Become A Data Analyst - (Etl, Sql, Power Bi, Python,R )

Data Analysis Unleashed: ETL, SQL, Power BI, Python, Jupyter Notebook,Pandas and R for Impactful Business Decisions

What you'll learn

The basic principles of data analysis and its importance in decision-making.

The roles and responsibilities of a data analyst.

Understanding the concept of ETL (Extract, Transform, Load) processes.

How to extract data from different data sources.

Techniques to clean and transform raw data for analysis.

How to load transformed data into an appropriate data storage system.

Best practices for ETL processes.

SQL fundamentals for retrieving and manipulating data in relational databases.

Advanced SQL concepts, including subqueries and joins.

Utilizing SQL clauses such as Between, IN, LIKE, and UNION.

Python basics, including data types, variables, and control flow.

Data manipulation in Python using Pandas.

Basic data visualization techniques in Python using libraries like Matplotlib and Seaborn.

Basic data manipulation and analysis techniques in R.

Understanding the concept of Power BI and its role in data analysis.

Transforming and shaping data to fit the needs of your analysis using Power BI.

Creating various data visualizations using Power BI.

Requirements

Basic Computer Skills: Students should be comfortable with basic computer operations, such as downloading and installing software, browsing the internet, and managing files and directories.

Fundamental Understanding of Mathematics: While the course does not delve into advanced mathematics, having a foundational understanding of basic mathematical concepts (like mean, median, mode, percentage, etc.) would be beneficial for comprehending data analysis concepts.

Logical Thinking: Data analysis involves a significant amount of problem-solving and logical reasoning. So, having an inclination towards logical thinking and problem-solving would be beneficial.

Basic Programming Knowledge (Not Mandatory): Prior experience with any programming language would be helpful but is not required. We will cover the necessary programming concepts as we delve into Python and R.

An Enthusiasm to Learn: Data analysis is a vast field with a variety of tools and techniques. Being keen and open to learning new things would definitely be a plus.

Access to a Computer: As the course involves hands-on exercises and practice, having access to a computer where you can install and use tools like SQL, Python, R, and Power BI is required. Please note, some of these software may have system requirements, so ensure your computer meets those.

Remember, this course is designed to take you from beginner to proficient, so don't worry if you're not familiar with some of the concepts or tools we'll be covering. We'll walk through everything you need to know, step by step, to ensure you're comfortable and understand each topic.

Description

Are you intrigued by the world of data and aspire to become a proficient data analyst? Do you wish to master the essential tools and technologies used in data analysis? If yes, then this course, "Mastering Data Analysis: A Comprehensive Guide to ETL, SQL, Power BI, Python, and R" is for you!The ever-growing expanse of the digital universe has amplified the importance of data analysis across industries worldwide. Businesses, government agencies, and nonprofits are increasingly leveraging data to make strategic decisions, drive operational efficiency, and innovate. Thus, equipping yourself with data analysis skills will not only increase your employability but also provide a platform to significantly impact decision-making processes.Our comprehensive course starts from the fundamentals of data analysis, moving gradually towards more complex concepts and tools. We firmly believe in the power of practical learning, so the course is replete with real-life examples, hands-on exercises, and case studies to ensure you can apply the concepts you learn in real-world scenarios.In the initial modules, you'll gain a broad understanding of data analysis, its applications, and the crucial role of a data analyst. You will also be introduced to ETL (Extract, Transform, Load) processes, the backbone of any data-driven operation, learning how data is collected, cleaned, and stored.Subsequently, we delve into SQL, the language of databases. You'll learn to extract and manipulate data stored in relational databases, starting from simple queries to more advanced topics like subqueries and joins.The following section takes you through Python, a versatile language extensively used in data analysis. You'll get hands-on experience with libraries like Pandas for data manipulation, and Matplotlib and Seaborn for data visualization.Next, we introduce R, a powerful language designed specifically for statistical analysis and data visualization. You'll learn about data structures in R and its various applications, enabling you to handle and analyze complex datasets.Finally, we explore Power BI, Microsoft's flagship business analytics tool. You'll learn to create dashboards and reports, providing interactive visualizations that efficiently communicate your findings.By the end of this course, you will have developed a robust foundation in data analysis. You'll be well-versed in various tools and technologies, from SQL and ETL processes to Python, R, and Power BI. Most importantly, you'll be equipped with the skill to transform raw data into actionable insights, a vital ability in today's data-driven world.This course is suited for beginners with no prior experience, as well as those who wish to consolidate their knowledge in data analysis. Whether you're a student, a working professional, or someone curious about the field, this course provides a comprehensive and practical approach to learning data analysis.Enroll in "Mastering Data Analysis: A Comprehensive Guide to ETL, SQL, Power BI, Python, and R" today and step confidently into the world of data analysis. Your journey towards becoming a skilled data analyst starts here.

Overview

Section 1: Introduction to Data Analysis

Lecture 1 Introduction

Lecture 2 What is Data Analysis?

Lecture 3 Roles and Responsibilities of a Data Analyst

Lecture 4 Introduction to ETL, SQL, Power BI, Python, R

Lecture 5 Tools and Technologies used in Data Analysis

Section 2: SQL for Data Analysis

Lecture 6 Introduction to SQL

Lecture 7 MySQL Database Download for Windows

Lecture 8 MySQL Database Download for Mac

Lecture 9 Introduction to MySQL Workbench

Lecture 10 Installing MySQL Workbench for Mac

Lecture 11 Basic database concepts

Lecture 12 What is a Schema

Lecture 13 Database Schemas

Lecture 14 MySQL Data Types

Lecture 15 SQL basics (Select, Where, From)

Lecture 16 What are SQL Joins

Lecture 17 INNER JOIN

Lecture 18 LEFT JOIN

Lecture 19 RIGHT JOIN

Lecture 20 SELF JOIN

Lecture 21 SQL Group By

Lecture 22 SQL Having

Lecture 23 SQL Order By

Lecture 24 SQL Distinct

Lecture 25 SQL Character Function

Lecture 26 SQL Concat Function

Lecture 27 Introduction to Aggregate Functions

Lecture 28 AVG Function

Lecture 29 COUNT Function

Lecture 30 SUM Function

Lecture 31 MIN Function

Lecture 32 MAX Function

Lecture 33 Introduction to some SQL SQL keywords

Lecture 34 SQL BETWEEN

Lecture 35 SQL IN

Lecture 36 SQL LIKE

Lecture 37 SQL UNION

Lecture 38 SQL Subquery

Lecture 39 Nested Sub query

Section 3: ETL (Extract, Transform, Load) Processes

Lecture 40 Introduction to ETL

Lecture 41 ETL Tools

Lecture 42 Extracting Data

Lecture 43 Transforming Data

Lecture 44 Loading Data

Lecture 45 ETL Best Practices

Section 4: Setting Up Environment for ETL

Lecture 46 What is SQL Server

Lecture 47 SQL Server Version

Lecture 48 Note on Software versions

Lecture 49 SQL Server Download

Lecture 50 SQL Server Installation

Lecture 51 Install SQL Server Management studio -SSMS

Lecture 52 Connect SSMS to SQL Server

Lecture 53 Restore sample database

Lecture 54 Restore sample data warehouse database

Lecture 55 What is Visual Studio

Lecture 56 Install Visual studio

Lecture 57 Visual studio workloads

Lecture 58 Install SQL Server Data Tools - SSDT

Lecture 59 Install SSDT Extensions

Section 5: Implementing and ETL Process with SSIS

Lecture 60 Introduction to SSIS

Lecture 61 Create a new SSIS Project

Lecture 62 Add and configure flat file connection manager

Lecture 63 Remap column data types

Lecture 64 Add and configure OLE DB Connection manager

Lecture 65 Add a data flow task

Lecture 66 Add and configure flat file source

Lecture 67 Add and configure lookup transformation

Lecture 68 Add and configure lookup DateKey Transformation

Lecture 69 Add and configure OLE DB Destination

Lecture 70 Test and run SSIS Package

Section 6: Python Setup

Lecture 71 Introduction to Python

Lecture 72 What is Jupyter Notebook

Lecture 73 Install Python on Windows

Lecture 74 Install Python on Mac

Lecture 75 Install Jupyter Notebook with Anaconda

Lecture 76 Running Jupyter Notebook

Lecture 77 Some Jupyter Notebook Commands

Lecture 78 Jupyter Notebook components

Lecture 79 Jupyter Notebook dashboard

Lecture 80 Jupyter Notebook Interface

Lecture 81 Creating a new Notebook

Section 7: Python Basics

Lecture 82 Python Expressions

Lecture 83 Python Statements

Lecture 84 Python Code Comments

Lecture 85 Python Data Types

Lecture 86 Casting Data Types

Lecture 87 Python Variables

Lecture 88 Python List

Lecture 89 Python Tuples

Lecture 90 Python Dictionaries

Lecture 91 Python Operators

Lecture 92 Python Conditional Statements

Lecture 93 Python Loops

Lecture 94 Python Functions

Section 8: Data Analysis and Data Visualization with Python

Lecture 95 Introduction to Python for Data Analysis (Pandas)

Lecture 96 Introduction to Python for Data Visualization (Matplotlib, Seaborn)

Lecture 97 The dataset

Lecture 98 Tabular Data

Lecture 99 Exploring Pandas DataFrame

Lecture 100 Manipulating Pandas DataFrame

Lecture 101 What is data cleaning

Lecture 102 Perform Data cleaning

Lecture 103 What is data visualization

Lecture 104 Visualizing qualitative data

Lecture 105 Visualizing quantitative data

Section 9: Introduction to R

Lecture 106 What is R

Lecture 107 Installing R for windows

Lecture 108 Installing R for Mac

Lecture 109 What is R Studio

Lecture 110 Installing R Studio on Windows

Lecture 111 Installing R Studio on mac

Lecture 112 Exploring R Studio default interface

Lecture 113 Creating a new project in R Studio

Lecture 114 What are Packages

Lecture 115 How to install Packages

Lecture 116 Datasets vs Data Frames in R

Lecture 117 Loading Packages

Section 10: Data Analysis and Visualization with R

Lecture 118 Importing data into R Studio

Lecture 119 How to read data in a csv file with R

Lecture 120 Installing the Janitor Package

Lecture 121 Selecting a subset of the data

Lecture 122 Performing multiple operations using Pipe Operator

Lecture 123 Cleaning columns

Lecture 124 Creating new columns from existing columns

Lecture 125 Create a new project

Lecture 126 Load data into new project

Lecture 127 What is Data Wrangling

Lecture 128 Data Wrangling Steps

Lecture 129 Importance of Data Wrangling

Lecture 130 Perform Data Wrangling

Lecture 131 Create a Scatter Plot

Lecture 132 Create a bar graph

Section 11: Introduction to Power BI

Lecture 133 What is Power BI

Lecture 134 Setup Microsoft 365

Lecture 135 Getting started with Microsoft 365

Lecture 136 Adding user to Microsoft 365

Lecture 137 Installing Power BI Desktop

Lecture 138 Exploring Power BI Interface

Lecture 139 Power BI Overview - Part 1

Lecture 140 Power BI Overview - Part 2

Lecture 141 Power BI Overview - Part 3

Lecture 142 Components of Power BI

Lecture 143 Exploring Power BI Service

Section 12: Data Analysis and Visualization with Power BI

Lecture 144 Connect to web based data

Lecture 145 Clean and transform data - Part 1

Lecture 146 Clean and transform data - Part 2

Lecture 147 Combining data sources

Lecture 148 Creating Visualizations: Part 1

Lecture 149 Creating Visualizations: Part 2

Lecture 150 Publishing Reports to Power BI Service

Beginners in Data Analysis: Individuals who are just starting out and want to learn the basics of data analysis. This course starts from scratch, introducing the fundamental concepts before moving on to more complex topics.,Career Switchers: Professionals from non-data-oriented fields who are looking to switch their careers and break into the data analysis industry. This course provides a comprehensive understanding of the required tools and techniques.,Current Data Analysts: Existing data analysts who want to consolidate their knowledge and learn new tools and techniques. This course covers a wide range of tools used in the industry.,Students: College or university students who are studying a related field and wish to enhance their practical skills and knowledge to prepare for a career in data analysis.,Professionals Who Work With Data: Individuals who work with data in their current roles and want to enhance their data analysis skills. This could include roles in marketing, finance, product management, and more.,Aspiring Data Scientists: Those planning to become data scientists in the future. This course can be a stepping stone as it covers Python and R, two programming languages commonly used in data science.,Anyone interested in Data: Lastly, anyone who is curious about data analysis and wants to understand how to turn raw data into actionable insights. This course does not require any prerequisites, making it suitable for anyone with a keen interest in the field.