Advanced Data Science

Course Description

This course focuses on the fundamental knowledge of data science, emphasizing advanced methods and approaches for deriving insights from data. The students will learn advanced statistical approaches, machine learning algorithms, and data visualization strategies. We will explore real-world applications and practical projects.
Can non tech background learn Data Science?

Prerequisites

  • Proficiency in programming languages such as R or Python
  • Understanding probability theory and basic statistics is crucial.
  • familiarity with machine learning concepts and techniques.

Course Objectives

  • Learn advanced statistical methods for analyzing data.
  • Examine cutting-edge machine learning techniques and algorithms.
  • Gain expertise in the interpretation and visualization of data.
  • Use what you’ve learned through practical projects and case studies.

Foundation & Intermediate Syllabus:

  1. Data Collection and Acquisition:
  • Understanding various data sources, such as databases, APIs, web scraping, etc.
  • obtaining and gathering unprocessed data for analysis.
  1. Data Cleaning and Preprocessing:
  • Addressing data irregularities, outliers, and missing numbers.
  • Normalization and data transformation.
  • Feature engineering: enhancing model performance by generating additional features from preexisting data.
  1. Exploratory Data Analysis (EDA):
  • Data understanding tools include statistical summaries and data visualizations.
  • Locating links, patterns, and trends in the data.
  • Testing hypotheses to confirm presumptions.
  1. Statistical Analysis:
  • Theory of probability and distributions.
  • Inferential and Descriptive statistics.
  • Testing of hypotheses and confidence intervals.
  1. Machine Learning Fundamentals:
  • Regression and classification techniques under supervised learning.
  • Techniques for grouping and dimensionality reduction in unsupervised learning.
  • Model performance evaluation criteria, including accuracy, precision, recall, and F1-score, are used.
  • Model selection and fine-tuning of hyperparameters.
  1. Advanced Machine Learning Techniques:
  • Ensemble techniques: Gradient Boosting Machines (GBM), Random Forests, etc.
  • Deep learning and neural networks: recurrent neural networks (RNNs), feedforward networks, convolutional neural networks (CNNs), etc.
  • Reinforcement learning as well as other complex subjects.
  1. Data Visualization:
  • presenting data insights through the creation of interesting and educational graphics.
  • Tools and resources for visualization (e.g., Matplotlib, Seaborn, Plotly, Tableau).
  1. Big Data Technologies:
  • Utilizing distributed computing frameworks (e.g., Hadoop, Spark) to work with large-scale datasets.
  • Managing real-time analytics and streaming data.
  1. Model Deployment and Productionisation:
  • Introducing machine learning models into operational settings.
  • Tracking the performance of the model and updating it as necessary.
  • Constructing dependable and scalable data pipelines.
  1. Ethical and Legal Considerations:
  • Privacy, bias, and fairness in data science.
  • Compliance with regulations (e.g., GDPR, CCPA).
  • Ethical responsibilities of data scientists.
  1. Practical Applications and Projects:
  • Using data science methods to solve practical issues.
  • Completing data science projects from start to finish, including model deployment and data collecting.
  • Communication and teamwork abilities while presenting and interpreting results.

Advanced Syllabus:

  1. Advanced Statistical Methods
  • A multivariate analysis
  • Analyzing time series
  • Statistics using Bayesian methods
  • Non-parametric techniques
  1. Advanced Machine Learning
  • Methods of ensemble learning (Gradient Boosting, Random Forests)
  • The basics of deep learning
  • Convolutional Neural Networks (CNNs) are used to analyze image data.
  • Neural networks with recurrent sequential data: RNNs
  1. Natural Language Processing (NLP)
  • Text preprocessing methods
  • Word embeddings (GloVe, Word2Vec)
  • Classification and Text Generation with Recurrent Neural Networks
  • Transformer models (BERT, GPT) for advanced NLP jobs
  1. Big Data Analytics
  • Overview of frameworks for distributed computing (Hadoop, Spark)
  • Using Spark to handle massive datasets
  • Algorithms for distributed machine learning
  1. Time Series Forecasting
  • ARIMA models
  • Seasonal breakdown techniques
  • Tool for prophecy forecasting
  1. Data Visualization and Interpretation
  • Programs for advanced visualization (Plotly, Seaborn)
  • Engaging visuals
  • Data-driven storytelling
  1. Feature Engineering and Selection
  • Managing missing data
  • Normalization and scaling of features
  • Techniques for reducing dimensionality (PCA, t-SNE)
  1. Model Evaluation and Hyperparameter Tuning
  • Techniques for cross-validation
  • Techniques for hyperparameter optimization (Random and Grid Search)
  • Bias-variance balance
  1. Ethical Considerations in Data Science
  • Machine learning model bias and fairness
  • Data security and privacy
  • Data scientists’ obligations in terms of ethics
  1. Capstone Project
  • Practical data science project incorporating cutting-edge methods discovered throughout the training.
  • A focus on critical thinking, problem-solving, and successful results communication.

Advanced Data Science

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Course Description

This course focuses on the fundamental knowledge of data science, emphasizing advanced methods and approaches for deriving insights from data. The students will learn advanced statistical approaches, machine learning algorithms, and data visualization strategies. We will explore real-world applications and practical projects.

Can non tech background learn Data Science?

Prerequisites

  • Proficiency in programming languages such as R or Python
  • Understanding probability theory and basic statistics is crucial.
  • familiarity with machine learning concepts and techniques.

Course Objectives

  • Learn advanced statistical methods for analyzing data.
  • Examine cutting-edge machine learning techniques and algorithms.
  • Gain expertise in the interpretation and visualization of data.
  • Use what you've learned through practical projects and case studies.

Foundation & Intermediate Syllabus:

  1. Data Collection and Acquisition:
  • Understanding various data sources, such as databases, APIs, web scraping, etc.
  • obtaining and gathering unprocessed data for analysis.
  1. Data Cleaning and Preprocessing:
  • Addressing data irregularities, outliers, and missing numbers.
  • Normalization and data transformation.
  • Feature engineering: enhancing model performance by generating additional features from preexisting data.
  1. Exploratory Data Analysis (EDA):
  • Data understanding tools include statistical summaries and data visualizations.
  • Locating links, patterns, and trends in the data.
  • Testing hypotheses to confirm presumptions.
  1. Statistical Analysis:
  • Theory of probability and distributions.
  • Inferential and Descriptive statistics.
  • Testing of hypotheses and confidence intervals.
  1. Machine Learning Fundamentals:
  • Regression and classification techniques under supervised learning.
  • Techniques for grouping and dimensionality reduction in unsupervised learning.
  • Model performance evaluation criteria, including accuracy, precision, recall, and F1-score, are used.
  • Model selection and fine-tuning of hyperparameters.
  1. Advanced Machine Learning Techniques:
  • Ensemble techniques: Gradient Boosting Machines (GBM), Random Forests, etc.
  • Deep learning and neural networks: recurrent neural networks (RNNs), feedforward networks, convolutional neural networks (CNNs), etc.
  • Reinforcement learning as well as other complex subjects.
  1. Data Visualization:
  • presenting data insights through the creation of interesting and educational graphics.
  • Tools and resources for visualization (e.g., Matplotlib, Seaborn, Plotly, Tableau).
  1. Big Data Technologies:
  • Utilizing distributed computing frameworks (e.g., Hadoop, Spark) to work with large-scale datasets.
  • Managing real-time analytics and streaming data.
  1. Model Deployment and Productionisation:
  • Introducing machine learning models into operational settings.
  • Tracking the performance of the model and updating it as necessary.
  • Constructing dependable and scalable data pipelines.
  1. Ethical and Legal Considerations:
  • Privacy, bias, and fairness in data science.
  • Compliance with regulations (e.g., GDPR, CCPA).
  • Ethical responsibilities of data scientists.
  1. Practical Applications and Projects:
  • Using data science methods to solve practical issues.
  • Completing data science projects from start to finish, including model deployment and data collecting.
  • Communication and teamwork abilities while presenting and interpreting results.

Advanced Syllabus:

  1. Advanced Statistical Methods
  • A multivariate analysis
  • Analyzing time series
  • Statistics using Bayesian methods
  • Non-parametric techniques
  1. Advanced Machine Learning
  • Methods of ensemble learning (Gradient Boosting, Random Forests)
  • The basics of deep learning
  • Convolutional Neural Networks (CNNs) are used to analyze image data.
  • Neural networks with recurrent sequential data: RNNs
  1. Natural Language Processing (NLP)
  • Text preprocessing methods
  • Word embeddings (GloVe, Word2Vec)
  • Classification and Text Generation with Recurrent Neural Networks
  • Transformer models (BERT, GPT) for advanced NLP jobs
  1. Big Data Analytics
  • Overview of frameworks for distributed computing (Hadoop, Spark)
  • Using Spark to handle massive datasets
  • Algorithms for distributed machine learning
  1. Time Series Forecasting
  • ARIMA models
  • Seasonal breakdown techniques
  • Tool for prophecy forecasting
  1. Data Visualization and Interpretation
  • Programs for advanced visualization (Plotly, Seaborn)
  • Engaging visuals
  • Data-driven storytelling
  1. Feature Engineering and Selection
  • Managing missing data
  • Normalization and scaling of features
  • Techniques for reducing dimensionality (PCA, t-SNE)
  1. Model Evaluation and Hyperparameter Tuning
  • Techniques for cross-validation
  • Techniques for hyperparameter optimization (Random and Grid Search)
  • Bias-variance balance
  1. Ethical Considerations in Data Science
  • Machine learning model bias and fairness
  • Data security and privacy
  • Data scientists' obligations in terms of ethics
  1. Capstone Project
  • Practical data science project incorporating cutting-edge methods discovered throughout the training.
  • A focus on critical thinking, problem-solving, and successful results communication.


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Date : 10/06/2024 Language : English

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