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2. Module 1
   1. Getting Handy with Excel
      1. Knowing Excel Part 1
      2. Knowing Excel Part 2
   2. Basics of Excel
      1. Basic Arithmetic Operations SUM, MAX, MIN, SMALL, LARGE & MEDIAN
      2. Transpose Vs TRANSPOSE Formula
      3. Using COUNTBLANK, COUNT & COUNTA
      4. Quick Calculations using Status Bar
      5. Calculating Growth Percentage YoY & Using POWER Formula
      6. Uses of ROUND, ROUNDUP, ROUNDDOWN & MDOWN in Finance
      7. Tricks for ROUND Formulas
      8. Autofilling option for Numbers, Days, Dates, Months
      9. Hide & Unhine Options Moving Data in Worksheets
   3. Formatting Options in Excel
      1. Ways to Format Cells
      2. Formatting Numbers in different ways
      3. Formatting lengthy text using Wrap Text and Alignment
      4. All about Format Painter
      5. Working with Tables
      6. Fixing Rows and Columns using Freeze Panes
      7. Grouping and Gridlines
      8. Creating drop down list
      9. Activating Developer Mode in Excel
      10. All about Comments
   4. Sort and Filter Option
      1. Sorting Numbers and Dates in Different ways
      2. Sorting text alphabetically (A-Z) or (Z-A)
      3. Sorting by Cell Colour
      4. 2 Level Sorting & Tricks for Sorting
      5. Basics of Filter by using SUM & SUBTOTAL
      6. Advanced Filter Vs General Filter
      7. Increasing productivity with slicer
      8. OFFTOPIC - How to use Cell Referencing ($) in formulas
   5. Working with Dates
      1. Dates in Excel and Calculating the duration using two dates with timestamp
      2. Using Date Formulas
      3. Use of EDATE & EMONTH
      4. Experimenting with Date Formulas
      5. Using WORKDAY.INTL and NETWORKDAYS.INTL Formulas
   6. Logical Functions
      1. Basic Logical Formulas
      2. Using multiple Logical formulas - IF, AND, OR & Implementing on Text, Numbers, & Errors including IFERROR
   7. Practical Data Cleaning
      1. Removing duplicate values and error values
      2. Using Data Cleaning Formulas UPPER, PROPER, LOWER, TRIM, VALUE, LEN, RIGHT, LEFT & MID
      3. SEARCH vs FIND and SEARCH & MID
      4. Using Go To (Special) technique
      5. Splitting data values Using Text to Column feature
      6. Text to Column feature
      7. Using CONCATENATE and Ampersand (&) to join values
      8. Using Find & Replace feature
   8. Pivot Table A-2-Z
      1. Getting started with Pivot Table
      2. Grouping and Multiple tables in Pivot Table
      3. Pivot Table for grouping Via Date, Blank Cells
      4. Custom Grouping, Conditional Formatting in Pivot Table
      5. Refresh Data, Sheets Via Division, ColorScale in Pivot Table
   9. Everything about LOOKUp Formulas
      1. Basics of VLOOKUp
      2. More about VLOOKUp
      3. Vlookup vs. Hlookup
      4. Using VLOOKUp with TRUE
      5. Basics of MATCH formula
      6. 2D Lookup using VLOOKUP & MATCH
      7. Inter-worksheet 2D Lookup using VLOOKUP & MATCH
      8. LOOKUP vs. VLOOKUP vs. HLOOKUP vs. MATCH
      9. Reverse Lookup using INDEX & MATCH
      10. Fuzzy Lookup
      11. Using VLOOKUP & INDIRECT formula
      12. 3D Lookup using VLOOKUP, MATCH, INDIRECT & name ranges?
      13. Using OFFSET formula for Dynamic Ranges
   10. Conditional Aggregation
       1. Basics of COUNTIF, SUMIF, AVERAGEIF formula
       2. COUNTIFS & SUMIFS
       3. Exercises for SUMIFS, COUNTIFS & VLOOKUP
   11. Conditional Formatting
       1. Conditional Formatting - Color cells using conditions
       2. Formula-based Conditional Formatting
   12. Analysis using What-If
       1. Basics of Data Table feature of What-If Analysis
       2. What-If Analysis with INDIRECT formula
       3. More about What-If Analysis
   13. Report Consolidation
       1. Hidden trick of Consolidation using SUM for multiple sheets
       2. Using CONSOLIDATE feature of Excel
       3. Using SUBTOTAL feature of Excel using one criterion
   14. Basics of Macros
       1. What is VBA Macros?
       2. How to enable Developer Tab in Excel?
       3. Creating, Running and Saving a Macro
       4. Running a Macro in Different Ways
       5. Understanding VBA Workspace and Recoring the Macro
       6. How to get VBA Codes?
3. Module 2
   1. Lesson 2 - Data Analytics Overview
      1. 2.01 Introduction
      2. 2.02 Data Analytics - Importance
      3. 2.03 Digital Analytics: Impact on Accounting
      4. 2.04 Data Analytics Overview
      5. 2.05 Types of Data Analytics
      6. 2.06 Descriptive Analytics
      7. 2.07 Diagnostic Analytics
      8. 2.08 Predictive Analytics
      9. 2.09 Prescriptive Analytics
      10. 2.10 Data Analytics - Amazon Example
      11. 2.11 Data Analytics Benefits Decision-Making
      12. 2.12 Data Analytics Benefits: Cost Reduction
      13. 2.13 Data Analytics Benefits: Amazon Example
      14. 2.14 Data Analytics: Other Benefits
      15. 2.15 Key Takeaways
   2. Lesson 3 - Dealing with Different Types of Data
      1. 3.1 Introduction
      2. 3.2 Terminologies in Data Analytics - Part One
      3. 3.3 Terminologies in Data Analytics - Part Two
      4. 3.4 Types of Data
      5. 3.5 Qualitative and Quantitative Data
      6. 3.6 Data Levels of Measurement
      7. 3.7 Normal Distribution of Data
      8. 3.8 Statistical Parameters
      9. 3.9 Key Takeaways
   3. Lesson 4 - Data Visualization for Decision making
      1. 4.1 Introduction
      2. 4.2 Data Visualization
      3. 4.3 Understanding Data Visualization
      4. 4.4 Commonly Used Visualizations
      5. 4.5 Frequency Distribution Plot
      6. 4.6 Swarm Plot
      7. 4.7 Importance of Data Visualization
      8. 4.8 Data Visualization Tools - Part One
      9. 4.9 Data Visualization Tools - Part Two
      10. 4.10 Languages and Libraries in Data Visualization
      11. 4.11 Dashboard Based Visualization
      12. 4.12 BI and Visualization Trends
      13. 4.13 BI Software Challenges
      14. 4.14 Key Takeaways
   4. Lesson 5 - Data Science, Data Analytics, and Machine Learning
      1. 5.01 Introduction
      2. 5.02 The Data Science Domain
      3. 5.03 Data Science, Data Analytics, and Machine Learning - Overlaps
      4. 5.04 Data Science Demystified
      5. 5.05 Data Science and Business Strategy
      6. 5.06 Successful Companies Using Data Science
      7. 5.09 E-commerce and Crime agencies
      8. 5.10 Analytical Platforms across Industries
      9. 5.11 Key Takeaways
      10. 5.7 Travel Industry
      11. 5.8 Retail
   5. Lesson 6 - Data Science Methodology
      1. 6.01 Introduction
      2. 6.02 Data Science Methodology
      3. 6.03 From Business Understanding to Analytic Approach
      4. 6.04 From Requirements to Collection
      5. 6.05 From Understanding to Preparation
      6. 6.06 From Modeling to Evaluation
      7. 6.07 From Deployment to Feedback
      8. 6.08 Key Takeaways
   6. Lesson 7 - Data Analytics in Different Sectors
      1. 7.01 Introduction
      2. 7.02 Analytics for Products or Services
      3. 7.03 How Google Uses Analytics
      4. 7.05 How Amazon Uses Analytics
      5. 7.08 Media and Entertainment Industry
      6. 7.09 Education Industry
      7. 7.10 Healthcare Industry
      8. 7.11 Government
      9. 7.12 Weather Forecasting
      10. 7.13 Key Takeaways
      11. 7.04 How LinkedIn Uses Analytics
      12. 7.06 Netflix- Using Analytics to Drive Engagement
      13. 7.07 Netflix- Using Analytics to Drive Success
   7. Lesson 8 - Analytics Framework and Latest trends
      1. 8.1 Introduction
      2. 8.2 Case Study: EY
      3. 8.3 Customer Analytics Framework
      4. 8.4 Data Understanding
      5. 8.5 Data Preparation
      6. 8.6 Modeling
      7. 8.7 Model Monitoring
      8. 8.8 Latest Trends in Data Analytics
      9. 8.9 Graph Analytics
      10. 8.10 Automated Machine Learning
      11. 8.11 Open Source AI
      12. 8.12 Key Takeaways
4. Module 3
   1. Course Introduction
      1. Python can be used for Finance?
      2. Introduction to Google Colab
      3. Worksheet 1
      4. Worksheet 1 Solutions
   2. Python Fundamentals
      1. Tokens
      2. Variable & Assignments
      3. Worksheet 2.1
      4. Worksheet 2.1 Solutions
      5. Worksheet 2.2
      6. Worksheet 2.2 Solutions
   3. Data Handling
      1. Data Types
      2. Operators
      3. Mutable & Immutable Data
      4. Expressions
      5. Working with Math Modules
      6. Using Python for basic TVM Problems
      7. Worksheet 3.1
      8. Worksheet 3.1 Solutions
      9. Worksheet 3.2
      10. Worksheet 3.2 Solutions
      11. Worksheet 3.3
      12. Worksheet 3.3 Solutions
   4. Flow of Control
      1. Types of Statements in Python
      2. Statement Flow Control
      3. If Statement & If-Else Statement
      4. Elif Statement & Nested If Statement
      5. The Range() Function
      6. Iteration/Looping Statements
      7. Worksheet 4.1
      8. Worksheet 4.1 Solutions
      9. Worksheet 4.2
      10. Worksheet 4.2 Solutions
      11. Worksheet 4.3
      12. Worksheet 4.3 Solutions
   5. List Manipulation
      1. Creating and Accessing Lists
      2. List Operations
      3. Making True Copy of the List
      4. List Functions and Methods
      5. Worksheet 5.1
      6. Worksheet 5.1 Solutions
      7. Worksheet 5.2
      8. Worksheet 5.2 Solutions
      9. Worksheet 5.3
      10. Worksheet 5.3 Solutions
   6. Dictionaries
      1. Introduction Dictionary – Key: Value Pair
      2. Working with Dictionaries
      3. Dictionary Functions and Methods
      4. Worksheet 6.1
      5. Worksheet 6.1 Solutions
      6. Worksheet 6.2
      7. Worksheet 6.2 Solutions
      8. Worksheet 6.3
      9. Worksheet 6.3 Solutions
   7. Functions
      1. Introduction to Functions
      2. Building Functions with Python
      3. Worksheet 7.1
      4. Worksheet 7.1 Solutions
      5. Worksheet 7.2
      6. Worksheet 7.2 Solutions
      7. Worksheet 7.3
      8. Worksheet 7.3 Solutions
   8. Working with Files
      1. Working with a Text File
      2. Creating and Renaming
      3. Working with other files using Pandas
5. Module 4
   1. Numpy Crash Course
      1. Why Numpy? Numpy Array VS Python List
      2. How to Use Numpy
      3. Basic Operations in Numpy
      4. Numpy Arrays: Boolean Indexing
      5. Generating Random Numbers
      6. Performance Issues
      7. Statistics with Numpy
      8. Numpy 2D Array
   2. n Dimensional Numpy Arrays
      1. How to work with nested Lists
      2. 2-dimensional Numpy Arrays
      3. Slicing 2-dim Numpy Arrays
      4. performing row-wise and column-wise Operations on n-dimensional arrays
      5. Reshaping and Transposing 2-dim Numpy Arrays
      6. Creating 2-dim Numpy Arrays from Scratch
      7. Arithmetic & Vectorized Operations with 2-dim Numpy Arrays
      8. The keepdims parameter
      9. Adding & Removing Elements
      10. Merging and Concatenating Numpy Arrays
   3. Importing Data - Pandas
      1. Importing csv-files with pd.read_csv
      2. Importing messy csv-files with pd.read_csv
      3. Importing Data from Excel with pd.read_excel()
      4. Importing messy Data from Excel with pd.read_excel()
      5. Importing Data from the Web with pd.read_html()
   4. Cleaning Data
      1. First Inspection & Handling of inconsistent Data
      2. String Operations
      3. Changing Datatype of Columns with astype()
      4. Intro NA values / missing values
      5. Detection of missing Values
      6. Removing missing values
      7. Replacing missing values
      8. Intro Duplicates
      9. Detection of Duplicates
      10. Handling / Removing Duplicates
      11. Detection of Outliers
      12. Handling / Removing Outliers
      13. Categorical Data
   5. Merging, Joining, and Concatenating Data
      1. Adding Rows with append() and pd.concat()
      2. Arithmetic with Pandas Objects / Data Alignment
      3. Inner & Outer Joins with merge()
      4. Outer Joins (without Intersection) with merge()
      5. Left & Right Joins (without Intersection) with merge()
      6. Joining on different Column Names / Indexes
      7. Joining on more than one Column
      8. pd.merge() and join()
   6. GroupBy Operations
      1. Understanding the GroupBy Object
      2. Splitting with many Keys
      3. split-apply-combine explained
      4. split-apply-combine applied
      5. Advanced aggregation with agg()
      6. GroupBy Aggregation with Relabeling (NEW - Pandas Version 0.25)
      7. Transformation with transform()
      8. Replacing NA Values by group-specific Values
      9. Generalizing split-apply-combine with apply()
      10. Hierarchical Indexing with Groupby
      11. stack() and unstack()
   7. Reshaping and Pivoting DataFrames
      1. Transposing Rows and Columns
      2. Pivoting DataFrames with pivot()
      3. Limits of pivot()
      4. pivot_table()
      5. pd.crosstab()
      6. melting DataFrames with melt()
   8. Data Preparation and Feature Creation
      1. Arithmetic Operations
      2. Transformation/Mapping with map()
      3. Conditional Transformation
      4. Discretization and Binning with pd.cut()
      5. Discretization and Binning with pd.qcut()
      6. Scaling / Standardization
      7. Creating Dummy Variables
      8. String Operations
   9. Time Series in Pandas
      1. Importing Time Series Data from csv-files
      2. Converting strings to datetime objects with pd.to_datetime()
      3. Initial Analysis / Visualization of Time Series
      4. Indexing and Slicing Time Series
      5. Creating a customized DatetimeIndex with pd.date_range()
      6. More on pd.date_range()
      7. Downsampling Time Series with resample()
      8. The PeriodIndex object
      9. Advanced Indexing with reindex()
   10. Importing Financial Data from Yahoo Finance
       1. Getting Data of TESLA Stock by YFinance
       2. Customising the Stock Data by YFinance
       3. Stock Split and Dividends by YFinance
       4. Exporting to CSV/ Excel File by YFinance
       5. Importing multiples stocks and Financial Indexes Data by YFinance
       6. Importing Currency Exchange & CryptoCurrency Data by YFinance
       7. Importing ETFs and MF Data by YFinance
       8. Stock Fundamentals, Meta Info and Performance Metrics by YFinance
       9. Financials (Balancesheet, Cashflows, P&L) by YFinance
       10. Put and Call Options by YFinance
       11. Stream Real Time data from YFinance
6. Module 5
   1. Exploratory Data Analysis
      1. 2D scatter plot
      2. 3D scatter plot
      3. Pair plots
      4. Limitations of Pair Plots
      5. Histogram and Introduction to PDF(Probability Density Function)
      6. Univariate Analysis using PDF
      7. CDF(Cumulative Distribution Function)
      8. Mean, Variance and Standard Deviation
      9. Median
      10. Percentiles and Quantiles
      11. IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)
      12. Box-plot with Whiskers
      13. Violin Plots
      14. Summarizing Plots, Univariate, Bivariate and Multivariate analysis
      15. Multivariate Probability Density, Contour Plot
      16. EDA Assignment
   2. Linear Algebra
      1. Why learn it ?
      2. Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector
      3. Dot Product and Angle between 2 Vectors
      4. Projection and Unit Vector
      5. Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane
      6. Distance of a point from a Plane/Hyperplane, Half-Spaces
      7. Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D)
      8. Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)
      9. Square ,Rectangle
      10. Hyper Cube,Hyper Cuboid
   3. Probability and Statistics
      1. Introduction to Probability and Statistics
      2. Population and Sample
      3. Gaussian/Normal Distribution and its PDF(Probability Density Function)
      4. CDF(Cumulative Distribution function) of Gaussian/Normal distribution
      5. Symmetric distribution, Skewness and Kurtosis
      6. Standard normal variate (Z) and standardization
      7. Kernel density estimation
      8. Sampling distribution & Central Limit theorem
      9. Q-Q plot:How to test if a random variable is normally distributed or not?
      10. How distributions are used?
      11. Chebyshev’s inequality
      12. Discrete and Continuous Uniform distributions
      13. How to randomly sample data points (Uniform Distribution)
      14. Bernoulli and Binomial Distribution
      15. Log Normal Distribution
      16. Power law distribution
      17. Box cox transform
      18. Applications of non-gaussian distributions?
      19. Co-variance
      20. Pearson Correlation Coefficient
      21. Spearman Rank Correlation Coefficient
      22. Correlation vs Causation
      23. How to use correlations?
      24. Confidence interval (C.I) Introduction
      25. Computing confidence interval given the underlying distribution
      26. C.I for mean of a random variable
      27. Confidence interval using bootstrapping
      28. Hypothesis testing methodology, Null-hypothesis, p-value
      29. Hypothesis Testing Intution with coin toss example
      30. Resampling and permutation test
      31. K-S Test for similarity of two distributions
      32. Code Snippet K-S Test
      33. Hypothesis testing: another example
      34. Resampling and Permutation test: another example
      35. How to use hypothesis testing?
      36. Proportional Sampling
7. Module 6
   1. Dimensionality reduction and Visualization:
      1. What is Dimensionality reduction?
      2. Row Vector and Column Vector
      3. How to represent a data set?
      4. Data Preprocessing: Feature Normalisation
      5. Data Preprocessing: Column Standardization
      6. Co-variance of a Data Matrix
      7. MNIST dataset (784 dimensional)
   2. PCA(principal component analysis)
      1. Why learn PCA?
      2. Geometric intuition of PCA
      3. Mathematical objective function of PCA
      4. Alternative formulation of PCA: Distance minimization
      5. Eigen values and Eigen vectors (PCA): Dimensionality reduction
      6. PCA for Dimensionality Reduction and Visualization
   3. Visualize MNIST dataset
      1. Limitations of PCA
      2. PCA Code example
      3. PCA for dimensionality reduction (not-visualization)
      4. (t-SNE)T-distributed Stochastic Neighbourhood Embedding
      5. What is t-SNE?
      6. Neighborhood of a point, Embedding
      7. Geometric intuition of t-SNE
      8. Crowding Problem
      9. How to apply t-SNE and interpret its output
      10. t-SNE on MNIST
8. Module 7
   1. History And Story Of Data8
      1. What Is a Database?
      2. I Didn't Learn Anything, Try Again...
      3. Database Management System (DBMS)
      4. Exercise: Building Amazon
      5. Exercise: Building Amazon 2
      6. 5 Types Of Databases
      7. Exercise: What Is A Database?
   2. Databases + SQL Fundamentals
      1. SQL Playground
      2. What Is SQL?
      3. What Is A Query?
      4. Exercise: Setting Up Your First Database
      5. Imperative vs Declarative
      6. History of SQL
      7. Optional: History of SQL Deep Dive
      8. Exercises: The Select Statement
      9. SQL Standards
      10. What Is A Database? Revisited
      11. Database Oriented Approach
      12. Exercise: SQL Starter Quiz
      13. Database Models
      14. Hierarchical And Networking Model
      15. Relational Model
      16. DBMS Revisited
      17. Relational Model Revisited
      18. Tables
      19. Columns
      20. Rows
      21. Primary And Foreign Keys
      22. OLTP vs OLAP
      23. Exercise: OLTP vs OLAP
      24. Exercise: Relational Model Quiz
      25. Endorsements On LinkedIN
      26. Environment Setup
      27. Why PostgreSQL
      28. Environment Tooling
      29. Having Trouble Registering A Serial Key For ValentinaDB?
      30. SQL Tooling Alternatives
      31. Command Line 101
      32. Getting Help With The Setup
      33. WINDOWS Setup
      34. Optional: Setting Up Windows For Command Line
      35. MAC Setup
      36. MAC Commandline tools
      37. LINUX Setup
      38. Importing The Databases
      39. Exercise: Imposter Syndrome
   3. SQL Deep Dive
      1. Query Along
      2. Starting With Query
      3. Exercise: Simple Queries
      4. Changing Column Names in a SELECT Query
      5. Concat Function
      6. What Is A Function In SQL?
      7. Aggregate Functions
      8. Exercise: Aggregate Functions
      9. Commenting Your Queries
      10. Common SELECT Mistakes
      11. Filtering Data
      12. AND and OR
      13. Exercise: Filtering Data
      14. The NOT Keyword
      15. Exercise: The Where Clause
      16. Comparison Operators
      17. Exercise: Comparison Operators
      18. Logical Operators
      19. Operator Precedence
      20. Operator Precedence 2
      21. Operator Precedence Extra
      22. Exercise: Operator Precedence
      23. Checking For NULL Values
      24. IS Keyword
      25. NULL Coalescing
      26. Exercise: Null Value Coalescing
      27. 3 Valued Logic
      28. Exercise: 3 Valued Logic
      29. BETWEEN + AND
      30. Exercise: BETWEEN + AND
      31. IN Keyword
      32. Exercise: IN Keyword
      33. LIKE
      34. Exercise: Like Keyword
      35. Dates And Timezones
      36. Setting Up Timezones
      37. How Do We Format Date And Time?
      38. Timestamps
      39. Date Functions
      40. Date Difference And Casting
      41. Age Calculation
      42. Extracting Information
      43. Intervals
      44. Exercise: Date and Timestamp
      45. DISTINCT
      46. Exercise: Distinct Keyword
      47. Sorting Data
      48. Exercise Sorting Data
      49. Multi Table SELECT
      50. Inner Join
      51. Self Join
      52. Outer Join
      53. Less Common Joins
      54. Inner-Join Exercises
      55. USING Keyword
   4. Advanced SQL
      1. GROUP BY
      2. Group By Exercises
      3. HAVING Keyword
      4. Having Exercises
      5. Ordering Grouped Data
      6. Group By Mental Model
      7. Grouping Sets
      8. Rollup
      9. Window What?
      10. Looking Through The Window
      11. PARTITION BY
      12. Order By Acting Strange
      13. Using Framing In Window Function
      14. Solving For Current Salary
      15. FIRST_VALUE
      16. LAST_VALUE
      17. SUM
      18. ROW_NUMBER
      19. Window Function Exercises
      20. Conditional Statements
      21. Conditional Statement Exercise
      22. NULLIF
      23. NULLIF Exercise
      24. Views...What Are They Good For?
      25. View Syntax
      26. Using Views
      27. Views Exercises
      28. Indexes
      29. Index Types
      30. Index Algorithms
      31. Quick Note On Index Algorithms
      32. What Are Subqueries?
      33. Subqueries vs Joins
      34. Subquery Guidelines As Types
      35. Using Subqueries
      36. Quick Note: Titles For Employees
      37. Getting The Latest Salaries
      38. Subquery Operators
      39. Subquery Exercises
   5. Database Management
      1. Before We Get Started
      2. Time To Create Some Stuff!
      3. Types Of Databases In A RDBMS
      4. Default PostgreSQL Database
      5. Template Databases
      6. Creating A Database
      7. Database Organization
      8. Roles In Postgres
      9. Role Attributes And Creation
      10. Creating Users And Configuring Login
      11. Privileges
      12. Granting Privileges and Role Management
      13. Best Practices For Role Management
      14. Data Types & Boolean Type
      15. Storing Text
      16. Storing Numbers
      17. Storing Arrays
      18. Data Models And Naming Conventions
      19. CREATE TABLE
      20. Extra information on CREATE TABLE
      21. Column Constraints
      22. Table Constraints
      23. Regexes!
      24. UUID Explained
      25. Custom Data Types And Domains
      26. Creating The Tables For ZTM
      27. Extra information on ALTER TABLE
      28. Adding Students And Teachers
      29. Creating A Course
      30. Adding Feedback To A Course
      31. A Tale Of 2 Feedbacks
      32. SQL Exercises
      33. SQL Quiz
      34. Backups And Why They Are Important
      35. Backing Up In Postgres
      36. Restoring A Database
      37. Transactions
   6. Database Design
      1. System Design And SDLC
      2. SDLC Phases
      3. System Design Deep Dive
      4. Top-Down vs Bottom-Up
      5. DRIVEME Academy
      6. Top Down Design
      7. ER Model
      8. Step 1: Determining Entities
      9. Tooling For Diagramming
      10. DRIVEME Academy Entities
      11. Step 2: Attributes
      12. Relational Model Extended
      13. Relational Schema And Instance
      14. Super Key and Candidate Key
      15. Primary Key and Foreign Key
      16. Compound Composite And Surrogate Key
      17. DRIVEME Attributes
      18. Step 3: Relationships
      19. DRIVEME Relationships
      20. Step 4: Solving Many To Many
      21. Step 5: Subject Areas
      22. DRIVEME Subject Areas
      23. Exercise: Painting Reservations
      24. Exercise: Movie Theatre
      25. Bottom Up Design
      26. Anomalies
      27. Normalization
      28. Functional Dependencies
      29. Functional Dependencies 2
      30. The Normal Forms
      31. Going from 0NF to 1NF
      32. Going from 1NF to 2NF
      33. Going from 2NF to 3NF
      34. Boyce-Codd Normal Form
      35. Why 4NF And 5NF Are Not Useful
      36. Exercise: Database Design Quiz
   7. Database Landscape, Performance and Security
      1. Scalability
      2. Sharding
      3. Replication
      4. Backups
      5. Distributed vs Centralized Databases
      6. Database Security
      7. Exercise: SQL Injection
      8. Optional: All About Injections Attacks
      9. Optional: Storing Passwords
      10. Optional: How To Store Passwords
      11. Relational vs NoSQL, PostgreSQL vs MongoDB Databases
      12. Future Of Relational Databases
      13. Elasticsearch
      14. S3 Object Storage
      15. Top Databases To Use
9. Module 8
   1. Overview of Tableau
      1. Tableau: An Introduction
      2. Overview of Tableau's Products
      3. Tableau Desktop installation (free trial)
      4. Before we begin, let's do a quick setup check
   2. Tableau Starter Guide
      1. How to get Data?
      2. How to create a datasource in Google Sheets
      3. Getting friendly with Tableau and connecting Data
      4. Basic Data Operations
      5. Dimensions Vs Measures
      6. First Visualization - Building Simple Bar Chart
      7. Understanding Discrete and Continuous using Line Charts
      8. How Show me can Help?
      9. Bar Charts - Formatting using Tableau
      10. Line Charts - Formatting using Tableau
      11. Area Charts - Formatting using Tableau
      12. Pie Charts - Formatting using Tableau
      13. Table Calculations
      14. Buidling our First Dashboard
      15. Exporting the Dashboard
      16. Publishing the dashboard on Tableau Public
   3. Live Connections, Data Extracts
      1. Data Connection Examples Oracle Server
      2. Automatic and Custom Splitting
      3. The Impact of Changing Data Types Text to Numbers
      4. More Pre-Filtering Options
   4. Logic (Boolean) and Numerical Formulas
      1. Aggregates and Different Ways to Create Them
      2. Foundational If Functions The Big Mac Example
      3. More IF Functions Null, IsNull (New Excel Download)
      4. If Functions using AND and OR Functions
      5. Rounding Formulas Round and Integer
      6. The Absolute Function
   5. Text and Date Formulas
      1. Left, Right and Mid Text Functions
      2. The FIND and LEFT Function Combination
      3. Trimming Off Trailing Spaces
      4. The Romanian Concatenate Function
      5. How to Easily Convert to Upper or Lower Case Characters
      6. Using the Replace Function
      7. Extracting Year, Month and Day
      8. Calculating the Difference between Two Dates
      9. Today() vs Now()
      10. How to Create your own Date Column
   6. Quick Table Calc's, Filtering, Legends and Mapping
      1. Quick Table Calculations: Using Directions in a Table
      2. Quick Table Calculations: The Calculation Assistant
      3. Quick Table Calculations: Refresher and Bonus Items
      4. Before we do Filters and Legends, Let's Clean Up Shall We
      5. Filtering Top and Bottom Method, Legends and Filtering Types
      6. Mapping Features Multi-Map Views, Dual Axis Plots with Pie Charts, Keep On
      7. Annotation Features Marks and Points
   7. Scatter, Analytics Menu, Forecast, Trends, Clusters
      1. Scatter Plots Colouring, Level of Detail and Shapes
      2. Introduction to Scatter Plots and Parameters
      3. Trendlines and the Different Types
      4. Analytics Menu Aggregate Options with Table, Pane and Cell Variations
      5. Forecast Modelling (DISCLAIMER)
      6. Clustering Method
   8. Exports, Dashboard Interactions, Dashboard Design
      1. Fresh Data Set and Hierarchies on the Fly
      2. Keep Only and Exclude Features Plus Export Methods
      3. Additional Keep Only and Exclude Methods + CSV Export
      4. 3 Methods for Sharing and Exporting your File
10. Module 9
    1. Section 1 - Unions, Joins and Blending
       1. Wildcard Union for Multiple Sheets and Multiple Documents
       2. Unioning Sheets from Excel - WORKAROUND Method
       3. Extracting Information from the Path or Sheet Data Fields
       4. Tableau 2020 - Joins and Blends Explained
       5. Basic Join Theory - One to One (PDF Cheat Sheet)
       6. Many to Many Joins
       7. Your First Single Join - The Ambassadors
       8. More Single Join Examples + Excel Comparison Example
       9. CHALLENGE - Multiple Single-Joins (Spiderweb Design)
       10. Multi-Joins x2 Examples
       11. Joining Non-Excel Files Together (CSV, Excel, Access)
       12. Joining Before or In Tableau
       13. Data Blending
    2. Section 2 - Parameters and Sets
       1. Reference Line Basics
       2. Controlling the Range of Values
       3. Top 10 Parameter Example and Scatter Plots
       4. Multiple Parameters and Analysis on the Fly
       5. Using Parameters to Change the Visualization Field
       6. Parameter Data Type Options
       7. Introduction to Sets Creating and Overlapping
       8. Sets Analysis - Looking at Overlaps using Condition Sets
       9. Performing IF Functions on Sets
    3. Section 3 - Level of Detail (LOD) Calculations
       1. Level of Detail - Fixed
       2. Level of Detail - Exclude
       3. Level of Detail - Include
    4. Section 4 - New Visualizations
       1. Histograms - Details and Quick Method
       2. Funnel Graphs
       3. Gantt Charts - Multiple Methods (Attach Data)
       4. Bump, Bump, Bump
       5. Donut Charts...mmmmmm Donut
       6. Packed Bubbles
       7. Hierarchy and Tree Maps
       8. Maps on a Scatter Plot
       9. Dot Time and Jitter Plot
       10. Waterfall Chart
       11. Calendar and Large Heat Maps
       12. Moving Average Dual Axis
       13. Mapping Paths - Simple and Complex
       14. London Pathways
       15. A Simple Mapping Dashboard (Bonus Video)
    5. Section 5 - Dashboard Actions and Animation (Pages)
       1. Introduction to Actions - Hover, Select, Menu
       2. Single Select Function and Deselect Bahaviour
       3. Target Filters and Actions
       4. Highlight, URL and Go To Sheets
       5. Actions to Change Parameters
       6. Actions to Control Sets
       7. Ignore Actions
       8. The Power of Animation in Telling Stories
       9. Introduction to Animation using Pages
       10. Pages - Dashboard and Synchronization
    6. Section 6 - Mobile, Dashboard Best Practices and Story Creation
       1. Tableau Mobile: iPads, iPhone, Tablets and Android
       2. Mobile and Tablet View Design
       3. Who are you building for and what are you telling them
       4. Dashboards - The Good, The Bad and The Ugly
       5. Fail Fast Development
       6. Story Mode Creation
       7. Story Mode Creation Tips and Advice
11. Module 10
    1. Section 1 - Tableau Online - Site Creation, First Published Dashboard and Cloud Features
       1. What is Tableau Online and How Much
       2. Site Creation
       3. Dashboard Design and Publication
       4. Collaboration 1: Favorites, Project Details, Custom Views and Subscriptions
       5. Collaboration 2: Web Editing, Comments, Share View, Embed and Alerts
       6. Collaboration 3: Revert, Refresh, Data Details, Downloads and Mobile/Tablet
    2. Section 2 - Tableau Online - Site/Server Administration
       1. Tableau Online vs. On-Premises vs. Cloud
       2. User Management - Add Users, CSV Import and Site Roles
       3. Site Roles
       4. License Management and Purchasing
       5. Data Governance - Principles
       6. Data Governance - Dashboard Views and Site Performance
       7. Published Data Sources Part 1
       8. Published Data Sources Part 2
       9. Permission Setting at a Dashboard and Project Level
       10. "Ask Data" Natural Language Processing (NLP)
       11. Tableau Bridge
       12. Tableau "Explain Data" (In Production)
       13. Tableau Conductor (In Production)
    3. Section 3 - Tableau Prep - Part 1 - Data Preparation and ETL (Extract, Transform, Load) Tool
       1. Tableau Prep
       2. Installation, Concept and Pricing
       3. Raw Data Sets Explained - The Dirty Super Store
       4. Loading your Data - Menu Explained
       5. Cleaning Step - Interface Explained
       6. Renaming Fields, Keep Only, Exclude, Data Types
       7. Branches, Multiple Outputs, Outputting your Results - CSV, Hyper, Preview
       8. Remapping a File to a New Location
       9. Data Interpreter and NotePad Plus
       10. Filtering Numbers and Strings
       11. Grouping and Replacing
       12. Ungrouping
       13. Cleaning Text - Spaces, Letters, Numbers, Punct, REPLACE
       14. Automatic and Custom Splitting
       15. Replace with Null
       16. Using Aggregates
       17. Unpivotting
    4. Section 4 - Tableau Prep - Part 2 - Data Preparation and ETL (Extract, Transform, Load) Tool
       1. Introduction to Unions
       2. Wildcard Unions
       3. Join Types Explained and PDF Download
       4. Single Join Example and your First Connection
       5. Single Join - More Examples and Rebuilding Joins
       6. Multi-Joins and Troubleshooting
       7. If functions, IsNull, Not Null, IfNull, AND/OR
       8. Rounding Decimal Places, Integer Function, Absolute
       9. Left, Right, Mid and Concatenate
       10. Find and Replace
       11. Date Functions - Year, Month, Day, Now, Today
       12. Assignments are on the Way! (In Production)
    5. Section 5 - Building Dashboards using Real World Data
       1. Zomato Dashboard
       2. Sales Dashboard
       3. HR Dashboard
       4. Supply Chain Dashboard
       5. Covid 19 Dashboard
12. Module 11
    1. Section 1 - Intoduction
       1. What is a Data Communication?
       2. Communicating with Data
    2. Section 2 - Good and Bad Data Vizualisations
       1. What Makes an Effective Data Communication?
       2. Effective Communication Examples
    3. Section 3 - Visual Design and Communicating Visually
       1. Visual Perception - Order
       2. Visual Perception - Hierarchy
       3. Visual Perception - Clarity
       4. Visual perception - Relationships
       5. Visual Perception - Convention
       6. Visual Design and the Application to Data Graphs
    4. Section 4 - The Right Graph for the Right Data
       1. Components of a Data Visualisation
       2. Different Types of Graphs
       3. Deadly Sins of Graph Design
       4. How to Avoid Being Mislead with Graphs
    5. Section 5 - Designing Your Graph to Tell a Story
       1. Create a Clear Graph
       2. Bringing Out the Story with Colour and Formatting
       3. Section 6 - Craft an Inspiring Narrative
       4. Analytics Value Chain
       5. Uncovering the Context
       6. BONUS: Anecdote - lessons from work: Learning the Role Context Plays
       7. Fundamental Data Narratives - with TEMPLATES
       8. Turning your Graph into a Story
       9. Case Studies
13. Module 12
    1. Section 1 - Getting Started
       1. Why Performance Matters
       2. Dashboard Types
       3. Key Performance Questions
       4. Internal vs. External Factors
       5. VizQL & Query Processing
       6. Performance Recording
       7. Interpreting Performance Results
       8. Event Types
       9. Workbook Performance Factors
    2. Section 2 - Data Design
       1. Intro to Data Design
       2. Data Modeling Options
       3. Live vs. Extract Connections
       4. Live Connection Optimization
       5. Extract Connection Optimization
       6. Data Source Options
       7. Performance Impact | Data Design
    3. Section 3 - Filter Optimization
       1. Intro to Filter Optimization
       2. Filtering Order of Operations
       3. Filter Optimization Tips
       4. Filter Options
       5. Date Filtering
       6. Filter Actions
       7. Parameter & Set Actions
       8. Performance Impact | Filter Optimization
    4. Section 4 - Calculation Optimization
       1. Intro to Calculation Optimization
       2. Calculation Types
       3. Aggregation
       4. Level of Detail Expressions
       5. Table Calculations
       6. Conditional Calculations
       7. Calculated Groups
       8. Common Pain Points
       9. Performance Review | Calculation Optimization
    5. Section 5 - Layout & Visuals
       1. Intro to Layout & Visual Optimization
       2. Mark & Sheet Counts
       3. Mapping Options
       4. Tooltips
       5. Pages
       6. Dashboard Properties
       7. Dashboard Objects
       8. User Experience Tips
       9. Performance Impact | Layout & Visuals
    6. Section 6 - Tableau Server & Online
       1. Intro to Server & Online
       2. Embedded vs. Published Sources
       3. Dashboard Caching
       4. Cache Warming
       5. Client vs. Server-Side Rendering
14. Book Now