Complete Data Analyst Bootcamp From Basics To Advanced
Complete Data Analyst Bootcamp From Basics To Advanced
Published 8/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 33.51 GB | Duration: 50h 51m
Master Data Analysis: Python, Statistics, EDA, Feature Engineering, Power BI, and SQL Server in Comprehensive Bootcamp
What you'll learn
Learn how to efficiently manipulate, analyze, and visualize data using Python and its powerful libraries such as Pandas, NumPy, Matplotlib, and Seaborn.
Develop the skills to retrieve, manipulate, and aggregate data using SQL. You'll work with SQL Server to manage complex databases and execute advanced queries.
Discover how to perform EDA to uncover insights, identify patterns, and prepare data for further analysis through effective data visualization
Learn to build interactive and insightful dashboards using Power BI, applying DAX for complex calculations, and integrating real-world data to produce reports
Requirements
A basic understanding of how to navigate your computer, including installing software and managing files, is essential.
Some experience with spreadsheet software like Microsoft Excel or Google Sheets will be helpful, as it will give you a foundation for data manipulation and basic analysis concepts
This course starts from the basics of Python, so no prior programming knowledge is necessary. However, a willingness to learn coding is important.
An eagerness to explore data, solve problems, and develop new skills is key to getting the most out of this bootcamp.
Description
Are you ready to embark on a rewarding career as a Data Analyst? Whether you're a beginner or an experienced professional looking to enhance your skills, this Complete Data Analyst Bootcamp is your one-stop solution. This course is meticulously designed to equip you with all the essential tools and techniques needed to excel in the field of data analysis.What You Will Learn:Python Programming for Data AnalysisDive into Python, the most popular programming language in data science. You'll learn the basics, including data types, control structures, and how to manipulate data with powerful libraries like Pandas and NumPy. By the end of this module, you'll be able to perform complex data manipulations and basic analyses with ease.Statistics for Data ScienceUnderstanding the language of data requires a solid foundation in statistics. This course will take you through the key concepts such as descriptive statistics, probability, hypothesis testing, and inferential statistics. You'll gain the confidence to make data-driven decisions and interpret statistical results accurately.Feature Engineering and Data PreprocessingData preparation is critical for successful analysis. This module covers all aspects of feature engineering, from handling missing data and encoding categorical variables to feature scaling and selection. Learn how to transform raw data into meaningful features that improve model performance and analysis outcomes.Exploratory Data Analysis (EDA)Before diving into data modeling, it's crucial to understand your data. EDA is the process of analyzing data sets to summarize their main characteristics, often with visual methods. You'll learn how to identify trends, patterns, and outliers using visualization tools like Matplotlib and Seaborn. This step is essential for uncovering insights and ensuring data quality.SQL for Data AnalystsSQL (Structured Query Language) is the backbone of database management and a must-have skill for any data analyst. This course will guide you from the basics of SQL to advanced querying techniques. You'll learn how to retrieve, manipulate, and aggregate data efficiently using SQL Server, enabling you to work with large datasets and perform sophisticated data analysis.Power BI for Data Visualization and ReportingData visualization is key to communicating your findings effectively. In this module, you'll master Power BI, a leading business intelligence tool. You'll learn how to create compelling dashboards, perform data transformations, and use DAX (Data Analysis Expressions) for complex calculations. The course also includes real-world reporting projects, allowing you to apply your skills and create professional-grade reports.Real-World Capstone ProjectsPut your knowledge to the test with hands-on capstone projects. You'll work on real-world datasets to perform end-to-end data analysis, from data cleaning and EDA to creating insightful visualizations and reports in Power BI. These projects are designed to simulate actual industry challenges, giving you practical experience that you can showcase in your portfolio.Who Should Enroll:Aspiring data analysts looking to build a comprehensive skill set from scratch.Professionals seeking to switch careers into data analysis.Data enthusiasts who want to gain hands-on experience with Python, SQL, and Power BI.Students and recent graduates aiming to enhance their job prospects in the data science industry.Why This Course?Comprehensive Curriculum: Covers everything from Python programming and statistics to SQL and Power BI, making you job-ready.Hands-On Learning: Work on real-world projects that mirror the challenges you'll face in the industry.Industry-Relevant Tools: Learn the most in-demand tools and technologies, including Python, SQL Server, and Power BI.Career Support: Gain access to valuable resources and guidance to help you kickstart or advance your career as a data analyst.Conclusion:By the end of this course, you'll have a strong foundation in data analysis and the confidence to tackle real-world data problems. You'll be ready to step into a data analyst role with a robust portfolio of projects to showcase your skills.Enroll now and start your journey to becoming a proficient Data Analyst!
Overview
Section 1: Introduction To The Course
Lecture 1 What Does A Data Analyst Do and Its Roadmap
Section 2: Getting Started With Python
Lecture 2 Getting Started With Google Colab
Lecture 3 Installation Of Anaconda And Visual Studio Code
Section 3: Complete Python With Important Libraries
Lecture 4 Getting Started With VS Code With Environments
Lecture 5 Python Basics-Syntax And Semantics
Lecture 6 Variables In Python
Lecture 7 Basic Data Types In Python
Lecture 8 Operators In Python
Lecture 9 Coding Excercise And Assignments
Lecture 10 Conditional Statements(if,elif,else)
Lecture 11 Loops In Python
Lecture 12 Coding Excercise And Assignments
Lecture 13 List And List Comprehrension In Python
Lecture 14 List Practise Code And assignments
Lecture 15 Tuples In Python
Lecture 16 Tuple Assignment And Practise Code
Lecture 17 Sets In Python
Lecture 18 Sets Assignment and Practise Code
Lecture 19 Dictionaries In Python
Lecture 20 Dictionaries Assignments and PRactise Questions
Lecture 21 REal World Usecases Of List
Lecture 22 Getting Started With Functions
Lecture 23 More Coding Examples With Functions
Lecture 24 Lambda functions
Lecture 25 Map functions In Python
Lecture 26 Filter Function In Python
Lecture 27 Function Assignments With Solution
Lecture 28 Import Modules And Packages In Python
Lecture 29 Standard Library Overview
Lecture 30 File Operation In Python
Lecture 31 Working With File Paths
Lecture 32 Exception Handling With Try Except else finally blocks
Section 4: Data Analysis With Python
Lecture 33 Numpy In Python
Lecture 34 Pandas-DataFrame And Series
Lecture 35 Data Manipulation With Pandas And Numpy
Lecture 36 Numpy Assignments With solution
Lecture 37 Reading Data From Various Data Source Using Pandas
Lecture 38 Data Visulaization With Matplotlib
Lecture 39 Data Visualization With Seaborn
Section 5: Getting Started With Statistics
Lecture 40 Introduction To Statistics
Lecture 41 Types Of Statistics
Lecture 42 Population And Sample Data
Lecture 43 Types Of Sampling Techniques
Lecture 44 Types Of Data
Lecture 45 Scales Of Measurement Of Data
Section 6: Descriptive Statistics
Lecture 46 Measure Of Central Tendency(Mean,Median And Mode)
Lecture 47 Measures Of Dispersion(Range,Variance,Standard Deviation)
Lecture 48 Why Sample Variance is divided by n-1
Lecture 49 Random Variables
Lecture 50 Percentiles And Quartiles
Lecture 51 5 Number Summary
Lecture 52 Histogram And Skewness
Lecture 53 Covariance And Correlation
Section 7: Probability Distribution Function And Types OF Distribution
Lecture 54 Pdf, PMF, CDF
Lecture 55 Types OF Probability Distribution
Lecture 56 Bernoulli Distribution
Lecture 57 Binomial Distribution
Lecture 58 Poisson Distribution
Lecture 59 Normal or Gaussian Distribution
Lecture 60 Standard Normal Distribution
Lecture 61 Uniform Distribution
Lecture 62 Log Normal Distribution
Lecture 63 10-Power Law Distribution
Lecture 64 11-Pareto Distribution
Lecture 65 Central Limit Theorem
Lecture 66 Estimates
Section 8: Inferential Stats And Hypothesis Testing
Lecture 67 Hypothesis Testing And Mechanism
Lecture 68 P value And Hypothesis Testing
Lecture 69 Z test Hypothesis Testing
Lecture 70 Student t Distribution
Lecture 71 T stats With T Test and Hypothesis Testing
Lecture 72 Z test vs T test
Lecture 73 Type1 And Type 2 Error
Lecture 74 Baye's Theorem
Lecture 75 Confidence Interval And Margin Of Error
Lecture 76 What is Chi Square Test
Lecture 77 Chi Square Goodness Of Fitness
Lecture 78 What is Anova
Lecture 79 Assumptions Of Anova
Lecture 80 Types Of Annova
Lecture 81 Partioning OF Annova
Section 9: Feature Engineering With Python
Lecture 82 Feature Engineering-Handling Missing Data
Lecture 83 Feature Engineering-Handling Imbalanced Dataset
Lecture 84 Feature Engineering-SMOTE
Lecture 85 Handling Outliers With Python
Lecture 86 Data Encoding-Nominal/One Hot Encoding
Lecture 87 Label And Ordinal Encoding
Lecture 88 Target Guided Ordinal Encoding
Section 10: Exploratory Data Analysis
Lecture 89 Red Wine Dataset EDA
Lecture 90 EDA Flight Price Dataset
Lecture 91 Part 1-Data Cleaning Google Playstore Dataset
Lecture 92 Part 2-EDA Google Play Store Dataset
Section 11: SQL : Course Introduction & Overview
Lecture 93 SQL Course Introduction
Lecture 94 SQL Overview
Lecture 95 SQL Server Download & Install
Section 12: Microsoft SQL Server basics
Lecture 96 SQL Select Statement
Lecture 97 SQL Select Distinct
Lecture 98 SQL Temp Tables
Lecture 99 SQL Where Clause
Lecture 100 SQL Order By Clause
Lecture 101 SQL AND & OR Operator
Lecture 102 SQL NOT, BETWEEN & IN Operators
Lecture 103 SQL Insert Into
Lecture 104 SQL Null Operator
Lecture 105 SQL Update Statement
Lecture 106 Delete, Drop & Truncate
Lecture 107 SQL Comments & TOP N
Lecture 108 SQL MAX & Group BY
Lecture 109 SQL MIN Function & Group BY
Lecture 110 SUM, AVG, COUNT & Group BY
Lecture 111 Group BY Concept
Lecture 112 Group BY Example SQL Server
Lecture 113 SQL Having Clause
Lecture 114 SQL Where & Having Clause Difference
Lecture 115 Inner Join Concept
Lecture 116 Inner Join Eample
Lecture 117 Left Join Concept
Lecture 118 Left Join Eample
Lecture 119 Right Join Concept
Lecture 120 Right Join Example
Lecture 121 Left & Right Anti Join
Lecture 122 Left & Right Anti Join Example
Lecture 123 Full Outer Join
Lecture 124 Self Join
Lecture 125 Union & Union All
Lecture 126 SQL Like Operator
Lecture 127 SQL Case in Select statement & Order BY Clause
Lecture 128 Nested CASE statement
Lecture 129 SQL Data Types
Lecture 130 SQL Create Table
Lecture 131 Inserting Records into All Columns of the Table
Lecture 132 Inserting Records into Certain Columns in a Table
Lecture 133 Copying Data From One Table to Another
Lecture 134 Sub Queries
Lecture 135 Not Null Constraint
Lecture 136 Unique Constraint
Lecture 137 Check Constraint
Lecture 138 Default Constraint
Lecture 139 Primary & Foreign Key Concept
Lecture 140 Primary Key Constraint
Lecture 141 Foreign Key Constraint
Section 13: SQL Basics Questions
Lecture 142 Questions Set - 1
Lecture 143 Questions Set - 2
Section 14: SQL Assignments
Section 15: SQL Functions
Lecture 144 Rank, Dense Rank & Row Number Window Functions - 1
Lecture 145 Rank, Dense Rank & Row Number Window Functions - 2
Lecture 146 Window Functions - Lead Function
Lecture 147 Window Functions - Lag Function
Lecture 148 ISNULL & Coalesce Functions
Section 16: Advanced SQL
Lecture 149 Common Table Expressions - 1
Lecture 150 Common Table Expressions - 2
Lecture 151 Recursive Common Table Expressions
Lecture 152 Stored Procedure in MS SQL Server
Lecture 153 Views in MS SQL Server
Lecture 154 Indexes in MS SQL Server
Section 17: SQL Important Interview Questions
Lecture 155 Nth Highest Salary
Lecture 156 Reportee & Manager Question
Lecture 157 Deleting Duplicates Q1
Lecture 158 Deleting Duplicates Q2
Section 18: Power BI Course Introduction
Lecture 159 Microsoft Power BI Course Introduction
Section 19: Introduction to Power BI
Lecture 160 General Workflow Power BI
Lecture 161 Downloading & Installing Power BI Desktop
Lecture 162 Creating a Free Power BI Account
Section 20: Data Visualization
Lecture 163 Creating a Bar Chart
Lecture 164 Creating a Column Chart
Lecture 165 Creating a Pie & a Donut Chart
Lecture 166 Creating a Clustered Column & Bar Chart
Lecture 167 Creating a Line & Area Chart
Lecture 168 Creating a Ribbon Chart
Lecture 169 Creating a line & stacked column chart
Lecture 170 Creating a Line & Clustered Column Chart
Lecture 171 Creating a Scatter Plot
Lecture 172 Creating a Bubble Map Visual
Lecture 173 Creating a Table & Matrix Visual
Lecture 174 Formatting Table & Matrix Visual
Lecture 175 Creating a Funnel Chart
Lecture 176 Gauge chart & KPI Visual
Lecture 177 AI Visuals in Power BI
Section 21: Power Query Editor
Lecture 178 Detecting Data Types in Power BI Desktop
Lecture 179 Data Profiling
Lecture 180 Column Distribution Example
Lecture 181 Appending Queries
Lecture 182 Merge Inner Join
Lecture 183 Left Outer Join
Lecture 184 Right Outer Join
Lecture 185 Left & Right Anti Join
Lecture 186 Full Outer Join
Lecture 187 Group By in Power Query Editor
Lecture 188 Pivot, Unpivot & Transpose
Lecture 189 Add/Transform Columns
Section 22: DAX
Lecture 190 DAX Lecture 1
Lecture 191 DAX Lecture 2
Lecture 192 DAX Lecture 3
Lecture 193 DAX Lecture 4
Lecture 194 DAX Lecture 5
Lecture 195 DAX Lecture 6
Section 23: Power BI Project 1, Sales Data Analysis
Lecture 196 Business Requirements
Lecture 197 Loading Data to PBI Desktop
Lecture 198 Data Profiling & Data Transformations Part 1
Lecture 199 Data Transformations Part 2
Lecture 200 Primary & Foreign Key
Lecture 201 Cardinality
Lecture 202 Star Schema
Lecture 203 Data Model Overview
Lecture 204 Different Types of Filters in Filters Pane
Lecture 205 Top Bottom 5 Products By Sales, Quantity and Profit
Lecture 206 Sales Trends Over Time
Lecture 207 Other Requirements
Lecture 208 Requirement 4 DAX
Lecture 209 Requirement 4 Edit Interactions
Lecture 210 Other Remaining Requirements
Lecture 211 Changing Filter Behaviour for Dimension Table Slicers
Section 24: Power BI Project 2, Insurance Data Analysis
Lecture 212 Downloading & Installing MSSQL Server
Lecture 213 Importing Data to MSSQL Server
Lecture 214 Loading Data to Power BI Desktop
Lecture 215 Table View & Data Profiling
Lecture 216 Adding Slicers & Text
Lecture 217 Adding New Card Visuals
Lecture 218 Adding a Multi Row Card & a Ribbon Chart
Lecture 219 Adding a Bar & a Line Chart
Lecture 220 Adding a Donut Chart & Matrix Visual
Lecture 221 Publishing the Report to Power BI Service
Lecture 222 Scheduling Refresh
Lecture 223 Drill Through Filter
Lecture 224 Testing Scheduled Refresh & Publishing the updated report
Lecture 225 Creating & Testing Roles in PBI Desktop
Lecture 226 Testing & Implementing RLS in Power BI Service
Lecture 227 Power BI Reports & Dashboards
Lecture 228 Sentiment Analysis Power Query
Lecture 229 Sentiment Analysis Adding Visuals to the Report
Section 25: Power BI Project 3, UPI Transactions Data Analysis
Lecture 230 Loading Data into Power BI Desktop
Lecture 231 Data Profiling
Lecture 232 Size & Position of slicers
Lecture 233 Formatting the Slicers
Lecture 234 Adding a Page & Age Group Column
Lecture 235 Adding a Line Chart
Lecture 236 Adding a Matrix Visual
Lecture 237 Syncing Slicers & Applying Conditional Formatting
Lecture 238 Adding Bookmarks for Transactions
Lecture 239 Adding Bookmarks for Remaining Balance
Lecture 240 Publishing the Report to Power BI Service
Individuals looking to start a career in data analysis and gain a comprehensive skill set from the ground up.,Professionals from other fields who want to transition into data analysis and need a structured, all-inclusive learning path.,Those pursuing degrees in fields like computer science, statistics, business, or related areas who want to enhance their job prospects with practical, industry-relevant skills.,Anyone with an interest in data, who wants to learn how to analyze, visualize, and make data-driven decisions, whether for professional development or personal projects.,Individuals already in the data industry or related fields who wish to sharpen their skills, learn new tools like Python, SQL, and Power BI, and take on more advanced data analysis tasks.
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What you'll learn
Learn how to efficiently manipulate, analyze, and visualize data using Python and its powerful libraries such as Pandas, NumPy, Matplotlib, and Seaborn.
Develop the skills to retrieve, manipulate, and aggregate data using SQL. You'll work with SQL Server to manage complex databases and execute advanced queries.
Discover how to perform EDA to uncover insights, identify patterns, and prepare data for further analysis through effective data visualization
Learn to build interactive and insightful dashboards using Power BI, applying DAX for complex calculations, and integrating real-world data to produce reports
Requirements
A basic understanding of how to navigate your computer, including installing software and managing files, is essential.
Some experience with spreadsheet software like Microsoft Excel or Google Sheets will be helpful, as it will give you a foundation for data manipulation and basic analysis concepts
This course starts from the basics of Python, so no prior programming knowledge is necessary. However, a willingness to learn coding is important.
An eagerness to explore data, solve problems, and develop new skills is key to getting the most out of this bootcamp.
Description
Are you ready to embark on a rewarding career as a Data Analyst? Whether you're a beginner or an experienced professional looking to enhance your skills, this Complete Data Analyst Bootcamp is your one-stop solution. This course is meticulously designed to equip you with all the essential tools and techniques needed to excel in the field of data analysis.What You Will Learn:Python Programming for Data AnalysisDive into Python, the most popular programming language in data science. You'll learn the basics, including data types, control structures, and how to manipulate data with powerful libraries like Pandas and NumPy. By the end of this module, you'll be able to perform complex data manipulations and basic analyses with ease.Statistics for Data ScienceUnderstanding the language of data requires a solid foundation in statistics. This course will take you through the key concepts such as descriptive statistics, probability, hypothesis testing, and inferential statistics. You'll gain the confidence to make data-driven decisions and interpret statistical results accurately.Feature Engineering and Data PreprocessingData preparation is critical for successful analysis. This module covers all aspects of feature engineering, from handling missing data and encoding categorical variables to feature scaling and selection. Learn how to transform raw data into meaningful features that improve model performance and analysis outcomes.Exploratory Data Analysis (EDA)Before diving into data modeling, it's crucial to understand your data. EDA is the process of analyzing data sets to summarize their main characteristics, often with visual methods. You'll learn how to identify trends, patterns, and outliers using visualization tools like Matplotlib and Seaborn. This step is essential for uncovering insights and ensuring data quality.SQL for Data AnalystsSQL (Structured Query Language) is the backbone of database management and a must-have skill for any data analyst. This course will guide you from the basics of SQL to advanced querying techniques. You'll learn how to retrieve, manipulate, and aggregate data efficiently using SQL Server, enabling you to work with large datasets and perform sophisticated data analysis.Power BI for Data Visualization and ReportingData visualization is key to communicating your findings effectively. In this module, you'll master Power BI, a leading business intelligence tool. You'll learn how to create compelling dashboards, perform data transformations, and use DAX (Data Analysis Expressions) for complex calculations. The course also includes real-world reporting projects, allowing you to apply your skills and create professional-grade reports.Real-World Capstone ProjectsPut your knowledge to the test with hands-on capstone projects. You'll work on real-world datasets to perform end-to-end data analysis, from data cleaning and EDA to creating insightful visualizations and reports in Power BI. These projects are designed to simulate actual industry challenges, giving you practical experience that you can showcase in your portfolio.Who Should Enroll:Aspiring data analysts looking to build a comprehensive skill set from scratch.Professionals seeking to switch careers into data analysis.Data enthusiasts who want to gain hands-on experience with Python, SQL, and Power BI.Students and recent graduates aiming to enhance their job prospects in the data science industry.Why This Course?Comprehensive Curriculum: Covers everything from Python programming and statistics to SQL and Power BI, making you job-ready.Hands-On Learning: Work on real-world projects that mirror the challenges you'll face in the industry.Industry-Relevant Tools: Learn the most in-demand tools and technologies, including Python, SQL Server, and Power BI.Career Support: Gain access to valuable resources and guidance to help you kickstart or advance your career as a data analyst.Conclusion:By the end of this course, you'll have a strong foundation in data analysis and the confidence to tackle real-world data problems. You'll be ready to step into a data analyst role with a robust portfolio of projects to showcase your skills.Enroll now and start your journey to becoming a proficient Data Analyst!
Overview
Section 1: Introduction To The Course
Lecture 1 What Does A Data Analyst Do and Its Roadmap
Section 2: Getting Started With Python
Lecture 2 Getting Started With Google Colab
Lecture 3 Installation Of Anaconda And Visual Studio Code
Section 3: Complete Python With Important Libraries
Lecture 4 Getting Started With VS Code With Environments
Lecture 5 Python Basics-Syntax And Semantics
Lecture 6 Variables In Python
Lecture 7 Basic Data Types In Python
Lecture 8 Operators In Python
Lecture 9 Coding Excercise And Assignments
Lecture 10 Conditional Statements(if,elif,else)
Lecture 11 Loops In Python
Lecture 12 Coding Excercise And Assignments
Lecture 13 List And List Comprehrension In Python
Lecture 14 List Practise Code And assignments
Lecture 15 Tuples In Python
Lecture 16 Tuple Assignment And Practise Code
Lecture 17 Sets In Python
Lecture 18 Sets Assignment and Practise Code
Lecture 19 Dictionaries In Python
Lecture 20 Dictionaries Assignments and PRactise Questions
Lecture 21 REal World Usecases Of List
Lecture 22 Getting Started With Functions
Lecture 23 More Coding Examples With Functions
Lecture 24 Lambda functions
Lecture 25 Map functions In Python
Lecture 26 Filter Function In Python
Lecture 27 Function Assignments With Solution
Lecture 28 Import Modules And Packages In Python
Lecture 29 Standard Library Overview
Lecture 30 File Operation In Python
Lecture 31 Working With File Paths
Lecture 32 Exception Handling With Try Except else finally blocks
Section 4: Data Analysis With Python
Lecture 33 Numpy In Python
Lecture 34 Pandas-DataFrame And Series
Lecture 35 Data Manipulation With Pandas And Numpy
Lecture 36 Numpy Assignments With solution
Lecture 37 Reading Data From Various Data Source Using Pandas
Lecture 38 Data Visulaization With Matplotlib
Lecture 39 Data Visualization With Seaborn
Section 5: Getting Started With Statistics
Lecture 40 Introduction To Statistics
Lecture 41 Types Of Statistics
Lecture 42 Population And Sample Data
Lecture 43 Types Of Sampling Techniques
Lecture 44 Types Of Data
Lecture 45 Scales Of Measurement Of Data
Section 6: Descriptive Statistics
Lecture 46 Measure Of Central Tendency(Mean,Median And Mode)
Lecture 47 Measures Of Dispersion(Range,Variance,Standard Deviation)
Lecture 48 Why Sample Variance is divided by n-1
Lecture 49 Random Variables
Lecture 50 Percentiles And Quartiles
Lecture 51 5 Number Summary
Lecture 52 Histogram And Skewness
Lecture 53 Covariance And Correlation
Section 7: Probability Distribution Function And Types OF Distribution
Lecture 54 Pdf, PMF, CDF
Lecture 55 Types OF Probability Distribution
Lecture 56 Bernoulli Distribution
Lecture 57 Binomial Distribution
Lecture 58 Poisson Distribution
Lecture 59 Normal or Gaussian Distribution
Lecture 60 Standard Normal Distribution
Lecture 61 Uniform Distribution
Lecture 62 Log Normal Distribution
Lecture 63 10-Power Law Distribution
Lecture 64 11-Pareto Distribution
Lecture 65 Central Limit Theorem
Lecture 66 Estimates
Section 8: Inferential Stats And Hypothesis Testing
Lecture 67 Hypothesis Testing And Mechanism
Lecture 68 P value And Hypothesis Testing
Lecture 69 Z test Hypothesis Testing
Lecture 70 Student t Distribution
Lecture 71 T stats With T Test and Hypothesis Testing
Lecture 72 Z test vs T test
Lecture 73 Type1 And Type 2 Error
Lecture 74 Baye's Theorem
Lecture 75 Confidence Interval And Margin Of Error
Lecture 76 What is Chi Square Test
Lecture 77 Chi Square Goodness Of Fitness
Lecture 78 What is Anova
Lecture 79 Assumptions Of Anova
Lecture 80 Types Of Annova
Lecture 81 Partioning OF Annova
Section 9: Feature Engineering With Python
Lecture 82 Feature Engineering-Handling Missing Data
Lecture 83 Feature Engineering-Handling Imbalanced Dataset
Lecture 84 Feature Engineering-SMOTE
Lecture 85 Handling Outliers With Python
Lecture 86 Data Encoding-Nominal/One Hot Encoding
Lecture 87 Label And Ordinal Encoding
Lecture 88 Target Guided Ordinal Encoding
Section 10: Exploratory Data Analysis
Lecture 89 Red Wine Dataset EDA
Lecture 90 EDA Flight Price Dataset
Lecture 91 Part 1-Data Cleaning Google Playstore Dataset
Lecture 92 Part 2-EDA Google Play Store Dataset
Section 11: SQL : Course Introduction & Overview
Lecture 93 SQL Course Introduction
Lecture 94 SQL Overview
Lecture 95 SQL Server Download & Install
Section 12: Microsoft SQL Server basics
Lecture 96 SQL Select Statement
Lecture 97 SQL Select Distinct
Lecture 98 SQL Temp Tables
Lecture 99 SQL Where Clause
Lecture 100 SQL Order By Clause
Lecture 101 SQL AND & OR Operator
Lecture 102 SQL NOT, BETWEEN & IN Operators
Lecture 103 SQL Insert Into
Lecture 104 SQL Null Operator
Lecture 105 SQL Update Statement
Lecture 106 Delete, Drop & Truncate
Lecture 107 SQL Comments & TOP N
Lecture 108 SQL MAX & Group BY
Lecture 109 SQL MIN Function & Group BY
Lecture 110 SUM, AVG, COUNT & Group BY
Lecture 111 Group BY Concept
Lecture 112 Group BY Example SQL Server
Lecture 113 SQL Having Clause
Lecture 114 SQL Where & Having Clause Difference
Lecture 115 Inner Join Concept
Lecture 116 Inner Join Eample
Lecture 117 Left Join Concept
Lecture 118 Left Join Eample
Lecture 119 Right Join Concept
Lecture 120 Right Join Example
Lecture 121 Left & Right Anti Join
Lecture 122 Left & Right Anti Join Example
Lecture 123 Full Outer Join
Lecture 124 Self Join
Lecture 125 Union & Union All
Lecture 126 SQL Like Operator
Lecture 127 SQL Case in Select statement & Order BY Clause
Lecture 128 Nested CASE statement
Lecture 129 SQL Data Types
Lecture 130 SQL Create Table
Lecture 131 Inserting Records into All Columns of the Table
Lecture 132 Inserting Records into Certain Columns in a Table
Lecture 133 Copying Data From One Table to Another
Lecture 134 Sub Queries
Lecture 135 Not Null Constraint
Lecture 136 Unique Constraint
Lecture 137 Check Constraint
Lecture 138 Default Constraint
Lecture 139 Primary & Foreign Key Concept
Lecture 140 Primary Key Constraint
Lecture 141 Foreign Key Constraint
Section 13: SQL Basics Questions
Lecture 142 Questions Set - 1
Lecture 143 Questions Set - 2
Section 14: SQL Assignments
Section 15: SQL Functions
Lecture 144 Rank, Dense Rank & Row Number Window Functions - 1
Lecture 145 Rank, Dense Rank & Row Number Window Functions - 2
Lecture 146 Window Functions - Lead Function
Lecture 147 Window Functions - Lag Function
Lecture 148 ISNULL & Coalesce Functions
Section 16: Advanced SQL
Lecture 149 Common Table Expressions - 1
Lecture 150 Common Table Expressions - 2
Lecture 151 Recursive Common Table Expressions
Lecture 152 Stored Procedure in MS SQL Server
Lecture 153 Views in MS SQL Server
Lecture 154 Indexes in MS SQL Server
Section 17: SQL Important Interview Questions
Lecture 155 Nth Highest Salary
Lecture 156 Reportee & Manager Question
Lecture 157 Deleting Duplicates Q1
Lecture 158 Deleting Duplicates Q2
Section 18: Power BI Course Introduction
Lecture 159 Microsoft Power BI Course Introduction
Section 19: Introduction to Power BI
Lecture 160 General Workflow Power BI
Lecture 161 Downloading & Installing Power BI Desktop
Lecture 162 Creating a Free Power BI Account
Section 20: Data Visualization
Lecture 163 Creating a Bar Chart
Lecture 164 Creating a Column Chart
Lecture 165 Creating a Pie & a Donut Chart
Lecture 166 Creating a Clustered Column & Bar Chart
Lecture 167 Creating a Line & Area Chart
Lecture 168 Creating a Ribbon Chart
Lecture 169 Creating a line & stacked column chart
Lecture 170 Creating a Line & Clustered Column Chart
Lecture 171 Creating a Scatter Plot
Lecture 172 Creating a Bubble Map Visual
Lecture 173 Creating a Table & Matrix Visual
Lecture 174 Formatting Table & Matrix Visual
Lecture 175 Creating a Funnel Chart
Lecture 176 Gauge chart & KPI Visual
Lecture 177 AI Visuals in Power BI
Section 21: Power Query Editor
Lecture 178 Detecting Data Types in Power BI Desktop
Lecture 179 Data Profiling
Lecture 180 Column Distribution Example
Lecture 181 Appending Queries
Lecture 182 Merge Inner Join
Lecture 183 Left Outer Join
Lecture 184 Right Outer Join
Lecture 185 Left & Right Anti Join
Lecture 186 Full Outer Join
Lecture 187 Group By in Power Query Editor
Lecture 188 Pivot, Unpivot & Transpose
Lecture 189 Add/Transform Columns
Section 22: DAX
Lecture 190 DAX Lecture 1
Lecture 191 DAX Lecture 2
Lecture 192 DAX Lecture 3
Lecture 193 DAX Lecture 4
Lecture 194 DAX Lecture 5
Lecture 195 DAX Lecture 6
Section 23: Power BI Project 1, Sales Data Analysis
Lecture 196 Business Requirements
Lecture 197 Loading Data to PBI Desktop
Lecture 198 Data Profiling & Data Transformations Part 1
Lecture 199 Data Transformations Part 2
Lecture 200 Primary & Foreign Key
Lecture 201 Cardinality
Lecture 202 Star Schema
Lecture 203 Data Model Overview
Lecture 204 Different Types of Filters in Filters Pane
Lecture 205 Top Bottom 5 Products By Sales, Quantity and Profit
Lecture 206 Sales Trends Over Time
Lecture 207 Other Requirements
Lecture 208 Requirement 4 DAX
Lecture 209 Requirement 4 Edit Interactions
Lecture 210 Other Remaining Requirements
Lecture 211 Changing Filter Behaviour for Dimension Table Slicers
Section 24: Power BI Project 2, Insurance Data Analysis
Lecture 212 Downloading & Installing MSSQL Server
Lecture 213 Importing Data to MSSQL Server
Lecture 214 Loading Data to Power BI Desktop
Lecture 215 Table View & Data Profiling
Lecture 216 Adding Slicers & Text
Lecture 217 Adding New Card Visuals
Lecture 218 Adding a Multi Row Card & a Ribbon Chart
Lecture 219 Adding a Bar & a Line Chart
Lecture 220 Adding a Donut Chart & Matrix Visual
Lecture 221 Publishing the Report to Power BI Service
Lecture 222 Scheduling Refresh
Lecture 223 Drill Through Filter
Lecture 224 Testing Scheduled Refresh & Publishing the updated report
Lecture 225 Creating & Testing Roles in PBI Desktop
Lecture 226 Testing & Implementing RLS in Power BI Service
Lecture 227 Power BI Reports & Dashboards
Lecture 228 Sentiment Analysis Power Query
Lecture 229 Sentiment Analysis Adding Visuals to the Report
Section 25: Power BI Project 3, UPI Transactions Data Analysis
Lecture 230 Loading Data into Power BI Desktop
Lecture 231 Data Profiling
Lecture 232 Size & Position of slicers
Lecture 233 Formatting the Slicers
Lecture 234 Adding a Page & Age Group Column
Lecture 235 Adding a Line Chart
Lecture 236 Adding a Matrix Visual
Lecture 237 Syncing Slicers & Applying Conditional Formatting
Lecture 238 Adding Bookmarks for Transactions
Lecture 239 Adding Bookmarks for Remaining Balance
Lecture 240 Publishing the Report to Power BI Service
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