Syllabus

Learning Outline

  • 1

    Welcome to Utiva!

    • SQL Installation Kit

    • Power BI Installation Kit

    • Python Installation Kit
  • 2

    Introduction to Spreadsheet

    • Learning Outcomes

    • Learning Slides : Excel

    • Introduction

    • Introduction to Spreadsheets

    • Save Workbooks, Access recent workbooks _ Pin workbooks

    • Excel Templates

    • Tabs, Ribbons _ Groups

    • Cells, Rows, Columns _ Ranges

    • Inputting _ Formatting Data

    • Creating Basic Excel Formulas

    • Relative _ Absolute Cell References
  • 3

    Structure of an Excel Function

    • Learning Outcomes

    • Structure of an Excel Function

    • SUM() Function

    • MIN() _ MAX() Functions

    • AVERAGE() Function

    • COUNT() Function

    • Adjacent Cell Errors

    • Autosum
  • 4

    Apply simple Formats

    • Learning Outcomes

    • Applying Simple Formats

    • Conditional Formatting

    • Named Ranges and Tables
  • 5

    The IF Command

    • Learning Outcome

    • The IF() Function

    • Nested-IF()

    • IFS() Function

    • COUNTIF() Function

    • SUMIF() Function

    • IF ERROR() Function
  • 6

    LookUp, Index and Match

    • Learning Outcomes

    • VLOOKUP() Function

    • HLOOKUP() Function

    • INDEX() Function

    • MATCH() Function

    • INDEX() _ MATCH() Functions
  • 7

    Basic Excel Charts A

    • Learning Outcomes

    • Basic Excel Charts

    • Pivot Tables

    • Grouping Pivot Tables

    • Calculated Fields in Pivot Tables

    • Filtering Pivot Table Data

    • PivotCharts

    • Filtering with Slicers
  • 8

    Basic Excel Charts (B)

    • Learning Outcomes

    • Dashboard - 1

    • Dashboards - 2

    • PowerPivot

    • Activating the PowerPivot Add-in

    • Data Models with PowerPivot

    • Creating PivotTables from Data Models
  • 9

    What - IF Analysis

    • Learning Outcomes

    • What If Analysis - Goal Seek Tool

    • What If Analysis - Solver Tool

    • What If Analysis - Data Tools

    • What If Analysis - Scenario Manager
  • 10

    Data Cleansing

    • Learning Outcomes

    • Data Cleaning in Excel

    • Importing Data from External Sources - Web Example
  • 11

    Excel Capstone Project

    • Capstone Project

    • Submit your Project

    • Feedback Survey
  • 12

    LinkedIn Optimization Session [Mid-Term]

    • LinkedIn Optimisation I

    • LinkedIn Optimisation II

    • LinkedIn Optimisation III
  • 13

    Introduction to Power Bi

    • Learning Slide

    • Learning Outcome

    • Welcome to PowerBI

    • Introduction to PowerBI

    • Introduction to Formatting [Must Watch]
  • 14

    Data Preparation and Transformation

    • Learning Slide

    • Learning Outcome

    • Data Preparation and Transformation

    • Assessment
  • 15

    Data Model and Dax

    • Learning Slides

    • Learning Outcomes

    • Data Model and Dax
  • 16

    Data Visualization

    • Learning Slide

    • Learning Outcomes

    • Data Visualisation [Creating Dashboard]
  • 17

    Build Interactive Dashboards for Storytelling

    • Learning Slide

    • Learning Outcomes

    • Creating Interactive Dashboards
  • 18

    Power BI Capstone Project

    • Power BI Capstone Project

    • Submit your Project

    • Feedback Survey
  • 19

    Introduction to PostgreSQL

    • Introduction to PostgreSQL
  • 20

    Introduction to Databases

    • Learning Outcomes

    • Introduction to databases I

    • Introduction to Databases II

    • Introduction to Databases [Presentation]

    • Learning Outcomes

    • Introduction to databases III

    • Introduction to Databases [Presentation II]
  • 21

    Introduction to SQL

    • Learning Outcomes

    • PostgreSQL Installation

    • The PostgreSQL GUI I

    • The PostgreSQL GUI II

    • The PostgreSQL GUI III

    • Importing CSV Files to pgAdmin I

    • Importing CSV Files to pgAdmin II
  • 22

    Querying, Sorting, Filtering and Grouping Data

    • Learning Outcomes

    • Simple SELECT statements I

    • Simple SELECT statement II

    • Simple SELECT statement III

    • Filtering Query Results with WHERE Clause I

    • Filtering Query Results with WHERE Clause II

    • Filtering Query Results with WHERE Clause III

    • Aggregate Functions
  • 23

    Subqueries

    • Learning Outcomes

    • Subqueries in the SELECT Clause

    • Subqueries in the FROM Clause

    • Subqueries in the WHERE Clause

    • Subqueries ANY _ ALL Operators
  • 24

    CV Review Session [Mid-Term]

    • Class Recording
  • 25

    SQL Joins

    • Learning Outcomes

    • Basics of Joins

    • Inner Joins

    • Left Joins

    • Right Joins

    • Full Joins

    • Union and Union All
  • 26

    Data Cleaning

    • Learning Outcome

    • SQL Data Cleaning (String and CONCAT() Function)

    • REPLACE() Function

    • UPPER() _ LOWER() Functions

    • LENGTH() Function

    • LTRIM(), RTRIM() and BTRIM() Functions

    • SUBSTRING(), LEFT() and RIGHT() Functions
  • 27

    Date Functions, Views and Case Statement in SQL

    • Learning Outcomes

    • Date Function I

    • Date Function II

    • PostgreSQL Views

    • CASE Statements

  • 28

    Wrap Up

    • SQL Shell

    • Wrap up _ Capstone Project

    • SQL Feedback Survey
  • 29

    Introduction to Anaconda

    • Setting Course Expectation

    • Introduction to Anaconda _ Jupiter Notebook

    • Variables in Python

    • Operators in Python
  • 30

    Data Types in Python

    • Introduction to Data Types in Python

    • String Data Type II

    • List Data Type 1

    • Lists Data Type 2

    • Tuple Data Types

    • Dictionary Data Type
  • 31

    Conditions, Methods and Functions in Python

    • Sets Data Type

    • Conditions in Python

    • Methods in Python

    • Functions in Python

    • Improving Your Python Code 1

    • Improving Your Python Code 2
  • 32

    Data Processing

    • Introduction to Data Processing

    • Introduction to Data Processing II

    • Indexing and Selection
  • 33

    Data Analytics with Numpy

    • Data Analytics with Numpy 1

    • Data Analytics with Numpy 2

    • Data Analytics with Numpy 3

    • Basic Image Processing with Numpy
  • 34

    Data Analytics with Pandas

    • Introduction to Data Analytics with Pandas

    • Pandas Series 2

    • Pandas DataFrame 1

    • Pandas DataFrames 2
  • 35

    Working with Data

    • Working with Missing Data

    • Merge, Join, Group _ Concat

    • Pandas Operation

    • Data Visualization with Python
  • 36

    Feature Engineering

    • Welcome to Feature Engineering

    • Equal Width Discretization 2

    • User-defined Interval Discretization

    • Discretization With Decision Trees

    • Outliers Engineering with IQR Rule
  • 37

    Feature Scaling and Engineering

    • Outliers with Mean and STD

    • Capping Outliers at Zero

    • Feature Scaling

    • Engineering Date-Time

    • Engineering Date-Time 2
  • 38

    Feature Selection, Logistic Regression and Support Vector Machine

    • Feature Selection

    • Regression

    • Regression_Multi

    • Logistic Regression

    • Support Vector Machines
  • 39

    Decision Tree

    • Decision Tree - Classification

    • Decision Tree - Regression

  • 40

    Wine Quality Prediction

    • Wine Quality Prediction 1

    • Wine Quality Prediction 2