Data analysis has become an essential component of all modern companies. Due to today’s technology and business landscape, organizations accumulate massive amounts of data. The ability to interpret, manage and filter this data to meet business objectives is crucial.
For this reason, data analytics—and by extension, data analysts—is among the most sought-after majors in both IT and non-technical fields. But what exactly is data analysis and what does a data analyst do for work? This article will answer all your questions.
First of all, we invite you to take a look at the AI, Python, and Data Science courses offered by the Software Development Academy – all relevant to the role of Data Analyst that we will discuss in this article.
What is a data analyst?
A data analyst is an expert who notices patterns and trends in data and describes them.
In general, the day-to-day work of a data analyst can be summarized in 5 interconnected tasks:
Identifying the data to be analyzed.
Collection of respective data.
Data filtering.
Data analysis.
Interpretation of analysis results.
Of course, the job description may vary slightly depending on the company and the nature of the industry in which the expert works.
In addition, the actual data analysis is a complex process that may require the use of some type of data analysis to reach the relevant conclusions. Each type of analysis answers a specific question.
Recent mobile phone number list is the collection recent mobile phone number list of verified customer with updated number in telemarketing field. It is very important to select verified number. If we do not collect an updated number list, we can face some difficulties when we use the expired data.
Descriptive analysis.
This type of analysis answers the question “What happened?”. Thus, it consists in describing and summarizing existing quantitative data in the form of statistics.
Diagnostic analysis. This analysis methodology answers the question “Why did it happen?”. Basically, diagnostic analysis uses historical data to reveal the factors and variables that influenced the current situation. For example, a diagnostic analysis can use data from descriptive analysis to determine that the reason users are leaving the product after X amount of time is because a certain key functionality is not quite visible in the UI.
Predictive analytics. This type of analysis answers the question “What could we do to solve X problem?”. Predictive analytics uses the data and methodology of the other two types of analytics to form future.
Action plans and recommendations.
As you may have noticed, data analysis is a complex discipline that combines elements from several fields. In order to ask the right questions, a data analyst must be on the same wavelength with all departments (whether we are talking about developers or marketers) and know the particularities of the industry in which they operate extremely well. More than that, he needs to know how to present the data, and for that, he needs to know how his role fits into the company’s ecosystem.
This framing is, however, more complicated than it seems, due to the existence of the roles of data scientist and business analyst. Although the tasks and areas of expertise of these roles intersect in many places, the specializations of data scientist, data analyst, and business analyst could not be more different.
What is the difference between a data scientist and a data analyst?
Although both roles consist of Иностранный пример analyzing and manipulating data, there are some crucial differences in education, qualifications and responsibilities to keep in mind.
To access a job in data analysis, candidates need:
Bachelor’s degree in statistics, mathematics, economics or other relevant field.
Knowledge of programming (Python or R) and data management and expertise in tools such as SQL or Excel.
To become a data scientist, candidates need:
Master’s or PhD in a research bhb directory field, preferably technical. Although the fields of study are similar, data scientists have more advanced knowledge of computer science or even engineering.
The educational background of data scientists consists of advanced statistics, machine learning, programming, data management, and artificial intelligence. To this we also add advanced knowledge of Python, R and scala, as well as big data technologies (Hadoop, Spark).
Responsibilities of a data analyst include:
Data collection and preparation.
Routine analyses.
Data reporting and visualization.
Carrying out the descriptive analysis of the data.
Collaboration with other departments.
In addition to the Data Science course, Software Development Academy also offers an in-depth course in Artificial Intelligence and Python. See the dedicated course pages for more details.
Although the two positions also intersect at the level of responsibilities, in the case of data scientists, they are somewhat more specific and in-depth:
Collection and management of ava full of data. Since they work with structured and unstructured data, data scientists are not concerned with collecting data as much as they are concerned with creating and implementing collection infrastructures. In other words, if a data analyst collects and interprets data, a data scientist sets up the tools and processes that enable them to accomplish their tasks.
Advanced data analysis. Data scientists use machine learning algorithms and other statistical methodologies to create predictive models.
Development of AI solutions and machine learning models. Data scientists develop algorithms and models that automate processes and simulate results.
Product research and innovation.
Interdepartmental projects. Data scientists are involved in (or even coordinate) projects that target several departments of the same company, such as marketing, sales, product development, the executive team.
Of course, these criteria are not hard and fast – you don’t need a bachelor’s degree to become a data analyst, just like you don’t need a PhD in statistics to access a data scientist role. For example, the Software Development Academy offers a comprehensive Data Science course where you can accumulate all the necessary knowledge to enter this field.
The even better part is that this niche benefits from surprisingly high career mobility – meaning you can become a data scientist as a data analyst and vice versa.
What is the difference between a business analyst and a data analyst?
Business analyst and data analyst are often used interchangeably. But as you will see, there are a number of major differences between these roles.
If data analytics involves analyzing vast amounts of data to create predictions and action plans, business analytics involves using data to understand the performance of a business and to recommend improvement or recovery initiatives. This is the major difference – business analytics focuses more on business performance indicators and improving a company’s efficiency and processes.
