Data Analytics And Business Intelligence - Why And How?

LEARN HOW TO READ,CLEAN,ANALYZE AND VISUALIZE DATA USING EXCEL, SQL,PYTHON,TABLEAU,SEABORN,STATISTICS,MATPLOTLIB

What you'll learn

Foster critical thinking and problem solving abilities through hands-on projects and real-world case studies

Equip students with essential skills in data manipulation and analysis and using tools like Python, Excel, SQL, Tableau and Statistics for data visualization

To familiarize students with predictive analytics , forecasting and trend analysis, correlations, statistics and more

Teach effective data visualization techniques to communicate insights clearly and persuasively to various audiences

Emphasize data visualization and techniques to clearly convey insights and findings to stakeholders

To guide students in creating a portfolio of projects scratch that showcase their analytical skills and knowledge to potential employers

To guide students for careers in data analytics by equipping them with the necessary skills, knowledge and practical experience to

succeed in a data-driven world.

At the end of this course you will have your time to shine by making your own portfolio to showcase what you learn from this course.

To build your confidence in reading, analyzing data and work effectively in a fast phase environment.

Requirements

Familiar with Excel, MYSQL, Python/Jupiter Notebook, Tableau,Matplotlib,Seaborn

Basic Math, Analytical Mind, Problem-solver driven, Curiosity,

Computer or laptop and internet services

Excel, MYSQL, Pandas Jupiter Notebook, Tableau tools, these are all free and downloadable

No programming experience needed.

Description

Foster critical thinking and problem solving abilities through hands-on projects and real-world case studiesEquip students with essential skills in data manipulation and analysis and using tools like Python, Excel, SQL, Tableau and Statistics for data visualization such MATPLOTLIB AND SEABORNTo familiarize students with predictive analytics , forecasting and trend analysis, correlations, statistics and moreTeach effective data visualization techniques to communicate insights clearly and persuasively to various audiencesEmphasize data visualization and techniques to clearly convey insights and findings to stakeholdersTo guide students in creating a portfolio of projects scratch that showcase their analytical skills and knowledge to potential employersTo guide students for careers in data analytics by equipping them with the necessary skills, knowledge and practical experience to succeed in a data-driven world.At the end of this course you will have your time to shine by making your own portfolio to showcase what you learn from this course.To build your confidence in gathering, reading, analyzing and visualizing data and work effectively in a fast phase environment.Are there any course requirements or prerequisites?Familiar with Excel, SQL, Python/Jupiter Notebook, TableauBasic Math, Analytical Mind, Problem-solver driven, CuriosityWELCOME and THANK YOU very much for taking this course. I am really excited to have you here and learn the world of data. Think of my logo, RUBIX CUBE, it seems complicated at first but the more you are familiar with the Issues and Techniques the easier it becomes. So relax and enjoy the course.

Overview

Section 1: Introduction

Lecture 1 Welcome!

Lecture 2 What is data?

Lecture 3 Course Structure Outline

Lecture 4 Download Course Resources

Section 2: Data Collection

Lecture 5 Introduction and downloads

Lecture 6 Identifying Objectives

Lecture 7 Selecting Methods

Lecture 8 Choosing Data Sources

Lecture 9 Ensuring Quality

Lecture 10 Gathering Data

Lecture 11 Organizing Data

Section 3: DATA CLEANING PART 1 - EXCEL

Lecture 12 Intro

Lecture 13 1. Sequence

Lecture 14 2. Countif

Lecture 15 3. Countblank

Lecture 16 4. Filter

Lecture 17 Pivot Table

Lecture 18 5. -Conditional Formatting

Lecture 19 7. Duplicates

Lecture 20 8. Outliers

Lecture 21 9. Irrelevant Data

Lecture 22 10. Typos and Errors

Lecture 23 11. Data Redundancy

Lecture 24 12. Non-standardized Data

Lecture 25 13. Lack of Documentations

Lecture 26 Sequence Quiz

Lecture 27 Countif Quiz

Lecture 28 Countblank Quiz

Lecture 29 Filter Quiz

Lecture 30 Conditional Formatting Quiz

Lecture 31 Pivot Table Quiz

Lecture 32 Irrelevant Data Quiz

Lecture 33 Non Redundancy Quiz

Lecture 34 Typos and Errors Quiz

Lecture 35 Non Standardized Data Quiz

Lecture 36 Lack of Documentation Quiz

Section 4: Data Cleaning - MYSQL

Lecture 37 Intro to MYSQL

Lecture 38 1. Distinct

Lecture 39 2. Duplicate Table

Lecture 40 3. Finding Missing Data

Lecture 41 4. Commit and Rollback

Lecture 42 5. Null

Lecture 43 6. Coalesce

Lecture 44 Replace

Lecture 45 7. Trim

Lecture 46 8. Concatenate

Lecture 47 9. Primary Key and Auto_Increment

Lecture 48 10. Substring

Lecture 49 11. Groupby

Lecture 50 12. Having Clause

Lecture 51 13. Partition by

Lecture 52 14. Subqueries

Lecture 53 15. Case Statement

Lecture 54 16. Constraint

Lecture 55 17. Ntile

Lecture 56 18. Row Number, Rank, Dense_Rank

Lecture 57 Distinct and Duplicates Quiz

Lecture 58 Null Quiz

Lecture 59 Coalesce Quiz

Lecture 60 Replace Quiz

Lecture 61 Trim and Concat Quiz

Lecture 62 Substring & Insert Quiz

Lecture 63 Primary Key and Auto Increment Quiz

Lecture 64 Case Statement Quiz

Lecture 65 Subqueries Quiz

Lecture 66 Partition by Quiz

Section 5: Data Cleaning - Python ( Pandas Jupiter Notebook)

Lecture 67 Intro to Python

Lecture 68 Duplicates

Lecture 69 Isna, Isnull, Notna

Lecture 70 Dropna

Lecture 71 Saving Files

Lecture 72 Fillna

Lecture 73 Astype

Lecture 74 Replace

Lecture 75 Pd.to_datetime

Lecture 76 Rename

Lecture 77 Drop rows and drop columns

Lecture 78 Split

Lecture 79 Aggregate

Lecture 80 Contains and Set Options

Lecture 81 Isna, Isnull, Notna, Fillna, Save File Quiz

Lecture 82 Astype, Pd. To_Datetime, Split, Rename, Replace Quiz

Lecture 83 Aggregate, Concat, Drop Columns & Rows Quiz

Section 6: Standard Deviation Python

Lecture 84 Introduction and Downloads

Lecture 85 Quantity

Lecture 86 Gross Sales

Lecture 87 Gross Profit

Lecture 88 Discounts

Section 7: KPI-EDA- Data Visualization Excel

Lecture 89 Intro

Lecture 90 Gross Sales

Lecture 91 Profit Ratio

Lecture 92 Total Transactions

Lecture 93 Sales Frequency

Lecture 94 Sale of Day

Section 8: KPI-EDA- Data Visualization- MYSQL and Excel

Lecture 95 Gross Sales

Lecture 96 Gross Sales w/ Excel

Lecture 97 Category

Section 9: KPI-EDA- Data Visualization- Tableau, Matplotlib, Seaborn

Lecture 98 Gross Sales

Lecture 99 Gross Profit

Lecture 100 Total Transactions

Lecture 101 Matplotlib and Seaborn Graph and how to access them.

Lecture 102 Matplotlib and Seaborn- how to access them

Section 10: Final Project

Lecture 103 Preview to Final Project

Lecture 104 Bonus and Final Statement!!

Beginner in Data analytics and curious of the following,To see the actual/day to day job of a data analyst,To advance or shift their careers in data-driven world,To see how technology is being use to manipulate tons of data,Business driven and loves the logic of statistical world.,Enjoy problem solving, curious and think outside the box.,Curious how software change our technologies for advancements