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The Data Analyst's Toolkit: Excel, SQL, Python, Power BI

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The Data Analyst's Toolkit: Excel, SQL, Python, Power BI, available at $49.99, has an average rating of 4.05, with 139 lectures, based on 31 reviews, and has 120 subscribers.

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You will learn about 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. This course is ideal for individuals who are Aspiring data analysts: Individuals who want to start a career in data analysis and are looking to acquire foundational skills in the field. or 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. or 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. or 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. or 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. or Researchers: Professionals involved in research who need to analyze and visualize large datasets to extract meaningful insights. or 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. or Freelancers and consultants: Professionals who provide data analysis services to clients and want to expand their toolkit to offer a wider range of services. or 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. It is particularly useful for Aspiring data analysts: Individuals who want to start a career in data analysis and are looking to acquire foundational skills in the field. or 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. or 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. or 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. or 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. or Researchers: Professionals involved in research who need to analyze and visualize large datasets to extract meaningful insights. or 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. or Freelancers and consultants: Professionals who provide data analysis services to clients and want to expand their toolkit to offer a wider range of services. or 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.

Enroll now: The Data Analyst's Toolkit: Excel, SQL, Python, Power BI

Summary

Title: The Data Analyst's Toolkit: Excel, SQL, Python, Power BI

Price: $49.99

Average Rating: 4.05

Number of Lectures: 139

Number of Published Lectures: 139

Number of Curriculum Items: 139

Number of Published Curriculum Objects: 139

Original Price: $119.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

  • 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.

Target Audiences

  • 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.

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.

Course Curriculum

Chapter 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

Chapter 2: Excel Fundamentals

Lecture 1: Introduction to Excel

Lecture 2: Opening a new workbook

Lecture 3: Entering data in Excel

Lecture 4: Basic data entry in Excel

Lecture 5: Entering data with autofil

Lecture 6: Entering date

Lecture 7: Entering time

Lecture 8: Undo and redo changes

Lecture 9: Adding comments

Lecture 10: Adding a title to worksheet

Lecture 11: Saving your work

Lecture 12: Introduction to Excel Functions and Formulas

Lecture 13: Using formulas for arithmetic tasks

Lecture 14: Re-using formulas

Lecture 15: Calculating YTD Profits

Lecture 16: Calculating percentage change

Lecture 17: Relative and absolute reference

Lecture 18: Using Rank Function

Lecture 19: STD Function

Lecture 20: Small and Large Functions

Lecture 21: Median Function

Lecture 22: Count and Counta Functions

Lecture 23: Exploring fonts

Lecture 24: Adjusting column width and row height

Lecture 25: Using alignment

Lecture 26: Designing borders

Lecture 27: Formatting Numbers

Lecture 28: Conditional formatting

Lecture 29: Creating tables

Lecture 30: Inserting shapes

Chapter 3: Data Analysis & Visualization with Excel

Lecture 1: What is Power Query

Lecture 2: Connecting to a data source

Lecture 3: Please Read

Lecture 4: Preparing the query

Lecture 5: Cleaning the data

Lecture 6: Enhancing the query

Lecture 7: What is Power Pivot

Lecture 8: How to enable Power Pivot

Lecture 9: Create a data model

Lecture 10: Importing data and creating relationships

Lecture 11: Creating lookups with DAX

Lecture 12: Analyze data with Pivot Tables

Lecture 13: Analyze data with Pivot Charts

Lecture 14: Refreshing source data

Lecture 15: Updating queries

Lecture 16: Creating new reports

Chapter 4: SQL and MySQL Fundamentals

Lecture 1: Introduction to SQL

Lecture 2: Introduction to MySQL

Lecture 3: MySQL Installation (Windows)

Lecture 4: MySQL Installation (Mac)

Lecture 5: What is MySQL Workbench

Lecture 6: Basic database concepts

Lecture 7: What is a Schema

Lecture 8: Database Schema

Lecture 9: MySQL Data Types

Lecture 10: Joining Multiple Tables with INNER Join

Lecture 11: Joining Multiple Tables with LEFT Join

Lecture 12: Joining Multiple Tables with RIGHT Join

Lecture 13: Joining Multiple Tables with SELF Join

Lecture 14: Removing duplicates from query results

Lecture 15: Group data by combing rows

Lecture 16: Filter grouped results

Lecture 17: Sort query results

Lecture 18: Filtering rows of data

Lecture 19: Introduction to aggregate functions

Lecture 20: Using COUNT Aggregate Function

Lecture 21: Using SUM Aggregate Function

Lecture 22: Using AVG Aggregate Function

Lecture 23: Using MIN Aggregate Function

Lecture 24: Using MAX Aggregate Function

Lecture 25: What are Subqueries

Lecture 26: Using Nested Subqueries

Chapter 5: Python Fundamental

Lecture 1: What is Python

Lecture 2: Installing Python on Windows

Lecture 3: Installing Python on Macs

Lecture 4: What is Jupyter Notebook

Lecture 5: Installing Jupyter Notebook

Lecture 6: Running Jupyter Notebook Server

Lecture 7: Some Jupyter Notebook Commands

Lecture 8: Jupyter Notebook Components

Lecture 9: The Notebook Dashboard

Lecture 10: The Notebook user interface

Lecture 11: Creating a new notebook

Lecture 12: Python expressions

Lecture 13: Python statements

Lecture 14: Python Comments

Lecture 15: Python data types

Lecture 16: Casting data types

Lecture 17: Python Variables

Instructors

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 0 votes
  • 3 stars: 5 votes
  • 4 stars: 13 votes
  • 5 stars: 12 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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