Master Data Analysis: Learn Python, EDA, Stats, MS Excel, SQL, Power BI, Tableau, Predictive Analytics & ETL Basics
What you'll learn
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.
Requirements
No pre-requisites are required for this course
Description
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