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Professional Diploma in Data Science

SCTP-NICF-Diploma in Infocomm Technology (Data) (Synchronous and Asynchronous E-Learning)

9 months Part time / 4 months Full Time (Bootcamp) Instructor-led Live & Mentor-led Blended Learning



Become a skilled professional in Data Science and design trending Solutions using R Programming, Statistics, and Azure Machine learning.

What do I Get?

Get Skilled with R and Python programming

Learn R and Python programming to import, clean, prepare, visualize, analyze, and predict structured and unstructured data. Perform statistical analysis and understand the integration and deployment using Azure Machine Learning. 

Acquire Statistical Skills

Learn the fundamentals and the basic syntax to perform data analysis using statistics and apply the essential statistical concepts of basic probability, random variables, sampling and confidence intervals, and hypothesis testing.

Attain Data Visualization Skills through Power BI

Learn the fundamentals of the Power BI interface, import data, understand the steps of creating reports and getting relevant insights and earn skills like creating visualization reports and dashboards and assessing the data in Power BI.

Develop Competency in Machine Learning

Learn how to implement the data science life cycle, probability, and visualization by learning data exploration, cleansing, transformation, and machine learning and acquire skills to create and evaluate Supervised, Unsupervised Machine learning models, manage the imbalanced and balanced datasets using Python.

Gain Basics Knowledge of Spark and Azure Cloud

Learn and explore various data engineering patterns and data ware housing ingestion techniques using Databricks and gain skills in Data Warehousing using Azure Synapse to analyze data using Spark SQL and PySpark notebooks.

Audience and Certificates

Target Audience

  • Individuals who are interested in a Data Science career

Prerequisite

Age: Minimum 21 years

Academics: 3 GCE A Level passes or an equivalent 

Work Experience: Minimum 1 year of experience in statistics or programming 

Graduation Requirements

  • Minimum 75% attendance in all sessions of each module
  • Should be assessed Competent (C) in each module

Certificate(s)

  • NICF-Diploma in Infocomm Technology (Data) (Synchronous and Asynchronous E-Learning)

  • Statement of Attainment by SSG, Singapore: ICT-DES-4001-1.1 Data Design

  • Statement of Attainment by SSG, Singapore: ICT-DIT-4006-1.1 Data Visualisation

  • Statement of Attainment by SSG, Singapore: ICT-SNA-4009-1.1 Data Strategy

  • Statement of Attainment by SSG, Singapore: ICT-DIT-4005-1.1 Data Engineering

  • Statement of Attainment by SSG, Singapore: ICT-DIT-4001-1.1 Analytics and Computational Modelling

  • Statement of Attainment by SSG, Singapore: ICT-SNA-4011-1.1 Emerging Technology Synthesis

  • Statement of Attainment by SSG, Singapore: ICT-OUS-3011-1.1 Problem Management

  • Statement of Attainment by SSG, Singapore: ICT-PMT-4001-1.1 Business Needs Analysis

Blended Learning Journey

(643 Hours)

E-Learning

90 hours

Projects / Assignments

180 hours

Flipped Class/Mentoring

90 hours

Additional Practice – for Bootcamp only

280 hours

Assessment

3 hours

Modules

NICF-Data Queries and Visualization Basics (SF) (Synchronous and Asynchronous E-Learning)

Start off your journey by understanding the basic datasets, and use libraries in Python to get insights from the data. Learn to modify the data by querying using SQL. Apply visuals on the data and then analyse them by using Power BI. Obtain the competency to create data models and get meaningful insights from the data visualizations during the data analysis.

Session Plan

More Details

Learning Outcome

Knowledge

By the end of this module, you will gain following knowledge:

  • Different libraries and work on the datasets
  • On functions and control flow needed for analysis
  • Use visualisation concepts and get meaningful insights from the data
  • Difference between reports and dashboards
  • Explore the layout of Power BI tool
  • Data visualisation tools like Power BI
  • Different types of data visualisation techniques using Power BI

Skills

By the end of this module, you will gain following skills:

  • Implement different libraries in Python and work on the datasets
  • Work with Python for visualisation and get meaningful insights from the data
  • Generate reports using Power BI visualisation techniques
  • Implement a data visualisation model that shares relevant insights
  • Develop Power BI dashboards
  • Querying using T-SQL
  • Share Power BI reports and dashboards to users
     

Other Information

Funding Validity Period: Until 31-Dec-2022

Course Developer: Lithan Academy

Qualification Course Code: TGS-2019503390

NICF-Basic R Programming (SF) (Synchronous and Asynchronous E-Learning)

You will learn the basics of R programming, how to handle data structures such as vectors, matrices, lists and data frames and create your own stunning data visualisations.

Session Plan

More Details

Learning Outcome

Knowledge

By the end of this module, you will gain following knowledge:

  • Basics of R language fundamentals and basic syntax
  • R Framework to perform data analysis
  • Variables and basic operations, handle data structures such as vectors, matrices, data frames and lists.
  • Graphical capabilities of R, and create your own stunning data visualizations.
  • Creating Matrices and Data frames
  • Work with data in R
     

Skills

By the end of this module, you will acquire following skills:

  • Explain the analytics architecture requirements to deploy the predictive model
  • Design and develop predictions in Azure Machine Learning(AML) studio
  • Create R scripts and integrate in AML
  • Monitor and tune the deployed model to ensure that it delivers the expected outcome and minimize the error predictions
  • Evaluate Machine Learning model in Azure studio
     

Other Information

Funding Validity Period: Until 31-Dec-2022

Course Developer: Lithan Academy

Qualification Course Code: TGS-2019503390

NICF-Data Science Essentials (SF) (Synchronous and Asynchronous E-Learning)

Get Introduced to the key concepts and techniques used to conduct the data science activities such as statistical analysis, predictive analysis, data cleansing and transformation, data visualization with Python, and Modelling using Microsoft Azure Machine Learning.

Session Plan

More Details

Learning Outcome

Knowledge

By the end of this module, you will gain following knowledge:

  • The Data science process
  • Processes and practices of data exploration and visualisation
  • Implementation on various research practices
  • Data ingestion, cleansing, transformation, and machine learning
  • Data exploration and visualization
  • Principles of the supervised and the unsupervised learning techniques
  • Operation of classifiers and understand the way to use logistic regression as a classifier
     

Skills

By the end of this module, you will acquire following skills:

  • Explore and apply the data science process
  • Design the process of predictive analysis to transform extracted dataset into models using R
  • Implement Data extraction, cleansing,transformation and machine learning
  • Perform Data exploration and visualization
  • Implement the supervised and the unsupervised learning techniques for modelling
  • Explore and apply predictive analysis
     

Other Information

Funding Validity Period: Until 31-Dec-2022

Course Developer: Lithan Academy

Qualification Course Code: TGS-2019503390

NICF-Statistical Thinking for Data Science and Analytics (SF) (Synchronous and Asynchronous E-Learning)

Learn descriptive and inferential statistics, basic probability, random variables, sampling and confidence intervals and hypothesis testing using Excel for data analysis.

Session Plan

More Details

Learning Outcome

Knowledge

By the end of this module, you will gain following knowledge:

  • Descriptive statistics and Inferential statistics
  • Range of statistical and advanced computational modelling techniques
  • Advanced mathematical models and theories like Variance and standard deviation
  • Elements of various Statistics and probability
  • Features, pros and cons of various statistical approaches, algorithms and storytelling
  • Hypothesis Testing procedures to evaluate statistical models
     

Skills

By the end of this module, you will acquire following skills:

  • Convey analytics story
  • Learn and implement probability analysis
  • Differentiation and derivatives
  • Perform matrix transformations
  • Understand and perform Hypothesis testing
  • Perform sampling distribution
     

Other Information

Funding Validity Period: Until 31-Dec-2022

Course Developer: Lithan Academy

Qualification Course Code: TGS-2019503390

NICF-Principles of Machine Learning (SF) (Synchronous and Asynchronous E-Learning)

Get hands-on experience with the Machine Learning concepts using Python and Azure Machine learning from setting up a proper data study to making valid inferences from data experiments. Build and derive insights by leveraging Azure cloud services to perform machine learning models.

Session Plan

More Details

Learning Outcome

Knowledge

By the end of this module, you will gain following knowledge:

  • Principles of the supervised and the unsupervised learning techniques
  • Operation of classifiers and understand the way to use logistic regression as a classifier
  • Manage experiment environments that ensure consistent runtime consistency to create and use compute targets for experiment runs.
  • Learn the operation of regression models and understand the to use linear regression for prediction and forecasting
  • Encapsulate data processing and model training code, and use them to train machine learning models.
  • Metrics used to evaluate classifiers and regression models
  • Operation of Regression models and how to use linear regression for prediction and forecasting
     

Skills

By the end of this module, you will acquire following skills:

  • Identify data analytics solution and platform requirements
  • Develop a standardised set of data analytics artifacts with relevant stakeholders
  • Modify a machine learning solution to ensure that it produces expected results
  • Use regularisation on over-parameterised models
  • Apply cross-validation to estimate model performances
  • Apply and evaluate k-means and hierarchical clustering models
  • Apply Machine Learning models to real-life situations
     

Other Information

Funding Validity Period: Until 31-Dec-2022

Course Developer: Lithan Academy

Qualification Course Code: TGS-2019503390

NICF-Spark on Azure HDInsight (SF) (Synchronous and Asynchronous E-Learning)

Learn various data engineering patterns and data ware housing ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines. 

Session Plan

More Details

Learning Outcome

Knowledge

By the end of this module, you will gain following knowledge:

  • Explore core compute technologies used for data engineering in Azure
  • Programming language and tools for big data analytics and how they integrate with big data technologies
  • Understand querying data in an interactive way on files placed in Azure Storage using Azure Synpase Analytics.
  • Emerging trends across business domains
  • Building real-time machine learning solutions in Azure
  • Software development methodologies with emphasis on requirement gathering for data science projects
  • Role of stakeholders and their level of involvement in data science projects
  • Gather Functional and non-functional requirements of data science projects and documentation
  • Understand supervised learning and Build Machine Learning Solutions in Spark
     

Skills

By the end of this module, you will acquire following skills:

  • Explore data using Spark Notebooks
  • Implement the Machine Learning models on Spark and working with data in Spark
  • Identify dependencies for the identified business requirements
  • Execute code-free transformation at scale with Azure Data Factory and Azure Synapse pipelines.
  • Run large data engineering workloads in the cloud combined with Azure Synapse Analytics.
  • Apply unsupervised learning and K-Means clustering and build Machine Learning Solutions in Spark
  • Ingest data with Apache Spark notebooks, transform data with DataFrames, integrate SQL and Apache Spark pools in Azure Synapse Analytics, while monitoring and managing the workloads
     

Other Information

Funding Validity Period: Until 31-Dec-2022

Course Developer: Lithan Academy

Qualification Course Code: TGS-2019503390

Pricing and Funding

SGD 18000.00

Fee Description

Detailed Breakdown

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