The world is changing at a very high pace, so is the research. Although humans are building new technologies, Artificial Intelligence and Machine learning are now taking over the manual work with advance learning techniques. For these machines to learn, there has to be a clear set of training examples which can be sent as input to the training model.
Handling the data with limited resources is sufficient enough for smaller models, but in the cases where a magnanimous set of data are used, there has to be a management technique to clean, modify and present the statistical analysis of the data. The subject of data science deals with this massive amount of data collection and modification.
Exploring Data Science:
Data science field has emerged massively with the increase of applications in Artificial Intelligence. Since every machine works with humongous data input, this field has become the most studied and researched field. This subject requires in-depth knowledge of statistics, data analysis, and machine learning.
Data science has to lead to huge innovations, especially for business analysts and statisticians. In addition to this, it has also become one of the best-seeking careers for budding engineers. Let’s explore further what all fields are involved in bringing up Data Science.
Scientific Methods
Scientific Methods deal with the perspective of real-world applications. It is more like a composition of different evaluation techniques and other essential information that was used to summarize the data model. It deals with the observation of data, able to question about the gathered information, hypothesize about the observation that was made, test the hypothesis, and make a conclusion based on this hypothesis.
Visualization
Data Visualization refers to a pictorial representation of data in different graphs. This is very important while displaying the statistics of the data as these charts provide the decision-makers to see the analytics visually which will make them easy to analyze the overview of the chart and identify the different patterns and concepts.
Statistical Modeling
Various statistical modeling techniques are used for concluding the data models and implement them in machine learning tools like Tensorflow, Apache Spark, etc. Some of these methods are Linear Regression, Classification, Resampling, Subset Selection, Dimension Reduction, Tree-based methods, Shrinkage, etc
Business Intelligence
Business Intelligence of the system refers to modeling the data and representing the statistical outcomes of it in a much more visualized way so that the intelligence system can keep updating the infographics concerning the business perspective.
Market Analysis
Market analysis is all about the survey details that were collected, gathered and cleaned to make an effective representation of the data. During market analysis, the data is collected from a huge crowd and different attributes are generated based on most frequently occurring data instance or keywords. This is important data for the machines as they take the input values from these accumulated data.
Data Computing
Here comes the most crucial task which is computing the data collected from different survey methods. Data computing plays an important role in deploying these models and run these large datasets.
Different roles offered in the industry:
Data science is the most chosen field for a computer science grad to pursue a good career. On the whole, one who has good knowledge of data science and machine learning are capable of contributing to most of the evolving technologies these days. One must surely look into the different roles offered to Data Science Engineer.
Analytics Manager-
They are expected to have a good grip over Database Systems, Programming languages and must have good interpersonal communication. Experienced industry experts can take up this job.
Business Analyst-
Business analysts must be able to take up any kind of projects and improve the business processes between the IT and business industry.
Database Administrator-
A database administrator ensures that the required databases are available to the relevant users. They are also responsible for the security measures to maintain the authentication of the authorized user.
Data Engineer-
A Data Engineer usually works with tools like SQL, Hive Ruby, etc. to develop the applications and perform various tests on them so that they function properly. However, he must be able to fix any kind of bug that is found during the analysis.
Data Analyst-
Data Analysts perform the role of a Statistician and work with intuitive data junk which is necessary for the development of the application. They must be curious enough and perform the right analysis of the given data.
Data Scientist-
The role of a data scientist is a mixture of every little role performed individually. A Data Scientist must be able to clean and organize the data. He must be versatile with a variety of technologies.
The above-mentioned roles have different pay packages depending on the role that they are working on and the place where they are located.
Usually, the highest pay packages are offered to the Data Scientists, Analytical Managers, and Data Architects.
The next bunch of pay packages is offered to Data Analysts, Business Analysts and Database Administrators.
To summarise, Data Science is a huge field and cannot be learned within a span of a few days or so. These skills are acquired only through continuous learnings and work experience. To succeed in this field, one must start with the fundamental tools and technologies and then dig deeper to master the subject.
