In the article published in Higher Education Review, Dr Abhijit Dasgupta (Assistant Professor and Director - Bachelor of Data Science) provides a roadmap for starting a career in data science from an academic perspective. He highlights the importance of formal education in mathematics, statistics, and computer science, along with the role of online courses and certifications for skill development.
In the article, he highlights the essential technical skills such as programming (Python, SQL), statistical foundations, machine learning, and data visualization and stresses the need for practical experience through internships, projects, and competitions like Kaggle.
The key points of the article are:
- - Data science is a rapidly growing field driven by the need for data-driven decision-making.
- - Online courses and certifications from platforms like Coursera and edX can help, but hands-on experience is crucial.
- - Proficiency in programming languages like Python and SQL is necessary for data manipulation and analysis.
- - A solid understanding of probability, linear algebra, and statistics is important for building accurate models.
- - Expertise in machine learning, deep learning, and AI frameworks like TensorFlow and PyTorch is highly valuable.
- - Data processing and visualization using tools like Pandas, Matplotlib, and Tableau help in effectively communicating insights.
- - Continuous learning and adaptability are necessary to keep up with the rapidly evolving field of data science.
To read the full article, please click here.