Data Science Lesson 26 – CI & p-Values | Dataplexa

Data Science

I. Data Science Foundations
1. What is Data Science? 2. DS Workflow 3. Types of Data 4. Python for DS
II. Data Wrangling & Cleaning
5. Missing Values 6. Outliers 7. Transformations 8. Encoding 9. Feature Scaling 10. Merging & Joining
III. Exploratory Data Analysis (EDA)
11. Univariate Analysis 12. Bivariate Analysis 13. Multivariate Analysis 14. Summary Statistics 15. Real-World EDA
IV. Feature Engineering
16. Feature Creation 17. Feature Selection 18. Dimensionality Reduction 19. PCA 20. Domain Features
V. Statistics & Probability
21. Descriptive Statistics 22. Probability Basics 23. Random Variables 24. Distributions 25. Hypothesis Testing 26. CI & p-Values
VI. Data Visualization
27. Matplotlib 28. Seaborn 29. Plotly 30. Dashboard Basics 31. Advanced Visualizations
VII. SQL for Data Science
32. SQL Select & Filters 33. Joins 34. Aggregations 35. Window Functions 36. Subqueries
VIII. Databases & Storage
37. Relational Databases 38. NoSQL 39. Data Modeling 40. BigQuery 41. Redshift
IX. Python Libraries
42. NumPy 43. Pandas 44. SciPy 45. Scikit-Learn 46. Joblib & Pickle
X. Machine Learning
47. Regression 48. Classification 49. Clustering 50. ML Metrics 51. Train/Test/Validation
XI. Data Pipelines & ETL
52. ETL Basics 53. Data Ingestion 54. Apache Airflow 55. Batch vs Streaming 56. ETL Pipelines
XII. Big Data Basics
57. Hadoop 58. Spark Basics 59. Spark DataFrames 60. Distributed Systems
XIII. Cloud for Data Science
61. S3 Storage 62. Cloud Compute 63. Cloud ETL
XIV. Real-World Projects
64. EDA Project 65. Predictive Modeling 66. Recommendation System 67. Business Case 68. Capstone Project
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