Business Problems and Data Science Solutions

Business Problems and Data Science Solutions

1. There are sets of common tasks that underlie the business problems. Subtasks can be composed to solve the overall problem. Here we use datamining.

2. Finding correlations are the most basic examples of classification and regression tasks.

3. The following are other important tasks: similarity matching, clustering, co-occurrence grouping, profiling, link prediction, data reduction and causal modeling.

4. There are supervised and unsupervised methods for data mining problems.

5. If a specific target can be provided, the problem can be phrased as a supervised one. The purpose is to predict the target.

6. We have to make an important distinction, the difference between mining data to find patterns and building models, and using the results of data mining.

7. The data mining process is: business understanding, data understanding, data preparation, modeling, evaluation and deployment.

8. There are some important implications for managing the data science team. An example is to view the data mining process as a software development cycle.

9. Other analytics techniques and technologies can be used and are mentioned in the next cloud.

10. Statistics, database querying, data warehousing, regression analysis, machine learning and data mining.

11. We have to consider a set of questions that may arise and the technologies that would be appropriate for answering them.