5 Key Points That Will Empower Your Data Science Journey
By: Soner Yıldırım
Tags: Machine Learning, Data Science, Deep Learning, Artificial Intelligence

5 Key Points That Will Empower Your Data Science Journey

Key topics that will boost your learning process.

Data science has gained tremendous popularity in recent years. More and more businesses in many different domains are investing in data science to create value out of data. In some domains, new businesses are created in the light of data science. The advancements in computation power and technology are likely to bring more attention to the field of data science. So, it is not a surprise that there is a high demand for data scientists and data engineers which motivate a lot of people to make a career change to work in the field of data science. I was one of the motivated people who started the data science journey about one and a half years ago. In this post, I would like to share 5 key points that I think will make your path to becoming a data scientist more robust and agile. The points I will discuss are usually not given high importance and kind of overlooked to some extent. I want to emphasize their importance and contributions to your data science journey.

1. Simple is better

Deep learning models with many layers might seem charming to you but most of the time, they are more complex than necessary. It is always desirable to accomplish the task with a simpler model. An image recognition task may require a deep learning model but we don’t need that amount of complexity for churn prediction. The business requirements also play a critical role here. If 90% accuracy satisfies the needs, there is no need for additional complexity. As models get more complex, they require more computation power which means additional costs. Moreover, deep learning models are data-hungry. We need to feed them an excess amount of data in order to obtain an accurate and not-overfitting model. Both data and computation power mean cost so simple is better when appropriate.

2. Linear Algebra Does The Magic

Linear algebra is like the hero behind the scenes. Data comes in many different formats such as numbers, texts, images, sound waves. However, the data must be converted to numbers for a model to make sense of it. The data is in tabular format (i.e. rows and columns) which is represented with matrices. Thus, lots of computations are done during the training of a model and this is where linear algebra comes into play. These computations are based on matrix or vector operations which are at the core of linear algebra. When the gradient descent algorithm works its way through the model convergence, matrix/vector computations are done under the hood. We don’t see them. We are more interested in the end result which is the accuracy or loss. However, in order to take our understanding one step further and go beyond accuracy and loss, we need to have a comprehensive understanding of linear algebra concepts. It may be boring at first but gets exciting once you get a hang of it. I strongly suggest sparing time to learn linear algebra because it is one of the fundamentals in your data science journey.

3. Domain Knowledge is Key to Success

Data science applications are designed to solve problems or improve processes. For instance, machine learning can be applied to make informed business decisions. But, what are those problems and processes? What is the purpose of that decision which we aim to apply machine learning techniques?

We need to have domain knowledge to answer these questions. We may be very good at data preprocessing, feature engineering, or model selecting and implementation. However, our skillset is more complete with domain knowledge. For example, if we are building a machine learning product to be used in oil production, we need to know the details of the crude oil distillation process. What kind of data is collected and what they mean are important questions that reveal valuable information for our model. I’m not saying we should be an expert in oil production but it is absolutely beneficial to learn the dynamics of processes.

Domain knowledge helps us in data preprocessing steps as well. We can make informed decisions when handling missing values. Correlations among certain features make more sense if we have an understanding of underlying processes.

4. Do Projects

Data science journey starts with learning the basics. There are lots of great sources to get you started. Introduction to data science courses helps to get familiar with the field. More practical courses may focus on using particular tools or packages such as TensorFlow or PyTorch. The documentation of open source packages is also very informative. Depending on your background, you may need to take some programming or software classes as well.

Once the basics are complete. It is time to start doing projects. What we learn from projects are not bounded with certain topics or tools. But, the most valuable skill to learn from projects is the ability to correctly approach a problem. How we approach a problem is key to create a robust and valuable model or product. Doing lots of projects in different areas will not only improve our practical skills but also enhance our way of thinking as a data scientist.

5. Optimization Algorithms Matter A Lot

We take the raw data. Clean it. Apply data preprocessing and feature engineering techniques. Create a model. Then train the model with data we prepared. Models are trained to determine model parameters so that relationships between features and target are mapped as correct as possible.

The process of learning the parameters is called optimization and the techniques used in this process are optimization algorithms. For instance, stochastic gradient descent is a widely used optimization algorithm in machine learning and deep learning. A comprehensive understanding of optimization algorithms help to better interpret the models. We can make more sense of the accuracy and other performance metrics. Thus, we will be able improve our model in a structured way.

The five key points we have discussed do not cover all of the materials you need to learn in the field of data science but emphasize very important concepts that might easily be overlooked.

Thank you for reading. Please let me know if you have any feedback.

References