Data Analysis Essentials - (Excel,Sql,Power Bi,Python)

Data-Driven Decisions with Excel, SQL, Power BI, Python

What you'll learn

Understand the foundational concepts of data analysis and its importance in decision-making processes.

Gain proficiency in using Microsoft Excel for data manipulation, analysis, and visualization.

Master SQL fundamentals for data retrieval, manipulation, and querying in relational databases.

Learn to leverage Power BI for creating interactive dashboards and reports to visualize data insights.

Develop essential programming skills in Python for data analysis, including data manipulation and visualization.

Acquire techniques for data cleaning and preparation to ensure data quality and accuracy for analysis.

Explore advanced Excel features for pivot tables, data validation, and conditional formatting to enhance data analysis workflows.

Understand the principles of data visualization and create effective charts, graphs, and dashboards in Excel and Power BI.

Learn to use Python libraries such as pandas for data manipulation, analysis, and visualization.

Requirements

Basic computer skills: Learners should be comfortable with using a computer, navigating software interfaces, and performing basic tasks such as opening files, saving documents, and browsing the internet.

Familiarity with spreadsheet software: While not mandatory, having some familiarity with spreadsheet software like Microsoft Excel would be beneficial. However, the course will cover basic Excel concepts from scratch, making it accessible to beginners.

Eagerness to learn: The most important requirement for taking the course is a willingness to learn and explore the field of data analysis. Curiosity and a desire to acquire new skills will greatly enhance the learning experience.

Description

In today's data-driven world, the ability to analyze and interpret data is a valuable skill that opens doors to a wide range of career opportunities and empowers individuals to make informed decisions in both personal and professional contexts. Whether you're a student eager to explore the field of data analysis, a seasoned professional looking to enhance your analytical capabilities, or an entrepreneur seeking to leverage data for business growth, this comprehensive course is designed to equip you with the knowledge and practical skills needed to excel in the exciting world of data analysis.Spanning across multiple modules, this course covers everything you need to know to become proficient in data analysis, regardless of your prior experience or background. From understanding the fundamental concepts of data analysis to mastering popular tools and techniques used by data professionals, each module is carefully crafted to provide a structured learning path that caters to learners of all levels.The journey begins with an introduction to the foundational concepts of data analysis, exploring its role in decision-making processes and its significance across various industries and domains. You'll then dive into hands-on tutorials on using Microsoft Excel, SQL, Power BI, and Python – four essential tools in the data analyst's toolkit. Through step-by-step guidance and practical exercises, you'll learn how to manipulate, analyze, and visualize data efficiently, unlocking valuable insights that drive strategic decision-making.Throughout the course, you'll explore a wide range of topics, including statistical analysis, data cleaning and preparation, data visualization, regression analysis, and more. You'll discover how to apply statistical techniques to analyze data distributions, measure central tendency, and assess variability, enabling you to derive meaningful insights from your data with confidence.But this course is more than just theoretical knowledge – it's about practical, real-world application. With hands-on projects and case studies inspired by real-world scenarios, you'll have the opportunity to put your skills to the test and solve data analysis challenges commonly encountered in today's professional landscape. Whether you're analyzing sales trends, forecasting financial performance, or uncovering patterns in customer behavior, you'll emerge from this course with the practical skills and confidence to tackle any data analysis task with ease.By the end of this course, you'll have the expertise and confidence to embark on a rewarding journey in the field of data analysis, equipped with the skills needed to excel in a variety of roles and industries. Whether you're pursuing a career as a data analyst, business analyst, marketing analyst, or entrepreneur, this course will empower you to unlock the full potential of data and drive impactful decision-making in your personal and professional life.

Overview

Section 1: Introduction to Data Analysis

Lecture 1 Introduction

Lecture 2 Understanding the role of data analysis in decision-making

Lecture 3 Overview of tools and technologies used in data analysis

Lecture 4 Introduction to Excel, SQL, Power BI, and Python for data analysis

Section 2: Excel Essentials for Data Analysis

Lecture 5 Introduction to Excel for data manipulation and analysis

Lecture 6 Data cleaning and preparation techniques in Excel

Lecture 7 Basic statistical analysis using Excel functions

Lecture 8 Data visualization with charts and graphs in Excel

Lecture 9 What is Power Pivot

Lecture 10 Enabling Power Pivot in Excel

Lecture 11 What is Power query

Lecture 12 Connecting to data source

Lecture 13 Preparing your query

Lecture 14 Cleansing data

Lecture 15 Enhancing your query

Lecture 16 Creating a data model

Lecture 17 Build data relationships

Lecture 18 Analyse data using Pivot tables

Lecture 19 Create lookups as new fields with DAX

Lecture 20 Analyse data with Pivot charts

Lecture 21 Refreshing source data

Lecture 22 Updating queries

Lecture 23 Creating new reports

Section 3: Data Analysis with SQL

Lecture 24 What is SQL

Lecture 25 What are relational databases

Lecture 26 MySQL

Lecture 27 Basic database concepts

Lecture 28 MySQL Installation for Windows

Lecture 29 MySQL Installation for Macs

Lecture 30 MySQL Workbench

Lecture 31 Installing MySQL Workbench for Macs

Lecture 32 Data export and import methods

Lecture 33 Data export and import using table wizard

Lecture 34 SQL data export and import wizard

Lecture 35 Using result data export and import

Lecture 36 SQL Statement and query

Lecture 37 Analyzing data using SQL Joins

Lecture 38 Analyzing data with INNER Join

Lecture 39 Analyzing data with LEFT Join

Lecture 40 Analyzing data with RIGHT Join

Lecture 41 Analyzing data with SELF Join

Lecture 42 Analyzing data with Sub queries

Lecture 43 Analyzing data with nested sub query

Lecture 44 Analyzing data with derived tables

Lecture 45 Analyzing data with Between operator

Lecture 46 Analyzing data with IN Operator

Lecture 47 Analyzing data with LIKE Operator

Lecture 48 Analyzing data with UNION Operator

Lecture 49 Analyzing data with aggregate functions

Lecture 50 Analyzing data with AVG aggregate functions

Lecture 51 Analyzing data with COUNT aggregate functions

Lecture 52 Analyzing data with SUM aggregate functions

Lecture 53 Analyzing data with MIN aggregate functions

Lecture 54 Analyzing data with MAX aggregate functions

Lecture 55 Analyzing data with character lenght function

Lecture 56 Analyzing data with concat function

Lecture 57 Analyzing distinct data

Lecture 58 Analyzing data by grouping

Lecture 59 Analyzing data using having clause

Lecture 60 Sorting data with order by clause

Lecture 61 Filtering data with where clause

Section 4: Data Analysis with Power BI

Lecture 62 What is Power BI

Lecture 63 What is Power BI Desktop

Lecture 64 Setup Microsoft 365

Lecture 65 Getting started with Microsoft 365

Lecture 66 Adding user to Microsoft 365

Lecture 67 Installing Power BI Desktop

Lecture 68 Exploring Power BI Interface

Lecture 69 Power BI Overview - Part 1

Lecture 70 Power BI Overview - Part 2

Lecture 71 Power BI Overview - Part 3

Lecture 72 Components of Power BI

Lecture 73 Exploring Power BI Service

Lecture 74 Connect to web based data

Lecture 75 Clean and transform data - Part 1

Lecture 76 Clean and transform data - Part 2

Lecture 77 Combining data sources

Lecture 78 Creating Visualizations: Part 1

Lecture 79 Creating Visualizations: Part 2

Lecture 80 Publishing Reports to Power BI Service

Section 5: Data Analysis with Python

Lecture 81 What is Python

Lecture 82 What is Jupyter Notebook

Lecture 83 Install Python on Windows

Lecture 84 Install Python on Mac

Lecture 85 Install Jupyter Notebook with Anaconda

Lecture 86 Jupyter Notebook components

Lecture 87 Jupyter Notebook dashboard

Lecture 88 Jupyter Notebook Interface

Lecture 89 Creating a new Notebook

Lecture 90 The dataset

Lecture 91 Tabular Data

Lecture 92 Exploring Pandas DataFrame

Lecture 93 Manipulating Pandas DataFrame

Lecture 94 What is data cleaning

Lecture 95 Perform Data cleaning

Lecture 96 What is data visualization

Lecture 97 Visualizing qualitative data

Lecture 98 Visualizing quantitative data

Section 6: Career opportunities and pathways in the field of data analysis

Lecture 99 Career opportunities and pathways in the field of data analysis

Students: College or university students studying business, economics, mathematics, computer science, or related fields who want to complement their academic knowledge with practical skills in data analysis.,Professionals transitioning to data-related roles: Individuals looking to transition into data analysis, business analysis, or similar roles within their current organization or in a new industry.,Business professionals: Professionals working in marketing, finance, operations, healthcare, or any field where data plays a crucial role in decision-making and who want to improve their data analysis skills.,Entrepreneurs and business owners: Entrepreneurs and small business owners seeking to harness the power of data to drive business growth, optimize operations, and make informed strategic decisions.,Career changers: Individuals considering a career change into the field of data analysis who want to acquire the necessary skills and knowledge to pursue new opportunities in this rapidly growing field.,Anyone curious about data analysis: Individuals with a general interest in data analysis and a desire to learn how to extract insights from data to solve real-world problems and make better decisions.