Data Science For Everyone
Published 9/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.29 GB | Duration: 9h 26m
Data Science Essentials for Beginners
[b]What you'll learn[/b]
Basics of data science
Basics of machine learning
Basics of statistical inference
Basics of data-driven decision making
[b]Requirements[/b]
None
[b]Description[/b]
Welcome to this course, data science for everyone. In this series of lectures, I will provide you with essentials of data science.This course is targeted for managers who are not data scientist but need to manage data analytic projects. It is also targeted for managers who want to introduce data-driven management. So, the knowledge provided in this course is both theoretical and pragmatic, but not includes details of mathematics and coding. However, anyone who are beginners in data science are also welcome because this course can provide you with essentials for learning technical aspects of data science.You will learn: - Essentials concepts and theories for learning technical aspects of data science.- Pragmatic knowledge for interpreting data and results of data analytics.- Not includes mathematics details and coding.Target Audience:- Managers who are not data scientist but need to manage data analytic projects.- Managers who want to introduce data-driven management.- Anyone who are beginners in data scienceThis course covers the following topics. As you can see, the contents include fundamental concepts of data science, and basics of descriptive, diagnostic, and predictive analytics. This course also covers the very basics of deep learning. In the final two chapters, you can gain a basic but essential and robust understanding of artificial neural networks.I hope you enjoy this course.Contents:- Data Literacy and DIKW- Data-Driven Decision Making- Exploratory Data Analysis: Probability theory, Descriptive Statistics- Data Preprocessing- Data Visualization- Diagnostic Analytics: Hypothesis Testing (Theory and Methods)- Predictive Analytics: Machine Learning, Deep Learning
Overview
Section 1: Introduction
Lecture 1 Why Do We Need Data Literacy?
Lecture 2 What is Data Science?
Lecture 3 Data Science Workflow
Lecture 4 Data Type
Lecture 5 DIKW Pyramid
Section 2: Analytics for Decision Making
Lecture 6 Data-Driven Decision Making
Lecture 7 Business Analytics
Lecture 8 Machine Learning
Section 3: Exploratory Data Analysis Part 1
Lecture 9 What is EDA?
Lecture 10 Stevens' Typology
Lecture 11 Univariate Analysis
Lecture 12 Multivariate Analysis
Section 4: Exploratory Data Analysis Part 2
Lecture 13 Probability Basics
Lecture 14 Conditional Probability
Lecture 15 Bayes Theorem
Section 5: Data Preprocessing
Lecture 16 Data Cleaning
Lecture 17 Handling Missing Data
Lecture 18 Data Transformation
Lecture 19 Data Reduction
Section 6: Data Visualization
Lecture 20 Data Visualization for Univariate Analysis Part 1
Lecture 21 Data Visualization for Univariate Analysis Part 2
Lecture 22 Data Visualization for Univariate Analysis Part 3
Lecture 23 Data Visualization for Bivariate Analysis
Lecture 24 Data Visualization for Higher Dimensions
Section 7: Diagnostic Analytics Part 1
Lecture 25 Statistical Hypothesis Testing
Lecture 26 Probability Distribution
Lecture 27 Law of Large Numbers and Central Limit Theorem
Lecture 28 Hypothesis Testing Part 1
Lecture 29 Hypothesis Testing Part 2
Section 8: Diagnostic Analytics Part 2
Lecture 30 t-test
Lecture 31 Two-sample t-test
Lecture 32 Chi-Squared Test
Section 9: Diagnostic Analytics Part 3
Lecture 33 Correlation
Lecture 34 Regression
Lecture 35 Hypothesis Testing by Correlation and Hypothesis
Lecture 36 Multiple Regression Analysis
Section 10: Predictive Analytics Part 1
Lecture 37 Types of Machine Learning
Lecture 38 Regression
Lecture 39 Performance Metrics of Regression Models Part 1
Lecture 40 Performance Metrics of Regression Models Part 2
Section 11: Predictive Analytics Part 2
Lecture 41 What is Classification?
Lecture 42 Logistic Regression
Lecture 43 Decision Tree
Lecture 44 Ensemble Learning
Lecture 45 Performance Metrics of Classification Models Part 1
Lecture 46 Performance Metrics of Classification Models Part 2
Section 12: Cluster Analysis
Lecture 47 What is Clustering?
Lecture 48 Distance-Based Clustering
Lecture 49 K-Means Clustering
Lecture 50 Example: Customer Segmentation
Section 13: Deep Learning Part 1
Lecture 51 What is Deep Learning?
Lecture 52 Perceptron
Lecture 53 Multilayer Perceptron
Lecture 54 Artiricial Neural Network
Section 14: Deep Learning Part 2
Lecture 55 How to Train a Neural Network
Lecture 56 Optimization
Lecture 57 Regularization Part 1
Lecture 58 Reguralization Part 2
Anyone who want to learn data science,Managers who implement data-driven management
https://ddownload.com/l8vbcnrknauj/.Data.Science.for.Everyone.2024-9.rar
https://rapidgator.net/file/c28daa12d905d76f1adb9422305cafa8/.Data.Science.for.Everyone.2024-9.rar
https://turbobit.net/4j8unw99jw6t/.Data.Science.for.Everyone.2024-9.rar.html
