Data scientists are a new breed of analytical data experts with the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved. 

What is Data Science? 

 Data science is an interdisciplinary discipline that uses scientific techniques, procedures, algorithms, and structures to extract value from data. Data scientists combine various skills, including statistics, computing, and business knowledge, to analyze data collected from the web, smartphones, customers, sensors, and other sources. 

Data science reveals trends and generates information that companies can use to make better decisions and create more innovative products and services. Data is the foundation of innovation, but its value comes from the information scientists can extract and then use from it. 

What process does a Data Scientist follow? 

Based on his knowledge in the field, the data scientist poses large databases that can answer a question that he believes. To answer it, follow the following process, which can be summarized in 8 steps: 

 1) Obtaining the data: Massive data usually comes from multiple sources (Variety); they can be of different volumes (Volume); they are generated quickly (Speed) and, since there are so many, it is necessary to check that they are correct (Veracity). They are the four “vees” of Big Data. 

2) Data pre-processing: An initial treatment of the data is carried out, where information that does not meet quality criteria, is not of interest to the study, contains errors. 

3) Transformation and integration: Homogenize the data from multiple sources to be comparable between them. This may be due to the structuring (data in table format) or the data (data in any other format such as text, images …). 

4) Data analysis: Process data using different algorithms and statistical methods to obtain results that answer the questions posed by data scientists. 

5) Interpretation of the data: It is at this point where the data scientist evaluates the result of the analysis and applies the experience he has in the field to understand, complete, and correct the information obtained by the computer. 

6) Data validation: See if these data are robust or change due to the data’s biases. It can be validated in multiple ways: using information external to the process, using techniques different from those used in the study. But they must always obtain a result similar to those initially obtained to affirm that the results are real and not due to chance bias. 

7) Design new analyzes or experiments if necessary: In the scientific procedure, this part is defined as “Validate the hypothesis.” Suppose the data has not been validated or more information is needed to obtain conclusive results to the data scientists’ questions. In that case, more data is included in the analyzes, or the algorithms are reformulated to ask other items to the data scientists.  

8) Visualize and present the data graphically results: It is a fundamental process in any work with large databases to graph the resulting information entirely and with as many layers as possible. The graphs are quick ways to interpret the data to make decisions. The trend in all scientific articles and everyday life, in general, is to complicate and complete the amount of information obtained in a single image. 

What does a data scientist do? 

The role of the data scientist is critical to the development of this production strategy. It is becoming increasingly fashionable, and apparently, it is here to stay in the commercial field. 

  • You must be a creative and curious person, capable of generating tactics that help improve marketing effectiveness and assist in consulting and training other professionals so that they can understand your organization’s data. 
  • It is a professional who must have some fundamental characteristics: 
  • Mathematical knowledge 
  • Through mathematics, it is possible to find solutions to many business problems. It is not only about understanding statistical values but also about building successful mathematical models. 
  • Technological skills 
  • Have the ability to find solutions through technological knowledge. A data scientist must use technology to create codes and prototypes to find creative options for improvement. 
  • This feature is extremely important. The more knowledge you have of technical language, the broader your capacity for understanding, creativity, and reaction. 
  • Business vision 
  • Being a strategist in business is paramount. The fact that a data scientist has access to such detailed information allows him to have extensive knowledge that not everyone will have the possibility to access. 
  • Having the ability to analyze and translate each data received and transform it into strategies helps solve problems successfully. 
The benefits of data science for companies 
  • Advertising on search networks, social networks, web traffic analytics, display networks, videos, installations and interactions with applications, web pages, CRM, databases, in today’s marketing, we are faced with a lot of data from different sources, with large volumes and arriving at an increasingly high speed. 
  • One of today’s marketing firms’ major problems is processing all this knowledge and extracting high-level strategic intelligence from it. And it is that with a successful data science application, we will collect critical knowledge for brands. 
  • To make more educated choices and decrease market risk, forecast potential consumer activities. 
  • Detect malfunctions such as cyber-attacks or theft, minimizing damages that can be very high for the company. 
  • Anticipate the needs of the customer to give them extremely tailored deals and content and therefore with greater conversion possibilities (as is currently the case with companies such as Netflix or Amazon) 
  • Establish trends and patterns that allow innovations with greater possibilities of success to be designed. 
  • And generally, reaching degrees of marketing segmentation and customer engagement that we could only think of before now. 
Challenges of implementing data science 

 

  • Despite the promise of data science and large investments in data science equipment, many companies do not realize their data’s full value. In their career hiring talent and creating data science programs, some companies have experienced inefficient team workflows, with multiple people using different tools and processes not working properly together. Without more disciplined central management, executives may not get a full return on their investments. This chaotic environment presents many challenges. 
  • Data scientists cannot work efficiently. Because an information technology administrator must grant access to data, data scientists frequently have a lengthy wait for the data and services they need to evaluate it. The data science team will examine the data through different, potentially incompatible methods once they have access. For example, a scientist could develop a model using the R language, but the application in which it will be used is written in a different language. That is why the implementation of models in useful applications can take from weeks to even months. 
  • Application developers cannot access usable machine learning. The machine learning models obtained by developers must often be recorded or are not ready to be implemented in applications. Also, since access points can be inflexible, it is impossible to enforce templates in all situations, and scalability is left to the program’s creator. 
  • Information Technology administrators spend too much time on support. Information Technology has an ever-growing list of applications to aid because of the abundance of open source tools. For example, a data scientist in marketing might use different instruments than a data scientist in finance. Teams can also have different workflows, which means that IT must continually rebuild and update environments. 
  • Business managers are a long way from data science. Data science workflows are not always integrated into business decision-making processes and systems, making it difficult for business managers to collaborate with data scientists intelligently. Without better integration, business managers have difficulty understanding why it takes so long to go from prototype to production. They are less likely to support investment in projects they consider too slow. 

Data Science at TechSur 

 TechSur team of data scientists works with clients across industries to solve their most pressing business challenges and take advantage of timely market opportunities. 

 During TechSur data science engagements, teams typically: 

  • Review current computational resources and sources of data 
  • Determine a situation of a strong market worth for executable use 
  • Iteratively create theoretical models, adapt, and optimize them. 
  • Operationalize by integrating predictive observations into business analysis and intuitive technology.