Data science is a solid, rapidly growing field with plenty of untapped potential. It has become a major part of the modern-day business world, playing an important role in helping businesses make critical business decisions through logic rather than intuition alone. The journey to being a data scientist isn’t an easy road, but with focus, determination, and consistency, one is able to thrive in this field.
The world is constantly changing, with new technologies being introduced on a daily basis, and for organizations and businesses to remain competitive enough, there is a need to ensure that their data plays a useful role in driving decisions. Data science calls for analyzing big data to find patterns and insights that can drive business decisions and enable organizations to increase profits.
To a beginner, it may seem challenging and impossible to become a data scientist. However, with a great roadmap, the journey can be quite smooth for an aspiring data scientist to put in the work to become one.
As a beginner, the learning process involves several tools and concepts and practicing and implementing what one has learned. Below is a stepwise roadmap that also requires persistence and determination to help you get started in data science.
SQL is a query language used for managing and manipulating data stored in databases. SQL allows a data scientist to query data from various sources and retrieve, filter, and aggregate the data. A beginner may use tutorials, boot camps, and SQL challenges to gain a strong foundation in SQL programming language before embarking on the next step.
Python and R are the widely used programming languages for data science, and a good foundation on this is important for data manipulation, visualization, and machine learning models. Knowing the use of variables, tuples, functions, loops, and other frameworks such as Numpy, Pandas, and sci-kit-learn is really helpful.
The next step after having learned and gained a good foundation in SQL and Python/R is learning how to use visualization tools to create interactive and visually appealing dashboards, graphs, and charts to communicate your data. Tolls such as PowerBI, Tableau, and QlikView can be of great help for visualization. As a beginner, it’s better to choose one tool and focus on it first before exploring other visualization tools.
Basic statistics concepts in statistics, probability, and linear regression are crucial for machine learning and data science in general. As a beginner, you are required to have a strong background in these concepts. Machine learning is a subset of data science that uses algorithms to learn from and make predictions from data. To get started, one can use online resources and tutorials.
Machine learning algorithms play a major part in the entire data science process, and upon getting the needed skills in statistics as a beginner, you’ll be required to learn machine learning algorithms such as linear regression, decision trees and K-means clustering, to begin with, and online tutorials and courses can be of great advantage to start.
As it is commonly known, practice makes perfect, and as a beginner, this phrase is of great importance. A beginner is required to work on several projects and exercises to apply their learned skills. Participating in hackathons and online communities can assist in learning from others and getting feedback for projects worked on. One is required to work on real-world data sets to help solve a real-world problem and to apply their skills to analyze data effectively. Building models and testing them on unseen data can help a beginner gain practical experience and build a good portfolio to kick start their career in data science.
For individuals interested in data to solve complex problems and make good decisions, data science is the path to follow. And with good communication skills, knowledge, and experience, data science can prove to be a rewarding career.