Master Advanced Data Science - Data Scientist Aiml Experts Tm

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Master Advanced Data Science -Data Scientist Aiml Experts Tm
Published 10/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 31h 29m | Size: 15.6 GB​

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 Fundamentalsata 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 & Analysisata 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 Scienceimensionality 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 Studiesata 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.
Who this course is for
Anyone who wants to learn future skills and become Data Scientist, Sr. Data Scientist, Ai Scientist, Ai Engineer, Ai Researcher & Ai Expert.
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