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Full Stack Data Analyst - fessridakla - 05-31-2023
![[Image: 3l2nco-Jbg-Cyi19q4ay-E1bxc-WRFWQs-EWk.jpg]](https://i.postimg.cc/zDHv67rn/3l2nco-Jbg-Cyi19q4ay-E1bxc-WRFWQs-EWk.jpg)
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.
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