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Business Analytics with Python 2021

Video Introducing this tutorial

1. Analytics - General:
1. What is Analytics (BI, BA, Levlels etc)
2. Why Analytics (Appl in various domains)
3. Different Roles in Analytics
4. Tools and Technqiues in Analytics
5. Data Science, Data Mining, Statistics, Machine Learning, Supervised and Non-Supe
6. CRISP Modeling Framework
7. Scales of Measurements

2. Python Environment:
1. Anaconda - Download & Setup
2. IDEs - Jupyter, Spyder, PyCharm
3. Git - Setup and Configuration with IDEs
4. Creating and Managing Analytics ML Projects

3. Basic Programming and Data Structures:
1. Basic Data Structures & Programming Contructs
2. Libraries
3. Numpy
4. Pandas
5. Matplotlib

4. Data Manipulation and Descriptive Summary:
1. Group Summaries
10. Managing Indexes in Pandas Coding
11. Partitioning Data into Train and Test Set- Theory
12. Partitioning Data into Train and Test Set- Coding
13. Scaling of Data (Useful for Clustering)
2. Crosstab, Pivot and Reshape data
3. Managing Missing Values Theory
4. Managing Missing Values Coding
5. Outliers Detection Theory
6. Outliers Detection
7. Various types of Joins, Merge Theory
8. Various types of Joins, Merge Coding
9. Managing Indexes in Pandas Theory

5. Statistics:
1. Basic Statistics (mean, median, mode)- Theory
2. Basic Statistics (mean, median, mode)- Coding
3. Other Statistics (sd, var, quantile, skewness, kurtosis)- Theory
4. Other Statistics (sd, var, quantile, skewness, kurtosis)- Coding
5. Hypothesis Tests (t-test, Chi-sq tests etc)- Theory
6. Hypothesis Tests (t-test, Chi-sq tests etc)- Coding
7. Probability Distributions (normal, binomial etc)- Theory
8. Probability Distributions (normal, binomial etc)- Coding
9. Sampling Techniques
10. Selection of Graph
11. Basic Graphs (histogram, barplot, boxplot, pie etc)- Theory
12. Basic Graphs (histogram, barplot, boxplot, pie etc)- Coding
13. Libraries (matplotlib, seaborn, plotnine)- Theory
14. Managing Plot Parameters (size, title, axis, legend etc)- Theory
15. Managing Plot Parameters (size, title, axis, legend etc)- Coding
16. Advanced Graphs (correlation, heatmap, mosaic etc)- Theory
17. Advanced Graphs (correlation, heatmap, mosaic etc)- Coding
18. Exporting graphs- Theory
19. Exporting graphs- Coding
20. Simple Linear Regression- Theory
21. Simple Linear Regression- Coding
22. Multiple Linear Regression
23. Libraries - sklearn, statsmodel
24. Predict DV on IVs- Theory
25. Predict DV on IVs- Coding
26. Metrics of Linear Regression (R2, RMSE, p-values)
27. Applications of Linear Regression
28. Assumptions of Linear Regression
29. Difference between Linear and Logistic
30. Logistic Regression- Theory
31. Logistic Regression- Coding
32. Metrics of Logistic Regression (confusion matrix, ROC curve)- Theory
33. Metrics of Logistic Regression (confusion matrix, ROC curve)- Coding
34. Predict probability of DV on IV- Theory
35. Predict probability of DV on IV- Coding
36. Applications of Logistic Regression
37. Difference between Classification and Regression Decision Trees from CART Models
38. Understanding Tree from the Plot- Theory
39. Understanding Tree from the Plot- Coding
40. Classification Tree - Predict class, plot, Accuracy- Theory
41. Classification Tree - Predict class, plot, Accuracy- Coding
42. Regression Tree - Predict Numerical value, plot, RMSE- Theory
43. Regression Tree - Predict Numerical value, plot, RMSE- Coding
44. Improving Tree Accuracy using Random Forests- Theory
45. Improving Tree Accuracy using Random Forests- Coding
46. Bagging and Boosting- Theory
47. Bagging and Boosting- Coding
48. Applications of Decision Tree
49. KNN (K-nearest neigbours)- Theory
50. KNN (K-nearest neigbours)- Coding
51. Neural Networks
52. Gradient Descent
53. SVM (Support Vector Machine)- Theory
54. SVM (Support Vector Machine)- Coding
55. Clustering for Grouping Data
56. Types - Hierarchical & Non-Hierarchical
57. Kmeans - Output Metrics (iter, error, plot)- Theory
58. Kmeans - Output Metrics (iter, error, plot)- Coding
59. Hierarchical (Agglomerative & Divisive) - Dendrogram, Visual plot- Theory
60. Hierarchical (Agglomerative & Divisive) - Dendrogram, Visual plot- Coding
61. Extracting the Data in Clusters, Cluster Centers- Theory
62. Extracting the Data in Clusters, Cluster Centers- Coding
63. Applications of Clustering
64. Applying AR to Grocery Store for Market Basket Analysis- Theory
65. Applying AR to Grocery Store for Market Basket Analysis- Coding
66. Metrics- Support, Confidence, Lift
67. Frequent Items Sets and Rules; Filtering rules
68. Applications of AR
69. Managing Unstructured Data; Unstructured to Structured Data- Theory
70. Managing Unstructured Data; Unstructured to Structured Data- Coding
71. Extracting Tweets from Twitter- Theory
72. Extracting Tweets from Twitter- Coding
73. Extracting words for Sentiment Analysis- Theory
74. Extracting words for Sentiment Analysis- Coding
75. Wordcloud to Visualise the frequency of Occurance of Words in Text- Theory
76. Wordcloud to Visualise the frequency of Occurance of Words in Text- Coding
77. Applications of Text Mining
78. Creating & Managing Dates and Times- Theory
79. Creating & Managing Dates and Times- Coding
80. Creating and managing Time Series data- Theory
81. Creating and managing Time Series data- Coding
82. Download Stock Prices Data using Libraries and Analysing them- Theory
83. Download Stock Prices Data using Libraries and Analysing them- Coding
84. Time Series Components(Seasonal, Irregular, Trend) & Types (Additive & Multiplic
85. Time Series Analysis - Simple Moving Average, Exponential Smootheing, ARIMA
86. Time Series Analysis - Simple Moving Average, Exponential Smootheing, ARIMA
87. Plotting Time Series, Candlesticks Diagram for Stocks- Theory
88. Plotting Time Series, Candlesticks Diagram for Stocks- Coding