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Complete Data Analyst Bootcamp From Basics To Advanced

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Complete Data Analyst Bootcamp From Basics To Advanced, available at $44.99, has an average rating of 4.71, with 247 lectures, based on 84 reviews, and has 2145 subscribers.

You will learn about 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 This course is ideal for individuals who are Individuals looking to start a career in data analysis and gain a comprehensive skill set from the ground up. or Professionals from other fields who want to transition into data analysis and need a structured, all-inclusive learning path. or 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. or 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. or 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. It is particularly useful for Individuals looking to start a career in data analysis and gain a comprehensive skill set from the ground up. or Professionals from other fields who want to transition into data analysis and need a structured, all-inclusive learning path. or 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. or 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. or 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.

Enroll now: Complete Data Analyst Bootcamp From Basics To Advanced

Summary

Title: Complete Data Analyst Bootcamp From Basics To Advanced

Price: $44.99

Average Rating: 4.71

Number of Lectures: 247

Number of Published Lectures: 247

Number of Curriculum Items: 252

Number of Published Curriculum Objects: 252

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

  • 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.

Target Audiences

  • 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.

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:

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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!

Course Curriculum

Chapter 1: Introduction To The Course

Lecture 1: What Does A Data Analyst Do and Its Roadmap

Chapter 2: Getting Started With Python

Lecture 1: Getting Started With Google Colab

Lecture 2: Installation Of Anaconda And Visual Studio Code

Chapter 3: Complete Python With Important Libraries

Lecture 1: Getting Started With VS Code With Environments

Lecture 2: Python Basics-Syntax And Semantics

Lecture 3: Variables In Python

Lecture 4: Basic Data Types In Python

Lecture 5: Operators In Python

Lecture 6: Coding Excercise And Assignments

Lecture 7: Conditional Statements(if,elif,else)

Lecture 8: Loops In Python

Lecture 9: Coding Excercise And Assignments

Lecture 10: List And List Comprehrension In Python

Lecture 11: List Practise Code And assignments

Lecture 12: Tuples In Python

Lecture 13: Tuple Assignment And Practise Code

Lecture 14: Sets In Python

Lecture 15: Sets Assignment and Practise Code

Lecture 16: Dictionaries In Python

Lecture 17: Dictionaries Assignments and PRactise Questions

Lecture 18: REal World Usecases Of List

Lecture 19: Getting Started With Functions

Lecture 20: More Coding Examples With Functions

Lecture 21: Lambda functions

Lecture 22: Map functions In Python

Lecture 23: Filter Function In Python

Lecture 24: Function Assignments With Solution

Lecture 25: Import Modules And Packages In Python

Lecture 26: Standard Library Overview

Lecture 27: File Operation In Python

Lecture 28: Working With File Paths

Lecture 29: Exception Handling With Try Except else finally blocks

Chapter 4: Data Analysis With Python

Lecture 1: Numpy In Python

Lecture 2: Pandas-DataFrame And Series

Lecture 3: Data Manipulation With Pandas And Numpy

Lecture 4: Numpy Assignments With solution

Lecture 5: Reading Data From Various Data Source Using Pandas

Lecture 6: Data Visulaization With Matplotlib

Lecture 7: Data Visualization With Seaborn

Chapter 5: Getting Started With Statistics

Lecture 1: Introduction To Statistics

Lecture 2: Types Of Statistics

Lecture 3: Population And Sample Data

Lecture 4: Types Of Sampling Techniques

Lecture 5: Types Of Data

Lecture 6: Scales Of Measurement Of Data

Chapter 6: Descriptive Statistics

Lecture 1: Measure Of Central Tendency(Mean,Median And Mode)

Lecture 2: Measures Of Dispersion(Range,Variance,Standard Deviation)

Lecture 3: Why Sample Variance is divided by n-1

Lecture 4: Random Variables

Lecture 5: Percentiles And Quartiles

Lecture 6: 5 Number Summary

Lecture 7: Histogram And Skewness

Lecture 8: Covariance And Correlation

Chapter 7: Probability Distribution Function And Types OF Distribution

Lecture 1: Pdf, PMF, CDF

Lecture 2: Types OF Probability Distribution

Lecture 3: Bernoulli Distribution

Lecture 4: Binomial Distribution

Lecture 5: Poisson Distribution

Lecture 6: Normal or Gaussian Distribution

Lecture 7: Standard Normal Distribution

Lecture 8: Uniform Distribution

Lecture 9: Log Normal Distribution

Lecture 10: 10-Power Law Distribution

Lecture 11: 11-Pareto Distribution

Lecture 12: Central Limit Theorem

Lecture 13: Estimates

Chapter 8: Inferential Stats And Hypothesis Testing

Lecture 1: Hypothesis Testing And Mechanism

Lecture 2: P value And Hypothesis Testing

Lecture 3: Z test Hypothesis Testing

Lecture 4: Student t Distribution

Lecture 5: T stats With T Test and Hypothesis Testing

Lecture 6: Z test vs T test

Lecture 7: Type1 And Type 2 Error

Lecture 8: Baye's Theorem

Lecture 9: Confidence Interval And Margin Of Error

Lecture 10: What is Chi Square Test

Lecture 11: Chi Square Goodness Of Fitness

Lecture 12: What is Anova

Lecture 13: Assumptions Of Anova

Lecture 14: Types Of Annova

Lecture 15: Partioning OF Annova

Chapter 9: Feature Engineering With Python

Lecture 1: Feature Engineering-Handling Missing Data

Lecture 2: Feature Engineering-Handling Imbalanced Dataset

Lecture 3: Feature Engineering-SMOTE

Lecture 4: Handling Outliers With Python

Lecture 5: Data Encoding-Nominal/One Hot Encoding

Lecture 6: Label And Ordinal Encoding

Lecture 7: Target Guided Ordinal Encoding

Chapter 10: Exploratory Data Analysis

Lecture 1: Red Wine Dataset EDA

Lecture 2: EDA Flight Price Dataset

Instructors

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  • 2 stars: 2 votes
  • 3 stars: 2 votes
  • 4 stars: 26 votes
  • 5 stars: 54 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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