Free Download Data Analysis Masterclass A-Z Data Analysis In Python
Published 4/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.27 GB | Duration: 14h 28m
Master Python for A-Z Data Analysis and Become Pro Data Analyst with Basics to Hands-on Coding Exercises and Assignments
What you'll learn
You will get proficient in Python for thorough data analysis. Prepare for a career as a data analyst by acquiring practical skills and expertise.
You will master the fundamentals of data analytics, including facts and theories, statistical analysis, hypothesis testing, and machine learning.
You will learn the important Python programming basics such as variables naming, data types, lists, dictionaries, dataframes, sets, loops, functions etc.
You will master a range of methods and techniques for data cleaning, sorting, filtering, data manipulation, transformation, and data preprocessing in Python.
You will learn to use Python for data visualizations, exploratory data analysis, statistical analysis, hypothesis testing methods and machine learning models.
You will work on practical data analysis projects to apply learned skills. Enhance problem-solving abilities through hands-on data analysis exercises.
You will pass practical assignments, 85+ coding exercises, 10 quizzes with 100+ questions, on all the topics over the entire course.
You will accomplish one capstone project on Sport data analysis at the end to get the full view of data analysis workflow in Python.
Requirements
Access to computer and internet
Basic computer literacy
No coding experience required
Description
Welcome to the Data Analysis Bootcamp: A-Z Data Analysis in Python! In this comprehensive course, you'll embark on a journey from Python novice to proficient data analyst, equipped with the essential skills and knowledge to excel in the field.Throughout this course, you will delve deep into the realm of Python programming, focusing on its application in data analysis. Starting from the basics, you'll master fundamental concepts such as variable naming, data types, lists, dictionaries, dataframes, sets, loops, and functions. With a solid foundation in Python, you'll seamlessly transition to advanced topics, including data cleaning, sorting, filtering, manipulation, transformation, and preprocessing.But that's not all. As you progress, you'll learn how to harness the power of Python for data visualization, exploratory data analysis, statistical analysis, hypothesis testing, and even delve into the exciting world of machine learning. Through a combination of theoretical understanding and hands-on practice, you'll gain proficiency in a wide range of methods and techniques essential for data analysis.What sets this course apart is its emphasis on practical application. You won't just learn the theory; you'll put your newfound knowledge to the test through practical data analysis projects and hands-on exercises. With over 85 coding exercises, 10 quizzes featuring 100+ questions, and practical assignments covering all topics, you'll have ample opportunities to reinforce your skills and enhance your problem-solving abilities.As the culmination of your journey, you'll undertake a capstone project focused on sports data analysis. This final project will allow you to apply all the skills you've acquired throughout the course, providing you with a comprehensive understanding of the data analysis workflow in Python.Whether you're a seasoned professional looking to upskill or someone just starting their journey in data analysis, this course is designed to equip you with the expertise and confidence needed to succeed. Join us on this exciting adventure and unlock your potential as a data analyst in Python.
Overview
Section 1: Start Here: MUST Follow the Instructions
Lecture 1 Instructions to accomplish the course
Lecture 2 Python cheatsheet for data analysis
Lecture 3 Resources used in the course
Section 2: Data Analysis and Its Application
Lecture 4 Understanding analyzing data
Lecture 5 Real-world application of data analysis
Section 3: Data Analysis Tools, Techniques and Methods
Lecture 6 Various aspects of data cleaning
Lecture 7 Various aspects of Joining datasets
Lecture 8 Methods of exploratory data analysis Part 1
Lecture 9 Methods of exploratory data analysis Part 2
Lecture 10 Methods of exploratory data analysis Part 3
Section 4: Statistical Analysis Methods and Techniques
Lecture 11 Population v/s sample and its methods
Lecture 12 Types of statistical data analysis
Lecture 13 A Recap on descriptive statistics methods
Lecture 14 Inferential statistics Part 1 – T-tests and ANOVA
Lecture 15 Inferential statistics Part 2 – Relationships measures
Lecture 16 Inferential statistics Part 3 – Linear regression
Section 5: Clarifying the Concept of Hypothesis Testing
Lecture 17 Hypothesis testing for inferential statistics
Lecture 18 Selecting statistical test and assumption testing
Lecture 19 Confidence level, significance level, p-value
Lecture 20 Making decision and conclusion on findings
Lecture 21 A-Z statistical analysis and hypothesis testing
Section 6: Data Transformation and Visualisation Methods
Lecture 22 Techniques for data transformation Part 1
Lecture 23 Techniques for data transformation Part 2
Lecture 24 Several methods of data visualization Part 1
Lecture 25 Several methods of data visualization Part 2
Lecture 26 Several methods of data visualization Part 3
Section 7: Data Modeling with Machine Learning Model
Lecture 27 Importance of ML in data analytics
Lecture 28 Widely used machine learning models
Lecture 29 Steps in developing machine learning model
Section 8: Setting Up Python and Jupyter Notebook
Lecture 30 Installing Python and Jupyter Notebook – Mac
Lecture 31 Installing Python and Jupyter Notebook – Windows
Lecture 32 More alternative methods – Check the article
Section 9: Starting with Variables to Data Types
Lecture 33 Getting started with first python code
Lecture 34 Assigning variable names correctly
Lecture 35 Various data types and data structures
Lecture 36 Converting and casting data types
Lecture 37 Starting with Variables to Data Types
Section 10: Various Operators in Python Programming
Lecture 38 Arithmetic operators (+, -, *, /, %, **)
Lecture 39 Comparison operators (>, =,
