WHAT IS DATA SCIENCE ?
Data Science is a combination of algorithms, tools and machines learning technique which helps you to find common hidden patterns from the given data. Data science is the process of collecting , modelling and analyzing, Visualization data to extract insights that support making. There are several methods, process and techniques to perform depending on the industry and the aim of the analysis.
HOW TO BUILT A DATA SCIENCE SUCCESSFUL CARRIER ?
Now-a-days, Almost very much required job in Data science field. So mostly learn many important topics as per industry need to grow your skill and built a successful carrier and get a better job in anywhere in the world. Now several Institute and University provided courses in Data science field but what topics to know as per industry required to easily get a well paid job in the market that is important, so here explain myself research study about Data science and its job related topics.
WHAT TOPICS LEARN TO GROW YOUR SKILL?
If anybody as a students or a professional interested to built an excellent carrier in Data science field so mostly know at first what is Data science ? And its subjects to learn to grow your skill then enroll for studies. Thus here Briefly described all details topics covered about Data Science skills to become a successful Data science carrier.
1. PYTHON BASICS AND ADVANCED
2. STRINGS OBJECTS
3. LIST OF OBJECTS BASICS
4. TUPLES,SETS AND DICTIONARIES
5. MEMORY MANAGEMENT
6. OOPs CONCEPTS
7. FILES KNOWLEDGE
8. EXCEPTION HANDLING
9. GUI - FRAMEWORK
10. DATA-BASE
11. WEB-API
12. FLASK
13. Django
14. STREAM LIST
15. PANDA BASICS AND ADVANCE
16. DASK
17. NUMPY
18. VISUALIZATION
19. STATISTICS BASICS AND ADVANCE
20. PROBABILITY DISTRIBUTION
21. LINEAR ALGEBRA
22. SOLVING STATISTICS PROBLEM IMPLEMENTATION
23. MACHINE LEARNING AND PIPELINE
24. FUTURE ENGINEERING
25. FUTURE SELECTION
26. EXPLORATORY DATA ANALYSIS
27. REGRESSION
28. LOGISTIC REGRESSION
29. DECISSION TREE
30. SUPPORT VECTOR MACHINE
31. NAIVE BAYES
32. ENSEMBLE TECHNIQUE
33. BOOSTING
34. STACKING
35. KNN
36. DIMENSIONALITY REDUCTION
37.CLUSTERING
38. ANOMALY DETECTION
39. TIME SERIES
40. NLP BASICS
41. MODEL RETAINING APPROACH
42. AUTO MACHINE LEARNING
43. NEURAL NETWORK
44. HARDWARE SETUP- GPU
45. TENSORFLOW AND JS
46. PYTORCH
47.Mx NET
48. KERAs TUNER
49. CNN OVERVIEW
50. ADVANCE COMPUTER VISION
51. CUSTOM OBJECTS
52. OBJECTS SEGMENTATION
53. OBJECTS TRACKING
54. OCR
55. IMAGE CAPTIONING
56. MODEL CONVERSION
57. ADVANCE NLP AND DEEP LEARNING
58. TEXT PROCESSING IMPORTING
59. SPACY
60. RNN
61. WORD EMBEDDING
62. ATTENTION BASED MODEL
63. TRANSFER LEARNING IN NLP
64. DEPLOYMENT MODEL
65. API - FOR SPEECH AND VISION
66. BIG DATA
67. HADOOP
68. SPARK
69. KAFKA
70. MACHINE LEARNING OPS
71. SQL
72. ADVANCE EXCEL
73. TABLEU
74. POWER - BI
75. GPT - 3
76. GAN
77. REINFORCEMENT LEARNING
78. TAKE DIFFERENT PROJECTS
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