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Data Analytics Online

Data Analytics Curriculum

 The intensive program will give you the analytical skills to help you further your personal and professional goals.

Unit 1:

 Intro to Data Analytics 1.1 Introduction to Big Data and Analytics: Fundamental Concepts, Industry Practice and Applications, Technical Domains and Roles

 Unit 2:

Probability Theory and Statistics Review with Excel 2.1 Probability Theory Fundamentals 2.2 Statistics Review: Descriptive Statistics 2.3 Introduction to Excel 2.4 Statistics

Review : Inferential Statistics 2.5 Prob/Stats and Data Problem Solving with Advanced Excel

  Unit 3: Introduction to R and R Analytics 3.1 Introduction: RStudio, Syntax and File I/O 3.2 R Data Types and Data Objects, Basic Data Operations and Manipulation Utilities 3.3 Effective Use of Conditional Arguments, Loops and Functions 3.4 Statistical Analysis and Regression in R 3.5 Basic R Plotting and Parameter Setting 3.6 Stattleship XCase

Unit 4: Predictive Analytics and Case Studies 4.1 Introduction to End-to-end Predictive Modeling Method 4.2 Introduction to Logistic Regression and Case Study I: Dental Visit 4.3 Data Exploration and Pre-processing Demonstration with Case Study II: Fraud Detection 4.4 Problem-solving Approach and Classification Model Evaluation Criteria in Case Study II: Fraud Detection Unit 5: Introduction to Tableau 5.1 Introduction to Tableau: Applications, Architecture and Working Elements 5.2 Building Data Views and Effective Data Manipulation in Tableau 5.3 Effective Use of Analytics Functions and Calculations in Tableau 5.4 Tableau Case Studies

Real Projects : Each student is paired with an industry-leading employer partner on a 1:1 capstone project, where students apply data skills to real world cases and give final presentations to employers.

Unit 6: Introduction: Relational Database and SQL 6.1 Relational Database, SQL Working Environment and MySQL Workbench 6.2 Relational Data Modeling - EntityRelationship Modeling (ERM) 6.3 MySQL Fundamentals: Basic Database Management Operations, Data Types, Basic Table Operation Commands and Functions 6.4 Essentials of MySQL Queries and Data Problem Solving 6.5 Burning Glass XCase

Unit 7: Introduction to Machine Learning: Concepts, Algorithms and Applications 7.1 Introduction to Machine Learning 7.2 Unsupervised learning 7.3 Supervised learning 7.4 End-to-end ML in practice

Unit 7: Introduction to Machine Learning: Concepts, Algorithms and Applications 7.1 Introduction to Machine Learning 7.2 Unsupervised learning 7.3 Supervised learning 7.4 End-to-end ML in practice

Unit 8: Data Visualization in R 8.1 Advanced R plotting

Optional Topics :  Basics of NoSQL, Applications and Fundamentals Introduction: Use of RStudio Shiny, File Structure, Share On Web Input, Output and Render functions in Shiny Reactivity in Shiny Format and layout of Shiny apps Experiential: Capstone Project One-on-one Project with Employer Partner Use Data Sets to help Solve Business Problem Present to Class & Employer

Professional Development & Career Advancement (Throughout Course) Hiring Tests & Informational Interviews with Employers Resume and Interview Workshops Industry Speakers and Site Visits

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