Top Tools That You Must Consider in Data Science

If you are starting in the world of Big Data, you will probably find yourself a little lost in the salad of terms that experts handle. Artificial Intelligence, Machine Learning, Internet of Things and some more. These are pieces of a puzzle that we must fit together to understand how this data works and be aware of all the juice that we can get out of it. One of the key components of the picture is the Data Science, which plays a crucial role in turning raw logs into information.

 

What is Data Science?

The Data Science is a discipline that combines mathematical techniques and technological tools for extracting, studying and analyzing data. Data Science aims to infer useful conclusions to anticipate trends and guide decision-making from a careful observation of reality.

 

Data Science is based on three pillars:

• Math. Although the academic origin of data scientists is very diverse, it is usual that we meet a large majority of mathematicians and statisticians. This is due to the great weight that the generation of algorithms and models and the application of logic have in Data Science. The scientist’s data are based on the techniques of data mining that oversimplify, are nothing more than complex equations that, given certain variables, seeking to clear one or more unknowns.

• Technology. When the data sets that are handled are manually vast, Data Science has to resort to technology, that is, to programming and computing.

• Business vision. The analysis of the data must be carried out with a tangible objective, that is, with an eye to issuing predictions to guide the next steps of an individual, organization, system or business. Data Science does not only imply only knowing, but it tries to use the knowledge generated as a roadmap to improve products and services, as well as to gain speed, efficiency and profitability.

 

Data Science is closely linked with Big Data and data mining. There are also other concepts of Data Science that it is essential to master if you want to make your passion for data your profession.

 

What is Data Science for?

In Data Science, you can take advantage from many different areas. We can think about Health, with the development of increasingly accurate diagnostic models; or in Human Resources, where Data Science will help us find the perfect candidate, analyze employee performance or retain talent.

 

But the list is almost endless, since other sectors such as finance, insurance, digital marketing, media, industry or logistics can also take advantage of the applications of this discipline. In general, companies are enhancing their Business Intelligence areas with Data Scientists to have a more analytical vision of the business and optimize key decision-making processes.

 

What does it take to work as a data scientist?

The profile of the data scientist requires a mixture of mathematical and analytical thinking, spiced with the ability to generate insights and transfer them to others in a simple and understandable way.

 

Most data scientist job openings stress the importance of knowing how to program with Python or R and being familiar with Apache Spark. In any case, if you do not have these computer skills, it is not an insurmountable barrier, since Master's degrees in Data Science include these disciplines as part of their training program.

 

Data Science tools

Data science tools intervene throughout the data exploitation chain, from storage to modeling, including tools to prepare the data and IDEs to work. In a few years, we have seen the number of solutions available on the market multiply. Choosing the right solutions can be a tedious job. To save you time, we have compiled a comparison of the best data science tools for you. In this article, we'll explore the different categories of tools that make up the data science sphere, in order to present our selection of the best tools according to their strengths and price ranges. Without further ado, the summary:

 

Data storage tools

Before choosing a solution, you must first analyze the volume of data to be stored, the type of databases you use (SQL, NoSQL etc.) as well as the end use of this data.

 

Faced with the multiple solutions on the market, the main differences lie in the scalability of storage systems, the possibilities of connections with other platforms and the computing power of requests. Another factor to consider is whether you want to be billed based on time of use or rather as a subscription. Here is our selection:

 

Amazon s3

• Leader in its category: Very flexible, scalable ecosystem with the most features

• 3 types of storage available: Object, file or block storage

• Fast SQL queries: performed in your warehouse and without traditional ETL

• Very large number of backups, archiving, data restoration partners

 

Google Big Query

• Satisfactory performance solution with automatic maintenance operations

• Automate data formatting and resource provisioning

• BigQuery performs its in-memory shuffle in a separate sub-service

• Import data via a variety of third-party software (Tableau, Looker, Qlikview, etc.) and the Google suite (Drive, Sheet etc.)

 

Amazon Redshift

• Complete cloud solution, which currently has 1,400 employees

• Architecture composed of several virtual warehouses, specifiable according to each business

• Supports the most popular data formats like JSON, Avro, Parquet, ORC and XML

• SQL native query language

• Instant scalability during periods of high demand

 

Microsoft Azure

• Solution very similar to Amazon competitively, and widely adopted by very large companies

• Lots of tools to deploy applications (Cloud service, container service etc.)

• Open to hybrid cloud systems, and efficient with Microsoft tools (MySQL, Office etc.)

• Complete and varied storage service: Files, blobs, datalakes, disks, archives

 

Data preparation tools

According to Experian, 92% of businesses don't trust their data. The tools in this category allow companies to obtain standardized, processed, enriched data if necessary in order to make them clean and usable. Each data preparation tool should simplify these steps as much as possible by automating scripts, macros for your future incoming data. The 3 essential factors to consider are:

• The compatibility of your data sources with data preparation tools

• The functional depth of the different operations available: Exploration, cleaning, enrichment, transformation

• The intuitiveness of the platforms and the ease of implementation

 

Conclusion

Now, you know some of the tools that will be vital for you when you consider data science. These tools will ensure that your experience with data science is seamless and you can utilize this amazing discipline to the maximum. Our developers at HData Systems can help you with the development of excellent data science tools.

 

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