The online programming industry is lucrative and growing rapidly. In the United States, programmers ear over $100,000 a year. Qualified experts are so scarce around the world that it has become the star topic at executive and recruiter meetings.
The data scientist is one of today’s most promising professions. In a few years, it will become a key part of any organization. But you have to understand the nature of the industry and how to execute code to create data projects correctly, regardless of your preferred language. Programming is very complicated.
We live in a world driven by the urgency of data. We all want as much information as possible. We also want it immediately. We are not satisfied with waiting for results, we want all the data NOW.
The most interesting thing is that this great value of the data contrasts with its current abundance (it is calculated that every day we generate 2.5 trillion bytes all over the planet). However, what is really significant about these data is not the massive volume, but the complexity of processing it.
The Challenges of Processing Data Through Programming
Until relatively recently, it was impossible to do anything with the growing troves of data. At the end of the last century, machine learning technologies began to face the dilemma.
This has resulted in a reduction in the price of these solutions. It is a common commodity among many companies, which has increased demand for data scientists in charge of analysing and interpreting large databases.
The current market has generated an enormous demand for this type of professionals that, in many cases, cannot be covered by people with generalized skill sets. This new role does not find the perfect professional who, in many cases, is “inventing” himself, in a self-taught way. The programming languages that he dominates are his best letter of introduction. Also, one can use Entity Extraction, which is a text analysis technique that uses Natural Language Processing (NLP) to automatically pull out specific data from unstructured text, and classifies it according to predefined categories.
As a result, there is a strong demand for data scientists in recent years. This demand does not match the number of data scientists available. In many cases, the aim is to close this gap with self-taught training that completes the basic skills of any training plan. Therefore, we find very diverse profiles, among the first professionals in this field, from mathematics and statistics, computer engineering or other specific engineering.
Understanding the concept of data analysis, we see that a common definition would be to determine it as a process of inspecting, cleaning and transforming data in order to highlight useful information, suggest conclusions, and support decision making.
Data analysis has multiple facets and approaches that encompass diverse techniques in a variety of names in different businesses and sectors. Data is collected and analyzed to answer questions, test hypotheses and conjectures, or prove the invalidity of certain theories.
In recent years the legislation of certain sectors has hardened considerably, added to the competitiveness in a globalized market has led to the need for different companies and organizations to make optimal management and decision making based on available information. In order to do this, it is extremely important to be able to make the most of the information by carrying out good data analysis.
Independently of the numerous applications and solutions that exist today for data analysis, there are several programming languages commonly used by professionals to perform this task in which it is worth highlighting:
Programming language R. With statistical approach and very popular among data scientists. It is the open source version of the S. R language. It is a very useful language, besides allowing you to manipulate and organize data in graphs.
SAS programming language. SAS is also used for statistical analysis. It is a powerful tool for transforming information from databases to readable formats such as HTML or PDF, as well as tables and graphs.
Python programming language. While R and SAS languages are typical in the world of analysis, Python has established itself as one of the major competitors. One of the biggest benefits is the variety of libraries and statistical functions it offers. It is an open source language and probably the easiest to learn with many available resources.
SQL programming language. SQL, which stands for Structured Query Language, does not relate to statistics, but focuses on information management and relational databases. It is the most widely used database language and is open source, so data scientists should not ignore it. With SQL it is possible to create SQL databases (like the nosql databases), manage the data that integrate them and use relevant functions.