Full Stack Data Analyst

Full Stack Data Analyst
Published 5/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 17.02 GB | Duration: 36h 22m

Full Microsoft Excel | SQL | Python |Statistics| R Programming | Power BI | Data Story telling | Full Data Visualization

What you'll learn
Master Complete ????? for Data Analysis
Master Complete ??? for Data Analysis
Master Complete ?????? programming for Data Analysis
Master Complete Data Visualization : ??????? & ????????? ????? ??
Data ???????????? and ???? ????????????
Complete ??? ???????? for Data Analysis
?????????? For Data Analysis in Python and Excel
How to prepare and draw insights from data using Python, Excel, SQL, Tableau, and Power BI
Understand the Data Analysis Ecosystem
Lifetime Access
Master Complete ? ??????????? for Data Analysis

Requirements
This is a beginner to advanced course and the instructor with many years of experience in the industry and classroom breaks the concepts down for anyone at any level to understand. A laptop, internet connections and willingness to learn is enough to succeed in this comprehensive course.

Description
Data Analytics is bringing innovations to the world. The recent incident of Covid and ChatGPT is making the demand for Data Analyst and scientists even more crucial in organizations.Employees with data science and analytical skills are highly valued and paid for in organizations. If you are interested in learning and understanding the field of data analytics, then this course is for you.This is the most COMPREHENSIVE and STRUCTURED course that covers EVERYTHING you need in order to become a Data Analyst.The course is designed for a period of 4-months of intensive lessons and projects. The topics included in this course are update and includes critical areas of demand in 2023 and beyond.Why Should You Enroll In This Particular Course?If you are aiming to become a Data Analyst or switch from one domain to data analysis or even looking to further dive into machine learning or AI field, you should first look at job requirement of the Data Analysis field and see the skills that are needed in order for you to get a job in that field.While creating this course, I have taken time to research many company job postings, including vacancies in my own company and created the course to give students the exact skills that they need in order to crack their Data Analytics interviews and also be successful when employed.In simple terms, this course is:Well structured : a 4-month plan to your dream jobIncludes the exact skills that you need in order to secure a data analytics job.Over 15 diverse projects in different domains : Telecommunication, Banking, Health, E-commerce, Tech, etc.From very BASIC to very ADVANCED concepts.Step-by-step walkthrough of concepts.straight to the point and not wasting time on unnecessary topics that you'll never use in the industry.In the course, you have access to : Industry SQL skills (full course)Industry Python skills (full course)Industry Statistics for Data Analytics skills (full course)Industry R ProgrammingData processingData CleaningDealing with messy dataData VisualizationIndustry Data Story Telling and Presentation SkillsIndustry Microsoft Power BI skills (full course)Github for Data AnalyticsOver 15 diverse Hands-On Real World ProjectsGuide and techniques for searching and applying for Internships and Jobslifetime accessIn this course, you will get to understand the core areas of Data Analytics and the various career opportunities in Data Analytics. After that, you will start with SQL for Data Analytics to give you solid grounds for your data analytics career.This should give you the foundation needed for advanced concepts in data analytics such as Python programming, R programming and cleaning and dealing with messy data.The instructor for this course has considerable years of industry experience as well as classroom experience in working with data in production. He is also instructor of top online courses and books. He combines his industry and academic experience in delivering the lessons. The lessons are broken down for easy understanding. A step by step approach is followed in order to cater for diverse audience such as beginners, intermediate as well as advanced learners.You only need your laptop, internet and willingness to learn and you are good to go.See you in the course.

Overview
Section 1: SQL FOR DATA ANALYTICS

Lecture 1 Overview Of Existing Databases

Lecture 2 The SELECT Statement in Details

Lecture 3 The ORDER BY Clause

Lecture 4 The WHERE Clause

Lecture 5 Operation with SELECT statement

Lecture 6 Aliasing in SQL

Lecture 7 Exercise 1 and solution

Lecture 8 The DISTINCT Keyword

Lecture 9 WHERE Clause with SQL Comparison operators

Lecture 10 Exercise 2 and Solution

Lecture 11 The AND, OR and NOT Operators

Lecture 12 Exercise 3 and Solution

Lecture 13 The IN Operator

Lecture 14 Exercise 4 and Solution

Lecture 15 The BETWEEN Operator

Lecture 16 Exercise 5 and Solution

Lecture 17 The LIKE Operator

Lecture 18 Exercise 6 and Solution

Lecture 19 The REGEXP Operator

Lecture 20 Exercise 7 and Solution

Lecture 21 IS NULL & IS NOT NULL Operator

Lecture 22 Exercise 8 and Solution

Lecture 23 The ORDER BY Clause in Details

Lecture 24 The LIMIT Clause

Lecture 25 Exercise 9 and Solution

Section 2: SQL JOINS

Lecture 26 Introduction To SQL JOINS

Lecture 27 Exercise 10 and Solution

Lecture 28 Joining Across Multiple Databases

Lecture 29 Exercise 11 and Solution

Lecture 30 Joining Table to Itself

Lecture 31 Joining Across Multiple SQL Tables

Lecture 32 LEFT and RIGHT JOIN

Lecture 33 Exercise 12 and Solution

Lecture 34 Exercise 13 and Solution

Section 3: WORKING WITH EXISTING SQL TABLE

Lecture 35 INSERTING Into Existing Table

Lecture 36 INSERTING Multiple Data Into Existing Table

Lecture 37 Creating A Copy of a Table

Lecture 38 Updating Existing Table

Lecture 39 Updating Multiple Records In Existing Table

Section 4: SQL VIEW

Lecture 40 Create SQL VIEW

Lecture 41 Using SQL VIEW

Lecture 42 Alter SQL VIEW

Lecture 43 Drop SQL View

Section 5: SQL DATA SUMMARIZATION: AGGREGATION FUNCTIONS

Lecture 44 COUNT () Function

Lecture 45 SUM() Function

Lecture 46 AVG() Function

Lecture 47 SQL Combined Functions

Section 6: ADVANCE SQL FUNCTIONS

Lecture 48 Count Function in Details

Lecture 49 The HAVING() Function

Lecture 50 LENGTH() Function

Lecture 51 CONCAT() Function

Lecture 52 INSERT() Function

Lecture 53 LOCATE() Function

Lecture 54 UCASE() & LCASE() Function

Section 7: SQL : ADVANCED LEVEL

Lecture 55 Overview

Section 8: SQL STORED PROCEDURE

Lecture 56 Create a Stored Procedure

Lecture 57 Stored Procedure with Single Parameter

Lecture 58 Stored Procedure with Multiple Parameter

Lecture 59 Alter Stored Procedure

Lecture 60 Drop Stored Procedure

Section 9: TRIGGERS

Lecture 61 Introduction to Triggers

Lecture 62 BEFORE Insert Triggers

Lecture 63 AFTER Insert Trigger

Lecture 64 DROP Triggers

Section 10: TRANSACTIONS

Lecture 65 Creating Transactions

Lecture 66 Rollback Transactions

Lecture 67 Savepoint Transactions

Section 11: 4TH MONTH

Lecture 68 Overview

Section 12: MASTER PYTHON FOR DATA ANALYSIS

Lecture 69 Overview

Lecture 70 Lecture resources 1

Lecture 71 Lecture resource 2

Lecture 72 Install and Write Your First Python Code

Section 13: INTRODUCTION TO GOOGLE COLAB

Lecture 73 Google Colab

Section 14: DATASETS

Lecture 74 Download datasets

Section 15: HANDS-ON WITH PYTHON

Lecture 75 Lecture resources

Lecture 76 Python Hands-On: Introduction

Lecture 77 Hands-On With Python: Keywords And Identifiers

Lecture 78 Hands-On Coding- Python Comments

Lecture 79 Hands-On Coding- Python Docstring

Lecture 80 Hands-On Coding- Python Variables

Lecture 81 Hands-On Coding- Rules and Naming Conventions for Python Variables

Section 16: PYTHON OUTPUT(), INPUT() AND IMPORT() FUNCTIONS

Lecture 82 Hands-On Coding- Output() Function In Python

Lecture 83 Hands-On Coding- Input() Function In Python

Lecture 84 Hands-On Coding- Import() Function In Python

Section 17: PYTHON OPERATORS

Lecture 85 Hands-On Coding- Arithmetic Operators

Lecture 86 Hands-On Coding- Comparison Operators

Lecture 87 Hands-On Coding- Logical Operators

Lecture 88 Hands-On Coding- Bitwise Operators

Lecture 89 Hands-On Coding- Assignment Operators

Lecture 90 Python Hands-On- Special Operators

Lecture 91 Hands-On Coding- Membership Operators

Section 18: PYTHON FLOW CONTROL

Lecture 92 If Statement

Lecture 93 If...Else Statement

Lecture 94 ELif Statement

Lecture 95 For loop

Lecture 96 While loop

Lecture 97 Break Statement

Lecture 98 Continue Statement

Section 19: WEEK 2: PYTHON FUNCTIONS

Lecture 99 User Define Functions

Lecture 100 Arbitrary Arguments

Lecture 101 Function With Loops

Lecture 102 Lambda Function

Lecture 103 Built-In Function

Section 20: PYTHON GLOBAL AND LOCAL VARIABLES

Lecture 104 Local Variable

Lecture 105 Global Variable

Section 21: WORKING WITH FILES IN PYTHON

Lecture 106 Python Files

Lecture 107 The Close Method

Lecture 108 The With Statement

Lecture 109 Writing To A File In Python

Section 22: PYTHON MODULES

Lecture 110 Python Modules

Lecture 111 Renaming Modules

Lecture 112 The from...import Statement

Section 23: PYTHON PACKAGES AND LIBRARIES

Lecture 113 Python Packages and Libraries

Lecture 114 PIP Install Python Libraries

Section 24: DATA TYPES IN PYTHON

Lecture 115 Lecture resources

Lecture 116 Lesson 1: Integer & Floating Point Numbers

Lecture 117 Lesson 2: Complex Numbers & Strings

Lecture 118 Lesson 3: LIST

Lecture 119 Lesson 4: Tuple & List Mutability

Lecture 120 Lesson 5: Tuple Immutability

Lecture 121 Lesson 6: Set

Lecture 122 Lesson 7: Dictionary

Lecture 123 Range In Python

Section 25: EXTRA CONTENT

Lecture 124 LIST

Lecture 125 Working On List

Lecture 126 Splitting Function

Lecture 127 List Comprehension In Python

Section 26: NUMPY

Lecture 128 Lecture resources

Lecture 129 Introduction To Numpy

Lecture 130 Numpy: Creating Multi-Dimensional Arrays

Lecture 131 Numpy: Arange Function

Lecture 132 Numpy: Zeros, Ones and Eye functions

Lecture 133 Numpy: Reshape Function

Lecture 134 Numpy: Linspace

Lecture 135 Numpy: Resize Function

Lecture 136 Numpy:Generating Random Values With random.rand

Lecture 137 Numpy:Generating Random Values With random.randn

Lecture 138 Numpy:Generating Random Values With random.randint

Lecture 139 Numpy: Indexing & Slicing

Lecture 140 Numpy: Broadcasting

Lecture 141 Numpy: How To Create A Copy Dataset

Lecture 142 Numpy- DataFrame Introduction

Section 27: NUMPY ASSIGNMENT

Lecture 143 Numpy Assignment

Section 28: PANDAS

Lecture 144 Pandas- Series 1

Lecture 145 Pandas- Series 2

Lecture 146 Pandas- Loc & iLoc

Lecture 147 Pandas- DataFrame Introduction

Lecture 148 Pandas- Operations On Pandas DataFrame

Lecture 149 Pandas- Selection And Indexing On Pandas DataFrame

Lecture 150 Pandas- Reading A Dataset Into Pandas DataFrame

Lecture 151 Pandas- Adding A Column To Pandas DataFrame

Lecture 152 Pandas- How To Drop Columns And Rows In Pandas DataFrame

Lecture 153 Pandas- How To Reset Index In Pandas Dataframe

Lecture 154 Pandas- How To Rename A Column In Pandas Dataframe

Lecture 155 Pandas- Tail(), Column and Index

Lecture 156 Pandas- How To Check For Missing Values or Null Values(isnull() Vs Isna())

Lecture 157 Pandas- Pandas Describe Function

Lecture 158 Pandas- Conditional Selection With Pandas

Lecture 159 Pandas- How To Deal With Null Values

Lecture 160 Pandas- How To Sort Values In Pandas

Lecture 161 Pandas- Pandas Groupby

Lecture 162 Pandas- Count() & Value_Count()

Lecture 163 Pandas- Concatenate Function

Lecture 164 Pandas- Join & Merge(Creating Dataset)

Lecture 165 Pandas-Join

Lecture 166 Pandas- Merge

Section 29: DATA VISUALISATION: MATPLOTIIB AND SEABORN

Lecture 167 Lecture resources

Lecture 168 Matplotlib | Subplots

Lecture 169 Univariate | Bivariate | Multivariate Data Visualisation

Lecture 170 Seborn | Scatterplot | Correlation | Boxplot | Heatmap

Section 30: WEB SCRAPING

Lecture 171 Lecture resources

Lecture 172 Introduction To Web Scraping Libraries

Lecture 173 Library- Requests

Lecture 174 Library- BeautifulSoup

Lecture 175 Library- Selenium

Lecture 176 Library- Scrapy

Section 31: PROJECT: WIKIPEDIA WEB SCRAPING

Lecture 177 Web Scraping On Wikipedia

Section 32: ONLINE BOOK STORE WEB SCRAPPING

Lecture 178 Critical Analysis Of Web Pages

Lecture 179 PART 1- Examining And Scraping Individual Entities From Source Page

Lecture 180 PART 2- Examining And Scraping Individual Entities From Source Page

Lecture 181 Data Preprocessing On Scraped Data

Section 33: JOB BOARD DATA WEB SCRAPING AUTOMATION WITH PYTHON

Lecture 182 lecture resources

Lecture 183 Problem Statement & Dataset

Lecture 184 Demystify The Structure Of Web Page URLs

Lecture 185 Formulating Generic Web Page URLs

Lecture 186 Forming The Structure Of Web Page URLs

Lecture 187 Creating A DataFrame For Scraped Data

Lecture 188 Creating A Generic Auto Web Scraper

Section 34: UBER DATA ANALYSIS WITH PYTHON

Lecture 189 Lecture resources

Lecture 190 PROJECT 1: Analyse The Top Movie Streaming | NETFLIX | Amazon Prime | Hu

Lecture 191 Uber Data Analysis With Python

Section 35: 3RD MONTH

Lecture 192 Overview

Section 36: STATISTICS FOR DATA ANALYTICS

Lecture 193 Overview

Section 37: WEEK 1 :: MASTER STATISTICS FOR DATA ANALYTICS

Lecture 194 Lecture resources

Lecture 195 Statistics For Data Analytics Curriculum

Lecture 196 Why Statistics Is Important For Data Analytics

Lecture 197 How Much Maths Do I Need To Know?

Section 38: STATISTICAL METHODS DEEP DIVE

Lecture 198 Statistical Methods Deep Dive

Lecture 199 Types Of Statistics

Lecture 200 Common Statistical Terms

Section 39: DATA

Lecture 201 What Is Data?

Lecture 202 Data Types

Lecture 203 Data Attributes and Data Sources

Lecture 204 Structured Vs Unstructured Data

Section 40: FREQUENCY DISTRIBUTION

Lecture 205 Frequency Distribution

Section 41: CENTRAL TENDENCY

Lecture 206 Central Tendency

Lecture 207 Mean,Median, Mode

Section 42: MEASURES OF DISPERSION

Lecture 208 Measures of Dispersion

Lecture 209 Variance and Standard Deviation

Lecture 210 Example of Variance and Standard Deviation

Lecture 211 Variance and Standard Deviation In Python

Section 43: COEFFICIENT OF VARIATIONS

Lecture 212 Coefficient of Variations

Section 44: THE FIVE NUMBER SUMMARY & THE QUARTILES

Lecture 213 The Five Number Summary & The Quartiles

Lecture 214 The Quartiles: Q1 | Q2 | Q3 | IQR

Section 45: THE NORMAL DISTRIBUTION

Lecture 215 Introduction To Normal Distribution

Lecture 216 Skewed Distributions

Lecture 217 Central Limit Theorem

Section 46: CORRELATION

Lecture 218 Introduction to Correlation

Lecture 219 Scatterplot For Correlation

Lecture 220 Correlation is NOT Causation

Section 47: WEEK 2 :: PROBABILITY

Lecture 221 Why Probability In Data Analytics?

Lecture 222 Probability Key Concepts

Lecture 223 Mutually Exclusive Events

Lecture 224 Independent Events

Lecture 225 Rules For Computing Probability

Section 48: BAYE'S THEOREM

Lecture 226 Baye's Theorem

Section 49: HYPOTHESIS TESTING

Lecture 227 Introduction To Hypothesis

Lecture 228 Null Vs Alternative Hypothesis

Lecture 229 Setting Up Null and Alternative Hypothesis

Lecture 230 One-tailed Vs Two-tailed test

Lecture 231 Key Points On Hypothesis Testing

Lecture 232 Type 1 vs Type 2 Errors

Lecture 233 Process Of Hypothesis testing

Lecture 234 P-Value

Lecture 235 Alpha-Value or Alpha Level

Lecture 236 Confidence Level

Section 50: PROJECT: STATISTICS FOR DATA ANALYTICS

Lecture 237 Implementation of the Stats Concepts

Lecture 238 Problem Statement

Lecture 239 Sample Solution

Section 51: R PROGRAMMING FOR DATA ANALYTICS

Lecture 240 Introduction to R Programming for Data Analytics

Lecture 241 R programming Installation

Lecture 242 The R Environments

Lecture 243 Introduction to RStudio

Lecture 244 Getting to Know the RStudio Environment

Lecture 245 Working with Raw Data in R Comments in R

Lecture 246 Install Packages in R

Lecture 247 Tidyverse Package

Lecture 248 The Piping Command

Lecture 249 Loading Inbuilt Datasets

Lecture 250 Loading External Datasets

Lecture 251 Using colors in R

Lecture 252 Creating bar charts

Lecture 253 Creating histograms

Lecture 254 Creating box plots

Lecture 255 Creating scatterplots

Lecture 256 Creating line charts

Lecture 257 Creating cluster charts

Lecture 258 Selecting cases and subgroups

Lecture 259 Recoding variables

Lecture 260 Computing new variables

Lecture 261 Computing frequencies

Lecture 262 Computing descriptives

Lecture 263 Computing correlations

Lecture 264 Computing contingency tables

Section 52: MICROSOFT POWER BI

Lecture 265 Lecture resource

Lecture 266 Power BI: An Introduction

Lecture 267 Installation

Lecture 268 Query Editor Overview

Lecture 269 Connectors and Get Data Into Power BI

Lecture 270 Clean up Messy Data (PART 1)

Lecture 271 Clean up Messy Data (PART 2)

Lecture 272 Clean up Messy Data (PART 3)

Lecture 273 Creating Relationships

Lecture 274 Explore Data Using Visuals

Lecture 275 Analyzing Multiple Data Tables Together

Lecture 276 Writing DAX Measure (Implicit vs. Explicit Measures)

Lecture 277 Calculated Column

Lecture 278 Measure vs. Calculated Column

Lecture 279 Hybrid Measures

Lecture 280 The 80/20 Rule

Lecture 281 Text, Image, Cards, Shape

Lecture 282 Conditional Formatting

Lecture 283 Line Chart, Bar Chart

Lecture 284 Top 10 Products/Customers

Section 53: DATA STORY TELLING & PRESENTATION SKILLS

Lecture 285 Lecture resources

Lecture 286 Introduction to story telling and data presentation

Lecture 287 Defining a Story

Lecture 288 Making Connections

Lecture 289 Story Helpers

Lecture 290 The 3 Phases of a Story

Lecture 291 Include plot : The 7 plots

Lecture 292 Create A Character

Lecture 293 Know Your Audience : The Warm Up Room

Lecture 294 The 5 Types of Audience

Lecture 295 Believe In Your Story

Lecture 296 Work with data

Lecture 297 Data Presentations

Section 54: GITHUB FOR DATA ANALYTICS

Lecture 298 Lecture resource

Lecture 299 Introduction To Github For Data Analytics

Lecture 300 Setting up Github account for Data Analytics projects

Lecture 301 Create Github Profile for Data Analytics

Lecture 302 Create Github Project Description for Data Analytics

Section 55: PROJECT: YOUTUBE VIDEO ANALYSIS

Lecture 303 Project resources

Lecture 304 Introduction: Youtube Video Analysis

Lecture 305 Youtube Video Analysis

Section 56: NUTRITIONAL ANALYSIS ON MCDONALD'S MENU

Lecture 306 Project resources

Lecture 307 Introduction : Nutritional Analysis On McDonald's menu

Lecture 308 Nutritional Analysis On McDonald's menu

Section 57: ANALYSIS OF AMERICAN UNIVERSITIES

Lecture 309 Project resources

Lecture 310 Introduction: University Analysis

Lecture 311 PART 1: University Analysis

Lecture 312 PART 2: University Analysis

Lecture 313 PART 3: University Analysis

Section 58: AUSTRALIAN SHOPPING CART ANALYSIS

Lecture 314 Project resources

Lecture 315 PART 1: Australian Shopping Cart Analysis

Lecture 316 PART 2: Australian Shopping Cart Analysis

Section 59: RECOMMENDED PROJECTS

Lecture 317 Recommended projects

Section 60: GUIDE TO FINDING INTERNSHIPS & JOBS

Lecture 318 Virtual Internship Overview

Lecture 319 Internships & Jobs

Students who want to become Data Analyst and are serious about their career,Working professionals who want to transition to the field of Data Analytics and Data Science,Anyone interesting in diving deeper into driving critical insights from data,Anyone interesting in diving deeper into knowing how to deal with messy data,Anyone finding it difficult to understand the field and concepts in Data Analytics and wants a breakdown step-by-step guide in understanding these concepts.,Anyone who wants to be career secured and not easily affected by layoffs in organizations,Anyone looking for salary hikes and increase in salary with a lucrative tech career.,NB: This course is not for lazy students who are not serious about their career. I spent a lot of time creating a comprehensive course like this, I expect you to be serious about your career. The course is good and there is no two ways about it. You just have to be serious to work towards your goals and we can achieve it together.

HOMEPAGE

 https://www.udemy.com/course/full-stack-data-analystics/

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