While coding has been the go-to skill for data science in recent years, this is quickly changing. Due to an influx of new data scientists with no programming background, many companies are hiring people who specialise in Data Science without any prior knowledge of programming.
So, is this really true or just a myth?
Data science is a field that can be entered by anyone with the right knowledge, and not just programming experience.
Did you know that 67% of data scientists don’t have backgrounds in computer science or programming?
Data science career transitions are often successful because the data scientist has prior programming knowledge. Programming can be a helpful skill, but it isn’t necessary for success.
Do you want to become a data scientist and don’t know any programming language?
There are six skills that data science professionals need in order to succeed. Programming is one of those skills, which you could learn on your own.
To learn about the skill sets required in data science, it’s important to first understand what Data Science is and if a data science career suits you.
If you want to pursue a data science career, it is clear that not only coding and programming skills will be required on the job. The data science field is vast. Data science is a highly specialised field that can be difficult to break into for one person.
In this blog, you will learn the differences between different sub-domains of data science, artificial intelligence, and machine learning. Next, we will take a close look at the modules for each subdomain.
Seek out data science fundamentals, and you will know that your skillset will get you an entry-level position as a data scientist.
To have a good understanding of data science learning modules, you can read related blogs or watch sample video lectures from data science courses.
If you are a working professional, consider the scope of your data science career. With an understanding of data analytics, you may be able to pursue opportunities in your current field.
If you are considering a career change to data science, it is helpful to start learning a programming language on your own before taking an advanced course.
You will need strong mathematical and statistical skills.
Why is programming not number one on the list of must-have skills as a data science professional? Because to be a data scientist doesn’t require you to know how to code. Instead of programming, mathematical and statistical knowledge is the pillar of data science.
As a data scientist, you need to cover all the fundamental and advanced topics in mathematics, statistics, engineering, and computer science. While a lack of programming and coding knowledge will not prevent you from learning data science, an absence of intermediate to advanced mathematical skills may pose a problem.
Coding and Programming.
Non-programmers typically come to data science with no prior programming experience. But it’s not that hard to learn all the basics if you are interested in code and technology.
Choose python programming as the initial learning stage for your data science career journey. Learning Python, libraries, coding commands, CSS, styling, etc., is one of the best ways to start your journey towards becoming a data scientist.
Make yourself comfortable with several GUI tools. Practice materials for those who need the specialisation without prior knowledge of programming are available online. So, keep practising whatever you learn for it will improve your skills.
Start by downloading Python (it’s free) and Notepad++ (a lightweight text editor for sourcing code), or you can opt for Notepad (it’s already present in your system), which can perform the same task. Some data science tools can be used to perform certain programming tasks without code.
The best tools for getting started with data science are DataRobot, Tableau, Knime, Google Cloud Auto ML, and IBM Watson Studio. Learning to use these tools will prepare you when your assignments include complex programming requirements.
Opt for any online basic Python courses.
Data handling is one of the core areas of Data Science. It not only requires knowledge but also skills in dealing with it to solve real-life problems. In this subdomain, you will learn how to create a statistical environment for your data analysis process.
For self-study purposes, one can look up tutorials from YouTube or online forums to get help on how to get started with data handling.
Also, you can read up books on Data Structures and Algorithms in Python, Introduction to Data Science Using Python, and Machine Learning to have a better understanding of data handling.
Data science studies include examining, interpreting, and manipulating data. If your goal is to work in the field of AI or analytics, you need to study data analysis heavily. To land an entry-level data science job, you need to have intermediate-level data handling skills. If your target is machine learning and deep learning, you need to attain the maximum level of data-handling efficiency.
Did you hear the word ‘data manipulation?’ If so, you’re on the right track. A successful data scientist should be intelligent enough to take an accessible set of data and manipulate it.
Data manipulation is an important part of data science that makes the data reusable to get the desired output.
Attempting to read data without visuals, such as graphs and other explanations of the various lines on a chart, is an impossibility. Data science projects deal with millions of data points at the same time, making them difficult to read without graphical depictions.
If you want to do more than pie charts and bar graphs in Excel, you might need to learn to program. Those new to graphs should start learning about histograms, stack-line graphs, waterfall charts, and thermometer charts.
For beginners in data science, the best start is a tool like Tableau. After that, you need to learn graphical programming libraries like MatPlotlib. For data analysis, it’s also helpful to have an understanding of SPSS. In case you come from a statistical background, you probably know about SPSS already.
Prediction algorithm modelling, data interpretation, and the outcome are very important. To master machine learning algorithms, one must first understand some basic models. The application of the model is often specific to a particular domain, so research, reading blogs, and watching videos can help you get an idea of how modelling works.
Communication and critical thinking skills are what typifies a data scientist’s ability.
Data science is a broad field that covers the spectrum of data mining, data interpretation, and report creation. As a data scientist, it’s your responsibility to present the data interpretation to clients and colleagues.
Moreover, in order to find the data you need for your analysis, you have to search among different sources and people. These could be teams within companies or from external sources. Hence, you need presentable and polished communication skills.
In addition, data scientists must be deliberate thinkers to evaluate all possible aspects of data interpretation. Effective storytelling is an underrated skill in data science. For such skills, start by writing blogs (and stories) that promote the information and share them across social media.
Now, you are aware of the must-have skills and how to initiate learning of those, so what comes next in the path of becoming a data scientist?
Want to start applying for data science jobs? Slow down.
Before you get too invested, it’s worth considering your options. To make a successful change in your career, you’ll need to do data science courses and projects.
If you are not a programmer, focus on courses that offer guidance for non-programmers. While a certificate could really help employers recognise your level of knowledge, your goal should be to work on a good hands-on project.
The first step to enter the field of data science without experience in programming is real-time project time.
Waiting is a disservice to your career. Start with the first steps today. Check with RISE WPU for India’s most affordable and innovative professional PG Program in Data Science for both programmers and non-programmers.
If you are a non-programmer and are curious about what it’s like to be on the data science side of things, share your thoughts and doubts with us. We look forward to hearing from you!.