Course

Course Summary
Credit Type:
Course
ACE ID:
SKIL-0207
Organization:
Location:
Classroom-based
Length:
Self-paced, 28 hours
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Upper-Division Baccalaureate 3 Data Science
Description

Objective:

The course objective is to help prepare the learner for a role as a data scientist, with a focus on visualization, APIs, machine learning and deep learning algorithms.

Learning Outcomes:

  • Describe the principle of the four Vs of big data analytics
  • Implement data ingestion using various technologies including NiFi, Sqoop, and Wavefront
  • Apply and implement essential data correction techniques, transformation rules, deductive correction techniques, and predictive modelling using critical data analytical approaches
  • Create and use real time dashboards with Tableau
  • Use the NumPy, Pandas, and SciPy libraries to perform various statistical summary operations on real datasets
  • Use Matplotlib to visualize datasets
  • Use R to create plots and charts of data
  • Describe what a Recommendation Engine does, how it can be used, and the types and reasons they are used
  • Evaluate a Recommendation Engine by using known data and metrics to calculate the accuracy of recommendations
  • List sources of data anomaly and compare the differences between data verification and validation
  • Demonstrate how to facilitate contextual data and collective anomaly detection using scikit-learn
  • Use machine learning methods and visualization tools to manage anomalies and improvise data for better data insights and accuracy
  • Perform t-tests using the SciPy library to test hypotheses
  • Calculate the skewness and kurtosis of data using SciPy
  • Compute regressions using scikit-learn
  • Valuate data using descriptive and inferential methods
  • Discuss how the four Vs should be balanced in order to implement a successful big data strategy
  • Use Seaborn to perform various data visualization tasks
  • Analyze continuous and categorical variables in a dataset using various plotting options in Seaborn including box and violin plots and FacetGrids
  • Apply data research techniques, including JMP measurement
  • Implement data exploration using R, Python, linear algebra, and plots

General Topics:

  • The Four Vs of Data
  • Data Driven Organizations
  • Raw Data Ingestion and Statistical Analysis
  • Raw Data Management and Decision Making Insights
  • Real Time Dashboards with Tableau
  • Crafting a Story with Data
  • Storytelling with Tableau and PowerBI
  • Data Visualization Using Python and Seaborn
  • Advanced Data Visualization Using Python and Seaborn
  • Using Python to Compute and Visualize Statistics
  • Advanced Dashboards Using Python
  • R Data Visualization
  • Advanced Data Visualization Using R
  • Data Recommendation Engines
  • Data Insights, Anomalies, and Verification - Handling Anomalies
  • Data Insights, Anomalies, and Verification - Machine Learning and Visualization Tools
  • Applied Inferential Statistics
  • Data Research Techniques
  • Data Research Exploration Techniques
  • Data Research Statistical Approaches
  • Machine and Deep Learning Algorithms and Introduction
  • Machine and Deep Learning Algorithms Regression and Clustering
  • Machine and Deep Learning Algorithms Data Preperation in Pandas ML
  • Machine and Deep Learning Algorithms Imbalanced
  • Datasets Using Pandas ML
  • Creating Data APIs Using Node.js
Instruction & Assessment

Instructional Strategies:

  • Computer Based Training
  • Laboratory
  • Practical Exercises

Methods of Assessment:

  • Examinations
  • Quizzes

Minimum Passing Score:

70%
Supplemental Materials