Is the fear of learning how to code keeping you from getting started with THE most in-demand field of the 21st century?
If the answer is yes, then you are not alone in this irrational fear of not knowing what to do to make a successful transition to the exciting and mysterious field of data science analytics.
The (sometimes) horror of learning programming language
The demand for data scientists is burgeoning. People want to capitalize on this opportunity and make the transition to the data science field or upskill to enhance their analytics capabilities.
Many people interested in becoming a data scientist fail to take the first step as they get bogged down by the idea of learning a programming language. After all, there is this worldview that coding is for the geeks (not true) and a person coming from a non-technical background will find it extremely difficult, if not impossible to write code.
Can a non-technical person Learn Machine Learning?
Aspiring data scientists and analysts with little to no programming experience find themselves a victim of imposter syndrome. They are always self-doubting their abilities and feel they would not be able to match up with those who have a background in software engineering or mathematics. Want to read more about the relationship between ML and data science?
Here are the most common questions that people are asking on the internet these days:
- How much coding do data scientists do?
- Can I learn machine learning without programming knowledge?
- Is there a thing called “No Code” data science?
- Can I learn data science without coding?
- How to become a data scientist without a Computer Science degree?
Skills required for a Data Scientist
Data Science draws knowledge from multiple fields. You will need to develop a solid understanding of Linear Algebra, Calculus, Statistics & Machine Learning.
I know it can sound intimidating at first but trust me, the most important skill is a desire to learn.
There are just a few key concepts that you need to master to get started in data science. Having taken a high school or university course in calculus or statistics is a good starting point for you to start grasping the fundamental concepts of data science.
If you are passionate & curious about something, you will find a way to crack the code.
All Theory and no Implementation?
One of the most common mistakes people make is they immerse themselves in gaining theoretical knowledge but keep avoiding the real art of implementation.
It’s good to enroll in courses that go in-depth to teach you the mathematics of all the machine learning algorithms but data science is both an art and a science.
You need to start implementing what you are learning in the classroom or else you won’t be able to make sense of the real-world applications of data science.
Don’t learn it, Just DO IT Yourself
This brings us to the most important part of your journey. So how do you start implementing data science and machine learning algorithms if you do not know how to code?
Would you need to learn a programming language like R and Python first? How much time would that require?
Well, do not get discouraged if you don’t know how to code. The data science and machine learning landscape has matured significantly over the years. There are many tools and platforms in the market today that have completely automated data science.
The common algorithms are already known, coded, and optimized so you as a data scientist do not need to write them from scratch. Explicit coding is being replaced with drag-and-drop machine learning and AI tools.
The good news is that now you can implement data science algorithms without being a programmer.
Sounds GREAT, doesn’t it?
Data science & Machine Learning for Everyone
What if I tell you that there is an extremely user-friendly, intuitive, and visually appealing platform designed especially for users who want to venture into data science without any technical background?
Sounds too good to be true? Well, it isn’t.
As a data scientist, you need to focus on looking at data and start thinking about how you can turn that data into useful business insights that might help a company build efficient or innovative products or improve the customer experience. If we were living in a utopia, that would ring true, but in the real-world there is far more mundane elements.
There is a lot more to data science than just taking in raw data and applying fancy machine learning algorithms to build high-quality models. First, the data has to be cleaned, wrangled, and visualized before you can start playing around with it. In fact, 80% of a data scientist’s time could be spent on just cleaning the data. And then training models, lots of them, to try to identify the best fit. This time could have been put to much better use if there was a way to automate the data cleaning, data wrangling and model selection process.
Become a Self-taught Data Scientist
At PI.EXCHANGE, our mission is to make data science available to everyone by providing an accessible AI tool. Just because you do not have coding experience does not mean you should miss out on learning one of the most important skills of the 21st century.
Everyone can learn data science and get started with analytics and extracting business insights from data. This is also true for people from a non-technical background who have no prior coding experience.
I’ll give you a step by step tutorial on how to undertake your first data science project without writing a single lining of code.
A Friendly Guide to Help you Implement your first ML Model
Step 1: Get started with a free tool
Go to the AI & Analytics Engine webpage and create an account by entering your email and selecting a password. This will take only a few seconds, it's free, and there is no credit card required.
Step 2: Creating a Project
Once you log in, this is the interface you will see. You need to click on the NEW PROJECT button
Give your project a name like I have given (Titanic Dataset Analysis) and then click on the Next button:
It will ask you to add any users that you want. You can ignore that and simply click on the CREATE PROJECT button.
Step 3: Importing your first dataset
There are a plethora of datasets available online. I recommend you download the titanic dataset from Kaggle as I will be using it in this article.
Kaggle is a very popular site for aspiring data scientists with numerous datasets to experiment with.
Click on the + button and it will prompt you to add a new dataset.
Next, you need to give your data a name and then upload the CSV file you have downloaded from Kaggle or online and then click on the CREATE button.
Give your recipe a name as I have given (Process Titanic Data) and click on the Done button.
You have now successfully loaded your first dataset. This is what it will look like:
Step 4: Data Cleaning & Wrangling
In the titanic dataset, we are trying to predict whether a passenger survives or not given on a list of attributes or features. Hence, we type the name of the target column (Survived in our case) where it asks us to select the target column.
The platform uses AI to suggest the steps required to preprocess and clean the data.
All you need to do is click on the + button next to the suggestion. You should do this for all the range of suggestions generated by the platform as they are extremely helpful in building accurate models.
Next, you need to click on the Recipe button. It lists all the recommendations that you have selected.
Simply click on ‘COMMIT ACTIONS’ and then ‘FINALIZE & END’.
It will ask you if you want to complete the recipe and finalize your dataset. Simply click Yes.
Step 5: Visualize your data to get a better sense
The Engine will automatically create beautiful and easy to comprehend visualizations for both Numerical columns as well as categorical columns. You do not require any library such as Ggplot or Matplotlib to create these visualizations. They have all been done for you!
Step 6: Building your ML Model
On the left part of your screen, you will see the button of Models. Click on it.
Then, click on the NEW APP button
Give your model a name under the Application name field.
There are options available: Prediction & Forecasting. Since we know we are trying to predict whether a passenger survives or not, we click on the prediction button and select ‘Survived’ as the target column.
And then click Next.
Choose the default configuration for now (you can experiment with the different setting later once you get a handle on the default).
Step 7: Choosing An Algorithm (made easy)
Now that our model is ready, we need to train it by selecting an appropriate algorithm. But first let’s give our model a name and a brief description. Since I will be implementing Logistic Regression on the dataset, I have written that in the description.
Now it’s time to train a new model. Click on the + button as seen in the image above and select ‘Train New Model’. The following dialog box will appear:
Select the default value under the category of feature set and click Next.
Next, choose your algorithm. I have selected Logistic Regression for demonstration purposes since this is a binary classification problem and the rule is to start with the simplest possible model to see how effective it is.
Note: The list contains all the possible machine learning algorithms used in the industry. Now, building a model is as simple as clicking on it and within a few clicks, your model will be ready.
We have made it that simple and easy for you.
In the third step, you have to configure your model. For now, just select the default configuration as we are interested in building our model first.
The final step is to train your model. You simply need to click on TRAIN MODEL button highlighted in blue!
You would have noticed there is a list of hyperparameters that you can tweak to optimize the performance of the model. We can always come back to optimize our model by fine-tuning the hyperparameters later.
You have built your first machine learning model without writing even a single line of code.
Step 8: Evaluate your Model
Your model is now ready. Next, you have to evaluate the model. The platform automatically generates plots for various metrics such as confusion matrix, ROC curve, PR curve, etc.
The accuracy of our model using logistic regression is 0.894 or 89.4%
Clicking on the ROC button will generate a ROC plot.
Similarly, clicking on the PR button will generate a PR plot.
Accuracy is just one of the metrics that is used to evaluate the performance of a machine learning model. Most business cases require ROC and PR as the key metrics to optimize model performance. Hence, the platform will generate these plots to simplify the analysis part.
Final Thoughts & Wrap Up
Data science & machine learning without coding are now a reality. This is not to say that you should not learn programming. You definitely should, its a great skill to have. However, it is only a matter of time before the mundane tasks of data cleaning and preprocessing get automated so data scientists can better utilize their energies to solve complex business problems and deliver real business value. After all data science is about using statistics, scientific methods, and data analysis (any tool you have!) to extract value from data.
We have built an all in one platform that takes care of the entire machine learning lifecycle. From importing a dataset to cleaning and preprocessing it, from building models to visualizing the results and finally deploying it, you won’t have to write even a SINGLE line of code. The goal is to make machine learning and data science accessible to everyone!