Analysis From The Start To The End
Published 3/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English
| Size: 5.16 GB[/align]
| Duration: 7h 8m
part of the Big Bang of Data Science
[b]What you'll learn[/b]
Research map & the relevance to analysis
Threats to conclusion validity
Fundamentals of statistical powers
Improvement of conclusion validity
Map of analysis Framework
Data preparation: logging, accuracy control, structure, and transformation
LAB-01: Data Preparation; all the steps you need to prepare your data from research perspective
Fundamentals of EDA
Mass, and Density distributions
Central tendency: mean, median, mode, and proportion
Dispersion: rang, interquartile, variance, and standard deviation
Univariate, Bivariate, and Multivariate analysis- process
LAB-02: analysis using univariate, bivariate and multivariate techniques
Inferential Analysis: estimating parameters, and hypothesis testing
Statistical tests: t-test, Chi square, Pearson's, F-test, ANOVA, MANOVA, and many more
Intro to open-source statistical software Jamovi
LAB-03: implementation of all the outlines using Jamovi on synthetical datasets
LAB-04: implementation of all the outlines using Jamovi on real dataset
[b]Requirements[/b]
No specific experience needed. ONLY, your motivation to outcome a product that is good, successful, and intelligent
STRONGLY RECOMMENDED: the completion of the first book- Research from the Start to the End.
[b]Description[/b]
The ContentThis is the second element of the Big Bang of Data Science, that is [Analysis from the Start to The End].I don't want to stick to that abstract and direct definition from the academic book, on the meaning of analysis, but from the industrial one. So, I believe ANALYSIS is the co-concertmaster that sits in the second chair of the highest leadership position among all the other parts that are responsible for the outcome of a product. Analysis is an art that has the characteristics of being a two-edged sword. In other words, if your understanding of analysis is based on subjective, rigid ground then your answers; solutions; products are for sure questioned. However, if your analysis is based on objective, scientific grounds then your answers; solutions; products are for sure worthy of consuming. If you search any search engine the word of analysis, you should not be surprised with the astronomical number of results on your search. The problem with many of the materials which discuss the subject of analysis is that two perspectives are there:the first, the perspective of analysis as a bunch of graphs and tables,and the second, the perspective of analysis is a bunch of tests and tools that applies them.Well, one can argue there is nothing wrong with that, but the problem arises when one fails to understand the raw materials that are needed to present those tables and figures, in addition, the fundamentals of those tools and tests that produce them. To this end, this book aims to address this mis conceptual understanding about analysis; basically, the book materials are constructed in such way that one can:firstly, understand the important of data that come from solid research,secondly, to understand the fundamentals of analysis from philosophical and scientifical perspective,thirdly, complete grasp on the meaning of hypothesis, as forming, articulating, etc.,and finally, the comprehensive knowledge on the tests and tools are there to help you implement your analysis.To this end, the second book is carefully crafted to meet all the requirements to build your product on the right foundation of analysis. Here is a quick view of the content of the book.Introduction[✓] Research map[✓] THREATS TO CONCLUSION VALIDITY[✓] STATISTICAL POWER[✓] IMPOROVE CONCLUSION VALIDITY[✓] ANALYSISData Preparation[✓] LOGGING THE DATA[✓] DATA ACCURACY CONTROL[✓] DATABASE STRUCTURE[✓] ENTERING DATA TO THE COMPUTER[✓] DATA TRANSFORMATION[✓] LAB-01- Three parts- on data preparationDescriptive Statistics[✓] Introduction to EDA[✓] Distribution[✓] Central Tendency[✓] Dispersion[✓] Bivariate descriptive[✓] Multivariate descriptive[✓] LAB-02- analysis on univariate, bivariate, and multivariate Inferential Statistics[✓] Introduction[✓] Estimating Parameters[✓] Hypothesis TestingStatistical Software[✓] Introduction[✓] Statistical Software[✓] Intro- Implementation by JAMOVI[✓] LAB-03- analysis on two datasets using JAMOVILAB-Section -04- Analysis on real dataset using Jamovi[✓] Review[✓] EDA analysis[✓] Inferential Analysis[✓] LAB-04- implementation on the dataset from the first bookWho is this book for?This book is for anyone, regardless of the educational background, with the interest in building, creating and producing a professional product that has a vision of the future. You don't have to have specific skill in any way, but extreme enthusiasm to learn how to make the right decision. So, it is meant for an audience of: (1) students, under or postgraduate. (2) scholars, (3) researchers, (4) scientists, (5) executives, (6) managers, (7) professionals, (8) or laypersons.TipThe trainer strongly advice on learning the materials from the first book Research from the Start to the End; that can absolutely help you to perform way better in this book.Competitive advantages!as outlined above in the introduction, this book is the second book from The Big Bang of Data Science that means it's an element among other elements of a project. This implies that the outlines and the contents are not ONLY discussed from an analysis perspective, but also from a wider perspective of the entire project. This offers you an opportunity to excel in the subject of analysis from a wider range of disciplines.As I have outlined above in the introduction, so many materials discuss the subject of analysis, however, many of which fail to focus on the subject of orientation. If your analysis is subjective oriented, i.e., your analysis is controlled by external factors such as your background, education, environment, culture and many more, then your final solution is questionable. However, if your analysis is objective oriented, i.e., your analysis is based on methodical, and scientific facts, then your final product is worthy of consuming. This material is constructed based on the latter, that is objective oriented approach.The slogan of the Big Bang of Data Science is From academia to industry, this material is obligated to that. You will have two types of labs: the first is using synthetical type of data to implement the abstracts and theories you learn, and the second uses a real dataset that we have built from the first book Research from the Start to the End. As a result, you will master the idea from abstract to applied.Lastly, all the types of tests we are going to learn about will be executed using an open-source statistical tool, that is Jamovi. This tool offers several statistical tests that one needs to do research analysis. Notably, unlike other material that presents analysis within the framework of jamovi, this material coaches you how to understand the selection of the right test, first, then you can use this tool or any other tool of your choice to execute the test. So, this perceptive gives you confidence in relying on many other tools of your choice if you understand each test independently.
Overview
Section 1: About the trainer
Lecture 1 Dahman's Phi Services- initiative
Lecture 2 The Story of- The Big Bang of Data Science Project
Lecture 3 Introduction to- The Big Bang of Data Science- First Edition
Lecture 4 Introduction to- The Big Bang of Data Science- Second Edition
Section 2: Introduction to this course
Lecture 5 Why this course, and its competitive advantages
Lecture 6 The main contents- in form of chapters
Lecture 7 Screencast covers all the recorded lectures
Lecture 8 The PDF lecture slides
Section 3: Chapter One- Introduction
Lecture 9 Research Map & relevance to analysis
Lecture 10 Threats to conclusion validity
Lecture 11 Statistical power
Lecture 12 Improve conclusion validity
Lecture 13 Analysis framework
Section 4: Chapter Two- Data Preparation
Lecture 14 Logging the data
Lecture 15 Data accuracy control
Lecture 16 Database structure
Lecture 17 Entering data to computer
Lecture 18 Data preparation
Lecture 19 LAB-01- Data preparation- PART ONE
Lecture 20 LAB-01- Data preparation- PART TWO
Lecture 21 LAB-01- Data preparation- PART THREE
Section 5: Chapter three- Descriptive Analysis
Lecture 22 Introduction to EDA
Lecture 23 Distribution- Introduction
Lecture 24 Distribution- Mass type
Lecture 25 Distribution- Density type
Lecture 26 Central Tendency- Introduction
Lecture 27 Central Tendency- Mean
Lecture 28 Central Tendency- Median
Lecture 29 Central Tendency- Mode
Lecture 30 Central Tendency- Proportion
Lecture 31 Central Tendency- Recap
Lecture 32 Dispersion- introduction
Lecture 33 Dispersion- Range
Lecture 34 Dispersion- interquartile
Lecture 35 Dispersion- Variance & Standard Deviation
Lecture 36 Bivariate Analysis- PART ONE
Lecture 37 Bivariate Analysis- PART TWO
Lecture 38 Bivariate Analysis- PART THREE
Lecture 39 Bivariate Analysis- PART FOUR
Lecture 40 Bivariate Analysis- PART FIVE
Lecture 41 Bivariate Analysis- PART SIX
Lecture 42 Bivariate Analysis- PART SEVEN
Lecture 43 Bivariate Analysis- PART EIGHT
Lecture 44 Multivariate Analysis- Review
Lecture 45 LAB-02- Analysis; univariate, bivariate and multivariate
Section 6: Chapter Four- Inferential Analysis
Lecture 46 Introduction to Inferential analysis
Lecture 47 Estimating parameters- Point estimate
Lecture 48 Estimating Parameters- Interval estimate
Lecture 49 hypothesis testing- Introduction
Lecture 50 hypothesis testing- factors for selection tests- PART ONE
Lecture 51 hypothesis testing- factors for selection tests- PART TWO
Section 7: Chapter Five- Statistical Software
Lecture 52 Statistical software Introduction
Lecture 53 Introduction to Jamovi statistical tool
Lecture 54 LAB-03- analysis on synthetical dataset ONE using Jamovi- PART ONE
Lecture 55 LAB-03- analysis on synthetical dataset ONE using Jamovi- PART TWO
Lecture 56 LAB-03- analysis on synthetical dataset TWO using Jamovi- PART TWO
Section 8: Chapter Six- LAB-04- Real Project Analysis with Jamovi
Lecture 57 LAB-04- Overview
Lecture 58 LAB-04- Implementation part one
Lecture 59 LAB-04- Implementation part two
Lecture 60 LAB-04- Implementation part three
Lecture 61 LAB-04- Implementation part four
Lecture 62 LAB-04- Implementation part five
Section 9: Chapter Seve- Closing and Next vision
Lecture 63 Research & Analysis- Review and Vision of Prediction
Lecture 64 References
Section 10: What is Next?
Lecture 65 It's not goodbye but keep in touch!
Lecture 66 Congratulations! and next if you aim for it!
students- post/undergraduate; scholars, scientists, executives, managers, professionals, and layperson
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Udemy - Analysis From the Start to The End
