Daniel Caraway

Daniel Caraway

I put the fun in functions.

MSDS IST 718: Big Data Analytics

3 minute read

Big Data Analytics

MSDS - Q7: IST718

Description:

A broad introduction to analytical processing tools and techniques for information professionals. Students will develop a portfolio of resources, demonstrations, recipes, and examples of various analytical techniques.

Learning Objectives:

During the course, we will emphasize:

  1. Experiential learning through reading and practical exercises.
  2. Collaborative learning through online discussions between instructors and peers.
  3. Self-learning with appropriate instructional support and timely feedback using analytical case studies.

In order to be successful in this course, the student will:

  1. Pro-actively research solution options vs. relying solely on textbook content.
  2. Actively code while completing the reading assignments.
  3. Present results in a professional manner. Comments – Clarity – Correctness – Credit.
  4. Submit their assignments on time.

After taking this course, the students will be able to:

  1. Obtain data and explain data structures and data elements.
  2. Scrub data by applying scripting methods, to include debugging, for data manipulation in Python, R or other languages.
  3. Explore data by analyzing using qualitative techniques including descriptive statistics, summarization, and visualizations.
  4. Model relationships between data using the appropriate analytical methodologies matched to the information and the needs of clients and users.
  5. INterpret the data, model, analysis, and findings. Communicate the results in a meaningful way.
  6. Select an applicable analytical methodology for real problems in areas such as business, science, and engineering.

Deliverables:

Class Outline:

Assessments

  • Week 3 Lab
  • Week 5 Project Check-In Assignment
  • Week 6 Lab Assignment
  • Week 7 Project Check-In Assignment
  • Week 9 Lab Assignment
  • Week 11 Final Project Assignment
  • Course Participation
  • Group Discussions

Week 1 | Aligning

  • 1.1 Getting Started
  • 1.2 OSEMiN
  • 1.3 Crawl
  • 1.4 Walk
  • 1.5 Run
  • 1.6 Tools of the Trade
  • 1.7 Python
  • 1.8 Spark
  • 1.9 Introduction to Case Study

Week 2 | Describing and Modeling

  • 2.1 Scenario Introduction
  • 2.2 Data Review
  • 2.3 Model Review
  • 2.4 Recommendation
  • 2.5 Code Review
  • 2.6 Describing
  • 2.7 Describing
  • 2.8 Modeling
  • 2.9 Modeling
  • 2.10 Introduction to Case Study

Week 3 | Choosing

  • 3.1 Scenario Introduction
  • 3.2 Data Review
  • 3.3 Model Review
  • 3.4 Recommendation
  • 3.5 Code Review
  • 3.6 Customer Choice/Preference
  • 3.7 Product Promotion
  • 3.8 Evaluation Methods
  • 3.9 Increasing Complexity
  • 3.10 Introduction to Case Study

Week 4 | Forecasting

  • 4.1 Scenario Introduction
  • 4.2 Data Review
  • 4.3 Model Review
  • 4.4 Recommendation
  • 4.5 Code Review
  • 4.6 Time Series
  • 4.7 ARIMA
  • 4.8 Forecasting
  • 4.9 Data Structure
  • 4.10 Introduction to Case Study

Week 5 | Inferring

  • 5.1 Scenario Introduction
  • 5.2 Data Review
  • 5.3 Model Review
  • 5.4 Recommendation
  • 5.5 Code Review
  • 5.6 Text Analytics
  • 5.7 Sentiment Analysis
  • 5.8 Increasing Complexity
  • 5.9 Introduction to Case Study

Week 6 | Picking

  • 6.1 Scenario Introduction, Part I
  • 6.2 Data Review, Part I
  • 6.3 Poisson Distribution
  • 6.4 Negative Binomial
  • 6.5 Applications and Coding
  • 6.6 Scenario, Part II
  • 6.7 Data Review, Part II
  • 6.8 Trees and Forests
  • 6.9 Introduction to Case Study

Week 7 | Choosing Again

  • 7.1 Scenario Introduction
  • 7.2 Data Review
  • 7.3 Model Review
  • 7.4 Recommendation
  • 7.5 Code Review
  • 7.6 Chances Are …
  • 7.7 Bayes’ Theorem
  • 7.8 Those Probability Events
  • 7.9 Choice
  • 7.10 Introduction to Case Study

Week 8 | Machine Learning, Part I

  • 8.1 Introduction to Machine Learning
  • 8.2 Data Review
  • 8.3 Perceptron
  • 8.4 Adaline
  • 8.5 Code Review
  • 8.6 Neural Networks
  • 8.7 Propagation
  • 8.8 Template Matching
  • 8.9 Code Creation
  • 8.10 Introduction to Case Study

Week 9 | Machine Learning, Part II

  • 9.1 Continuing the Challenge
  • 9.2 Base Architectures
  • 9.3 Convolution Networks
  • 9.4 Single-Layer Image Networks
  • 9.5 Deep Neural Network
  • 9.6 Architectures
  • 9.7 Code Creation
  • 9.8 Code Creation
  • 9.9 Introduction to Case Study

Week 10 | Presenting

  • 10.1 Presenting Introduction
  • 10.2 Cat in the Hat
  • 10.3 SOAR
  • 10.4 Specify
  • 10.5 Observe
  • 10.6 Analyze
  • 10.7 Recommend
  • 10.8 Elder’s Rules
  • 10.9 Code to Slide
  • 10.10 Introduction to Case Study

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