Know Which Tools Help Data Scientists To Improve Their Productivity?

Nov 23, 2018 | 4143 Views

The role of a data scientist role is merely limited to data analysis or statistical analysis. You may consider a 360-degree function of a data scientist related to business data, he is going to deal. Hence, he needs to pitch in almost all the areas of business data handling all the functions from sourcing to execution. The inclination is more on the techniques they are using to solve a problem. However, data scientists tools and technologies also play a significant role to get a productive result.

Well, with the manifold of data science tools in the market, it is certainly a rising challenge for you as a data scientist or a blooming data scientist to sort out the best ones. Moreover, it depends on your solution approach towards the problem. However, every trade asks for some essential skills. Not required to mention, as a data scientist you must be getting acquainted with the available data scientists tools in the market and more importantly the essential ones.

Common Data Science Tools and Technologies in the Market
"Process, perform and visualize the data", Probably this is the key 'mantra' for a data scientist. Hence, a data scientist should possess a working knowledge of statistical programming languages. Along with it, he must be capable of constructing data processing systems, performing database operations, and handling visualization tools. In addition to that, the knowledge of programming language is a plus.  So, a fair understanding of programming tools and user-friendly graphical interface help them to build predictive models more productively.

Following are the standard tools for data scientists in the stack:

As per 2014 Data Science Salary Survey, data scientists tools fall into four clusters and that cover almost 35 tools in total. Each of the clusters depicts data scientist roles to get the best outcome with the tools and technologies used for that particular data scientist role.

1: Business Intelligence
2: Hadoop and Data Engineering
3: Machine Learning and Data Analytics
4: Data Visualization  
Apart from this, as reflected in the Gartner Magic Quadrant for Advanced Analytics, the new generations of data scientists tools are gaining traction. The sole purposes of these tools are helping data scientists to build and deploy data science applications more efficiently.

Open Source Data Science Tools and Technologies in the Market
When the world is moving around open source tools and technologies, numerous free data science tools have been there in the data scientists' plate. Some of them are:

Apache Giraph: Iterative graph processing improves scalability and productivity as a whole for a data scientist. Giraph is a way to unleash the potential of structured datasets on a massive scale.

Apache Hadoop: This open source software is useful for distributed processing of large datasets across clusters of computers.

Apache HBase: Data scientists use this tool to achieve random and real-time read/write access to Big Data

Apache Hive: This data warehouse tool is used to assist reading, writing, and managing large datasets in distributed storage using SQL.

Apache Kafka: This tool is useful for building real-time pipelining and streaming data.

Apache Mahout: This is an ideal tool to build an environment for scalable machine learning applications.

Apache Pig: This tool is great to analyze large datasets coupled with infrastructure appropriate for such programs.

Apache Spark: Ideal to access diverse data sources such as HDFS, Cassandra, HBase, and S3.

Fusion table: This is a data visualization web application that empowers data scientist to gather, visualize, and share data tables.

ggplot2: This is among one of the most robust visualization data scientists tools. It is a hassle-free plotting graphics with which you can produce complex and multi-layered graphics.

Jupyter: Jupyter notebook is an efficient way to allow data scientists to manage different types of documents like code, explanatory and shared ones.

KNIME: It is a data-driven innovative tool to help data scientists to uncover the hidden potential of data, insights and predict future from it.

MLBase: This tool integrates algorithms, machines, and the human brain to make sense of Big Data.

Pandas: This is an open source high-performance library that provides easy-to-use data structures along with data analysis tools for the Python programming language. Data scientists who use Python makes use of this tool.

RapidMiner: RapidMiner is a unified platform for data preparation, machine learning, and model deployment for data scientists. It helps to make data science fast and straightforward.

And the data science tools and technologies don't end here, there are much more on the list.

Which Tool Should We Pick?
As we have discussed, there are more than 30 data science tools and technologies available in the market, the next big question is do a data scientist need to learn all of them? Note that, some tools coincide with others, whereas others are very domain specific. Hence, the silver lining is know at least one of them. Learn at least one of them well and get familiar with others as they come into your path.

However, if you want to get a role of data scientist, the best way to get started is to learn R, SQL, and Hadoop. Once you get a good hold of these, start learning Python and other Big data tools like Hive, Pig, etc. It will give you an excellent start to become a data scientist.
Hence, if you are an aspiring data scientist, get yourself acquainted with at least one of the popular data scientists tools. 

Source: HOB