Transitioning from Tutorials to Independent Projects in Data Science
Tutorials are the entry point to data science learning Many beginners take tutorials for a reason. Tutorials are a great way to learn programming, statistical techniques, data analysis skills, machine learning algorithms, and visualisation tools. But eventually, students need to stop copying and start doing projects.
This change is an important step towards your way of Self Sufficient Data Science Professional. It will help the learners to think from all the perspectives, enhance the ability to learn the solution themselves and apply their learning practically. Sevenmentor Data Science Courses can surely guide you to establish a firm base.
Why Tutorials Are a Great Starting Point
Get started Where to start? Tutorials give an inexperienced user the framework for a trailblazing teacher. How to import datasets, clean data, visualize data, build models, validate, etc.
A tutorial you can follow on how to make a customer churn prediction model for a customer. From the tutorial it should be apparent how to make this. A step-by-step tutorial like this can help you to get to know an idea easier.
However, in practical implementation, there will not be a step-by-step guide book that instructs us how to do each step in sequence. Data scientists will have to determine what data they require, how to clean and process the data, and what modeling technique to employ and how.
That's why if you get past the tutorials – that's a big leap ahead!
Developing Independent Problem-Solving Skills
Learners have the opportunity to decide independently which way they will be headed when they start a project of their own. Instead of 'What do I do next in this tutorial?', they are asking learner-centred questions, such as:
What problem am I trying to solve?
What data do I need?
How should I clean the dataset?
Which variables are important?
Which algorithm should I test?
How will I measure model performance?
What does the final result actually mean?
These questions encourage analytical thinking.
7mentor Data Science course in pune can really help the student with the ideas and the exposure that they can use in their future independent projects.
Begin With an Issue and Not Simply a Data Set
The simplest way to run a side hustle is to begin with a significant problem.
Or: 1. Instead of just picking up the learn dataset, and in business, for example, having a go at several algorithms.
For example:
Q: Is customer trend or behavior predictable for a customer turning?
At this stage the student would then have finally go on to identify the appropriate data, the variables to use, clean the data, perform exploratory data analysis and finally build a predictive model.
In asking thisproblem-first question, the task becomes more engaging for the learner and shows them why they need to be doing each of the technical steps.
Learn to Explore Data Independently
This is one area that home practice can make the most difference.
Learners can examine:
Missing values
Duplicate records
Outliers
Data distributions
Relationships between variables
Categorical and numerical features
Trends and patterns
Instead of trying to re-create a visualisation from a tutorial, a user can choose the charts which they believe most effectively illustrate their results.
For example, put up a bar graph to compare classes or a scatter plot to see the correlation of numerical data.
This process gradually develops data intuition.
Experiment With Different Approaches
Projects don't always go as planned. In fact, testing out an idea could just be one of the most fun elements of project-based learning.
(What the learner will do) The learner will employ machine learning and will train a model then see that the model doesn't work well, but that the learner is able to formulate a hypothesis as to why.
They might try:
Different preprocessing techniques
Feature engineering
Alternative algorithms
Hyperparameter tuning
Different evaluation metrics
Additional data exploration
This places a focus on the learners to be able to understand the story behind their choices not simply copying code.
Documentation Makes Projects More Professional
So the last thing before tutorials, then, is to learn how to document our project.
A good project should explain:
The problem being addressed
The source and nature of the data
Data cleaning steps
Exploratory analysis
Features selected
Model development
Evaluation results
Key findings
Possible improvements
Documentation provides others with knowledge of the project; it can also help the learner when interviewing to explain his or her project.
Build Projects That Reflect Real-World Scenarios
Experience The more confident students will be as they work on fairly straightforward assignments, the more, little by little, they will start to conceive of somewhat more authentic examples.
Possible project ideas include:
Sales forecasting
Customer segmentation
Employee attrition analysis
House price prediction
Recommendation systems
Fraud detection
Sentiment analysis
Retail demand forecasting
Marketing campaign analysis
Building Confidence Through Practice
Independent project is scary because, I guess, there is not going to be a teacher there and I guess no step-by-step instructions.
Confidence develops through repeated practice.
The third tip is to begin with a quite simple example and not a very complex one. Once the user is used to the first sequence of step, he can switch to a more complex one.
The projects is a continuation of class work and exercises for 7mentor Data Science students.
From Copying Code to Understanding Code
The key disimilarity from tutorial learning and your own endeavor is...
In tutorials you could follow the code without understanding why. Independent projects tempt students to explore every step.
For instance: Learners may be prompted to ask a learner: Select one specific machine learning algorithm:
Why is this algorithm appropriate?
What assumptions does it make?
What are its strengths and limitations?
How does it compare with another approach?
How should its performance be evaluated?
These questions will active your learning.
Creating a Strong Project Portfolio
Perhaps they could even be maintained as a portfolio.
- Python. The portfolio may include: These skills.
SQL.
Statistics.
Machine Learning.
Data visualization. Analytical Thinking.
No copied tutorial projects shown a student could present a project that he/she had to develop decisions and confidently explain the entire process.
Sometimes, during an interview, it helps just as much to know how you got there, as it does to know what you have.
Keep Learning While Building
Projects do NOT mean no learning. Projects can be expected to lead to further ideas from where for further learning.
May uncover a new statistical technique, visualization, ML algorithm or data problem.
This creates a productive learning cycle:
Get started Practice Advance Construct Search gaps Learn more improve the project
This allows for practical data science bootcamps.
Conclusion
Your own project After completing the tutorials, the true next step in taking your data science skills up a notch is to do a project of your own. The tutorials build upon what you learn, but you are creating a project.
I'm not suggesting we need to eliminate all tutorials. I'm suggesting we transition from where we are today - simply copying tutorials for students to do, to the point where we enable them to determine for themselves what they need to do to their tutorial to get it to work.
