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Advanced Analytics and Machine Learning Executive Program

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April 5, 2023

Advanced Analytics and Machine Learning Executive Program

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BusinessData ScienceFinance

The Advanced Analytics and Machine Learning Executive Program is designed for senior executives, managers, and decision-makers who want to stay ahead of the curve when it comes to data analytics and machine learning. This program will help participants gain a deep understanding of the latest trends, tools, and techniques in advanced analytics and machine learning, and how they can be applied to solve complex business problems.

What Will I Learn?

  • Data exploration and visualization techniques
  • Predictive analytics and modeling
  • Machine learning algorithms
  • Deep learning and neural networks
  • Natural language processing
  • Reinforcement learning
  • Optimization techniques
  • Big data and cloud computing
  • Statistical analysis
  • Data science foundations
  • Advanced analytics applications
  • Decision-making using data
  • Business intelligence and analytics
  • Ethical considerations in analytics
  • Analyzing unstructured data
  • Techniques for feature engineering
  • Evaluating model performance
  • Hyperparameter tuning
  • Dimensionality reduction techniques
  • Applying machine learning to real-world problems
Course Curriculum
Introduction to Advanced Analytics and Machine Learning
Provides an overview of advanced analytics and machine learning and their applications in business.
  • 1.1 What is Advanced Analytics and Machine Learning?
  • 1.2 Why is Advanced Analytics and Machine Learning Important for Business?
  • 1.3 Applications of Advanced Analytics and Machine Learning
  • 1.4 Tools and Technologies for Advanced Analytics and Machine Learning
Foundations of Statistical Analysis
Introduces statistical concepts and methods used in data analysis, including hypothesis testing and probability distributions.
  • 2.1 Probability Distributions and Descriptive Statistics
  • 2.2 Inferential Statistics and Hypothesis Testing
  • 2.3 Correlation and Regression Analysis
  • 2.4 Sampling and Estimation Techniques
Introduction to Data Science
Covers the basics of data science, including data cleaning, preprocessing, and visualization.
  • 3.1 Data Cleaning and Preprocessing
  • 3.2 Data Visualization Techniques
  • 3.3 Exploratory Data Analysis
  • 3.4 Introduction to Data Wrangling
Data Exploration and Visualization
Teaches techniques for exploring and visualizing data to gain insights.
  • 4.1 Data Exploration Techniques
  • 4.2 Data Visualization Techniques
  • 4.3 Geospatial Data Visualization
  • 4.4 Interactive Data Visualization
Predictive Analytics and Modeling
Covers the fundamentals of predictive modeling and how to build models that make accurate predictions.
  • 5.1 Introduction to Predictive Modeling
  • 5.2 Linear Regression Analysis
  • 5.3 Classification Algorithms
  • 5.4 Model Selection and Evaluation Techniques
Machine Learning Algorithms
Introduces various machine learning algorithms, including decision trees, k-NN, SVM, and clustering.
  • 6.1 Decision Trees
  • 6.2 Random Forests
  • 6.3 k-Nearest Neighbors
  • 6.4 Support Vector Machines
  • 6.5 Clustering Algorithms
Natural Language Processing
Covers the basics of natural language processing and how it can be applied to text data.
  • 7.1 Introduction to NLP
  • 7.2 Text Preprocessing Techniques
  • 7.3 Text Classification Techniques
  • 7.4 Sentiment Analysis
Deep Learning and Neural Networks
Provides an introduction to deep learning and neural networks, and how they can be used to solve complex problems.
  • 8.1 Introduction to Deep Learning
  • 8.2 Artificial Neural Networks
  • 8.3 Convolutional Neural Networks
  • 8.4 Recurrent Neural Networks
  • 8.5 Autoencoders
Reinforcement Learning
Introduces reinforcement learning and how it can be used to train agents to make optimal decisions.
  • 9.1 Introduction to Reinforcement Learning
  • 9.2 Markov Decision Processes
  • 9.3 Q-Learning
  • 9.4 Policy Gradient Methods
  • 9.5 Applications of Reinforcement Learning
Optimization Techniques
Covers optimization techniques used in machine learning, including gradient descent, stochastic gradient descent, and Adam optimization.
  • 10.1 Gradient Descent
  • 10.2 Stochastic Gradient Descent
  • 10.3 Adam Optimization
  • 10.4 Conjugate Gradient Method
  • 10.5 Newton’s Method
Big Data and Cloud Computing
Introduces big data and cloud computing technologies used in advanced analytics and machine learning.
  • 11.1 Introduction to Big Data
  • 11.2 Hadoop and MapReduce
  • 11.3 Spark and Scala
  • 11.4 Cloud Computing Platforms
  • 11.5 Data Warehousing and Business Intelligence
Capstone Project and Case Studies
Participants will apply their knowledge and skills to a capstone project and analyze real-world case studies to gain practical experience in advanced analytics and machine learning.
  • 12.1 Applying Machine Learning Techniques to Real-World Problems
  • 12.2 Analysis of Real-World Case Studies
  • 12.3 Data Exploration and Preprocessing
  • 12.4 Model Building and Evaluation
  • 12.5 Presentation and Communication of Results
$1,699.00

Material Includes

  • Participants will have access to a range of materials, including recorded lectures, reading materials, case studies, and assignments. They will also have the opportunity to engage in discussion forums and collaborate with their peers on group projects.
Durations: 95 hours
Lectures: 54
Students: Max 0
Level: Expert
Language: English
Certificate: No

Material Includes

  • Participants will have access to a range of materials, including recorded lectures, reading materials, case studies, and assignments. They will also have the opportunity to engage in discussion forums and collaborate with their peers on group projects.

Requirements

  • Participants should have a strong foundation in statistics and programming, as well as a basic understanding of data analysis and machine learning. They should also have access to a computer with internet connectivity, as well as the ability to install and run software such as Python and Jupyter Notebooks. Participants will be expected to complete weekly assignments and a final capstone project to demonstrate their understanding of the course material.

Tags

Audience

  • Senior executives, managers, and decision-makers who are interested in learning about the latest trends, tools, and techniques in advanced analytics and machine learning.