The 21st century is all about data! Data has become the backbone of all industries, businesses and organisations. It has become an essential part of our daily lives and has allowed us to make better decisions and improve our lives and how we interact with the world. It serves as the foundation for all decisions and has developed into a vital tool for both corporations and governments to maintain competitiveness.
Data is often referred to as the “new oil” because, just like oil, it has the power to fuel growth and drive progress. In today’s digital age, data is being generated at an unprecedented rate, and it has become a valuable commodity for businesses and organisations of all sizes.
Data science is the process of using data to gain insights and make decisions. It involves collecting, cleaning, and analysing large amounts of data to identify patterns and trends.
It’s an excellent field involving math and computer skills to find insights and make predictions from data. Imagine you have a big box of puzzle pieces (that’s your data), and your job is to put those pieces together to find a hidden picture (those are your insights). It’s like a treasure hunt, but instead of treasure, you’re finding valuable information that can help businesses, organisations, and even individuals make crucial decisions.
Data Scientists are the detectives solving the mystery. The detective (data scientist) collects clues (data), sorts through them to find the important ones, and uses these clues to figure out what happened (gain insights). Now you figured that out. Let’s look at what data scientists do.
It’s critical to comprehend the issue before coming up with a remedy. The main goal is to determine if your problem is, in fact, a Data Science problem and, if so, what kind. Understanding how to transform a business idea into a clearly defined problem statement has immense benefits. Here are a few data scientist’s problem statements
Data collection is the process of gathering the information needed to address the problem statement. This phase takes the longest since it takes time to collect relevant data.
Depending on the nature of the problem statement, data collection involves gathering data from numerous resources, which can be structured or unstructured.
Once it’s collected, the next stage involves sorting out the relevant data, i.e., Data cleaning. It identifies and corrects errors, inconsistencies, and duplicates in data before it is used for analysis and modelling. The objective of data cleaning is to improve the quality and reliability of data so that it is suitable for analysis and decision-making.
Some of the steps involved in the Data cleaning process are
Once the data is collected and cleaned, the next stage is data analysis. Data scientists need to be knowledgeable about different modelling approaches and data analysis techniques for machine learning and deep learning algorithms. A data scientist must comprehend an algorithm’s mathematical formula to understand how different algorithms work. This will also make it simpler to select an algorithm that will work with the data.
The next phase involves testing and evaluating the model. A data scientist must understand the various model evaluation metrics, which are then assessed for their feasibility and predictive accuracy.
Data Scientists use various tools and techniques like statistical analysis, machine learning, and visualisation to understand data. It’s a pretty in-demand field, too, with many job opportunities for those with the right mix of technical skills and creativity. So, if you love problem-solving, enjoy working with numbers and computers, and want to make a real impact, then data science might be the perfect field for you!