Data Analysis By Excel, Sql, Python, And Power Bi

Become a Data Analyst, SQL Expert, or Business Analyst by locating the concept of Data Mining, Wrangling, and Cleaning.

What you'll learn

You will able to analyze Raw Data patterns and uncover most of the hidden Information.

Your research strategies will improve by using basics and Advanced functions of Excel, SQL, and Python.

You will get Hand-on Experience using Excel, SQL, Python, and Power BI.

You will learn Data Wrangling, Data Cleaning, Data Analyzation and Data Manipulation.

You will Identify Ideas and manage Business Decisions.

Becomes an Expert in Data Storytelling and Optimize the overall output using Power BI.

Able to provide your thought on crucial situations and solve them accordingly.

You will be able to code on Python and SQL.

You will merge different Datasets using Python, Excel and Power-BI.

Requirements

No programming experience needed. You will learn everything you need to know.

Should be eager to learn.

Description

Data analytics has been one of the fastest growing fields in the last five years. The use of major tools like Excel, SQL, and Python has elevated its importance, as these tools allow analysts to accurately and professionally uncover the story behind the data.This course is structured to provide a step-by-step guide to you, starting from the basics of each tool and gradually building up to more advanced concepts. Through hands-on exercises and real-world examples, you will learn how to manipulate data, perform statistical analyses, and create compelling visualizations and dashboards.Data Analysis with Excel.In this course, I cover Excel in detail, with a focus on its applications in data analysis. You will learn how to use Excel to manipulate and analyze data, as well as how to use its powerful features to save time and improve efficiency.The course is designed to make learning Excel easy and accessible, with clear explanations and plenty of examples and practice exercises. By the end of the course, you will have a solid understanding of Excel and its powerful features for data analysis, and will be able to use them to solve real-world business problems. The main topic which have covered in this section are,■ Introduction to Excel.■ Learn about types the Data Connection.■ Applying Formatting on Datasets.■ Performing data manipulation.■ How we perform Data Cleaning task.■ What are the methods for Descriptive Analysis.■ Learn about the Charts and Graphs for Data Visualization.■ Discovering these concepts with Case Study.Data Analysis with SQL ServerSQL (Structured Query Language) is a programming language used for managing and manipulating relational databases. It is widely used in data analytics because of its ability to efficiently retrieve and manipulate large amounts of data. This course covers all the commands and functions of SQL, from basic SELECT statements to more advanced topics such as subqueries, joins, and aggregate functions. Students will also learn how to use SQL with Python and other data analysis tools. SQL is an essential tool for data analysts and data scientists who need to extract insights and information from databases.  The main topics which have covered in this section are,■ Introduction to SQL and relational databases.■ Working with SQL Queries to retrieve data from databases for Analysis.■ Joining tables and combining data from multiple sources.■ Performing data manipulation.■ Learn how to apply different condition to datasets.■ Understand the concept of Sub-Queries or Inner Queries.■ Discovering these concepts with Case Study.Data Analysis with Python.Furthermore, Data Analysis covers Python in detail, with a particular focus on its applications in data analytics. You will learn how to use Python and its various packages for data manipulation, analysis, and visualization.The course is designed to make learning Python for data analytics accessible and enjoyable. Each topic is presented in a clear and easy-to-understand manner, with plenty of examples and practice exercises to reinforce learning. By the end of the course, you will have a solid understanding of Python and its packages for data analytics, and will be able to use them to analyze and visualize data in a variety of contexts. For example, the Pandas package provides powerful data structures for working with structured data, while NumPy provides efficient support for numerical operations. Additionally, Matplotlib and Seaborn packages are used for data visualization and creating visually appealing plots and charts. The main topics which have covered in this section are,■ Introduction to Python.■ Learn about the Basic of Python.■ Working with specific modules like PANDAS and NumPy for Data Analysis.■ Working on Analytical Method approach which lead to be a better Data Analyst.■ Learn about Data Story Telling with Matplotlib and Seaborn.■ Performing data manipulation with Python Function, Methods and Logics.■ Learn how to apply different condition to Datasets.■ Discover the concept of Data Wrangling.■ Discovering these concepts with Case Study.Data Analysis with Power BI.In the course, you will learn how to use Power BI to connect to various data sources, including databases, spreadsheets, and cloud-based data services. They will also learn how to transform and clean data using Power Query, a powerful data transformation tool built into Power BI.You will also learn how to create a range of powerful visualizations using Power BI's extensive library of charts, graphs, and tables. You will learn how to customize these visualizations to suit your specific needs, and how to create interactive dashboards that allow users to explore and analyze data in real-time.■ Introduction to Power BI.■ Learn how to do ETL on BI environment.■ Joining tables and combining data from multiple sources.■ Performing Data Cleaning on Power Query Editor.■ Creating Relationship between model.■ Discover the details of Visualization( Eye Catching Graphs and Charts).■ Formatting of Visuals through numerous style.■ Develop Dashboards and Reports.

Overview

Section 1: Data Analytics Foundation, and Connection with Other Fields, and Future Trends.

Lecture 1 Introduction to Data Analytics? Is it worth it ?

Lecture 2 Why Data Analytics?

Lecture 3 Types of Data Analysis

Lecture 4 Framework of Data Analytics Course

Lecture 5 The Course Content

Lecture 6 The Services you will provide.

Lecture 7 Future Trends

Section 2: Case Study of Real Life

Lecture 8 Case Study

Lecture 9 Learning Resources

Lecture 10 You have Achieved ….

Section 3: Data Analysis with EXCEL

Lecture 11 Overview of Excel Section

Lecture 12 Introduction to Excel

Lecture 13 Data Connections

Lecture 14 Data Formatting

Lecture 15 Data Cleaning

Lecture 16 Detailed Descriptive Analysis

Lecture 17 Where is the Option of Data Analysis?

Lecture 18 Descriptive Statistics

Lecture 19 VLookup

Lecture 20 Pivot Table Part 1

Lecture 21 Pivot Table Part 2

Lecture 22 Pivot Table Formatting

Lecture 23 Correlation

Lecture 24 How to Merge multiple data tables?

Lecture 25 What is Insert Slicer?

Lecture 26 Random Number Generation

Lecture 27 Charts and Graphs

Lecture 28 What have you achieved from this Excel Section?

Section 4: Data Analysis with SQL

Lecture 29 Overview of SQL Section

Lecture 30 Installation of SQL SERVER

Lecture 31 Discover the Interface

Lecture 32 How to Import Data?

Lecture 33 SELECT and FROM Clause

Lecture 34 SELECT DISTINCT

Lecture 35 Logical Operators

Lecture 36 Comparison Operators

Lecture 37 Aggregate Functions

Lecture 38 Where with Logical Operators ( AND & OR )

Lecture 39 WHERE Clause with Logical Operators Part 1

Lecture 40 WHERE Clause with Logical Operators Part 2

Lecture 41 Group By Part 1 with Aggregate Function

Lecture 42 Group By Part 2 with Data Type Change

Lecture 43 ORDER BY

Lecture 44 HAVING

Lecture 45 WHERE AND HAVING

Lecture 46 Primary Vs Foreign Key

Lecture 47 JOIN

Lecture 48 UNION

Lecture 49 SUBQURIES

Lecture 50 Introduction to Case Study

Lecture 51 Case Study Part 1

Lecture 52 Case Study Part 2

Lecture 53 What have you achieved from this SQL Section ?

Section 5: Data Analysis with PYTHON

Lecture 54 Overview of Python Section

Lecture 55 Installation of Anaconda ( For PYTHON )

Lecture 56 Opening Layout Introduction

Lecture 57 What are Data Types in Python?

Lecture 58 Variable, Value and Print

Lecture 59 Casting and Case Sensitive

Lecture 60 What is Module?

Lecture 61 Module Vs Library

Lecture 62 Numpy Library

Lecture 63 PANDAS Library

Lecture 64 Matplotlib Package

Lecture 65 Seaborn Library

Lecture 66 How to Import Data in Jupyter Notebook?

Lecture 67 What is Data Wrangling?

Lecture 68 Data Cleaning Part 1

Lecture 69 Missing Data

Lecture 70 Outliers

Lecture 71 Inconsistent Data

Lecture 72 Data Cleaning Part 2

Lecture 73 Invalid Data

Lecture 74 Duplicate Data

Lecture 75 Data Types Issues

Lecture 76 GroupBy

Lecture 77 Merge

Lecture 78 Cross-Tab

Lecture 79 Cut

Lecture 80 Analysis Methods Part 1 (a)

Lecture 81 Analysis Methods Part 1 (b)

Lecture 82 Analysis Methods Part 2

Lecture 83 Analysis Methods Part 3

Lecture 84 Unique Methods

Lecture 85 Graphs and Charts

Lecture 86 Case Study Part 1

Lecture 87 Case Study Part 2

Lecture 88 What you have achieved from this Section?

Section 6: Data Visualization with Power BI

Lecture 89 Installation of Power BI Desktop

Lecture 90 Explore Power BI

Lecture 91 How to upload Data from multiple sources

Lecture 92 Creation of Dashboard

Lecture 93 Understanding the Formatting of Visuals

Lecture 94 How to Create a Relationship between Data Tables?

Lecture 95 Conclusion

Section 7: End of THE BIG Course.

Lecture 96 End of Course

This course is particularly for those people who want to learn Data related things a student, a teacher, and Corporate sector people included.,Data curious guy who wanted to learn how to gather hidden information by using Excel, SQL, Python, and Power BI.,Students looking for a comprehensive, engaging, and highly interactive approach to learning Data Analysis.,A person who wants to improve the system and change the manual routine works into automatic work.,A Doctor, Teacher, Engineer, or even anyone who belongs to any particular domain can learn about Data Engineering.