Data Analytics Masters - From Basics To Advanced

Master Data Analysis: Learn Python, EDA, Stats, MS Excel, SQL, Power BI, Tableau, Predictive Analytics & ETL Basics

[b]What you'll learn[/b]

Discover how to effectively handle, analyze, and visualize data using Python and its robust libraries, including Pandas, NumPy, Matplotlib, and Seaborn.

Learn how to conduct Exploratory Data Analysis (EDA) to reveal insights, detect patterns, and prepare data for further analysis through effective visualization

Acquire the skills to extract, manipulate, and aggregate data using SQL. You will utilize MySQL to handle complex databases and execute sophisticated queri

Master the art of creating interactive and insightful dashboards using Power BI and Tableau. You'll apply DAX for complex calculations in Power BI and integrate

Explore the fundamentals of machine learning, including classification, regression, and time series analysis, to enhance your predictive analytics skills.

Learn the fundamentals of ETL processes to effectively extract, transform, and load data for analysis.

[b]Requirements[/b]

No pre-requisites are required for this course

[b]Description[/b]

Congrats on enrolling in the Data Analytics Masters Course!!Need of Data AnalyticsThe outburst of data is transforming businesses. Companies - big or small - are now expecting their business decisions to be based on data-led insight.Data specialists have a tremendous impact on business strategies and marketing tactics.The demand for data specialists is on the rise while the supply remains low, thus creating great job opportunities for individuals within this field.Today, it is almost impossible to find any brand that does not have a social media presence; soon, every company will need data analytics professionals.This makes it a wise career move that has a future in business.Job Roles after the courseThis course will help you to step forward in Data Analytics and choose the following rolesData AnalystBusiness AnalystBI AnalystBI DeveloperPower BI DeveloperTableau Developerand many more.Syllabus:Module 1: Python for Data AnalyticsModule 2: Exploratory Data AnalysisModule 3: Business StatisticsModule 4: SQLModule 5: Microsoft ExcelModule 6: Power BIModule 7: TableauModule 8: Predictive ModellingModule 9: Data Warehousing and ETLModule 10: Interview GuidesModule 11: Capstone ProjectsConclusion: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

Lecture 1 Welcome Page

Lecture 2 Welcome to the Course

Lecture 3 What is Data Analytics

Lecture 4 Importance of Data Analytics

Lecture 5 Types of Data

Lecture 6 Types of Statistical Analysis

Lecture 7 Steps to obtain a Data Analytics solution

Lecture 8 Business Understanding

Lecture 9 Data Understanding

Lecture 10 Data Collection

Lecture 11 Data Preparation

Lecture 12 Data Modelling

Lecture 13 Deployment

Lecture 14 Use Case

Section 2: Python

Lecture 15 Course Contents

Lecture 16 Introduction to Python

Lecture 17 Variables & Keywords

Lecture 18 Datatypes Operators

Lecture 19 Lists

Lecture 20 Tuples

Lecture 21 Sets

Lecture 22 Doctionary

Lecture 23 Loops & Iteration

Lecture 24 Functions

Lecture 25 Map Reduce Filter

Lecture 26 File Handling

Lecture 27 Control Structures

Lecture 28 OOPS

Lecture 29 NumPy

Lecture 30 Pandas

Lecture 31 Data Visualization

Lecture 32 Matplotlib

Lecture 33 Seaborn

Section 3: Business Statistics

Lecture 34 Course Contents

Lecture 35 Introduction

Lecture 36 Types of Data (Agenda)

Lecture 37 Descriptive Stats

Lecture 38 Inferential Stats

Lecture 39 Qualitative Data

Lecture 40 Quantitative Data

Lecture 41 Sampling Techniques (Agenda)

Lecture 42 Population vs Sample

Lecture 43 Why Sampling is important

Lecture 44 Types of Sampling

Lecture 45 Cluster Random Sampling

Lecture 46 Probability Sampling

Lecture 47 Non probability sampling

Lecture 48 Population Sampling

Lecture 49 Why n-1 and not n

Lecture 50 Descriptive Analytics (Agenda)

Lecture 51 Measures of Central Tendency

Lecture 52 Mean

Lecture 53 Median

Lecture 54 Mode

Lecture 55 Measures of Dispersion

Lecture 56 Range

Lecture 57 IQR

Lecture 58 Variance Standard Deviation

Lecture 59 Mean Deviation

Lecture 60 Probability (Agenda)

Lecture 61 Probability

Lecture 62 Addition Rule

Lecture 63 Independent Events

Lecture 64 Cumulative Probability

Lecture 65 Conditional Probability

Lecture 66 Bayes Theorem 1

Lecture 67 Bayes Theorem 2

Lecture 68 Probability Distrubution (Agenda)

Lecture 69 Uniform Distribution

Lecture 70 Binomial Distribution

Lecture 71 Poisson Distribution

Lecture 72 Normal Distribution Part 1

Lecture 73 Normal Distribution Part 2

Lecture 74 Skewness

Lecture 75 Kurtosis

Lecture 76 Calculating Probability with Z-score for Normal Distribution Part 1

Lecture 77 Calculating Probability with Z-score for Normal Distribution Part 2

Lecture 78 Calculating Probability with Z-score for Normal Distribution Part 3

Lecture 79 Covariance & Correlation (Agenda)

Lecture 80 Covariance

Lecture 81 Correlation

Lecture 82 Covariance VS Correlation

Lecture 83 ANOVA

Lecture 84 Hypothesis Testing

Lecture 85 Tailed Tests

Lecture 86 p-value

Lecture 87 Types of Test

Lecture 88 T Test

Lecture 89 Z Test

Lecture 90 Chi Square Test

Lecture 91 Correlation Test (Practicals)

Section 4: Exploratory Data Analysis

Lecture 92 Course Contents

Lecture 93 Agenda

Lecture 94 DA,DS Process

Lecture 95 What is EDA

Lecture 96 Visualization

Lecture 97 Steps involved in EDA (Data Sourcing)

Lecture 98 Steps involved in EDA (Data Cleaning)

Lecture 99 Handle Missing Values (Theory)

Lecture 100 Handle Missing Values (Practicals)

Lecture 101 Feature Scaling (Theory)

Lecture 102 Standardization Example

Lecture 103 Normalization Example

Lecture 104 Feature Scaling (Practicals)

Lecture 105 Outlier Treatment (Theory)

Lecture 106 Outlier Treatment (Practicals)

Lecture 107 Invalid Data

Lecture 108 Types of Data

Lecture 109 Types of Analysis

Lecture 110 Univariate Analysis

Lecture 111 Bivariate Analysis

Lecture 112 Multivariate Analysis

Lecture 113 Numerical Analysis

Lecture 114 Analysis Practicals

Lecture 115 Derived Metrics

Lecture 116 Feature Binning (Theory)

Lecture 117 Feature Binning (Practicals)

Lecture 118 Feature Encoding (Theory)

Lecture 119 Feature Encoding (Practicals)

Lecture 120 Case Study

Lecture 121 Data Exploration

Lecture 122 Data Cleaning

Lecture 123 Univariate Analysis

Lecture 124 Bivariate Analysis Part 1

Lecture 125 Bivariate Analysis Part 2

Lecture 126 EDA Report

Section 5: SQL

Lecture 127 Course Contents

Lecture 128 Installation

Lecture 129 Data Architecture - File server vs client server

Lecture 130 Introduction to SQL

Lecture 131 Constraints in SQL

Lecture 132 Table Basics - DDLs

Lecture 133 Table Basics - DQLs

Lecture 134 Table Basics - DMLs

Lecture 135 Joins

Lecture 136 Data Import Export

Lecture 137 Aggregation Functions

Lecture 138 String functions

Lecture 139 Date Time Functions

Lecture 140 Regular Expressions

Lecture 141 Nested Queries

Lecture 142 Views

Lecture 143 Stored Procedures

Lecture 144 Windows Function

Lecture 145 SQL Python connectivity

Section 6: Microsoft Excel

Lecture 146 Course Contents

Lecture 147 Pre-defined Functions

Lecture 148 Datetime Functions

Lecture 149 String Functions

Lecture 150 Mathematical Functions

Lecture 151 Lookup (Hlookup,Vlookup)

Lecture 152 Logical & Error Functions

Lecture 153 Statistical Functions

Lecture 154 Images in Excel

Lecture 155 Excel Formatting

Lecture 156 Custom Formatting

Lecture 157 Conditional Formatting

Lecture 158 Charts in Excel

Lecture 159 Data Analysis using Excel

Lecture 160 Pivot Tables

Lecture 161 Dashboarding in Excel

Lecture 162 Others

Lecture 163 What-If Tools - Scenario Manager, Goal Seek

Section 7: Power BI

Lecture 164 Course Contents

Lecture 165 Introduction

Lecture 166 Life Hack (How to have Power BI Pro License)

Lecture 167 Power BI Desktop

Lecture 168 Power BI Services

Lecture 169 Power Query Editor

Lecture 170 Data Profiling

Lecture 171 Group by Dialog

Lecture 172 Applied Steps

Lecture 173 Append vs Merge

Lecture 174 Power BI Visuals

Lecture 175 Power BI Charts

Lecture 176 Introduction to DAX

Lecture 177 Implicit Measures

Lecture 178 DAX Formula

Lecture 179 Basic DAX Functions

Lecture 180 Date Functions

Lecture 181 CALENDAR Functions

Lecture 182 Contexts Row vs Filter

Lecture 183 CALCULATE & FILTER

Lecture 184 IF ELSE Conditions

Lecture 185 Time Intelligence Functions

Lecture 186 X vs Non X Functions

Lecture 187 Tool Tips & Drill Throughs

Lecture 188 Power BI Relationships

Lecture 189 KPIs in Power BI

Lecture 190 Administration in Power BI

Lecture 191 Static Row Level Security

Lecture 192 Dynamic Row Level Security

Lecture 193 Formatting

Lecture 194 Best Practices

Lecture 195 EDA

Lecture 196 Live Projects

Section 8: Tableau

Lecture 197 Course Contents

Lecture 198 What is Data Visualization

Lecture 199 BI Process

Lecture 200 What is Tableau

Lecture 201 Features of Tableau

Lecture 202 How to use Tableau

Lecture 203 Tableau Architecture

Lecture 204 Tableau Desktop

Lecture 205 Tableau vs Power BI

Lecture 206 Relationships, Joins , Unions

Lecture 207 Sets in Tableau

Lecture 208 Groups in Tableau

Lecture 209 Hierarchies in Tableau

Lecture 210 Filters in Tableau

Lecture 211 Highlighting

Lecture 212 Device Deisgner

Lecture 213 Parameters

Lecture 214 Data Blending

Lecture 215 Transparency

Lecture 216 Date Aggregation

Lecture 217 Generated Fields

Lecture 218 Discrete vs Continuous

Lecture 219 Charts in Tableau

Lecture 220 Pivot Tables in Tableau

Lecture 221 LOD Expressions

Lecture 222 Calculated Fields

Lecture 223 Formatting

Lecture 224 Forecasting in Tableau

Lecture 225 Analytics in Tableau

Lecture 226 Dashboarding

Section 9: Predictive Analytics

Lecture 227 Course Contents

Lecture 228 Introduction

Lecture 229 Predictive Analytics Process

Lecture 230 How model works

Lecture 231 Why Predictive Analytics

Lecture 232 Applications

Lecture 233 What is Machine Learning

Lecture 234 Types Of Machine Learning

Lecture 235 Classification

Lecture 236 KNN

Lecture 237 KNN Excel example

Lecture 238 Classification Practical

Lecture 239 KNN Code

Lecture 240 Decision Tree Example

Lecture 241 Decision Tree Code

Lecture 242 Random Forest

Lecture 243 Random Forest Code

Lecture 244 Boosting

Lecture 245 Boosting Code

Lecture 246 Regression Theory

Lecture 247 Regression Theory Code

Lecture 248 Clustering

Lecture 249 Clustering Procticals

Lecture 250 Time Series

Lecture 251 Time Series Forecasting Code

Section 10: ETL

Lecture 252 Course Contents

Lecture 253 Introduction

Lecture 254 What is ETL

Lecture 255 ETL Tools

Lecture 256 What is Data Warehouse

Lecture 257 Benefits of Data Warehouse

Lecture 258 Data Warehouse Structure

Lecture 259 Why do we need Staging

Lecture 260 What are Data Marts

Lecture 261 Data Lake

Lecture 262 Data lake vs Data Warehouse

Lecture 263 Elements of Data lake

Section 11: Interview Q&A Guides

Lecture 264 Interview Guides

Section 12: Capstone Projects

Lecture 265 Churn Analysis (Power BI)

Lecture 266 HR Analytics (Tableau)

Complete beginners interested to learn Data Analytics can join this program,Any Technical or Non Technical person can enroll for this program

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