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Complete Data Analyst Bootcamp From Basics To Advanced

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Complete Data Analyst Bootcamp From Basics To Advanced

Master Data Analysis: Python, Statistics, EDA, Feature Engineering, Power BI, and SQL Server in Comprehensive Bootcamp

4.5

(11031)

59510 enrolled on this course

Last updated 11/2021

Krish Naik

₹ 1799.0

₹ 3299.0

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30-Day Money-Back Guarantee

Lessons

Quizzes

Duration

13 Hours

Skill level

Beginner

Language

English

Certificate

Yes

Full lifetime access

Yes

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 Analysis
Dive 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 Science
Understanding 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 Preprocessing
Data 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 Analysts
SQL (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 Reporting
Data 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 Projects
Put 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!
Who this course is for:
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.

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

You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
No previous design experience is needed.
No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures

Expand All Sections

1-Introduction To The Course

1.1-What Does A Data Analyst Do and Its Roadmap

2-Getting Started With Python

2.1-Getting Started With Google Colab

2.2-Installation Of Anaconda And Visual Studio Code

3-Complete Python With Important Libraries

3.1-Getting Started With VS Code With Environments

3.2-Python Basics-Syntax And Semantics

3.3-Variables In Python

3.4-Basic Data Types In Python

3.5-Operators In Python

3.6-Coding Excercise And Assignments

3.7-Conditional Statements(if,elif,else)

3.8-Loops In Python

3.9-Coding Excercise And Assignments

3.10-List And List Comprehrension In Python

3.11-List Practise Code And assignments

3.12-Tuples In Python

3.13-Tuple Assignment And Practise Code

3.14-Sets In Python

3.15-Sets Assignment and Practise Code

3.16-Dictionaries In Python

3.17-Dictionaries Assignments and PRactise Questions

3.18-REal World Usecases Of List

3.19-Getting Started With Functions

3.20-More Coding Examples With Functions

3.21-Lambda functions

3.22-Map functions In Python

3.23-Filter Function In Python

3.24-Function Assignments With Solution

3.25-Import Modules And Packages In Python

3.26-Standard Library Overview

3.27-File Operation In Python

3.28-Working With File Paths

3.29-Exception Handling With Try Except else finally blocks

4-Data Analysis With Python

4.1-Numpy In Python

4.2-Pandas-DataFrame And Series

4.3-Data Manipulation With Pandas And Numpy

4.4-Numpy Assignments With solution

4.5-Reading Data From Various Data Source Using Pandas

4.6-Data Visulaization With Matplotlib

4.7-Data Visualization With Seaborn

5-Getting Started With Statistics

5.1-Introduction To Statistics

5.2-Types Of Statistics

5.3-Population And Sample Data

5.4-Types Of Sampling Techniques

5.5-Types Of Data

5.6-Scales Of Measurement Of Data

6-Descriptive Statistics

6.1-Measure Of Central Tendency(Mean,Median And Mode)

6.2-Measures Of Dispersion(Range,Variance,Standard Deviation)

6.3-Why Sample Variance is divided by n-1

6.4-Random Variables

6.5-Percentiles And Quartiles

6.6-5 Number Summary

6.7-Histogram And Skewness

6.8-Covariance And Correlation

7-Probability Distribution Function And Types OF Distribution

7.1-Pdf, PMF, CDF

7.2-Types OF Probability Distribution

7.3-Bernoulli Distribution

7.4-Binomial Distribution

7.5-Poisson Distribution

7.6-Normal or Gaussian Distribution

7.7-Standard Normal Distribution

7.8-Uniform Distribution

7.9-Log Normal Distribution

7.10-10-Power Law Distribution

7.11-11-Pareto Distribution

7.12-Central Limit Theorem

7.13-Estimates

8-Inferential Stats And Hypothesis Testing

8.1-Hypothesis Testing And Mechanism

8.2-P value And Hypothesis Testing

8.3-Z test Hypothesis Testing

8.4-Student t Distribution

8.5-T stats With T Test and Hypothesis Testing

8.6-Z test vs T test

8.7-Type1 And Type 2 Error

8.8-Baye's Theorem

8.9-Confidence Interval And Margin Of Error

8.10-What is Chi Square Test

8.11-Chi Square Goodness Of Fitness

8.12-What is Anova

8.13-Assumptions Of Anova

8.14-Types Of Annova

8.15-Partioning OF Annova

9-Feature Engineering With Python

9.1-Feature Engineering-Handling Missing Data

9.2-Feature Engineering-Handling Imbalanced Dataset

9.3-Feature Engineering-SMOTE

9.4-Handling Outliers With Python

9.5-Data Encoding-Nominal/One Hot Encoding

9.6-Label And Ordinal Encoding

9.7-Target Guided Ordinal Encoding

10-Exploratory Data Analysis

10.1-Red Wine Dataset EDA

10.2-EDA Flight Price Dataset

10.3-Part 1-Data Cleaning Google Playstore Dataset

10.4-Part 2-EDA Google Play Store Dataset

11-SQL : Course Introduction & Overview

11.1-SQL Course Introduction

11.2-SQL Overview

11.3-SQL Server Download & Install

12-Microsoft SQL Server basics

12.1-SQL Select Statement

12.2-SQL Select Distinct

12.3-SQL Temporary Tables

12.4-SQL Where Clause

12.5-SQL Order By Clause

12.6-SQL AND & OR Operator

12.7-SQL NOT, BETWEEN & IN Operators

12.8-SQL Insert Into

12.9-SQL Null Operator

12.10-SQL Update Statement

12.11-Delete, Drop & Truncate

12.12-SQL Comments & TOP N

12.13-SQL MAX & Group BY

12.14-SQL MIN Function & Group BY

12.15-SUM, AVG, COUNT & Group BY

12.16-Group BY Concept

12.17-Group BY Example SQL Server

12.18-SQL Having Clause

12.19-SQL Where & Having Clause Difference

12.20-Inner Join Concept

12.21-Inner Join Eample

12.22-Left Join Concept

12.23-Left Join Eample

12.24-Right Join Concept

12.25-Right Join Example

12.26-Left & Right Anti Join

12.27-Left & Right Anti Join Example

12.28-Full Outer Join

12.29-Self Join

12.30-Union & Union All

12.31-SQL Like Operator

12.32-SQL Case in Select statement & Order BY Clause

12.33-Nested CASE statement

12.34-SQL Data Types

12.35-SQL Create Table

12.36-Inserting Records into All Columns of the Table

12.37-Inserting Records into Certain Columns in a Table

12.38-Copying Data From One Table to Another

12.39-Sub Queries

12.40-Not Null Constraint

12.41-Unique Constraint

12.42-Check Constraint

12.43-Default Constraint

12.44-Primary & Foreign Key Concept

12.45-Primary Key Constraint

12.46-Foreign Key Constraint

12.47-SQL Order of Execution

13-SQL Basics Questions

13.1-Questions Set - 1

13.2-Questions Set - 2

13.3-Questions Set - 3 (Joins)

13.4-Questions Set - 4 (Joins)

14-SQL Assignments

14.1-SQL Assignment 1

14.2-SQL Assignment 2

14.3-SQL Assignment 3

14.4-SQL Assignment 4

14.5-SQL Assignment 5

15-SQL Functions

15.1-Rank, Dense Rank & Row Number Window Functions - 1

15.2-Rank, Dense Rank & Row Number Window Functions - 2

15.3-Window Functions - Lead Function

15.4-Window Functions - Lag Function

15.5-ISNULL & Coalesce Functions

15.6-First_Value() Window Function

15.7-Last_Value() Window Function

16-Advanced SQL

16.1-Common Table Expressions - 1

16.2-Common Table Expressions - 2

16.3-Recursive Common Table Expressions

16.4-Stored Procedure in MS SQL Server

16.5-Views in MS SQL Server

16.6-Indexes in MS SQL Server

16.7-Clustered Index

16.8-Non Clustered Index

17-SQL Important Interview Questions

17.1-Nth Highest Salary

17.2-Reportee & Manager Question

17.3-Deleting Duplicates Q1

17.4-Deleting Duplicates Q2

18-Power BI Course Introduction

18.1-Microsoft Power BI Course Introduction

19-Introduction to Power BI

19.1-General Workflow Power BI

19.2-Downloading & Installing Power BI Desktop

19.3-Creating a Free Power BI Account

20-Data Visualization

20.1-Creating a Bar Chart

20.2-Creating a Column Chart

20.3-Creating a Pie & a Donut Chart

20.4-Creating a Clustered Column & Bar Chart

20.5-Creating a Line & Area Chart

20.6-Creating a Ribbon Chart

20.7-Creating a line & stacked column chart

20.8-Creating a Line & Clustered Column Chart

20.9-Creating a Scatter Plot

20.10-Creating a Bubble Map Visual

20.11-Creating a Table & Matrix Visual

20.12-Formatting Table & Matrix Visual

20.13-Creating a Funnel Chart

20.14-Gauge chart & KPI Visual

20.15-AI Visuals in Power BI

21-Power Query Editor

21.1-Detecting Data Types in Power BI Desktop

21.2-Data Profiling

21.3-Column Distribution Example

21.4-Appending Queries

21.5-Merge Inner Join

21.6-Left Outer Join

21.7-Right Outer Join

21.8-Left & Right Anti Join

21.9-Full Outer Join

21.10-Group By in Power Query Editor

21.11-Pivot, Unpivot & Transpose

21.12-Add/Transform Columns

22-DAX

22.1-DAX Lecture 1

22.2-DAX Lecture 2

22.3-DAX Lecture 3

22.4-DAX Lecture 4

22.5-DAX Lecture 5

22.6-DAX Lecture 6

23-Power BI Project 1, Sales Data Analysis

23.1-Business Requirements

23.2-Loading Data to PBI Desktop

23.3-Data Profiling & Data Transformations Part 1

23.4-Data Transformations Part 2

23.5-Primary & Foreign Key

23.6-Cardinality

23.7-Star Schema

23.8-Data Model Overview

23.9-Different Types of Filters in Filters Pane

23.10-Top Bottom 5 Products By Sales, Quantity and Profit

23.11-Sales Trends Over Time

23.12-Other Requirements

23.13-Requirement 4 DAX

23.14-Requirement 4 Edit Interactions

23.15-Other Remaining Requirements

23.16-Changing Filter Behaviour for Dimension Table Slicers

24-Power BI Project 2, Insurance Data Analysis

24.1-Downloading & Installing MSSQL Server

24.2-Importing Data to MSSQL Server

24.3-Loading Data to Power BI Desktop

24.4-Table View & Data Profiling

24.5-Adding Slicers & Text

24.6-Adding New Card Visuals

24.7-Adding a Multi Row Card & a Ribbon Chart

24.8-Adding a Bar & a Line Chart

24.9-Adding a Donut Chart & Matrix Visual

24.10-Publishing the Report to Power BI Service

24.11-Scheduling Refresh

24.12-Drill Through Filter

24.13-Testing Scheduled Refresh & Publishing the updated report

24.14-Creating & Testing Roles in PBI Desktop

24.15-Testing & Implementing RLS in Power BI Service

24.16-Power BI Reports & Dashboards

24.17-Sentiment Analysis Power Query

24.18-Sentiment Analysis Adding Visuals to the Report

25-Power BI Project 3, UPI Transactions Data Analysis

25.1-Loading Data into Power BI Desktop

25.2-Data Profiling

25.3-Size & Position of slicers

25.4-Formatting the Slicers

25.5-Adding a Page & Age Group Column

25.6-Adding a Line Chart

25.7-Adding a Matrix Visual

25.8-Syncing Slicers & Applying Conditional Formatting

25.9-Adding Bookmarks for Transactions

25.10-Adding Bookmarks for Remaining Balance

25.11-Publishing the Report to Power BI Service

26-Miscellaneous Section Power BI

26.1-SQL + Power BI Scenario based question - 1

26.2-Power BI Scenario Based Question - 2

27-Getting Started with Microsoft Excel

27.1-Home Tab Font Group

27.2-Home Tab Alignment Group

27.3-Home Tab Number Group Part 1

27.4-Home Tab Number Group Part 2

27.5-Home Tab Styles Group Conditional Formatting 1

27.6-Home Tab Styles Group Conditional Formatting 2

27.7-Draw Tab & Cell Styles

27.8-Excel Functions Video 1

27.9-Excel Functions Video 2

27.10-Excel Functions Video 3

27.11-Excel Functions Video 4

27.12-Excel Functions Video 5

27.13-Excel Functions Video 6

27.14-Excel Functions Video 7

27.15-Excel Functions Video 8

27.16-Excel Functions Video 9 VLookUp Function

27.17-Excel Functions Video 10 XLookUp Function

28-Excel Dashboard 1

28.1-Pivot Tables Lecture 1

28.2-Pivot Tables Lecture 2

28.3-Creating & Formatting the Column Chart

28.4-Creating & Formatting the Bar Chart

28.5-Creating & Formatting a Line Chart

28.6-Creating & Formatting an Area Chart

28.7-Creating & Formatting a Pie Chart

28.8-Creating the Dashboard

29-Excel Dashboard 2

29.1-Data Understanding & Cleansing

29.2-Data Transformation (Removing Data Quality Issues using VLookUp Function)

29.3-Adding Age Group & Production Cost Per Unit Column

29.4-Adding a 3D Column Chart

29.5-Adding a 3D Bar Chart

29.6-Adding a 3D Line Chart

29.7-Adding a 3D Pie Chart

29.8-Creating the Dashboard

30-Power Query Editor (MS Excel)

30.1-Data Profiling (Column Distribution)

30.2-Data Profiling (Column Profile & Column Quality)

30.3-Combining Queries

30.4-Inner Join Concept

30.5-Inner Join Example

30.6-Left Join Concept

30.7-Left Join Example

30.8-Right Join Concept

30.9-Right Join Example

30.10-Full Outer Join Concept

30.11-Full Outer Join Example

30.12-Left & Right Anti Join Concept

30.13-Right & Left Anti Join Example

30.14-Nulls in SQL Server & Power Query Editor

30.15-Appending two tables

30.16-Appending three tables

30.17-Power Query Editor Exercise 1

30.18-Power Query Editor Exercise 2

30.19-Power Query Editor Exercise 3

30.20-Power Query Editor Exercise 4

30.21-Power Query Editor Exercise 5

31-Excel Activity (Importing Data From SQL Server)

31.1-Importing Data to SQL Server

31.2-Importing Data to Excel From SQL Server

31.3-Data Understanding & Creating the Pivot Table

31.4-Adding Category Slicer

31.5-Adding Other Slicers & Formatting Slicers & Pivot Table

31.6-Refreshing the Report

32-Tableau

32.1-Work of a Data Analyst (Story Telling)

32.2-Downloading & Installing Tableau Public

32.3-Loading Data into Tableau Public

32.4-Creating Column & Bar Chart

32.5-Saving Your Work to Tableau Public

32.6-Horizontal & Vertical Axis

32.7-Stacked & 100% Stacked Column Chart

32.8-Stacked & 100 % Stacked Bar Chart

32.9-Creating an Area Chart

32.10-Creating a Line Chart

32.11-Creating a Scatter Plot

32.12-Creating Pie Chart & Tree Map

32.13-Creating a Donut Chart

32.14-Creating a Lollipop Chart

32.15-Filters Shelf in Tableau 1

32.16-Filters Shelf in Tableau 2

32.17-Symbol & Filled Map

32.18-Funnel & Packed Bubble Chart

32.19-Sets in Tableau

32.20-Parameters in Tableau

32.21-Sets & Parameters

33-Tableau Dashboard 1

33.1-Importing Data

33.2-Creating & Formatting a Bar Chart

33.3-Creating & Formatting a Pie Chart

33.4-Creating & Formatting a Line Chart

33.5-Creating & Formatting a Scatter Plot

33.6-Creating the Dashboard

34-Tableau Dashboard 2

34.1-Importing Data

34.2-Transactions by City

34.3-Number of Transactions by Age Group

34.4-Transactions by Payment Method & Merchant Name

34.5-Adding Filters

34.6-Creating the Dashboard

35-Tableau Prep Builder

35.1-Downloading & Installing Tableau Prep Builder

35.2-Understanding the Dataset

35.3-Connecting to Text File & Microsoft Excel

35.4-Removing Additional Columns

35.5-Cleaning Orders Central Table

35.6-Cleaning Orders East Table

35.7-Cleaning Orders West Table

35.8-Assigning Data Roles to Geographical Fields

35.9-Combining Different Tables

35.10-Cleaning Returns Table

35.11-Joining Tables

35.12-Cleaning Combined Table

36-SQL + Tableau Project (Student Depression Data Analysis)

36.1-Importing Data to SQL Server

36.2-Modifying Gender Column

36.3-Adding the Age Group Column

36.4-Column Distribution remaining columns

36.5-Adding Index Column & Updating depression column

36.6-Downloading & Installing Tableau Desktop

36.7-Bringing Data from SQL Server to Tableau Desktop

36.8-Academic Pressure & Student Count

36.9-Financial Stress & Student Count

36.10-Study Satisfaction & Student Count

36.11-Sleep Duration & Student Count

36.12-Study Hours & Student Count

36.13-Creating the Student Count Analysis Dashboard

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