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.