Data Science for Beginners - Theory and Concepts

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Free Download Data Science for Beginners - Theory and Concepts
Published 4/2024
Created by Ndiaga Fall
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 38 Lectures ( 3h 34m ) | Size: 1.76 GB

Data science, AI, Machine Learning, Statistics
What you'll learn:
Data science
Artificial intelligence
Machine Learning
Statistics and data analysis
Requirements:
No prior experience in data science is required
Description:
Are you eager to dive into the world of data science and unleash the power of data-driven insights ? My Data Science Fundamentals course is designed to equip you with essential skills and techniques required to excel in this rapidly growing field. Whether you're a beginner looking to start a career in data science or a professional seeking to enhance your analytical abilities, this course is tailored to meet your learning needs.Course Overview:In this comprehensive course, you will:Explore Data Science Foundations: Understand the fundamental concepts of data science, including data manipulation, exploratory data analysis (EDA), and data visualization techniques.Learn Statistical Analysis: Acquire foundational knowledge in statistics, hypothesis testing, and probability theory to make data-driven decisions and draw meaningful insights from data.Learn Machine Learning : Discover the principles of supervised and unsupervised learning, regression, classification, clustering, and evaluation metrics to develop predictive models.Learn Machine Learning Algorithms: Discover the principles of supervised and unsupervised learning, regression, classification, clustering, and evaluation metrics to develop predictive models.Introduction to Deep Learning: Dive into the basics of neural networks, deep learning, computer vision and natural language processing.Who Should Enroll:This course is ideal for:Aspiring Data Scientists and AnalystsBusiness and IT professionals seeking to leverage data for decision-makingStudents and researchers interested in exploring the field of data scienceNo prior experience in data science is requiredBy the end of this course, you will be equipped with the skills and confidence to tackle data science challenges, make informed decisions based on data analysis, and embark on a rewarding career in the dynamic field of data science.Join me on this exciting data science journey and unlock the potential of data-driven innovation !
Who this course is for:
Beginners curious about data science
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Data Science For Beginners : Theory And Concepts
Published 4/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English
| Size: 968.41 MB
| Duration: 3h 34m
Data science, AI, Machine Learning, Statistics

What you'll learn

Data science

Artificial intelligence

Machine Learning

Statistics and data analysis

Requirements

No prior experience in data science is required

Description

Are you eager to dive into the world of data science and unleash the power of data-driven insights ? My Data Science Fundamentals course is designed to equip you with essential skills and techniques required to excel in this rapidly growing field. Whether you're a beginner looking to start a career in data science or a professional seeking to enhance your analytical abilities, this course is tailored to meet your learning needs.Course Overview:In this comprehensive course, you will:Explore Data Science Foundations: Understand the fundamental concepts of data science, including data manipulation, exploratory data analysis (EDA), and data visualization techniques.Learn Statistical Analysis: Acquire foundational knowledge in statistics, hypothesis testing, and probability theory to make data-driven decisions and draw meaningful insights from data.Learn Machine Learning : Discover the principles of supervised and unsupervised learning, regression, classification, clustering, and evaluation metrics to develop predictive models.Learn Machine Learning Algorithms: Discover the principles of supervised and unsupervised learning, regression, classification, clustering, and evaluation metrics to develop predictive models.Introduction to Deep Learning: Dive into the basics of neural networks, deep learning, computer vision and natural language processing.Who Should Enroll:This course is ideal for:Aspiring Data Scientists and AnalystsBusiness and IT professionals seeking to leverage data for decision-makingStudents and researchers interested in exploring the field of data scienceNo prior experience in data science is requiredBy the end of this course, you will be equipped with the skills and confidence to tackle data science challenges, make informed decisions based on data analysis, and embark on a rewarding career in the dynamic field of data science.Join me on this exciting data science journey and unlock the potential of data-driven innovation !

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Foundations of Data science

Lecture 2 What is data science ?

Lecture 3 Role of a data scientist

Lecture 4 History and evolution of data science

Lecture 5 Data Preprocessing

Lecture 6 Overview of data storage

Lecture 7 Data Exploration and Visualization

Lecture 8 Descriptive Statistics

Lecture 9 Data Visualizations Techniques

Section 3: Statistical Foundations

Lecture 10 Importance of statistics

Lecture 11 Statistical Features Part 1

Lecture 12 Statistical Features Part 2

Lecture 13 Correlation

Lecture 14 Causation

Lecture 15 Probability Distribution

Lecture 16 Skewness

Lecture 17 Dimensionality Reduction

Lecture 18 Multicollinearity

Lecture 19 Outliers

Lecture 20 Bayesian Statistics

Lecture 21 Central Theorem Limit

Lecture 22 Sampling

Lecture 23 Confidence interval & Significance Level

Lecture 24 Features transformation

Lecture 25 Probability Theory

Lecture 26 Hypothesis Testing

Lecture 27 Regression Analysis

Lecture 28 Domain Knowledge

Section 4: Advanced Machine Learning

Lecture 29 Machine Learning concepts

Lecture 30 Machine Learning algorithms

Lecture 31 Supervised & Unsupervised Learning

Lecture 32 Cross Validation Techniques

Lecture 33 Metrics for Regression

Lecture 34 Metrics for Classification

Lecture 35 Deep Learning

Lecture 36 Introduction to Neural Network

Lecture 37 Artificial Intelligence & Robotics

Section 5: Congratulations

Lecture 38 Outro

Beginners curious about data science

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