Data Science Guide For Beginners

Data Science

The data science profession has been among the firsts in recent years in the ranking of the best professions in researches conducted worldwide. This is why they create new jobs in companies and provide new jobs in different roles to provide human resources in this field and want to go one step further in generating data and value. In this article, we will give examples of data science professions, tools used in data science studies, and data science applications.

What is Data Science?

Data science is an interdisciplinary field of study, a field of study that results from the joint study of many disciplines such as mathematics, statistics, programming and business knowledge. It is to make meaningful results from raw, structured or unstructured data by using scientific methods, algorithms and different technologies and to adapt the results to the system to be automated.

In data science studies, the most equipped tools, the most advanced programming languages ​​and very advanced algorithms are used to draw meaningful results from the data and to ensure the implementation of these results.

We can collect it under the following headings;

Let’s say that we will go from point X to Y in Istanbul by car, the first thing we do before going to traffic for this process is to create a route to the point we will go with the help of a navigation device. The navigation device we use for this process draws routes for alternative routes to our destination and we set the route with the option that is most useful for our work in terms of time, cost, convenience, and here we go, the system that provides us this opportunity (Phone, Tablet, Computer, Navigation Device, etc.) advanced algorithm software and the operation is data analysis, that is, working discipline which is a sub-branch of Data Science.

Do you need Data Science?

It is a great need to draw meaningful conclusions from the data in this time when the data has increased inevitably in the digital world, while it is able to keep the data obtained previously in a basic excel file and visualize it again with basic business intelligence tools, now the capacity of the data has increased rapidly and accumulated. The need for data science has arisen to use the available data in the most beneficial way to overcome many problems (data) and to produce the most meaningful applications and results.

According to researches, approximately 1.7MB of data is produced per second in 2020, so the reasons such as obtaining and storing this data, visualizing it, obtaining the most beneficial outputs from the data increase the importance of the data for several main reasons:

With the help of data science technologies, we can visualize high-capacity (terabyte) data in the most meaningful way and obtain useful output.

Many technology companies worldwide include Google, Amazon, Alibaba etc. organizations and hundreds of users are evaluating the data at a high level to make the most of their users.

Data science is used in the autonomous (without human intervention) processes of many jobs, for example, driverless vehicles, robots, etc. It is widely used in such areas.

It makes use of the data science field in forecasting operations, for example, it makes important contributions through the forecasting algorithms in areas such as elections, weather conditions, stock market etc.

Let’s examine in detail the “Data Science” field that we are trying to explain in detail and the professions or lines of business formed in this field.

In order to increase the data rapidly and make this data meaningful in the world of today and tomorrow, the importance of the data in the field of “Data Science” is increasing day by day and the need for human resources for the field is increasing. According to estimates, the need for human resources in this area will be around 13 million in 2030.

What are the Profession Types in the Field of Data Science?

If we want to be a data scientist and work with data, it will be our benefit to know the lines of business in this area and to advance our development processes and plans within the framework of these majors.

Let’s try to explain some Data science professions below.

1. Data Analyst:

The Data Analyst is the person who is data mining and tries to understand the visual relationships between the data that model the data. At the end of the day, we can say that it is the individual who undertakes the task of analyzing and visualizing the data for the decision making and problem solving process and taking the reporting process tasks.

Competencies; If we want to be a good “Data Analyst”, we need to increase our competencies and knowledge in the fields of mathematics, statistics, business intelligence and data mining. At the same time, we need to have advanced knowledge and application level proficiency in at least one or two of programming languages ​​such as MATLAB, Python, R, SQL, Hive, Pig, JavaScript, Excel, Spark.

2. Machine Learning Specialist:

The Machine Learning engineer is mostly the person who works on the algorithm side of the job, that is, those who adapt and develop technical coding and algorithms such as regression, clustering, classification, decision trees, random forest algorithms.

Competencies: Languages ​​such as C / C ++, Python, R, Java, Hadoop, Scala, which are computer programming languages, are used to encode algorithms.

3. Data Engineer:

Data Engineers are people who work with large data sets. It is the individual responsible for creating the model architecture by following the data preparation and model creation processes in the creation of a data science project.

Qualifications: Qualifications required by a Data Engineer, Database managements, SQL, MongoDB, Cassandra, Hbase, Apache Spark competencies, together with some programming languages, such as Python, C / C ++, Java, Perl must.

4. Data Scientist:

Data Scientist is a multi-disciplinary individual who is responsible for processing high-capacity data, optimizing algorithms, producing and implementing the most optimal solution from available resources.

Competencies: Must have a high level of knowledge and experience in programming languages ​​such as R, Python, SQL, Hadoop, Apache Spark, MATLAB. At the same time, data scientist is the person who takes part in versatile studies that should have knowledge in fields such as mathematics, statistics, visualization and communication.

In this article, I tried to give summary information about the professions for data science. In the next article, I will share detailed information about the tools used in the field of data science, professions in the field of data science and their common points.

Sait Alay

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