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    Data Analysts Toolbox: Excel, Sql, Python, Power Bi, Tableau

    Posted By: ELK1nG
    Data Analysts Toolbox: Excel, Sql, Python, Power Bi, Tableau

    Data Analysts Toolbox: Excel, Sql, Python, Power Bi, Tableau
    Published 12/2023
    MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
    Language: English | Size: 3.93 GB | Duration: 10h 43m

    Gain Real World Skills By Mastering Excel, SQL, Python, Power BI, and Tableau for Data Analysts

    What you'll learn

    Develop a foundational understanding of data analysis principles and techniques.

    Master Excel functions and formulas for data manipulation, analysis, and visualization.

    Learn SQL for data querying, manipulation, and retrieval from relational databases.

    Acquire programming skills in Python for data handling, manipulation, and analysis.

    Create compelling data visualizations and dashboards using Power BI and Tableau.

    Apply learned skills to real-world data sets and scenarios.

    Requirements

    Basic Computer Skills: Familiarity with navigating computer systems, using software applications, and basic file management is recommended.

    Fundamental Math Skills: A foundational understanding of basic mathematical concepts like arithmetic, algebra, and statistics will be beneficial for comprehending data analysis principles.

    No Prior Programming Knowledge Required: While prior programming experience is not mandatory, a willingness to learn and engage with programming concepts is essential for the Python section of the course.

    Access to a Computer: Learners should have access to a computer or laptop with internet connectivity to participate in the course modules and complete exercises.

    Software and Tools: Access to software such as Microsoft Excel, SQL client (e.g., MySQL Workbench), Python (Anaconda distribution recommended), Power BI Desktop, and Tableau Public (free version) will be necessary for hands-on practice during the course. Free trials or open-source alternatives are available for some tools.

    For Beginners: No prior experience or specific skills are mandatory to embark on this course.

    Description

    The Data Analyst's Toolbox course is designed to equip learners with essential skills in Excel, SQL, Python, Power BI, and Tableau, enabling them to efficiently collect, analyze, visualize, and present data. Through hands-on exercises, real-world applications, and practical examples, participants will gain proficiency in utilizing these tools to extract insights from data, make informed decisions, and communicate findings effectively.Embark on an exhilarating journey into the world of data-driven insights with our comprehensive course, the "Data Analyst's Toolbox: Excel, SQL, Python, Power BI, Tableau." Dive headfirst into a dynamic learning experience tailored for aspiring data analysts, professionals seeking to upskill, or anyone intrigued by the power of data.In this transformative course, you'll unearth the fundamental pillars of data analysis through an immersive blend of theory and hands-on application. Beginning with the bedrock of Excel, you'll harness its prowess, unraveling its complex functions and formulas to wield data manipulation and visualization like a seasoned pro. Seamlessly transition into the realm of SQL, where you'll unravel the enigmatic databases, querying and manipulating data with finesse.But that's just the beginning. Brace yourself for an odyssey through Python, the programming language du jour in the data analytics sphere. From data wrangling with Pandas to crafting captivating visualizations using Matplotlib, you'll discover Python's unparalleled versatility in unlocking data's hidden tales.Yet, the adventure doesn't halt there. Traverse through the landscape of Power BI and Tableau, where you'll sculpt interactive dashboards and reports that breathe life into raw data. Witness how these tools metamorphose complex data sets into compelling visual narratives, empowering you to extract actionable insights effortlessly.This course transcends the confines of theory, intertwining real-world applications and case studies that beckon you to apply newfound skills to tangible scenarios. Engage in a transformative capstone project, amalgamating all your knowledge and skills into a masterpiece that showcases your prowess in the data realm.Join us on this transformative expedition, guided by seasoned industry experts committed to nurturing your analytical acumen. Whether you're stepping into the data universe for the first time or seeking to fortify your skill set, this course promises an immersive, exhilarating voyage that unlocks the gates to a world brimming with data-driven possibilities. Unleash your potential as a data maestro and chart your course toward becoming a proficient, sought-after data analyst.

    Overview

    Section 1: Introduction to Data Analysis

    Lecture 1 Introduction

    Lecture 2 Understanding the role of a data analyst

    Lecture 3 Overview of data analysis tools and their importance

    Lecture 4 Introduction to key concepts in data analysis

    Section 2: Excel for Data Analysis

    Lecture 5 Introduction to Excel for Data Analysis

    Lecture 6 Basic to advanced Excel functions and formulas

    Lecture 7 Hands-on Practice of Functions and Formulas

    Lecture 8 Using Formulas for Arithmetic Tasks

    Lecture 9 Re-using Formulas

    Lecture 10 Calculating YTD Profits

    Lecture 11 Calculating Percentage Change

    Lecture 12 Relative and Absolute Reference

    Lecture 13 Using RANK Function

    Lecture 14 STD Function

    Lecture 15 Small and Large Functions

    Lecture 16 Median Functions

    Lecture 17 COUNT and COUNTA Functions

    Lecture 18 What is Power Query

    Lecture 19 Connecting to a data source

    Lecture 20 Please Read

    Lecture 21 Preparing your query

    Lecture 22 Cleansing data

    Lecture 23 Enhancing query

    Lecture 24 What is Power Pivot

    Lecture 25 Creating a data model

    Lecture 26 Importing data and creating relationships

    Lecture 27 Create lookups with DAX

    Lecture 28 Analyze data with Pivot Tables

    Lecture 29 Analyze data with Pivot Charts

    Section 3: SQL Fundamentals

    Lecture 30 Introduction to SQL and its applications in data analysis

    Lecture 31 What is MySQL

    Lecture 32 MySQL Installation (Windows)

    Lecture 33 MySQL Installation (Mac)

    Lecture 34 What is MySQL Workbench

    Lecture 35 Installing MySQL Workbench ( Mac)

    Lecture 36 Database Concepts

    Lecture 37 What is a Schema

    Lecture 38 What is a Database Schema

    Lecture 39 MySQL Data Types

    Lecture 40 Exploring Basic SQL Commands

    Lecture 41 Showing existing databases

    Lecture 42 Showing lists of tables

    Lecture 43 Create a table

    Lecture 44 Displaying table structure

    Lecture 45 Changing table structure

    Lecture 46 Creating MySQL Database

    Lecture 47 Creating MySQL Table

    Lecture 48 Setting a default database

    Lecture 49 Primary Keys

    Lecture 50 Foreign Keys

    Lecture 51 SQL & MySQL SELECT Statement

    Lecture 52 SQL & MySQL INSERT Statement

    Lecture 53 SQL & MySQL UPDATE Statement

    Lecture 54 SQL & MySQL DELETE Statement

    Lecture 55 Introduction to Joins

    Lecture 56 INNER JOIN

    Lecture 57 LEFT JOIN

    Lecture 58 RIGHT JOIN

    Lecture 59 SELF JOIN

    Lecture 60 What is sub query

    Lecture 61 Nested sub query

    Lecture 62 Filtering data with WHERE Clause

    Lecture 63 Sorting data with ORDER BY Clause

    Lecture 64 Grouping data with GROUP BY Clause

    Lecture 65 What are aggregate functions

    Lecture 66 AVG Aggregate Function

    Lecture 67 COUNT Aggregate Function

    Lecture 68 SUM Aggregate Function

    Lecture 69 MIN Aggregate Function

    Lecture 70 MAX Aggregate Function

    Section 4: Data Analysis with Python and Jupyter Notebook

    Lecture 71 What is Python

    Lecture 72 Using Python in Data Analysis

    Lecture 73 What is Jupyter Notebook

    Lecture 74 Installing Jupyter Notebook Server

    Lecture 75 Running Jupyter Notebook Server

    Lecture 76 Jupyter Notebook Components

    Lecture 77 Jupyter Notebook Dashboard

    Lecture 78 Jupyter Notebook User Interface

    Lecture 79 Creating a new notebook

    Lecture 80 Kaggle Data Sets

    Lecture 81 Tabular Data

    Lecture 82 what is Pandas DataFrame

    Lecture 83 Exploring Pandas DataFrame

    Lecture 84 Manipulating a Pandas DataFrame

    Lecture 85 What is data cleaning

    Lecture 86 Basic data cleaning process

    Lecture 87 What is data visualization

    Lecture 88 Visualizing Qualitative Data

    Lecture 89 Visualizing Quantitative Data

    Section 5: Data Analysis with Power BI

    Lecture 90 What is Power BI

    Lecture 91 What is Power BI Desktop

    Lecture 92 Installing Power BI Desktop

    Lecture 93 Exploring Power BI Desktop

    Lecture 94 Power BI Overview: Part 1

    Lecture 95 Power BI Overview: Part 2

    Lecture 96 Power BI Overview: Part 3

    Lecture 97 Components of Power BI

    Lecture 98 Building Blocks of Power BI

    Lecture 99 Explore Power BI Service

    Lecture 100 Connect to web data

    Lecture 101 Clean and transform data: Part 1

    Lecture 102 Clean and transform data: Part 2

    Lecture 103 Combining Data Sources

    Lecture 104 Visualizing Data: Part 1

    Lecture 105 Visualizing Data: Part 2

    Lecture 106 Publishing Reports to Power BI Service

    Section 6: Data Analysis with Tableau

    Lecture 107 What is Tableau

    Lecture 108 Using Tableau for Data Analysis

    Lecture 109 Tableau Public Desktop

    Lecture 110 Tableau Public Desktop Overview : Part 1

    Lecture 111 Tableau Public Desktop Overview : Part 2

    Lecture 112 Tableau Online

    Lecture 113 Tableau Data Sources

    Lecture 114 Connecting to data source

    Lecture 115 Join related data sources

    Lecture 116 Join data sources with inconsistent fields

    Lecture 117 Data cleaning

    Lecture 118 Tableau Interface

    Lecture 119 Reordering Visualization

    Lecture 120 Change summary

    Lecture 121 Split text into multiple columns

    Lecture 122 Presenting data using stories

    Aspiring Data Analysts: Individuals keen on pursuing a career in data analysis or transitioning into the field will find immense value in this course. It serves as a comprehensive introduction to the essential tools and techniques used in the data analytics domain.,Professionals Seeking to Upskill: Professionals working in diverse fields such as business, finance, marketing, healthcare, or any industry reliant on data-driven decision-making will benefit from enhancing their data analysis skills. Whether you're a business analyst, financial analyst, marketing professional, or from another field, this course equips you with the tools to extract meaningful insights from data.,Students and Graduates: Students pursuing degrees in fields related to data science, computer science, business analytics, or any discipline with an interest in data will discover a solid foundation in data analysis methodologies and tools, setting them on the path to excel in their academic pursuits and future careers.,Entrepreneurs and Small Business Owners: Entrepreneurs and small business owners seeking to harness the power of data to drive business growth and make informed decisions will find this course invaluable. Understanding how to analyze and visualize data can aid in optimizing strategies, identifying trends, and making data-driven decisions crucial for success.,Anyone Curious About Data Analysis: Individuals with a general interest in data analysis, regardless of their professional background, will benefit from acquiring practical skills in Excel, SQL, Python, Power BI, and Tableau. Whether you're a hobbyist, a curious mind, or someone looking to explore new horizons, this course provides a comprehensive understanding of data analysis tools.,This course caters to a diverse audience by delivering a structured learning experience that accommodates various skill levels and backgrounds. It's designed to empower learners with the essential skills required to navigate the intricate landscape of data analysis confidently, fostering a community of individuals passionate about deriving insights from data.