Data Science is a skill set that will always be relevant. People have been participating in data science without an official title since before the age of computers. Thankfully, now there is an official title and skills are being identified to fill this important need. With the growing rate data is being collected, new tools and methods of analysis need to be employed to make proper use of the information collected . If you enjoy making sense of data, or are just looking for an interesting area to specialize in, Data Science is a great are to focus on.
A few of the skills aspiring data scientists should learn:
- R programming language
- Python programming language
- SQL (relational databases)
- NoSQL (not only relational databases)
Many languages are currently used for data aggregation, calculations, and formatting. Certain businesses have to use proprietary languages like Stata or Visual C#. There are certain industries that enforce rules like only using closed source proprietary languages for legal reasons. Languages like R, Python, Java, C++ are used heavily in both financial and scientific areas. Some languages like R were created with the purpose of easily processing large amounts of data.
R is a free software environment for statistical computing and graphic calculations ( as stated on their website ). A wonderful language that has many easy to use packages for calculations and aggregation of data. Once one learns the syntax, R is a wonderful language and will help to develop skills that all data scientists will benefit from. R also easily integrates with databases and has a wonderful REPL ( An interactive top level shell like environment to interact with ).
Wonderfully titled the easiest programming language to learn, python is a great general purpose programming language. Used for almost any need, Desktop applications, Server automation, data aggregation, website development, scientific and financial data analysis, python can be used almost anywhere there is a development need. With specialized libraries like scipy and numpy, python can be used for any specialized or general purpose needs.
SQL – Structured Query Language
SQL is the standard language used to communicate with Relational Databases. Other Databases like Cassandra or OrientDB use SQL, or SQL like subsets to work with data. Some of the benefits include data aggregation ( max, sum, average, min ), or combining data from different data collections. Understanding how to use SQL can help analyze data in a very efficient way.
MapReduce is a programming model and an associated implementation for processing and generating large data sets with a parallel, distributed algorithm on a cluster (google definition). Used with Hadoop, MongoDB, Cassandra, and other databases, MapReduce is a very powerful tool used to aggregate data and perform powerful calculations.
Microsoft Excel is the industry standard for spreadsheets. With a programmable back end, and an incredible amount of pre-built as well as custom calculation macros, Excel is a powerful tool for data analysis. Available for purchase, or a monthly rental fee, Microsoft excel is a great tool to allow data to be used by data scientists and business people alike. If the fee for excel is not desirable, or if working on Linux, free alternatives like LibreOffice are available.
Alternative databases built to fit needs that Relational Databases may not be suited for like:
- Incredibly large data sets
- Different ways to organize data
- Provide faster data access
- Organizing data in a non relational way
Not all NoSQL databases were created for speed, or large data. NoSQL databases were created with different use cases in mind, so the type of database used should depend on the intended design or the problem being addressed. A wonderful starting point into the NoSQL world is MongoDB. Mongo is one of the easiest databases to use. Storing and accessing data used together in document format, makes working with data incredibly easy.
Specializing in this new and exciting area can be a great resume booster to enter a new career. If you are already a programmer, even if the Data Science keyword stops trending, the skills learned will never be out of trend!