The Data Analyst'S Toolkit: Excel, Sql, Python, Power Bi

Data Mastery for the Modern Analyst: Excel, SQL, Python, and Power BI Techniques

What you'll learn

The roles and responsibilities of a data analyst

The importance of data-driven decision-making in organizations.

How to use Microsoft Excel for data manipulation and analysis.

Data cleaning and formatting techniques in Excel.

How to create and use pivot tables

Data visualization techniques using Excel charts.

Writing basic SQL queries for data retrieval from relational databases.

Advanced SQL techniques, such as filtering, sorting, aggregating, and joining multiple tables.

The basics of the Python programming language for data analysis.

How to use Python libraries like Pandas for data manipulation.

Data visualization techniques using Python libraries such as Matplotlib.

Connecting to data sources, data cleaning, and transformation in Power BI.

Creating interactive dashboards and reports using Power BI.

Requirements

Basic computer literacy: Students should be comfortable using computers and navigating various software applications, as well as have a general understanding of file management.

Familiarity with Microsoft Office Suite: A basic understanding of Microsoft Office applications, particularly Excel, will be helpful for students as they dive into more advanced data analysis techniques using Excel.

Problem-solving mindset: A curiosity for solving problems and a willingness to explore various approaches to data analysis will help students succeed in this course.

No prior programming experience is required, but a basic understanding of programming concepts and logic will be beneficial when learning Python and SQL.

Access to required software: Students should have access to a computer with Microsoft Excel, Power BI, and a Python development environment (e.g., Anaconda) installed. Access to a SQL database environment (e.g., MySQL, PostgreSQL, or SQL Server) is also recommended for practicing SQL queries.

Description

This course aims to provide students with a comprehensive understanding of the essential tools and techniques used by data analysts, including Excel, SQL, Python, and Power BI. This course is a comprehensive  course designed to equip aspiring data analysts and professionals with the essential skills and tools necessary to thrive in today's data-driven world. This course provides a solid foundation in data analysis, visualization, and communication, enabling students to make data-driven decisions and deliver actionable insights.The course begins with an introduction to data analysis, delving into the roles and responsibilities of a data analyst, and the importance of data-driven decision-making. Students will then explore Microsoft Excel, a widely-used tool for data manipulation, analysis, and visualization. Through hands-on exercises, students will learn essential Excel techniques such as data cleaning, formatting, formulas, functions, pivot tables, and chart creation.Next, the course introduces SQL, the standard language for managing and querying relational databases. Students will learn how to write basic SQL queries, filter, sort, aggregate data, join multiple tables, and use subqueries for advanced data retrieval. The course then dives into Python, a versatile programming language for data analysis. Students will learn  some Python basics, including data types, control flow, and functions, before progressing to data manipulation with  Pandas, as well as data visualization using Matplotlib.As the course advances, students will explore Power BI, a powerful business intelligence tool for creating interactive visualizations and sharing insights across organizations. The Power BI module covers data connection, cleaning, transformation, modeling, relationships, and an introduction to DAX (Data Analysis Expressions). Students will learn how to create visually appealing and interactive dashboards and reports, customize visuals and themes, and share their findings with various stakeholders.In the final weeks, the course will focus on integrating the tools and techniques learned throughout the program, including real-world case studies and applications in sales analysis, customer segmentation, social media analytics, operational efficiency, and financial analysis.Upon completion, students will have a comprehensive understanding of the data analyst's toolkit and be equipped to tackle complex data analysis tasks using Excel, SQL, Python, and Power BI.Whether you are an aspiring data analyst, a professional looking to enhance your skillset, or a business leader seeking to leverage data-driven insights, this course will provide you with the knowledge and tools necessary to succeed in today's data-driven world. Join us in this immersive learning experience and unlock the power of data analysis with the Data Analyst's Toolkit: Excel, SQL, Python, Power BI.

Overview

Section 1: Introduction to Data Analysis

Lecture 1 Introduction

Lecture 2 Course Introduction

Lecture 3 Data Analysis Overview

Lecture 4 Roles in Data Analysis

Lecture 5 Tasks of a Data Analyst

Lecture 6 Importance of Data-Driven Decision Making

Section 2: Excel Fundamentals

Lecture 7 Introduction to Excel

Lecture 8 Opening a new workbook

Lecture 9 Entering data in Excel

Lecture 10 Basic data entry in Excel

Lecture 11 Entering data with autofil

Lecture 12 Entering date

Lecture 13 Entering time

Lecture 14 Undo and redo changes

Lecture 15 Adding comments

Lecture 16 Adding a title to worksheet

Lecture 17 Saving your work

Lecture 18 Introduction to Excel Functions and Formulas

Lecture 19 Using formulas for arithmetic tasks

Lecture 20 Re-using formulas

Lecture 21 Calculating YTD Profits

Lecture 22 Calculating percentage change

Lecture 23 Relative and absolute reference

Lecture 24 Using Rank Function

Lecture 25 STD Function

Lecture 26 Small and Large Functions

Lecture 27 Median Function

Lecture 28 Count and Counta Functions

Lecture 29 Exploring fonts

Lecture 30 Adjusting column width and row height

Lecture 31 Using alignment

Lecture 32 Designing borders

Lecture 33 Formatting Numbers

Lecture 34 Conditional formatting

Lecture 35 Creating tables

Lecture 36 Inserting shapes

Section 3: Data Analysis & Visualization with Excel

Lecture 37 What is Power Query

Lecture 38 Connecting to a data source

Lecture 39 Please Read

Lecture 40 Preparing the query

Lecture 41 Cleaning the data

Lecture 42 Enhancing the query

Lecture 43 What is Power Pivot

Lecture 44 How to enable Power Pivot

Lecture 45 Create a data model

Lecture 46 Importing data and creating relationships

Lecture 47 Creating lookups with DAX

Lecture 48 Analyze data with Pivot Tables

Lecture 49 Analyze data with Pivot Charts

Lecture 50 Refreshing source data

Lecture 51 Updating queries

Lecture 52 Creating new reports

Section 4: SQL and MySQL Fundamentals

Lecture 53 Introduction to SQL

Lecture 54 Introduction to MySQL

Lecture 55 MySQL Installation (Windows)

Lecture 56 MySQL Installation (Mac)

Lecture 57 What is MySQL Workbench

Lecture 58 Basic database concepts

Lecture 59 What is a Schema

Lecture 60 Database Schema

Lecture 61 MySQL Data Types

Lecture 62 Joining Multiple Tables with INNER Join

Lecture 63 Joining Multiple Tables with LEFT Join

Lecture 64 Joining Multiple Tables with RIGHT Join

Lecture 65 Joining Multiple Tables with SELF Join

Lecture 66 Removing duplicates from query results

Lecture 67 Group data by combing rows

Lecture 68 Filter grouped results

Lecture 69 Sort query results

Lecture 70 Filtering rows of data

Lecture 71 Introduction to aggregate functions

Lecture 72 Using COUNT Aggregate Function

Lecture 73 Using SUM Aggregate Function

Lecture 74 Using AVG Aggregate Function

Lecture 75 Using MIN Aggregate Function

Lecture 76 Using MAX Aggregate Function

Lecture 77 What are Subqueries

Lecture 78 Using Nested Subqueries

Section 5: Python Fundamental

Lecture 79 What is Python

Lecture 80 Installing Python on Windows

Lecture 81 Installing Python on Macs

Lecture 82 What is Jupyter Notebook

Lecture 83 Installing Jupyter Notebook

Lecture 84 Running Jupyter Notebook Server

Lecture 85 Some Jupyter Notebook Commands

Lecture 86 Jupyter Notebook Components

Lecture 87 The Notebook Dashboard

Lecture 88 The Notebook user interface

Lecture 89 Creating a new notebook

Lecture 90 Python expressions

Lecture 91 Python statements

Lecture 92 Python Comments

Lecture 93 Python data types

Lecture 94 Casting data types

Lecture 95 Python Variables

Lecture 96 Python List

Lecture 97 Python Tuple

Lecture 98 Python dictionaries

Lecture 99 Python Operators

Lecture 100 Python Conditional statements

Lecture 101 Python Loops

Lecture 102 Python Functions

Section 6: Data Analysis and Visualization with Python and SQL

Lecture 103 Create a virtual environment on Windows

Lecture 104 Create a virtual environment on Macs

Lecture 105 Activate a virtual environment on Windows

Lecture 106 Activate a virtual environment on Macs

Lecture 107 Upgrade Pip

Lecture 108 Install Visual Studio Code

Lecture 109 Required Python Packages

Lecture 110 Installing Python Packages

Lecture 111 Import packages into a Python file

Lecture 112 The Sakilla Database

Lecture 113 Establishing a connection to the database

Lecture 114 Write a Python function to execute SQL queries

Lecture 115 Asking relevant questions about the data

Lecture 116 What are the most popular film categories rented by customers?

Lecture 117 How does the average rental duration vary across film categories?

Lecture 118 Which actors are featured in the most rented films?

Lecture 119 Are there any seasonal trends in the rental volume?

Lecture 120 What is the average rental cost by film category?

Lecture 121 How does the revenue contribution from different film categories compare?

Lecture 122 Are there any correlations between film length and rental frequency?

Lecture 123 Download the Python files

Section 7: Introduction to Power BI

Lecture 124 What is Power BI

Lecture 125 What is Power BI Desktop

Lecture 126 Install Power BI Desktop

Lecture 127 Explore Power BI Desktop Interface

Lecture 128 Microsoft 365 Setup

Lecture 129 Getting started with Microsoft 365

Lecture 130 Create a new user account in Microsoft 365

Lecture 131 Components of Power BI

Lecture 132 Getting data into Power BI Desktop

Section 8: Data Analysis and Visualization with Power BI

Lecture 133 Connect to data source

Lecture 134 Transform the data

Lecture 135 Model the data

Lecture 136 Visualize the data

Lecture 137 Publish report to Power BI Service

Lecture 138 Build a dashboard

Lecture 139 Collaborate and share

Aspiring data analysts: Individuals who want to start a career in data analysis and are looking to acquire foundational skills in the field.,Professionals seeking a career change: Professionals from other fields who want to transition to a data analysis role and need to develop their skillset in the most relevant tools and techniques.,Existing data analysts: Data analysts who want to expand their knowledge of specific tools, improve their proficiency, or stay up-to-date with the latest industry trends.,Business professionals and managers: Individuals involved in decision-making processes who want to leverage data-driven insights to make more informed decisions and gain a better understanding of the tools used by their data analysis teams.,Students: College or university students studying business, economics, computer science, or other related fields who want to complement their academic knowledge with practical skills in data analysis.,Researchers: Professionals involved in research who need to analyze and visualize large datasets to extract meaningful insights.,Small business owners and entrepreneurs: Individuals who want to utilize data analysis techniques to optimize their business operations, improve customer experience, or identify new opportunities for growth.,Freelancers and consultants: Professionals who provide data analysis services to clients and want to expand their toolkit to offer a wider range of services.,Overall, this course is designed for anyone looking to acquire the skills necessary to efficiently analyze, visualize, and communicate data insights using Excel, SQL, Python, and Power BI.