Data Science – Dataplexa

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Data Science Master Index

Complete Data Science roadmap covering Foundations, EDA, Statistics, Feature Engineering, Machine Learning, Big Data, ETL, Cloud and Real Projects.

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I. Data Science Foundations

1. What is Data Science? 2. DS Workflow 3. Types of Data 4. Python for DS (Refresher)

II. Data Wrangling & Cleaning

5. Handling Missing Values 6. Outliers 7. Transformations 8. Encoding Techniques 9. Feature Scaling 10. Merging & Joining

III. Exploratory Data Analysis (EDA)

11. Univariate Analysis 12. Bivariate Analysis 13. Multivariate Analysis 14. Summary Statistics 15. EDA on Real Data

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 Basics 39. Data Modeling 40. BigQuery Basics 41. Redshift Basics

IX. Python Libraries for Data Science

42. NumPy 43. Pandas 44. SciPy 45. Scikit-Learn Basics 46. Joblib & Pickle

X. Machine Learning for Data Science

47. Regression 48. Classification 49. Clustering 50. ML Metrics 51. Train/Test/Validation

XI. Data Pipelines & ETL

52. What is ETL? 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 Workflows

XIII. Cloud for Data Science

61. S3 Storage 62. Cloud Compute 63. Cloud ETL

XIV. Real-World Data Science Projects

64. EDA Project 65. Predictive Modeling 66. Recommendation System 67. Real Business Case 68. Capstone Project