Data science and Machine learning tools

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. 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?

These are known as the 3 V’s of big data:

• Volume
• Variety
• Velocity

The following tools are used for handling volume

1. Microsoft excel
2. Microsoft Access
3. SQL
4. Hadoop
5. Hive

Tools for handling variety 

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.

Tools for handling velocity

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. 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:

• CCTV
• Stock trading
• Fraud detection for credit card transaction
• Network data – social media (Facebook, Twitter, etc.)

Now, let’s head on to some of the commonly used data science tools to handle real-time data:

1. Apache Kafka : Kafka is an open-source tool by Apache. It is used for building real-time data pipelines. Some of the advantages of Kafka are – It is fault-tolerant, really quick, and used in production by a large number of organizations
2. Apache Storm : This tool by Apache can be used with almost all the programming languages. It can process up to 1 Million tuples per second and it is highly scalable. It is a good tool to consider for high data velocity.
3. python : This is one of the most dominant languages for data science in the industry today because of its ease, flexibility, open-source nature. It has gained rapid popularity and acceptance in the ML community.
4. R : It is another very commonly used and respected language in data science. R has a thriving and incredibly supportive community and it comes with a plethora of packages and libraries that support most machine learning tasks.

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.