Data Analytics is the fundamental step towards building a career in Data Science. It uses pre-existing data or historical data to solve a given problem. It includes tasks like checking hypothesis, creating dashboards & reports and gathering data from various sources.
One should be adept at working with tools like Power BI to generate reports, Microsoft Excel and SQL to work with structured or unstructured data, and proficient in R and Python.
Data Science is a multidisciplinary field. It includes Computer Science, Mathematics, Statistics, Economics, etc. It is NOT a subset of Machine Learning. Data Scientist may use machine learning to discover new insights from the data, but not necessarily.
Data Science consists of specific steps (but not limited to):
Collection of data.
Pre-processing or cleaning the data to fit the requirements of the problem & make the data uniform.
Analysis of the data.
Driving insights and using them to take steps to solve that problem.
Collection of data.
Pre-processing or cleaning the data to fit the requirements of the problem & make the data uniform.
Analysis of the data.
Driving insights and using them to take steps to solve that problem.
Machine Learning helps to produce predictions from the data. It is used to extract data, learn from it and predict trends. It enables our machine to learn on its own without being explicitly programmed. It is used to scale, automate and incorporate results in the user domain.
Core Machine Learning Engineers don't use the scikit learn to train their models. They build their own code to optimize the model to get the best results.
Machine Learning Engineers are from the Computer Science Domain, whereas Data Scientist can be from any domain.
Machine Learning Engineers are from the Computer Science Domain, whereas Data Scientist can be from any domain.
• • •
Missing some Tweet in this thread? You can try to
force a refresh
