Data and Machine Learning (ML) are two of the most valuable skills throughout the industry today, in an age where technology is heavily dependent on ML and data. Data and ML play an essential role in driving the innovations happening daily, from predicting customer trends to optimizing supply chain processes and powering recommendation engines that guide individual consumers in the marketplace, to enabling systems capable of autonomous learning and operation.
Theory provides the basis for learning the tools of data and ML, but there is a significant difference between theoretical understanding and mastery of ML, and Data gained through practical, hands-on experience. A place, such as the School of Tech, allows students to experience ML and Data Conceptually and then develop those skills through Real-World Experience. The School of Tech focuses on exciting, hands-on learning to provide students with the knowledge and experience to make them successful Data and ML professionals.
This blog will discuss the skills and training that the School of Tech provides to its students regarding hands-on learning experiences, to prepare them to be professional Data and ML developers.
- Why Hands-On Learning Matters in Data & ML
Hands-on experience is essential for success in data science and machine learning, as these topics are primarily application-based. Instead of simply memorizing facts, success in data roles involves:
- Implementation of complex, real-world data
- Selection of suitable algorithms and their tuning
- Creation of visual representations of insights
- Development of code that is both reusable and capable of being scaled
- Deployment of models to the production state
To become competent in data science and machine learning, it is necessary to engage in coding, conducting experiments, identifying and fixing bugs, and addressing actual challenges through the development of solutions.
- Real Projects from Day One
Project-based curriculum design is a key principle of hands-on learning at the School of Technology in that it presents students with task-oriented projects that replicate, as best as possible, activities an employee would encounter in the workplace or a real-world environment.
Sample Projects Students May Undertake:
Predictive Analytics: Customer churn prediction using Classification Models
Natural Language Processing: Sentiment Analysis of Social Media Data
Time Series Forecasting: Sales, Stock Prices, Demand Forecasting
Image Recognition: Object Detection using Deep Learning Models
Recommendation Systems: Personalized Recommendations for Users
Anomaly Detection: Fraud Detection in Financial Transactions
All projects will follow the complete Data Science Workflow: collect data, clean data
explore the data, create features, train the models, evaluate the results, etc.
These are not “toy” projects – they’re built on data sets that include the same level of complexity and ambiguity you would find in the industry.
- Tools and Technologies Students Actually Use
The School of Technology provides students access to the same tools now used by employers in the job market, based on what they will need as employees. These tools include:
Programming Languages
- Both Python (the language of choice for data science/ML) and R (the language used for statistical analysis and advanced analytics)
Data Manipulation and Analysis
- NumPy and Pandas for Data Manipulation and Analysis and SQL for database access and querying
Visualization Tools
- Matplotlib and Seaborn for professional visualization and Power BI/Tableau for business intelligence visualization.
Machine Learning Frameworks
- Scikit-Learn as well as TensorFlow, Pytorch, and other similar frameworks for ML application development
Big Data
- Only relevant to big data development and deployment; Apache Spark, and other big data tools from the Hadoop ecosystem; AWS and Microsoft Azure cloud technology
Cloud-based and Deployment Technologies
- AWS, Microsoft Azure, Google Cloud Platform (GCP); Docker, Flask, and FastAPI
The School of Technology trains all students to use the same tools that are currently being used in the job market, which means that what they learn will directly apply to their future jobs as software developers.
- Hands-On Labs and Interactive Sessions
The concept of “learning by doing” is not a novelty limited to project-based development. The School of Tech has incorporated interactive hands-on labs as well as live coding workshops into its entire curriculum, including the following components:
Live coding workshops – Teachers will complete coding tasks in front of a group of students, working together to troubleshoot and solve problems. This method demonstrates how experienced programmers use their knowledge and skills in problem-solving and debugging.
Practice labs – Students will have access to practice lab environments where they can conduct experiments on data sets, use various algorithms to produce different outputs, and create predictive models while being supervised by their instructors and/or peers.
Peer-code reviews – Like in the “real world” of software development, students review each other’s code and provide constructive criticism on what they observe and learn from each other’s work.
Q&A and mentoring support – If students ever get stuck, they can contact a mentor from the industry to provide them with real-time assistance, constructive criticism, and insights.
- Dataset Diversity: From Structured to Unstructured Data
Diversifying your exposure with multiple data types will be fundamental to learning and applying Data Science on a practical level:
Example 1: Structured Data
Typically, students will begin their practical experience with cleaned tabular datasets containing information such as sales transactions, customers, and financial records that need to have cleaned records created from the dataset, imputed records created from the dataset, and/or transformed records from a raw dataset into a tabular format.
Example 2: Unstructured Data
The most common types of unstructured data include text files that do not have a specific format. Examples include reviews, social media posts, e-mails, blog articles, etc. Most images are also unstructured data, including pictures taken by your camera or uploaded to a website, scanned documents, as well as pdf files. There are types of time-series data; this type of data can be either structured (e.g, one time series) or unstructured or semi-structured (a continuous stream of data).
When students gain real-world experience working with unstructured data, they learn how to apply various techniques to extract value from it. For example, you will learn about NLP (Natural Language Processing), computer vision, and time-series forecasting using ARIMA and LSTM models. These practical applications provide students with an understanding of how to manage and analyze large amounts of unstructured data and how to obtain valuable insights from it.
- Mentorship from Industry Experts
A major benefit to students enrolled in the School of Technology’s hands-on model of learning is having mentors actively working in the field.
- The following are some of the benefits of having an active mentor:
- Gain insight into the application of Data and Machine Learning to a live environment.
- Receive guidance on best practices in the areas of code quality, experimentations, model evaluations, etc.
- Be provided with direction for optimizing jobs regarding speed, accuracy & reliability.
- Receive guidance for career choices and specialization, portfolio building.
This mentorship fills the gap between the academic realm and what will be expected of students once they enter the working environment.
- Collaborative Tasks: Learn in Teams
In practice, Data Science is seldom a solitary endeavor. As part of the School of Tech Code Program, we advocate for:
- Working in small groups to complete a large project
- Collaborating on programming tasks with another person
- Conducting sprint cycles to produce results
Students learn from working in a team environment how to:
- Maintain their code within a version control system (like Git)
- Point out how they reached their design choices
- Separate responsibilities within a team project
- Bring together individual contributions into one cohesive project
Through this format of working, students not only build technical expertise but also develop their skillsets in areas such as teamwork, communication, and personal accountability.
- Frequent Assessments and Real-Time Feedback
a) Instructor Feedback from a Live Instructor
In practical lessons, instructors provide:
- Match code for every line
- Propose a better application of logic or optimisation
- Explain alternative solutions
- Highlight the industry standard on best practices
Students learn from experts how to think, rather than just how to act.
b) Assistance with Debugging
Rather than struggling on their own, students receive support in:
- Finding errors
- Learning the techniques for debugging errors
- Understanding how the error occurred
This creates resilience and develops problem-solving abilities in ‘real-world’ environments.
c) Feedback from Peers
Students also have the opportunity to:
- Review their classmates’ work
- Contrast different solutions with each other
- Gain exposure to many different solutions for the same issue
This encourages teamwork and collaborative effort, the essentials for any workplace.
- Internship Opportunities for Practical Exposure
The hands-on learning process occurs outside of the School of Tech’s classrooms. For example, students may have access to the following opportunities:
- Internships with or in partnership with local start-up organizations
- Project assignments with existing companies
- Live client briefings.
Real-world experience in a corporate environment provides students with the opportunity to put their skills to use solving real business problems and to add to their resumes.
Conclusion
To sum up, all of their programs (in Data Science and Machine Learning) teach students through hands-on learning experiences that merge the theoretical aspects of data science with real-life experiences working with data. Such a program allows students to create, test, and implement solutions to data-based problems as they learn the concepts of data science.
Students who focus on this type of training will develop fundamental analytical skills to solve data-driven problems. They will gain real-world experience working with actual data and the real-world tools used to create or manipulate that data.
Even though students receive a certificate upon completion of their program, what they are actually receiving is on-the-job experience. This on-the-job training provides the students with the skills they need to become employable in today’s tech-driven job market and allows them to adapt to the fast-changing marketplace as technology continues to advance rapidly.