1 Ask the right questions to begin the discovery process.Solving business problems through undirected research and framing open-ended industry questions
2. Acquire data.Extract huge volumes of structured and unstructured data. They query structured data from relational databases using programming languages such as SQL. They gather unstructured data through web scraping, APIs, and surveys.
3. Process and clean the data.Thoroughly clean data to discard irrelevant information and prepare the data for preprocessing and modeling
4. Integrate and store data.Perform exploratory data analysis (EDA) to determine how to handle missing data and to look for trends and/or opportunities
5. Initial data investigation and exploratory data analysis.Employ sophisticated analytical methods, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling
6. Choose one or more potential models and algorithms.Discovering new algorithms to solve problems and build programs to automate repetitive work
7. Apply data science techniques, such as machine learning, statistical modeling, and artificial intelligence
8. Measure and improve results.Recommend cost-effective changes to existing procedures and strategies
9. Present final result to stakeholders.Communicate predictions and findings to management and IT departments through effective data visualizations and reports
10. Make adjustments based on feedback
11. Repeat the process to solve a new problem
Â