Can I Become a Successful Data Scientist Without Programming Knowledge?

This question can be more than an interesting curiosity. It's an important question that most people understand and need to know because they are getting into the field of data science without knowing how to code. Today, Data science is the next big thing. Many believe it will be a "job for the future" or a new position to replace the old data analyst. But can you become a data scientist if you don't have programming knowledge?

Although coding has recently been the preferred skill for data science, this is rapidly shifting. Many businesses are recruiting data scientists with no programming experience due to the inflow of new data scientists in this field.

If you've always wanted to learn more about data science and get into the field without getting bogged down in programming requirements, this guide should help!

But first, What is Data science?

Data Science is a field where you will extract insights from extensive data. Data scientists are a breed that excels in using big data, machine learning, and statistics to make predictions about the future. They use various high-level languages to do so, like R and Python, but they also understand how to work with lower-level languages like C++ or Java.

Becoming a Data Scientist is difficult as this requires critical thinking and a strong emphasis on problem-solving skills. However, if you are proficient with at least one programming language, you can easily become a Data Scientist.

Does data science require coding?

So, is this a myth, or is it true?

This is an essential but often poorly understood point. First, one of the key things to realize when trying to become a data scientist is that programming isn't the only tool you'll use. When you're a data scientist, you'll spend most of your time with tools like Excel, R, and Python, which are great tools for analyzing data.

Also, you might be surprised to know that 67% of data scientists lack programming or computer science backgrounds. However, anyone with the relevant knowledge—not only those with programming experience—can work in the field of data science.

So, What are the Basic Skills to become a Data Scientist?

Thorough knowledge of statistics and probability

Passion for working with numbers

Ability to communicate with stakeholders and non-tech staff

Ability to extract insights from the dataset

Ability to analyze data from different perspectives

Data Visualization techniques for model building

If you want to become a data scientist but don't know how or aren't sure if your background qualifies you for the position (or if it even matters), plenty of resources can help guide your path. One of the best resources is enrolling in a data science certification course co-developed by IBM.

But, Where does programming is required in data science?

Sourcing data

Programs can help you extract the data from any place it is stored, regardless of the cloud platform or source. We can retrieve information and manipulate it simultaneously with the help of code.

Data Transformation

Knowing how to code can help with data manipulation, modification, and transformation as needed. But, again, this can be done on a variety of platforms. Python code, for instance, may be used with practically any cloud platform or service.

Exploratory Data Analysis (EDA)

Code can be used to identify patterns in data, but it's essential to analyze massive datasets to identify both obvious and hidden patterns.

Experimenting with Data

Coding can be used to test several hypotheses and determine whether a data-driven decision is supported.

ML and Modeling

Coding gives you the power to create models independently and apply machine learning to data.

Data visualization

A powerful tool allows a data scientist to visualize data in many ways. The process of problem-solving may change as a result since better data-driven decisions can be made by corporate stakeholders thanks to data visualization.

Now, we will look at what data science jobs require coding.

Believe it or not, all data science positions demand some level of programming expertise as well as technical tool and technology skills. Some of them are:

Data Engineer – Having some familiarity with Python, more knowledge of SQL, and preferably some knowledge of a cloud platform is also desirable.

ML Engineer – More Python, a little bit of SQL, and a strong desire to experiment with data

Data Scientist – Comprehensive knowledge of the data pipeline which needs to be coded.

Business Analyst – Strong Business understanding, familiarity with a visualization tool, and minimum coding (depending on a business analyst's company profile).

As the market develops, we might eventually have additional roles that don't require as much coding. Perhaps you might have heard of different "No-code" platforms. However, only a few businesses utilize these platforms despite how wonderful they would be. This is because they are incapable of handling all tasks and are not developed enough to offer the same level of flexibility as merely coding it out.

Developing a case for data scientists without coding

Programming skills were a must for data scientists ten years ago. However, there are various ways to get around sound coding skills today.

Standard algorithms are already developed and optimized. Thus they are already recognized.

Drag-and-drop interfaces, like those in Tableau and Trifacta, are taking the role of manual coding.

Data science is getting more automated thanks to tools like AutoML and DataRobot, which both assist you in selecting the best algorithm.

The Google Duplex demo has also made predictions about the evolution of AI, suggesting that rather than programming computers, data scientists in the future may interact with them.

Tips to become a Data Scientist Without Coding.

The best way to become a data scientist is to study the fundamentals of statistics, machine learning, and data science from an academic perspective.

Once you've learned your stuff well enough, you can take courses in programming languages like Python or R.

Because these languages will allow you to do more complex tasks than other languages will, such as reading large datasets or writing complex algorithms to make decisions based on your data.

A huge problem with data science is that it requires both domain knowledge and programming skills. However, even though you won't be able to do everything on your own without any programming knowledge, you will still be able to make an impact by using your domain knowledge along with the right tools and techniques.

Many tools available in the market today can help you build your own algorithms without any programming experience. You can use these tools as long as they comply with industry standards like SQL or Apache Spark, which are famous for building big data applications for companies like Facebook or Netflix.

However, knowing your current skill set's limitations is crucial to maximizing your ability to advance your career as a data scientist. If you are interested in advancing your career as a data scientist, I recommend taking a best data analytics courses that focuses on developing these skills.

What programming languages do you need to know to become a data scientist?

Python – It is an incredible programming language used for general purposes.

R – R is typically used for statistical analysis, data visualization, and other forms of data manipulation.

SQL – SQL is used to interact with databases and allows you to create queries and retrieve data from datasets.

SAS – It is mainly developed for business operations and complex arithmetic computerization.

So, Can you become a data scientist without a coding Background?

My answer is yes.

Programming is unquestionably a necessary skill for a job as a data scientist. Analyzing data and drawing actionable conclusions from it is the primary essential qualification for being a great data scientist. However, this does not imply that learning programming is not a valuable data science skill. On the contrary, you will undoubtedly advance in your data science career if you can build scripts in Python or R or understand how SQL queries operate. However, a lack of programming skills should not discourage you from pursuing a career in data science.

Just know that there is no shortcut to success. Learn to code a little, work on upskilling yourself, and keep yourself motivated. So, launch a successful career with a data science course with placement which provides special training for non-technical aspirants.