Master Advanced Data Science -Data Scientist Aiml Experts Tm

Real-World Case Studies and Practical Applications in Data Science

What you'll learn

Data Science Sessions Part 1 & 2: Understand the foundational methodologies and approaches in data science.

Data Science vs Traditional Analysis: Compare modern data science techniques to traditional statistical methods.

Data Scientist Journey Parts 1 & 2: Explore the skills, roles, and responsibilities of a data scientist.

Data Science Process Overview Parts 1 & 2: Gain insights into the end-to-end data science process.

Introduction to Python for Data Science: Learn Python programming for data science tasks and analysis.

Python Libraries for Data Science: Master key Python libraries like Numpy, Pandas, and Matplotlib.

Introduction to R for Data Science: Get acquainted with R programming for statistical analysis.

Data Structures and Functions in Python & R: Handle and manipulate data efficiently using Python and R.

Introduction to Data Collection Methods: Understand various data collection techniques, including experimental methods.

Data Preprocessing (Parts 1 & 2): Clean and transform raw data to prepare it for analysis.

Exploratory Data Analysis (EDA): Detect outliers and anomalies to understand your data better.

Data Visualization Techniques: Choose the right visualization methods to represent data insights.

Tableau and Data Visualization: Utilize Tableau for advanced data visualization.

Inferential Statistics for Hypothesis Testing: Apply inferential statistics to test hypotheses and determine confidence intervals.

Introduction to Machine Learning: Learn the fundamentals of machine learning and its applications.

Unsupervised Learning (Clustering, DBSCAN, Dimensionality Reduction): Discover patterns and clusters in unlabeled datasets.

Supervised Learning (Regression, Classification, Decision Trees): Build and evaluate predictive models using labeled data.

Evaluation Metrics for Regression & Classification: Use various metrics to assess machine learning model performance.

Model Evaluation and Validation Techniques: Improve model robustness through bias-variance tradeoffs and validation techniques.

Ethical Challenges in Data Science: Address ethical concerns in data collection and model deployment.

Requirements

Anyone can learn this class it is very simple.

Description

This comprehensive Data Science Mastery Program is designed to equip learners with essential skills and knowledge across the entire data science lifecycle. The course covers key concepts, tools, and techniques in data science, from basic data collection and processing to advanced machine learning models. Here's what learners will explore:Core Data Science Fundamentals:Data Science Sessions Part 1 & 2 – Foundation of data science methodologies and approaches.Data Science vs Traditional Analysis – Comparing modern data science techniques to traditional statistical methods.Data Scientist Journey Parts 1 & 2 – Roles, skills, and responsibilities of a data scientist.Data Science Process Overview Parts 1 & 2 – An introduction to the step-by-step process in data science projects.Programming Essentials:Introduction to Python for Data Science – Python programming fundamentals tailored for data science tasks.Python Libraries for Data Science – In-depth exploration of key Python libraries like Numpy, Pandas, Matplotlib, and Seaborn.Introduction to R for Data Science – Learning the R programming language basics for statistical analysis.Data Structures and Functions in Python & R – Efficient data handling and manipulation techniques in both Python and R.Data Collection & Preprocessing:Introduction to Data Collection Methods – Understanding various data collection techniques, including experimental studies.Data Preprocessing – Cleaning, transforming, and preparing data for analysis (Parts 1 & 2).Exploratory Data Analysis (EDA) – Detecting outliers, anomalies, and understanding the underlying structure of data.Data Wrangling – Merging, transforming, and cleaning datasets for analysis.Handling Missing Data and Outliers – Techniques to manage incomplete or incorrect data.Visualization & Analysis:Data Visualization Techniques – Best practices for choosing the right visualization method to represent data.Tableau and Data Visualization – Leveraging advanced data visualization software.Inferential Statistics for Hypothesis Testing & Confidence Intervals – Key statistical concepts to test hypotheses.Machine Learning Mastery:Introduction to Machine Learning – Core concepts, types of learning, and their applications.Unsupervised Learning (Clustering, DBSCAN, Dimensionality Reduction) – Discovering patterns in unlabeled data.Supervised Learning (Regression, Classification, Decision Trees) – Building predictive models from labeled data.Evaluation Metrics for Regression & Classification – Techniques to evaluate model performance (e.g., accuracy, precision, recall).Model Evaluation and Validation Techniques – Methods for improving model robustness, including bias-variance tradeoffs.Advanced Topics in Data Science:Dimensionality Reduction (t-SNE) – Reducing complexity in high-dimensional datasets.Feature Engineering and Selection – Selecting the best features for machine learning models.SQL for Data Science – Writing SQL queries for data extraction and advanced querying techniques.Ethical Challenges in Data Science – Understanding the ethical implications in data collection, curation, and model deployment.Hands-on Applications & Case Studies:Data Science in Practice Case Study (Parts 1 & 2) – Real-world data science projects, combining theory with practical implementation.End-to-End Python & R for Data Science – Practical coding exercises to master Python and R in real data analysis scenarios.Working with Data Science Applications – Applying data science techniques in real-world situations.By the end of this program, learners will be equipped to handle end-to-end data science projects, including data collection, cleaning, visualization, statistical analysis, and building robust machine learning models. With hands-on projects, case studies, and a capstone, this course will provide a solid foundation in data science and machine learning, preparing learners for roles as data scientists and AI/ML professionals.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Data Science Session 2

Lecture 2 Data Science Session 2

Section 3: Data Science Vs Traditional Analysis

Lecture 3 Data Science Vs Traditional Analysis

Section 4: Data Scientist Part1

Lecture 4 Data Scientist Part1

Section 5: Data Scientist Part2

Lecture 5 Data Scientist Part2

Section 6: Data Science Process Overview

Lecture 6 Data Science Process Overview

Section 7: Data Science Process Overview Part2

Lecture 7 Data Science Process Overview Part2

Section 8: Introduction to Python for Data Science

Lecture 8 Introduction to Python for Data Science

Section 9: Python Libraries for Data Science

Lecture 9 Python Libraries for Data Science

Section 10: Introduction to R for Data Science

Lecture 10 Introduction to R for Data Science

Section 11: R Programmig Basics AIML

Lecture 11 R Programmig Basics AIML

Section 12: Introduction to Python Programming

Lecture 12 Introduction to Python Programming

Section 13: Introduction to Python Programming Part2

Lecture 13 Introduction to Python Programming Part2

Section 14: Data Structures and Functions in Python

Lecture 14 Data Structures and Functions in Python

Section 15: End-to-End Python for AIML- Data Structures and Functions

Lecture 15 End-to-End Python for AIML- Data Structures and Functions

Section 16: Working with Libraries and Handling Files

Lecture 16 Working with Libraries and Handling Files

Section 17: Python Introduction to Numpy

Lecture 17 Python Introduction to Numpy

Section 18: Introduction to R Programming

Lecture 18 Introduction to R Programming

Section 19: Introduction to R Programming Part2

Lecture 19 Introduction to R Programming Part2

Section 20: Data Structures in R

Lecture 20 Data Structures in R

Section 21: Data Structures in R Part2

Lecture 21 Data Structures in R Part2

Section 22: R Programming

Lecture 22 R Programming

Section 23: R Programming Part2

Lecture 23 R Programming Part2

Section 24: Introduction to Data Collection Methods

Lecture 24 Introduction to Data Collection Methods

Section 25: Introduction to Data Collection Methods Experimental Studies

Lecture 25 Introduction to Data Collection Methods Experimental Studies

Section 26: Data Preprocessing

Lecture 26 Data Preprocessing

Section 27: Data Preprocessing Part2

Lecture 27 Data Preprocessing Part2

Section 28: Introduction to Exploratory Data Analysis EDA

Lecture 28 Introduction to Exploratory Data Analysis EDA

Section 29: EDA- Detecting Outliers and Anomalies in Data

Lecture 29 EDA- Detecting Outliers and Anomalies in Data

Section 30: Data Visualization in Data Science

Lecture 30 Data Visualization in Data Science

Section 31: Choosing the Right Visualization for Data

Lecture 31 Choosing the Right Visualization for Data

Section 32: Introduction to Statistical Analysis for Data Science

Lecture 32 Introduction to Statistical Analysis for Data Science

Section 33: Inferential Statistics for Hypothesis Testing & Confidence Intervals

Lecture 33 Inferential Statistics for Hypothesis Testing & Confidence Intervals

Section 34: Introduction to Data Science Tools and Software

Lecture 34 Introduction to Data Science Tools and Software

Section 35: Tableau and Data Visualization

Lecture 35 Tableau and Data Visualization

Section 36: Data Wrangling in Data Science

Lecture 36 Data Wrangling in Data Science

Section 37: Data Wrangling & EDA in Data Science

Lecture 37 Data Wrangling & EDA in Data Science

Section 38: Data Integration & Transformation for Data Science

Lecture 38 Data Integration & Transformation for Data Science

Section 39: Handling Missing Data and Outliers

Lecture 39 Handling Missing Data and Outliers

Section 40: Introduction to Machine Learning

Lecture 40 Introduction to Machine Learning

Section 41: ML Unsupervised Learning

Lecture 41 ML Unsupervised Learning

Section 42: Supervised Learning- Regression

Lecture 42 Supervised Learning- Regression

Section 43: Evaluation Metrics for Regression Models

Lecture 43 Evaluation Metrics for Regression Models

Section 44: Supervised Learning- Classification in Machine Learning

Lecture 44 Supervised Learning- Classification in Machine Learning

Section 45: Supervised Learning- Decision Trees

Lecture 45 Supervised Learning- Decision Trees

Section 46: Unsupervised Learning- Clustering

Lecture 46 Unsupervised Learning- Clustering

Section 47: Unsupervised Learning DBSCAN Clustering

Lecture 47 Unsupervised Learning DBSCAN Clustering

Section 48: Unsupervised Learning- Dimensionality Reduction

Lecture 48 Unsupervised Learning- Dimensionality Reduction

Section 49: Unsupervised Learning- Dimensionality Reduction with t-SNE

Lecture 49 Unsupervised Learning- Dimensionality Reduction with t-SNE

Section 50: Model Evaluation and Validation Techniques

Lecture 50 Model Evaluation and Validation Techniques

Section 51: Model Evaluation- Bias-Variance Tradeoffs

Lecture 51 Model Evaluation- Bias-Variance Tradeoffs

Section 52: Introduction to Python Libraries for Data Science

Lecture 52 Introduction to Python Libraries for Data Science

Section 53: Introduction to Python Libraries for Data Science Part2

Lecture 53 Introduction to Python Libraries for Data Science Part2

Section 54: Introduction to R Libraries for Data Science

Lecture 54 Introduction to R Libraries for Data Science

Section 55: Introduction to R Libraries for Data Science Statistical Modeling

Lecture 55 Introduction to R Libraries for Data Science Statistical Modeling

Section 56: Introduction to SQL for Data Science

Lecture 56 Introduction to SQL for Data Science

Section 57: SQL Queries for Data Science

Lecture 57 SQL Queries for Data Science

Section 58: SQL and Advanced Queries Part1

Lecture 58 SQL and Advanced Queries Part1

Section 59: SQL and Advanced Queries Part2

Lecture 59 SQL and Advanced Queries Part2

Section 60: Data Science in Practice- Case Study

Lecture 60 Data Science in Practice- Case Study

Section 61: Data Science in Practice- Case Study Part2

Lecture 61 Data Science in Practice- Case Study Part2

Section 62: Introduction to Data Science Ethics

Lecture 62 Introduction to Data Science Ethics

Section 63: Ethical Challenges in Data Collection and Curation

Lecture 63 Ethical Challenges in Data Collection and Curation

Section 64: Data Science Project Lifecycle

Lecture 64 Data Science Project Lifecycle

Section 65: Feature Engineering and Selection

Lecture 65 Feature Engineering and Selection

Section 66: Application- Working with Data Science

Lecture 66 Application- Working with Data Science

Section 67: Application Working with Data Science

Lecture 67 Application Working with Data Science

Anyone who wants to learn future skills and become Data Scientist, Sr. Data Scientist, Ai Scientist, Ai Engineer, Ai Researcher & Ai Expert.