# BigData Science/Analytics

Crack your dream company

The course aims at developing both math and programming skills required for a data scientist. It allows us to get insight into data analysis problems that arise in business verticals and solving those problems using statistical and machine learning approaches. The course also focus upon the understanding fundamental math underlying those models. This course is more of practical research oreinted course than developer oriented. It focuses on 6 most common data analysis problems that arise in most business verticals: Classification, Regression, Recommender Systems, Clustering, Association Analysis and Outlier Detection.

Category: Data Analytics

- Details
- Objectives
- Target Audience
- Syllabus

Duration | 50 Hours |

Prerequisites | Must have working knowledge of any object oriented programming language |

Class Room Course | Available |

Video Course | Unvailable |

1. Introduction to Data Science/Analytics | |

3. Tools for Data Science/Analytics | |

5. Linear Algebra for data scientist | |

7. Probability for data scientist | |

9. Optimization theory for data scientist | |

11. Applying deep learning approaches for image based analytics | |

13. Frequent Pattern Mining | |

15. Outlier Problem | |

17. Data Collection Techniques | |

19. EDA(Numerical + Graphical) and Feature Engineering | |

21. Classification and Regression: Decision Tree Model | |

23. Classification:Logistic Regression | |

25. Classification and Regression: Neural Network Model | |

27. Recommenders: Content based Recommendation | |

29. Recommenders:Item-Item KNN Model | |

31. Clustering: Iterative Models | |

33. Clustering: Density Models | |

35. Outliers: KNN Model | |

37. Association Analysis: Apriori Model | |

39. (Optional)Data Visualization | |

2. Data Analysis Problems/Usecases in Business | |

4. Mastering R/Python Language | |

6. Statistics for data scientist | |

8. Calculus for data scientist | |

10. Classification Problem | |

12. Recommendation Problem | |

14. Clustering Problem | |

16. Overview of Machine Learning Algorithms | |

18. Data Preparation Techniques | |

20. Classification and Regression: KNN Model | |

22. Classification and Regression: Naive Bayes Model | |

24. Classification and Regression: SVM Model | |

26. Classification and Regression: Ensemble Model | |

28. Recommenders: User-User KNN Model | |

30. Recommenders:Latent Factor Model | |

32. Clustering: Hierarchical Models | |

34. Outliers: Probabilistic Model | |

36. Outliers: Density Model | |

38. Distributed/BIGDATA Analytics | |

40. Project(4 day Hackathon) | |

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