NCC Advance Short Course
in Data Science
Introduction
The NCC Advanced Short Course in Data Science is designed for learners with foundational knowledge, offering advanced skills in data analysis, visualization, and machine learning using tools like R and Python.
NCC Advance Short Course in Data Science
Awarding Institution and Language of Study
Awarding Institution:
NCC Education
Language of Study:
English
Programme Overview
The NCC Advanced Short Course in Data Science is designed for individuals who have a foundational understanding of data science and want to delve deeper into more complex and advanced topics. This course covers advanced techniques and tools necessary for high-level data science tasks, including data analysis with R, machine learning, and data visualization.
Entry Requirements
- Good understanding of basic and intermediate data science concepts.
- Familiarity with programming languages, particularly Python and R.
- Strong analytical skills and experience with data handling.
Course Structure
The course comprises several advanced topics that equip learners with skills to perform complex data analyses, develop sophisticated data models, and create insightful visualizations.
Syllabus
- Introduction to Extract, Transform, and Load (ETL) Process
- ETL Introduction
- ETL Process
- ETL Tools
- ETL Testing Process
- ETL Test Scenarios and Test Cases
Learning Outcomes: 1, 2
- Linear Algebra I
- Matrices
- Matrix Transpose
- Matrix Addition and Subtraction
- Scalar Multiplication
- Conjugation of a Matrix
- Ranks of Matrices
- Echelon Form
- Full Rank Matrices
- Linear Transformation
Learning Outcome: 3
- Linear Algebra II
- Vector Inner Product
- Vector Outer Product
- Matrix Multiplication
- Properties of Matrix Multiplication
- Matrix Multiplication Algorithms
- Inverse of a Matrix
Learning Outcome: 4
- Data Analysis with R
- What is Data Analysis?
- Types of Data Analysis
- Data Analysis Process
- Tools Used for Data Analysis
- Introduction to R Programming
- Why R Programming?
- Applications of R Programming
- How R Works?
- Basic R Programming Data Types
- Conditional Statements in R
- Loops in R
Learning Outcome: 5
- Understanding R Data Structures
- Data Types and Data Structures in R
- Vectors and Properties of Vectors
- Types of Atomic Vectors and Accessing Vector Elements
- Vectors Arithmetic
- Recycling Vector Elements
- Sorting a Vector
- Lists and Accessing Elements of a List
- Add and Delete Elements of List
- Update Elements of Lists
- Matrices, Arrays, Data Frame, Factors
- Creating a Data Frame and Extracting Columns/Rows from Data Frame
Learning Outcome: 6
- R’s Graphic Packages & Visualizations
- R Visualization Packages
- R Graphics & Graphics Devices
- Advantages and Disadvantages of Data Visualization in R
- Data Visualization in R
- Base R Graphics
- Scatterplot, Bar Charts, Box Plots, Histogram in R Programming
- Lattice Plotting System
- Lattice Functions
- Lattice Ideas in a Nutshell
- Ggplot2 Package
Learning Outcomes: 7 & 8
- Automated Knowledge Acquisition
- Knowledge Engineering Process
- Development Cycle of a Knowledge-Based System
- Elicitation Methods
- Manual Methods
- Semi-Automated Methods
- Automated Methods
- Artificial Intelligence Rules
- Semantic Networks
- Frames
- Knowledge Relationship Representations
- Reasoning Program
- Expert System Development
Learning Outcome: 9
- Supervised and Unsupervised Learning
- Machine Learning Techniques
- Clustering – Why?
- Stereotypical Clustering
- Cluster Bias
- K-means Clustering
- K-means Properties
- Choosing Clustering Dimension
- DBSCAN, DBSCAN Clusters, and DBSCAN Algorithm
- DBSCAN Performance
- Matrix Factorization – Motivation
- Matrix Factorization with SGD and MCMC
- Alternating Least Squares (ALS)
- Performance – Offline/Online Performance
Learning Outcome: 9
- Analytics Scenarios for Different Industries and Machine Learning Models
- Evolution of Machine Learning
- What’s Required to Create Good Machine Learning Systems?
- Popular Machine Learning Use Cases
- Supervised Learning
- Unsupervised Learning
Learning Outcomes: 9 & 10
- Design Principles for Data Visualization
- Data Visualization
- What Makes Data Visualization Effective?
- Types of Big Data Visualization Categories
- Best Practices for Bar Chart Visualization
- Best Practices for Line Chart Visualization
- Best Practices for Scatterplot Visualization
- Best Practices for Sparkline Visualization
- Best Practices for Pie Chart Visualization
- Steps to Designing an Information Visualization
Learning Outcome: 11
- Exploratory, Descriptive, and Diagnostic Analysis
- What is EDA and Aim of EDA
- Exploratory vs Confirmatory Data Analysis
- EDA and Visualization
- Steps of EDA
- Classification of EDA
Learning Outcome: 12
- Data Story Presentation & Dashboard Design for Communication
- Visualization Definitions
- Uses for Data Visualizations
- Data Storytelling
- Why Data Storytelling?
- Why Data Storytelling the Future?
- Key Elements of Storytelling
- A Basic Recipe for Storytelling
- Dashboards
- Dashboards for Different Departments
- Dashboard Tab Types
- Tips for Designing a Dashboard
- Why Should Your Business Visualize Data on a Dashboard?
Learning Outcome: 12
Learning & Teaching Strategies
The program uses a blend of online learning methods, including:
Video Lectures
Detailed lectures to explain advanced data science concepts and techniques.
Tutorials
Practical exercises and hands-on sessions using data science tools.
Discussion Forums
Online platforms for peer-to-peer and instructor discussions.
Live Sessions
Interactive sessions to resolve queries and provide deeper insights into complex topics.
Assessment Strategy
Assessment includes:
Coursework
Projects and assignments that involve real-world data science problems and scenarios.
Examinations
Online assessments to test theoretical understanding and practical skills.
Learning Outcomes:
By the end of this course, students will be able to:
- Understand and apply advanced ETL processes.
- Master advanced concepts in linear algebra relevant to data science.
- Perform complex data analyses using R programming.
- Create sophisticated data visualizations using R.
- Develop and deploy machine learning models for various scenarios.
- Understand and implement advanced supervised and unsupervised learning techniques.
- Design and develop professional-grade dashboards for data storytelling.
Career and Professional Development
Graduates of this course can pursue roles such as:
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Data Visualization Specialist
- Business Intelligence Developer
Support for Student Learning:
Students will have access to:
Personal Tutors
Guidance and support from experienced instructors.
Learning Resources
Access to an extensive library of data science resources.
Discussion Forums and Live Sessions
Engaging and interactive learning environment.
Total Qualification Time:
Approximately 100 hours of study.
Conclusion
In conclusion, the NCC Advanced Short Course in Data Science equips learners with expert-level knowledge and skills in data science, empowering them to tackle complex data challenges and develop innovative solutions using advanced techniques and tools. Graduates are well-prepared to excel in high-demand roles such as Data Scientist, Machine Learning Engineer, or Data Visualization Specialist, making a significant impact in the dynamic field of data science.
