Deep Data Science Aiml End To End Master Class Tm

Real time case study

What you'll learn

Introduction to Data Science

Data Science Session Part 2 Overview

Data Science Vs Traditional Analysis Overview

Data Scientist Introduction to the role of a Data Scientist, including skills, tools, and responsibilities.

Data Science Process Overview

Introduction to Python for Data Science Overview

Python Libraries for Data Science Overview

Introduction to R for Data Science

Requirements

Anyone can learn this class it is very simple.

Description

1. Introduction to Data ScienceOverview:-This section provides a broad introduction to Data Science, its significance, and its impact across various industries.Topics Covered:-What is Data Science?The Importance of Data in the Modern WorldApplications of Data Science in Various DomainsKey Roles in Data Science: Data Scientists, Data Engineers, and Data AnalystsLearning Outcomes:-Understand the basics of Data Science and its relevance.Identify key roles and applications of Data Science.2. Data Science Session Part 2Overview:-A continuation of the introduction, diving deeper into the tools and technologies used in Data Science.Topics Covered:-Overview of Data Science Tools and TechnologiesIntroduction to Data Wrangling, Exploration, and VisualizationUnderstanding Big Data and its ChallengesReal-World Case Studies in Data ScienceLearning Outcomes:-Gain insight into the tools and techniques used in Data Science.Analyze real-world examples of Data Science in action.3. Data Science Vs Traditional AnalysisOverview:-This section contrasts Data Science with traditional data analysis techniques, highlighting the differences and advancements.Topics Covered:-Traditional Data Analysis TechniquesEvolution from Traditional Analysis to Data ScienceDifferences in Methodology, Tools, and OutcomesAdvantages of Data Science over Traditional AnalysisLearning Outcomes:-Understand the evolution from traditional analysis to Data Science.Identify the key differences and advantages of Data Science.4. Data Scientist Part 1Overview:-Introduction to the role of a Data Scientist, including skills, tools, and responsibilities.Topics Covered:-Who is a Data Scientist?Essential Skills for Data Scientists: Programming, Statistics, and Domain KnowledgeCommon Tools Used by Data ScientistsThe Day-to-Day Responsibilities of a Data ScientistLearning Outcomes:-Understand the role and importance of a Data Scientist.Identify the essential skills and tools required for a Data Scientist.5. Data Scientist Part 2Overview:-A deeper dive into the practical aspects of being a Data Scientist, including challenges and career opportunities.Topics Covered:-Challenges Faced by Data ScientistsEthical Considerations in Data ScienceCareer Pathways and Growth Opportunities for Data ScientistsBuilding a Portfolio and Gaining Practical ExperienceLearning Outcomes:-Gain insight into the challenges and ethical considerations in Data Science.Learn about career opportunities and how to build a strong Data Science portfolio.6. Data Science Process OverviewOverview:-An overview of the Data Science process, including stages and methodologies.Topics Covered:-The Data Science LifecycleUnderstanding Data Collection and Data CleaningExploratory Data Analysis (EDA)Model Building and EvaluationDeployment and Monitoring of Data Science ModelsLearning Outcomes:-Understand the stages involved in the Data Science process.Gain a high-level understanding of each step from data collection to model deployment.7. Data Science Process Overview Part 2Overview:-Continuation of the Data Science process, focusing on more advanced concepts and tools.Topics Covered:-Advanced Data Cleaning TechniquesFeature Engineering and SelectionHyperparameter Tuning and Model OptimizationIntroduction to Model InterpretabilityPost-Deployment Monitoring and MaintenanceLearning Outcomes:-Master advanced techniques in the Data Science process.Learn how to optimize models and ensure they remain effective post-deployment.8. Introduction to Python for Data ScienceOverview:-This section introduces Python as a primary tool for Data Science.Topics Covered:-Why Python for Data Science?Setting Up the Python Environment for Data ScienceIntroduction to Jupyter NotebooksBasic Python Syntax and OperationsOverview of Python Libraries for Data ScienceLearning Outcomes:-Set up and use Python for Data Science tasks.Write basic Python code and use Jupyter Notebooks.9. Python Libraries for Data ScienceOverview:-A focused introduction to essential Python libraries used in Data Science.Topics Covered:Overview of NumPy, Pandas, Matplotlib, and SeabornWorking with NumPy ArraysData Manipulation with PandasData Visualization with Matplotlib and SeabornIntroduction to SciPy and Scikit-Learn for Machine LearningLearning Outcomes:-Gain proficiency in using essential Python libraries for data manipulation and visualization.Prepare data for analysis and build simple visualizations.10. Introduction to R for Data ScienceOverview:-Introduces R as an alternative tool for Data Science, focusing on its strengths and ecosystem.Topics Covered:-Why R for Data Science?Setting Up the R EnvironmentBasic R Syntax and OperationsIntroduction to RStudioOverview of R Libraries for Data Science (e.g., dplyr, ggplot2)Learning Outcomes:-Set up and use R for Data Science tasks.Write basic R code and use RStudio effectively.

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

Lecture 4 Data Scientist

Section 5: Data Scientist Part 2

Lecture 5 Data Scientist Part 2

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

Lecture 11 R Programmig Basics

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: Python for AIML- Data Structures and Functions

Lecture 15 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 for Data Science

Lecture 18 Introduction to R for Data Science

Section 19: R Programmig Basics

Lecture 19 R Programmig Basics

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