How to Start Your Career In Data Science Without Any Prior Knowledge of Coding?

How to start your career in Data Science

Data science is an up-and-coming field these days! There’s much scope for a bright future in this area. However, non-programmers are often disappointed when it comes to choosing a Data Science career path. It is solely because people believe that one cannot pursue data science without prior knowledge of programming! Well, that’s untrue. 

If someone already knows how to code, it’s an advantage as it’s a great skill. But it’s not compulsory that you must know “how to code” as a prerequisite. So, if you certainly want to choose data science as your , you must be clear about its fundamentals. 

Data science has a very vast dimension. There are six skills that you must have an idea about to become a successful data scientist. Let’s see what they are!

Solid Mathematical and Statistical Ability

The first and the most important criteria to be a data scientist is not programming, so you can relax a little. To master data science, you need to have excellent mathematical and statistical abilities. They are often called the pillars of data science. If you don’t know the basics of algebra, calculus, coordinate geometry, etc., mathematics will create a roadblock.

Don’t miss Data Analytics Using Python

Master A Programming language for Coding in Data Science

Don’t you worry just because you don’t have any prior clue to programming! If you put your mind to coding and work efficiently, you can definitely master programming within a few months. To start with, select Python Programming Language. Python is a really readable, understandable and robust language. You can begin by learning its libraries and CSS. Make sure you get comfortable with different GUI tools. There are various online open learning sources where you can get much help. Keep practicing, and you’ll get better.

You can download Python completely free. You can also download Notepad++, which is free and a really lightweight source code editor. These will help you practice your python problems.

There are many tools to help non-programmers carry different programming tasks without coding! Some of them are Tableau, Google Cloud Auto ML, Knime, Datarobot, etc.

Learn Data Handling Techniques for Data Science

Data handling techniques that you must be basically familiar with are data analysis, interpretation and manipulation. If you aim to make your career in data analytics and AI, a basic understanding of data handling measures with ample practice will work for you. Whereas, if you aim for machine learning and deep learning, you’ll have to get efficient expertise in data handling measures.

Data analysis is used to observe data, analyze and use it for problem-solving. Data manipulation is the process of taking out valuable aspects of data to use it efficiently.

Read About Data Visualization

If you cannot represent your data on a graph, the interpretation of the data will not be effective in an understandable way.

In data science, one does not deal with 1000 of data, rather millions of it! Hence, interpreting data will become nearly important.

Visualization is not as simple as drawing a pie chart. You need to have hands-on experience with visualization tools. Tableau is the best for beginners to start with graphical tools. Also, there is the support of graphical programming language like Matplotlib that you might need. SPSS is a great tool that you might have heard of in data analysis if you’re from a statistical background.

Learn Algorithm Modelling

Algorithm modelling is essential when it comes to data interpretation and outcome prediction. The most popular way of modelling today is the machine learning algorithms. If you want to have a modelling idea, you can explore, watch videos and read blogs, as the applications and requirements are usually domain-specific.

Strengthen Your Communication Skills and Critical Thinking.

If you think that your role as a data scientist comes to an end after completing data mining, data interpretation, and submitting reports, you can’t be more wrong! One of the critical aspects of data science is the presentation of your data interpretation. 

With your presentation, you have the responsibility to convince your clients that your analytical outcome is apt. Also, you have the responsibility of collecting data from different sources, teams and companies. 

Moreover, you need polished and presentable communication skills for this purpose. You also have to answer all questions raised by your clients with the data observations you have made while working on it. In addition, you need to be confident when speaking about what you have done on a particular project and how you reached a conclusion.

Now, if you are not a critical thinker, the various aspects of data interpretation will not occur to you frequently. You might miss important details that way. So you really need to pay attention and think critically while working with data. A very underrated skill in communication is storytelling. 

If you can mold your data to present it in the form of a story, it’ll really have the audience’s attention and be more effective. Furthermore, if you want to hone your storytelling skills, you can start writing blogs in a storytelling format and promote them on different social media sites, see the audience’s response, and make changes to make them better.

Also Read: 7 easy tips to crack your first data science internship.

Now you know that you do not have to lose hope at all, even if you’re new to programming. If you want to become a data scientist, there are several ways for you. 

In the field of data science, choose your specific domain of interest. Research about the skills it requires majorly and start working on it. There’s no shortage of mentors in today’s world of networking. Also, you can always enroll in an online course of your choice and even get a certification. You can reach out to subject matter experts and get the help of any kind. 

Learn what you want to, get hands-on experience, and start your Data Science career whenever you want! 

With high hopes, explore data and use it in your favor. All the very best for your future Data Science career choices!

Also, don’t forget to read about How to get your first data science job to prepare yourself for your very first job role in the ocean of data science!

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