Does Data Analytics require coding?

Suppose you are interested in a career in Data Analysis but are unsure how much programming experience is necessary. In that case, we hope the information regarding “does business analytics require coding” provided in this article will address your concerns about the subject.

Who is a Data Analyst ? 

The primary responsibility of a data analyst is to assist companies and senior-level stakeholders in improving the quality of choices that they rely on data. However, this data must first go through several more complicated operations, including cleaning, managing, manipulating, and analyzing. For analysts to carry out these processes, they use a wide variety of tools and technologies that enable them to manage enormous volumes of unstructured data.

Does Data Analytics Require Coding?

The answer is yes. However, it does not call for highly developed programming abilities. Instead, it is required that you are proficient in a querying language like SQL and that you have learned the fundamentals of either Python or R. Thankfully, these languages are not difficult to pick up.

SAS or Julia may be required of you by certain employers, but this will depend on the sector of the economy in which you choose to work. Data analysts need to prepare themselves in dealing with numbers in a programmed setting in addition to having a passion for numbers themselves. 

Educate yourself in coding so that your work may be replicated by others and used as a foundation for further development. If you cannot put what you are doing into a computer programme you are left with two choices: educate people how to do it, or keep doing it yourself forever.

Which programming languages and other software applications are most likely helpful for a data analyst to be familiar with? SQL is necessary since it is the predominant language used to process data. Additional practical alternatives include:

• Python

• Pandas

• R

• Hadoop

• Spark

On the other hand,  The majority of these tools are cloud-based adaptations of point-and-click data visualization programmes, which are dependent on labor-intensive and unreplicable procedures for data analysis. If you need such tools, you won't have any problem learning how to utilize them if you are an analyst proficient in using a programming language. 

But, on the other hand, if most of your experience is in point-and-click analysis, it will be difficult for you to transfer to an environment that relies on analysis based on code, which is where serious data analysts do their job.

Coding for data analytics does not need the same level of in-depth expertise as coding for computer science, which is necessary for a degree in computer science. However, data analysts still need to know how to code and be familiar with at least one other programming language.

Data analytics and computer science are separate but related fields of study. The focus of data analytics is on understanding massive datasets. In a computer science class, you will learn about loops and loop statements. However, in data analytics, you may not come across this concept until the end of the course. 

This is because data analytics operations process an entire set simultaneously, and looping is only used infrequently. Therefore, a data analyst must be able to create code, but they do not necessarily require a degree in computer science to execute their job.

How Much Coding is Necessary for the Analysis of the Data?

Again, it is dependent on the position that you play as well as the technologies that are used inside the firm. Most businesses depend significantly on executing scalable analysis utilizing fundamental programming languages and BI tools such as R, Python, and SQL.

However, there are also roles available that call for less coding and put more emphasis on using a combination of tools such as Excel (advanced level – using VBA formulas, charting, pivot tables, aggregate reports, and more), Tableau, or Power BI, which are all programmes that have user-friendly interfaces that allow users to drag and drop data.

• The Ability to Code well is an Skill 

Learning Python or R beyond the fundamentals can help you get employed more quickly and negotiate a more excellent starting salary for yourself. In addition, companies value employees proficient in coding for objectives such as data analysis, the construction of pipelines, and the collecting and processing of data.

Python knowledge is essential for companies focused on expansion since they wish to automate data operations to guarantee scalability. In addition to having fundamental coding knowledge, analysts must be proficient in at least one data visualization tool, such as Tableau, Power BI, or Google Data Studio.

• Coding Exercises May Be Part of the Interview Process for Data Analyst Jobs

Candidates for Business Intelligence Analyst and Data Analyst jobs often have to pass coding exams administered by the firms that employ them.

Basic exercises/questions might look like this:

• What is the best way to read CSV files with Pandas?

• Python functions for filtering and counting

• How does one go about generating a dataframe with Python?

• How can I switch from the integer data type to the string data type in Python?

• How can CVSs be loaded and saved using R?

• In R, which function generates a summary of the statistics?

• Important to emphasize data analytics is not data science.

Data science roles require heavy programming, advanced knowledge of Machine Learning algorithms, building predictive models, and strong math and statistics skills as you get promoted, like senior data analyst salary. Whereas data analysis focuses more on the fundamentals of coding and more on analytical tools (such as Tableau), data science roles require heavy programming.

Conclusion 

We hope this article has helped you in understanding the question whether coding is a prerequisite for data analysts. In case you want to explore more about coding as a skill for data science, you can enroll for the courses offered by The IoT Academy. With dedicated mentors at work, you can learn more about data science modules, skills, applications and more in a much simplified manner.