Proficiency in programming languages like Python or R is essential for data manipulation, analysis, and model development.
Effective communication, both in written and verbal form, is necessary for conveying complex technical concepts and conclusions to a variety of stakeholders and audiences, including those who are not technical.
To build predictive and prescriptive models, you need to know a lot about both supervised and unsupervised machine learning methods, as well as their uses and limits.
A structured way to solve problems by using critical thought and creativity to solve hard problems with data.
Experience with using cloud services like Amazon Web Services, Microsoft Azure, or Google Cloud to store, process, and deploy data science projects at scale.
Understanding the specific industry or domain you're working in is crucial for contextualizing data insights and developing relevant solutions.
It is impossible to organize tests, draw conclusions, or construct prediction models without a solid grounding in statistical principles, probability theory, and linear algebra.
The ability to use tools like Matplotlib, Seaborn, ggplot2, or Tableau to create insightful and significant visualizations that may be used to convey insights and conclusions to non-technical stakeholders.
Ability to preprocess, clean, and transform raw data into usable formats using libraries like Pandas, dplyr, or SQL, ensuring data quality and accuracy.
Strong understanding of business concepts and the ability to convert data insights into recommendations that drive business value.
knowledge of how to choose, manipulate, and produce useful features from unprocessed data to enhance model performance and accuracy.
Ability to design and conduct controlled experiments to evaluate hypotheses and make data-driven decisions for optimization.