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خانه » فروشگاه » آموزش علوم درسی و دانشگاهی » آموزش Data Science » Data Science Skillpath: SQL, ML, Looker Studio & Alteryx

Data Science Skillpath: SQL, ML, Looker Studio & Alteryx

سرفصل های دوره

[4-in-1 Bundle] Covers SQL, Data viz using Google's Looker Studio, Machine Learning using Python and ETL using Alteryx


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
  • 4.1 Customer.csv
  • 4.2 Product.csv
  • 4.3 Sales.csv
  • 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 And 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 And FLOOR
  • 2. RANDOM
  • 3. SETSEED
  • 4. ROUND
  • 5. POWER

    • 17. Date-Time Functions
  • 1. CURRENT DATE And 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 And FOREIGN KEY
  • 3. ACID compliance
  • 4. Truncate

    • 24. Looker Studio
  • 1. Introduction
  • 2. Why Data Studio

    • 25. Terminologies And Theoretical concepts for Data Studio
  • 1. Data Studio Home Screen And Dataset vs Data Source
  • 2. Structure of Input data
  • 3. Dimensions vs Measures (new definition)

    • 26. Practical part begins here
  • 1. Opening Data Studio and preparing data
  • 2. Adding a data source
  • 3. Managing added data source

    • 27. Charts to highlight numbers
  • 1. Data Table
  • 2. Styling tab for data table
  • 3. Scorecards

    • 28. Charts for comparing categories Bar charts and stacked charts
  • 1. Simple Bar and Column chart
  • 2. Stacked Column chart

    • 29. Charting maps of a country, continent or a region - Geomaps
  • 1. GeoMap

    • 30. Charts to highlight trends Time series, Line and Area charts
  • 1. Time Series
  • 2. Update to Time Series chart
  • 3. Line Chart and Combo Chart

    • 31. Highlight contribution to total Pie chart And Donut Chart
  • 1. Pie Chart and Donut Chart
  • 2. Stacked Area Charts
  • 3.1 Areachart updated.csv
  • 3. Updated data for area charts.html

    • 32. Relationship between two or more variables Scatterplots
  • 1. Scatter Plots and Bubble charts

    • 33. Aggregating on two dimensions Pivot tables
  • 1. Pivot tables for cross tabulation

    • 34. All about a single Metric Bullet Chart
  • 1. Bullet Chart

    • 35. Chart for highlighting heirarchy TreeMap
  • 1. TreeMaps

    • 36. Branding a Report
  • 1. Branding a Report Brand Logo and Company Details
  • 2. Brand colors for report branding

    • 37. Giving the power to filter Data to viewers
  • 1. Filter controls for viewers

    • 38. Add Videos, Feedback form etc. to your Report
  • 1. URL Embed to include external content

    • 39. Sometimes data is in multiple tables
  • 1. Blending data from multiple tables
  • 2. Different types of Joins while blending data

    • 40. Sharing and collaborating on Data Studio report
  • 1. Downloading report as PDF and Page Management
  • 2. Sharing report and Data Credentials
  • 3. Sharing report using a link
  • 4. Scheduling emails
  • 5. Embeding report on Website

    • 41. Charting Best Practices
  • 1. Highlighting chart message
  • 2. Eliminating Distractions from the Graph
  • 3. Avoiding clutter
  • 4. Avoiding the Spaghetti plot

    • 42. Machine Learning in Python
  • 1. Introduction

    • 43. 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

    • 44. Basics of statistics
  • 1. Types of Data
  • 2. Types of Statistics
  • 3. Describing data Graphically
  • 4. Measures of Centers
  • 5. Measures of Dispersion

    • 45. Introduction to Machine Learning
  • 1. Introduction to Machine Learning
  • 2. Building a Machine Learning Model

    • 46. 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

    • 47. 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

    • 48. 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

    • 49. 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

    • 50. Linear Discriminant Analysis (LDA)
  • 1. Linear Discriminant Analysis
  • 2. LDA in Python

    • 51. 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

    • 52. Comparing results from 3 models
  • 1. Understanding the results of classification models
  • 2. Summary of the three models

    • 53. 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

    • 54. Simple Classification Tree
  • 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

    • 55. Ensemble technique 1 - Bagging
  • 1. Ensemble technique 1 - Bagging
  • 2. Ensemble technique 1 - Bagging in Python

    • 56. 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

    • 57. 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

    • 58. Alteryx
  • 1. The Problem Statement

    • 59. Case study and Alteryx Installation
  • 1. Installing Alteryx
  • 2. Alteryx Interface

    • 60. DATA EXTRACTION Extracting tabular data
  • 1. Manually entering data into Alteryx
  • 2. Importing Data from a CSV (Comma Separated Values) file
  • 3. Importing Data from a TXT (text) file
  • 4. Importing Data from an Excel file
  • 5. Importing Data from a ZIP file
  • 6. Importing Data from multiple files in a folder

    • 61. DATA EXTRACTION Extracting non-tabular data
  • 1. Probable Issue with Extraction from XML.html
  • 2.1 ProductXMLFull.csv
  • 2.2 productxmlfull.zip
  • 2.3 productxmlfull2.zip
  • 2. Extracting from XML

    • 62. Extracting from an SQL table
  • 1. Plan for importing sales Data
  • 2. Installing PostgreSQL and pgAdmin in your PC
  • 3. Creating Sales table in SQL
  • 4. Extracting from an SQL table

    • 63. Storing and Retrieving Data Cloud storage
  • 1. Storing Data on AWS S3
  • 2. Importing data from AWS S3

    • 64. Merging Data Streams
  • 1. Union tool - Merging Customer Data

    • 65. Data Cleansing and improving data quality
  • 1. Find and Replace Tool
  • 2. Data Cleaning Tool
  • 3. Autofield and Select Tool - For controlling Field order and data type

    • 66. Merging Sales and Product data
  • 1. Select and Unique Tools- For Removing duplicates from product data
  • 2. Date Parse - Changing Date format
  • 3. Select and union - Merging Sales data

    • 67. Sampling Data
  • 1. Select Records Tool
  • 2. Sample Tool
  • 3. Random Percent Sample Tool
  • 4. Train-Validation-Test Split sampling

    • 68. Data Preparation
  • 1. Multifield binning and Tile Tool - To create customer age categories
  • 2. Formula Tool - Conditional Formula for giving category titles
  • 3. Sort tool - Sorting customer Data based on ID
  • 4. Formula Tool - Sales order date And ship date
  • 5. Multifield Formula tool - Converting multiple currency fields
  • 6. Filtering and Sorting - Positive number of days
  • 7. Text to Columns - Splitting Product ID into 3 columns

    • 69. Outputting Cleaned Data
  • 1. Outputting Clean Customer And Product Data

    • 70. Merging tables to create a datamart
  • 1. The Joining Tool - Adding customer and Product data to Sales table
  • 2. Extracting more info from the Date values

    • 71. Performing Analytics Transformation on Datamart
  • 1. The Summarize tool
  • 2. Running Total Tool
  • 3. Crosstab tool for creating Pivot tables
  • 4. Transpose Tool - the opposite of Cross Tab tool
  • 5. The Count tool

    • 72. Creating a report in Alteryx
  • 1. Introduction to Reporting
  • 2. Interactive Chart tool - Bar chart to show region-wise sales
  • 3. Interactive Chart tool - Line chart to show Sales trend
  • 4. Table Tool - Formatting the Pivot table
  • 5. Text Tool - Adding static text to a report
  • 6. Visual Layout tool - Arranging charts, text and tables in a report
  • 7. Header tool - Adding header in a report
  • 8. Footer tool - Adding footer to a report
  • 9. Rendering tool - rendering report as a PDF, HTML or PNG
  • 10. Email Tool - Sending email with Alteryx
  • 11. Image tool - Adding image to a report
  • 12. Layout tool - Arranging charts, text or tables in a report

    • 73. Scheduling a workflow in Alteryx
  • 1. Schedule and Automate Alteryx workflow

    • 74. Congratulations And about your certificate
  • 1. Alternative to Alteryx.html
  • 2. The final milestone!
  • 3. Bonus Lecture.html
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