Become a Data Scientist: SQL, Tableau, ML & DL [4-in-1]

سرفصل های دوره

4-in-1 Bundle covering the 4 essential topics for a data scientist - SQL, Tableau, Machine & Deep Learning using Python

1. Introduction

1. Introduction

2. Installation and getting started

1. Installing PostgreSQL and pgAdmin in your PC

2. This is a milestone!

3. If pgAdmin is not opening .html

4. Course Resources.html

3. Case Study Demo

1. Case Study Part 1 - Business problems

2. Case Study Part 2 - How SQL is Used

4. Fundamental SQL statements

1. CREATE

2. INSERT

3. Import data from File

4. SELECT statement

5. SELECT DISTINCT

6. WHERE

7. Logical Operators

8. UPDATE

9. DELETE

10. ALTER Part - 1

11. ALTER Part - 2

5. Restore and Back-up

1. Restore and Back-up

2. Debugging restoration issues

3. Creating DB using CSV files

4. Debugging summary and Code for CSV files.html

6. Selection commands Filtering

1. IN

2. BETWEEN

3. LIKE

7. Selection commands Ordering

1. Side Lecture Commenting in SQL

2. ORDER BY

3. LIMIT

8. Alias

1. AS

9. Aggregate Commands

1. COUNT

2. SUM

3. AVERAGE

4. MIN & MAX

10. Group By Commands

1. GROUP BY

2. HAVING

11. Conditional Statement

1. CASE WHEN

12. JOINS

1. Introduction to Joins

2. Concepts of Joining and Combining Data

3. Preparing the data

4. Inner Join

5. Left Join

6. Right Join

7. Full Outer Join

8. Cross Join

9. Intersect and Intersect ALL

10. Except

11. Union

13. Subqueries

1. Subquery in WHERE clause

2. Subquery in FROM clause

3. Subquery in SELECT clause

14. Views and Indexes

1. VIEWS

2. INDEX

15. String Functions

1. LENGTH

2. UPPER LOWER

3. REPLACE

4. TRIM, LTRIM, RTRIM

5. CONCATENATION

6. SUBSTRING

7. LIST AGGREGATION

16. Mathematical Functions

1. CEIL & FLOOR

2. RANDOM

3. SETSEED

4. ROUND

5. POWER

17. Date-Time Functions

1. CURRENT DATE & TIME

2. AGE

3. EXTRACT

18. PATTERN (STRING) MATCHING

1. PATTERN MATCHING BASICS

2. ADVANCE PATTERN MATCHING - Part 1

3. ADVANCE PATTERN MATCHING - Part 2

19. Window Functions

1. Introduction to Window functions

2. Introduction to Row number

3. Implementing Row number in SQL

4. RANK and DENSERANK

5. NTILE function

6. AVERAGE function

7. COUNT

8. SUM TOTAL

9. RUNNING TOTAL

10. LAG and LEAD

20. COALESCE function

1. COALESCE function

21. Data Type conversion functions

1. Converting Numbers Date to String

2. Converting String to Numbers Date

22. User Access Control Functions

1. User Access Control - Part 1

2. User Access Control - Part 2

23. Nail that Interview!

1. Tablespace

2. PRIMARY KEY & FOREIGN KEY

3. ACID compliance

4. Truncate

24. TABLEAU

1. Why Tableau

2. Tableau Products

25. Installing and getting started

1. Installing Tableau desktop and Public

2. About the data

3. Connecting to data

4. Live vs Extract

26. Combining data to create Data model

1. Combining data from multiple tables

2. Relationships in Tableau

3. Joins in Tableau

4. Types of Joins in Tableau

5. Union in Tableau

6. Physical Logical layer and Data models

7. The visualization screen - Sheet

27. Data categorization in Tableau

1. Types of Data - Dimensions and Measures

2. Types of Data - Discreet and Continuous

3. Changing Data type in Tableau

28. Most used charts

1. Bar charts

2. Line charts

3. Scatterplots

29. Customizing charts using Marks shelf

1. Marks cards

2. Dropping Dimensions and Measures on marks card

3. Dropping Dimensions on Line chart

4. Adding marks in scatterplot

30. Other important charts

1. Text tables, heat map and highlight tables

2. Pie charts

3. Area charts

4. Creating custom hierarchy

5. Tree map

6. Dual combination charts

7. Creating Bins

8. Histogram

31. Grouping and Filtering data

1. Grouping Data

2. Filtering data

3. Dimension filters

4. Measure filters

5. Date-Time filters

6. Filter options

7. Types of filters and order of operation

8. Customizing visual filters

9. Sorting options

32. Map charts in Tableau

1. How to make a map chart

2. Considerations before making a Map chart

3. Marks card for customizing maps

4. Customizing maps using map menu

5. Layers in a Map

6. Visual toolbar on a map

7. Custom background images

8. Territories in maps

9. Data blending for missing geocoding

33. Calculation and Analytics

1. Calculated fields in Tableau

2. Functions in Tableau

3. Table calculations theory

4. Table calculations in Tableau

5. Understanding LOD expressions

6. LOD expressions examples

7. Analytics pane

34. Sets and Parameters

1. Understanding sets in Tableau

2. Creating Sets in Tableau

3. Parameters

35. Dashboard and Story

1. Dashboard part -1

2. Dashboard part - 2

3. Story

36. Appendix

1. Connecting to SQL data source

2. Connecting to cloud storage services

37. Machine Learning with Python

1. Introduction

38. Setting up Python and Jupyter notebook

1. Installing Python and Anaconda

2. Opening Jupyter Notebook

3. Introduction to Jupyter

4. Arithmetic operators in Python Python Basics

5. Strings in Python Python Basics

6. Lists, Tuples and Directories Python Basics

7. Working with Numpy Library of Python

8. Working with Pandas Library of Python

9. Working with Seaborn Library of Python

39. Basics of statistics

1. Types of Data

2. Types of Statistics

3. Describing data Graphically

4. Measures of Centers

5. Measures of Dispersion

40. Introduction to Machine Learning

1. Introduction to Machine Learning

2. Building a Machine Learning Model

41. Data Preprocessing

1. Gathering Business Knowledge

2. Data Exploration

3. The Dataset and the Data Dictionary

4. Importing Data in Python

5. Univariate analysis and EDD

6. EDD in Python

7. Outlier Treatment

8. Outlier Treatment in Python

9. Missing Value Imputation

10. Missing Value Imputation in Python

11. Seasonality in Data

12. Bi-variate analysis and Variable transformation

13. Variable transformation and deletion in Python

14. Non-usable variables

15. Dummy variable creation Handling qualitative data

16. Dummy variable creation in Python

17. Correlation Analysis

18. Correlation Analysis in Python

42. Linear Regression

1. The Problem Statement

2. Basic Equations and Ordinary Least Squares (OLS) method

3. Assessing accuracy of predicted coefficients

4. Assessing Model Accuracy RSE and R squared

5. Simple Linear Regression in Python

6. Multiple Linear Regression

7. The F - statistic

8. Interpreting results of Categorical variables

9. Multiple Linear Regression in Python

10. Test-train split

11. Bias Variance trade-off

12. Test train split in Python

13. Regression models other than OLS

14. Subset selection techniques

15. Shrinkage methods Ridge and Lasso

16. Ridge regression and Lasso in Python

17. Heteroscedasticity

43. Introduction to the classification Models

1. Three classification models and Data set

2. Importing the data into Python

3. The problem statements

4. Why cant we use Linear Regression

44. Logistic Regression

1. Logistic Regression

2. Training a Simple Logistic Model in Python

3. Result of Simple Logistic Regression

4. Logistic with multiple predictors

5. Training multiple predictor Logistic model in Python

6. Confusion Matrix

7. Creating Confusion Matrix in Python

8. Evaluating performance of model

9. Evaluating model performance in Python

45. Linear Discriminant Analysis (LDA)

1. Linear Discriminant Analysis

2. LDA in Python

46. K Nearest neighbors classifier

1. Test-Train Split

2. Test-Train Split in Python

3. K-Nearest Neighbors classifier

4. K-Nearest Neighbors in Python Part 1

5. K-Nearest Neighbors in Python Part 2

47. Comparing results from 3 models

1. Understanding the results of classification models

2. Summary of the three models

48. Simple Decision Trees

1. Introduction to Decision trees

2. Basics of Decision Trees

3. Understanding a Regression Tree

4. The stopping criteria for controlling tree growth

5. Importing the Data set into Python

6. Missing value treatment in Python

7. Dummy Variable Creation in Python

8. Dependent- Independent Data split in Python

9. Test-Train split in Python

10. Creating Decision tree in Python

11. Evaluating model performance in Python

12. Plotting decision tree in Python

13. Pruning a tree

14. Pruning a tree in Python

49. Simple Classification Trees

1. Classification tree

2. The Data set for Classification problem

3. Classification tree in Python Preprocessing

4. Classification tree in Python Training

5. Advantages and Disadvantages of Decision Trees

50. Ensemble technique 1 - Bagging

1. Ensemble technique 1 - Bagging

2. Ensemble technique 1 - Bagging in Python

51. Ensemble technique 2 - Random Forests

1. Ensemble technique 2 - Random Forests

2. Ensemble technique 2 - Random Forests in Python

3. Using Grid Search in Python

52. Ensemble technique 3 - Boosting

1. Boosting

2. Ensemble technique 3a - Boosting in Python

3. Ensemble technique 3b - AdaBoost in Python

4. Ensemble technique 3c - XGBoost in Python

53. Introduction - Deep Learning

1. Introduction to Neural Networks and Course flow

2. Perceptron

3. Activation Functions

4. Creating Perceptron model in Python - Part 1

5. Creating Perceptron model in Python - Part 2

54. Neural Networks - Stacking cells to create network

1. Basic Terminologies

2. Gradient Descent

3. Back Propagation Part - 1

4. Back Propagation - Part 2

5. Some Important Concepts

6. Hyperparameter

55. ANN in Python

1. Keras and Tensorflow

2. Installing Tensorflow and Keras

3. Dataset for classification

4. Normalization and Test-Train split

5. Different ways to create ANN using Keras

6. Building the Neural Network using Keras

7. Compiling and Training the Neural Network model

8. Evaluating performance and Predicting using Keras

9. Building Neural Network for Regression Problem - Part 1

10. Building Neural Network for Regression Problem - Part 2

11. Building Neural Network for Regression Problem - Part 3

12. Using Functional API for complex architectures

13. Saving - Restoring Models and Using Callbacks - Part 1

14. Saving - Restoring Models and Using Callbacks - Part 2

15. Hyperparameter Tuning

56. CNN Basics

1. CNN Introduction

2. Stride

3. Padding

4. Filters and feature map

5. Channels

6. Pooling Layer

57. Creating CNN model in Python

1. CNN model in Python - Preprocessing

2. CNN model in Python - structure and Compile

3. CNN model in Python - Training and results

4. Comparison - Pooling vs Without Pooling in Python

58. Project Creating CNN model from scratch in Python

1. Project - Introduction

2. Data for the project.html

3. Project - Data Preprocessing in Python

4. Project - Training CNN model in Python

5. Project in Python - model results

59. Project Data Augmentation for avoiding overfitting

1. Project - Data Augmentation Preprocessing

2. Project - Data Augmentation Training and Results

60. Transfer Learning Basics

1. ILSVRC

2. LeNET

3. VGG16NET

4. GoogLeNet

5. Transfer Learning

6. Project - Transfer Learning - VGG16 - Part - 1

7. Project - Transfer Learning - VGG16 - Part - 2

8. Project - Transfer Learning - VGG16 - Part - 3

9. The final milestone!

61. Congratulations & about your certificate

1. Bonus Lecture.html

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