For a data science professional, tools make possible the application of knowledge.

Tool category groupings

There isn't necessarily a common set of descriptions for the main tool category groups. Different Data Scientists and different employers might use varying ways to describe how they manipulate data and with what.

Even so, it is not unusual to see the following groupings used to describe generic toolsets:

● Data collection

● Data processing

● Analytics

● Machine learning

● Visualisation.

Languages

A classic debate you might hear of relates to whether programming languages should be included under the general descriptive heading of "tools."

There is no right or wrong answer to that, and the discussion can slip into a slightly sterile debate of semantics. The traditional view is that language is used to write functional packages. Those functional packages were generally described as "tools."

The argument goes that you wouldn't necessarily describe steel or wood as a tool. However, put them together, shape them and produce a saw – then you have a tool.

Choosing a tool

Nobody can tell an individual Data Scientist what the best tool for the job is.

A Data Scientist may have a highly variable set of requirements and demands placed upon them almost daily. It may well be that some software tools are far more suitable for one particular situation than another.

This is roughly analogous to a carpenter. If you ask a professional in that discipline what is the best tool for the job, they may find the question amusing and simply ask what type of job you were referring to.

As a Data Scientist, what is important is that you maintain an awareness of and familiarity with, the wide range of toolsets that are available to you for deployment. Your eventual choice may be heavily influenced by a number of different factors including ongoing maintainability, but your employer will expect you to select the right tool for the job in the right circumstances.

Of course, there are now so many different data science tools available that it is difficult to maintain an in-depth familiarity with all of them. Here are the five top tools that many Data Scientists would argue are invaluable, including appropriate training courses:

● R Programming.

R is one of the hottest and fastest-growing Data Analytics tools in the market. These skills are in huge demand and this certification in the R course will provide you with 40 hours of instructor-led training and a further 24 hours of self-paced video.

● Spark and Scala.

These tools are specifically designed for real-time data analysis and are becoming increasingly essential for a Data Scientist's portfolio. Apache Spark course is delivered through 15 hours of video at your own pace plus 32 hours of instructor-based tuition

● SQL.

Although relational databases and SQL are not associated with new wave Big Data analysis, the fact remains that a significant amount of traditional enterprise data sits in one form of RDBMS or another. It can't be exploited unless it's accessed. So, SQL continues to be regarded as one of the "must-have" toolset skills for many Data Scientists. SQL training is available through 180 days' access to online courses.

● SAS.

The perennially popular analytics package. Some surveys show that, in spite of its origins going back to the 1970s, still around 15-20% of job advertisements for Data Scientists specify SAS expertise as a requirement. SAS basics training is delivered through an online course comprising 10 hours of video tuition progressed at your own pace.

● Python.

This tool consistently appears at the top of Data Scientists' lists of essential skill sets. Although seen by some as a general-purpose language, it is very popular in data science due to its power and flexibility. You can learn Python through the Python for Data Science Certification Training Course. That involves 24 hours of learning videos plus 44 hours of instructor-led training.

The Future

It might surprise you to know that in addition to the above, expert-level knowledge of the simple Excel package is still often seen as being essential for Data Scientists! That has been the case for many years now and seems unlikely to change in the near future.

It also seems highly likely that the current high levels of demand for Data Scientists with the above skill sets will continue for the foreseeable future.

If you'd like to know more about the above tools and their associated training courses, it might be a good idea to make a move to find out right now.