1. Proficiency in programming languages such as Python, R, and SQL, as well as related libraries and frameworks for data analysis and visualization, such as NumPy, Pandas, and Matplotlib.
2. Strong understanding of statistical concepts and methods, including hypothesis testing, regression analysis, and time series analysis.
3. Experience with data wrangling and data cleaning techniques, including working with large and complex datasets, data extraction, and data transformation.
4. Familiarity with machine learning techniques and algorithms, such as linear and logistic regression, decision trees, random forests, and neural networks.
5. Knowledge of database management systems and experience working with relational and non-relational databases.
6. Experience with data visualization tools such as Tableau, Power BI, and ggplot, as well as web development technologies such as HTML, CSS, and JavaScript.
7. Familiarity with big data technologies such as Apache Hadoop, Apache Spark, and distributed file systems.
8. Proficiency in software development best practices, including version control, unit testing, and code review.
9. Understanding of cloud computing and experience working with cloud-based services such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure.
10. Strong analytical and problem-solving skills, including the ability to develop and evaluate hypotheses, design experiments, and interpret results.

Overall, individuals pursuing degrees in data science and engineering should focus on developing a strong foundation in programming, statistics, machine learning, data wrangling, and data visualization, as well as familiarity with big data technologies and cloud computing.

Some different vertical career tracks in data science, and the core skills that are relevant for each:

1. Data Analyst: A data analyst is responsible for analyzing and interpreting data to inform business decisions. Core skills for a data analyst include proficiency in SQL and data visualization tools such as Tableau or Power BI. A data analyst should also have a strong foundation in statistics and experience with data wrangling and cleaning techniques.
2. Data Scientist: A data scientist is responsible for developing and implementing machine learning models to analyze and predict data. Core skills for a data scientist include proficiency in Python or R, knowledge of statistical concepts and methods, and familiarity with machine learning techniques and algorithms. Data scientists should also have experience with data wrangling, database management, and data visualization.
3. Data Engineer: A data engineer is responsible for designing and building data infrastructure to support data analysis and modelling. Core skills for a data engineer include proficiency in big data technologies such as Hadoop and Spark, knowledge of database management systems, and experience with cloud computing. Data engineers should also have strong software development skills and proficiency in data wrangling and cleaning techniques.
4. Business Intelligence Analyst: A business intelligence analyst is responsible for analyzing and reporting on business performance and trends. Core skills for a business intelligence analyst include proficiency in SQL and data visualization tools such as Tableau or Power BI. They should also have experience with data wrangling and cleaning techniques and a strong understanding of statistical concepts and methods.
5. Machine Learning Engineer: A machine learning engineer is responsible for developing and implementing machine learning models at scale. Core skills for a machine learning engineer include proficiency in Python, knowledge of statistical concepts and methods, and familiarity with machine learning techniques and algorithms. They should also have experience with big data technologies, cloud computing, and software development best practices.

Overall, data science is a highly interdisciplinary field, and individuals pursuing careers in data science should focus on developing a range of skills across programming, statistics, machine learning, data wrangling, and data visualization, as well as familiarity with big data technologies, cloud computing, and software development best practices. The specific skills that are most relevant will depend on the individual’s career goals and the specific requirements of their role.

Dr Suresh R K