Descarga Data Science (Full Course) en PC con GameLoop Emulator

Data Science (Full Course) en PC

Data Science (Full Course), proveniente del desarrollador SikApps Academy, se ejecuta en el sistema Android en el pasado.

Ahora, puedes jugar Data Science (Full Course) en PC con GameLoop sin problemas.

Descárgalo en la biblioteca de GameLoop o en los resultados de búsqueda. No más mirar la batería o llamadas frustrantes en el momento equivocado nunca más.

Simplemente disfrute de Data Science (Full Course) PC en la pantalla grande de forma gratuita！

Data Science (Full Course) Introducción

This app is an excellent choice for beginners to learn Data Science through interactive video tutorials and lessons for free. Also, this app doesn't require any signup process which makes it extremely user-friendly and convenient to use.

★ What you'll learn in this course? ★

1. The course provides the entire toolbox you need to become a data scientist

2. Fill up your resume with in demand data science skills

3. Learn Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn

4. Learn Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow

5. Impress interviewers by showing an understanding of the data science field

6. Understand the mathematics behind Machine Learning

7. Start coding in Python and learn how to use it for statistical analysis

8. Perform linear and logistic regressions in Python

9. Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn

10. Apply your skills to real-life business cases

11. Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlow

12. Develop a business intuition while coding and solving tasks with big data

13. Unfold the power of deep neural networks

14. Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation

15. Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations

16. Become a Data Scientist and get hired

17. Master Machine Learning and use it on the job

18. Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0

19. Use modern tools that big tech companies like Google, Apple, Amazon and Meta use

20. Present Data Science projects to management and stakeholders

21. Learn which Machine Learning model to choose for each type of problem

22. Real life case studies and projects to understand how things are done in the real world

23. Learn best practices when it comes to Data Science Workflow

24. Implement Machine Learning algorithms

25. Learn how to program in Python using the latest Python 3

26. How to improve your Machine Learning Models

27. Learn to pre process data, clean data, and analyze large data.

28. Build a portfolio of work to have on your resume

29. Developer Environment setup for Data Science and Machine Learning

30. Supervised and Unsupervised Learning

31. Machine Learning on Time Series data

32. Explore large datasets using data visualization tools like Matplotlib and Seaborn

33. Explore large datasets and wrangle data using Pandas

34. Learn NumPy and how it is used in Machine Learning

35. A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided

36. Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry

37. The entire Data Science process

38. Cloud concepts & application in Data Science

39. Database concepts

40. Statistics fundamentals as needed in Data Science

41. Visualizations for data mining and presentation

42. An overview on Statistical Learning

43. The essentials of Machine Learning

44. More advanced Python to apply to Data Science

45. Understand the basics of probability

46. Be able to implement basic statistics

47. Understand how to use various statistical distributions

48. Apply statistical methods and hypothesis testing to business problems

49. Understand how regression models work

50. Implement one way and two way ANOVA

51. Be able to understand different types of data

★ Disclaimer ★

Developer claims no credit for any video embedded in this app unless otherwise noted. Videos embedded in this app are copyright to its respectful owners. If there is an video appearing in this app that belongs to you and you do not want it to appear in this app, please contact us via email and it will be promptly removed.

★ Icon Credit ★

https://www.flaticon.com/free-icons/data-science