, why? Because they don’t want to put in efforts. That being said, to ace anything in your life, you need to be honest with yourself. You need to take risks, you should be willing to give whatever it takes. As they say “as you sow, shall you reap”? So you’ve got to be prepared to sow your favorite fruit which you’d like to devour later.
Take the first step and follow the expert’s guidelines and pave your way towards your goal. Make sure to practice what you learn, this will help you become familiar with the tool. If there’s anything you’re stuck at, take help from the community. Talend, Python and BigML has a huge network of experts that are there to help you out.
We all know that data science requires an immense range of tools. The tools for data science are for analyzing data, making meaningful and graphical representation of data and making amazing predictive models applying AI algorithms. A majority of the data science tools convey complex data science tasks in a single place. This makes it simpler for the client to execute functionalities of data science without composing their code without any preparation.
Likewise, there are a few different tools that take into account the application domains of data science. So, if you are thinking to start your journey in data science, then you shouldenroll in data science academy to learn R, SQL, Matlab, Hadoop, and other data science tools. This will help you ace these tools as well by keeping you motivated.