Data scientists are in huge demand. At present, this seems to be the most promising and sustainable career path. Everyone is dying for this career. But is this craze making you disappointed for not having a coding background? Let’s Discus How to become a Data scientist with out programming background?

Wait. Maybe you like believing in myths.

Yes, ‘nonprogrammer ‘can’t switch to a data science career’ is nothing but a myth. The fact is nonprogrammer and candidates with the no-coding background can have a glorious career in the world of data science.

Many successful data science career transition stories are available, where the data scientist has prior programming knowledge. So, in data science, programming or coding knowledge is more like a skill; it’s not the criteria.

How to become a data scientist with no programming knowledge?

To become a data scientist, you need to acquire six must-have skills, amongst which programming is the one. But before heading into the skill sets required in data science learning, do need to understand the fundamentals of Data science and then need to analyze if a data science career suits you.

data scientist without any prior knowledge of programming, Data Science Course, Nonprogrammer, Python Programming, Data Science Institute in Bangalore, AI

Fundamental of Data science

Once you know the fundamentals of data science, you will realize why coding and programming are not only the skills required for a successful data science career transition. The expansion of the data science field is too vast. A single person can’t master every aspect of data science. 

Learn the differences between different sub-domains of data science, machine learning, artificial intelligence, data analyst, and deep learning. Next, have an in-depth look into the learning modules for each subdomain.

Researching data science fundamentals will help you understand that you have eligibility to earn the skills required for your dream career- data scientist.  To have clear ideas in data science learning modules, you can read related blogs, watch sample video classes of different EduTech companies, or listen to related podcasts. If you are a working professional, try to evaluate data scopes since a career in your domain. Once you become sure enough about your decision of a data science career transition, start your learning initiative on your own before you register for a certification course. 

Know the six must-have skills for a successful Data Science career and how to achieve those?

Skill #1: Strong Mathematical and Statistical Ability

Are you wondering why programming and coding are not the number 1 position on the list of must-have skills set for data science? Because that is not the foremost criteria for learning data science. Instead, you can say, mathematical and statistical knowledge is the key pillar of data science.

To become a data scientist, you need to learn the basic concepts of linear programming, regression, linear algebra, calculus, coordinate geometry, time-scale, probability, etc. While lack of programming or coding knowledge can’t stop you from learning data science, having no basic concept in intermediate to advanced levels of mathematics and statistics may become a serious roadblock for you.

Skill #2: Programming and Coding for Data Science

Being a nonprogrammer, you have to learn programming and coding from scratch. But it’s not that hard. You can become a programming expert within a year if you love coding and technology. 

Choose python programming as the initial learning stage to your data science career journey. Learn the basics of python, like coding commands, libraries, styling, CSS, etc.  Make yourself comfortable with several GUI tools. You can have plenty of help from various online open learning sources like W3Schools, Codeacademy, etc. Practice materials are also available in these online sources. So keep practicing whatever you learn for more with such practice material.

Initially, you can download python (it’s completely free) and Notepad++ (a free and extremely lightweight source code editor) and practice your python problems. The good news is that a range of data science tools is now available to help nonprogrammers carry out several programming tasks without coding. The most popular tools are DataRobot, Tableau, Knime, Google cloud auto ML, IBM Watson studio, and more. Learning these tools will help you with complex programming requirements.

You can opt for any free online basic python programming courses offered by Upgrade, Google’s Python class, Microsoft free python course etc.

Skill # 3: Data handling for Data Science

Data handling measures of the data science domain are mainly concerned with data analysis, interpretation, and manipulation. Suppose your target is data analytics and AI. In that case, the intermediate level of data handling ability is fair enough to have an acceptable data science career. Still, if your target is machine learning and deep learning, you need to acquire the maximum possible level of data handling efficiency.

Are you surprised to hear the word ‘data manipulation?’ Whatever, but you heard it right. Yes, a successful data scientist should be intelligent enough to manipulate an available able set of data. It’s an important aspect of data science to make the data reusable and optimize them to get the expected output.

Skill # 4: Data Visualization

Data interpretation without graphs is a kind of impossible matter. Because it’s not about 100 or 1000 data. Rather, data science projects deal with millions of data at the same time. So without graphical presentation, the analysis becomes hardly interpretable.

But if it’s not that easy, like making a pie chart or bar graph in excel. It would help if you learned programming, libraries, and tools related to data visualization. If you are not much aware of graphs, start collecting knowledge about histograms, stack-line graphs, waterfall charts, thermometer charts. For graphical tools, the tableau is the best option for beginners to start with. Later, you need to learn graphical programming libraries like MatPlotlib. For data analysis, SPSS is another handy tool. In case you belong to a statistical background, you are well aware of SPSS.

Skill #5: Algorithm modeling

For data interpretation and outcomes prediction algorithm modeling is very important. Machine learning algorithms are now the most popular way of modeling. So you need to learn about basic algorithm models. Application and requirements of the model usually remain domain-specific, so researching, reading blogs, and watching videos will help you have an idea of modeling.

Skills #6: Communication and critical thinking skills

Data science’s responsibilities do not end up with data mining, data interpretation, and submitting reports. Instead, as a data scientist, you become responsible for presenting the data interpretation to your clients/ peer groups and making them convinced with your analytical outcomes. Moreover, you have to gather data from different sources, teams, and even from different companies. Hence what you need is polished and presentable communication skills.

Besides, you have to be a critical thinker to evaluate the maximum possible aspects of a data interpretation. And yes, an underrated communication skill in data science is storytelling. To make your data interpretation and presentation more eye-catchy and appealing storytelling is a good approach. To earn such skills start writing, blogs and promoting them in storytelling format across different social media sites.

Now, you are aware of the must-have skills and how to initiate learning of those, so comes next in the path of becoming a data scientist?

Start applying for a data scientist job? Wait wait. You are in a huge rush. For a successful and sustainable data science career transformation, you need to do some data science courses and real-time industry projects. It’s time to search for a suitable course. Being a nonprogrammer, you need to focus on courses that offer special guidance to candidates with no coding background. While choosing a course, keep in mind, a certificate of completion is not your target. This doesn’t worth getting your dream data science job. Rather your goal should be working on a good hands-on project. You can check for Skills slash Data since and AI courses for a nonprogrammer. For nonprogrammers, real-time project experience is the first step to enter into the field of data science.
So, why wait? Take the first initiative to your career science transition career today. Happy learning!

Write a comment