Certified Data
Scientist
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Take the next step in your data science career
by becoming a certified data scientist today

5 Days / 40 Hours / 10 Modules
+ 3 month coaching + 1 year eLearning access

Get in Touch With Us
Certified Data
Scientist
Take the next step in your
data science career by becoming
a certified data scientist today

5 Days / 40 Hours / 10 Modules
+ 3 month coaching + 1 year eLearning access
logo
Get in Touch With Us
Take the next step in your data science career by becoming a certified data scientist today
The Certified Data Science Developer Certification provided by IABAC has global recognition, and facilitates for value addition in landing an illustrious career in data science. It covers concepts such as basics of Data Science, Programing languages like Python and R, Statistics for Data Science, Data Science Deployment Models.

Duration

5 days / 40 hours

Level

Beginner to Intermediate

Delivery

100% Online - Instructor Led
Request For Information

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    Key Features

    • 40 hours of instructor led training
    • Fully Online
    • Class recording available
    • Interactive Learning
    • Additional Coaching Session
    • 100% HRDF SBL-KHAS Claimable!

    Pre-Requisites

    • Certified Machine Learning Expert Course (CMLE) or Demonstrable competence at CMLE level
    • Recommended essential knowledge in :
      a. Mathematics: Calculus, Statistics, Linear Algebra, Probability
      b.Machine Learning and Python/R Programming
    • Training: Though formal training is not mandatory; it is recommended to attend IABAC® registered course through Registered Education Partners

    Who Should Join

    • The professionals working in technology-enabled domains.
    • The beginners and students, who aspire a career in data science
    • The enthusiast, who wanted to acquire high-level knowledge in Data Science
    Key Learning Outcomes
    Upon completion, participants should be able to demonstrate each of the following outcome:-
    • Build an expertise in the most widely-used analytics tools and technologies.
    • Develop the ability to independently solve business problems using analytics and data science.
    • Understand the applications and implications of data science in different industries.
    • Learn how to extract strategic business insights from data and efficiently communicate them to stakeholders
    • Build models to predict future trends and use them to inform business strategy.
    • Build a substantial body of work and an industry-ready portfolio in data science and analytics.
    IABAC Exam Information

    Materials Permitted

    • The examination is an ‘open book’
    • Candidates can refer to any material

    Exam duration & format

    • Exam format is through a Project Submission
    • The assessment duration is 8 hours.
    • The project is graded for three areas: Project Summary with recommendations, Machine Learning model performance and Exploratory Data Analysis

    Exam mode

    • Project needs to be submitted at IABAC project submit page, as per exam guidelines
    • Any copied work, ideas, concepts or a piece of text needs to be marked with reference as per IABAC project plagiarism guidelines

    Pass criteria

    • The candidate needs to score assessment grade A+, A, B+, B, C+, C as a PASS Criteria
    • The candidate needs to score 60% or higher in order to pass the examination
    • The results will be declared after validation of the exam recording video session and identity proof verification.

    Results timeline

    • The preliminary results are usually released within 9 days of the exam date
    • The official results are usually released within 15 days from the exam date

    Certificate Issuance

    • IABAC® e-certificate will be issued through the candidate’s registered email
    • The e-certificate is digital verifiable at https://www.iabac.org/verify-certificate
    • The candidate has license to share digital certificate validation in professional networking portals such as www.linkedin.com
    • The candidate has a license to print physical copy (hardcopy) of the certificate
    Course Modules Covered in the Certified Data Scientist program
    Module 1 - Data Science Foundation

    Data Science Foundation

    1. Introduction to Data Science
    2. Data Science vs Business Analytics vs Big Data
    3. Classification of Business Analytics
    4. Data Science Project Workflow
    5. Various Roles in Data Science
    6. Application of Data Science in various industries
    Module 2 - Python Data Science

    Python Data Science

    1. Introduction to Data Science with Python
    2. Python Basics: Basic Syntax, Data Structures
    3. Data objects, Math, Comparison Operators, Condition Statements, loops, lists, tuples, dicts, functions
    4. Numpy Package
    5. Pandas Package
    6. Python Advanced: Data Mugging with Pandas
    7. Python Advanced: Visualization with Matplotlib
    8. Exploratory Data Analysis: Data Cleaning, Data Wrangling
    9. Exploratory Data Analysis: Case Study
    Module 3 - Statistics For Data Science

    Statistics For Data Science

    1. Introduction to Statistics
    2. Harnessing Data
    3. Exploratory Analysis
    4. Distributions
    5. Hypothesis & Computational Techniques
    6. Correlation & Regression
    Module 4 - Visual Analytics Foundation

    Visual Analytics Foundation

    1. Visual Analytics Basics
    2. Basic Charts, Plots
    Module 5 - SQL For Data Science

    SQL For Data Science

    1. Install SQL packages and Connecting to DB
    2. RDBMS (Relational Database Management) Basics
    3. Basics of SQL DB, Primary key, Foreign Key
    4. SELECT SQL command, WHERE Condition
    5. Retrieving Data with SELECT SQL command and WHERE Condition to Pandas DataFrame.
    6. SQL JOINs
    7. Left Join, Right Joins, Multiple Joins
    Module 6 - Machine Learning Associate

    Machine Learning Associate

    1. Machine Learning Introduction
    2. What is ML? ML vs AI. ML Workflow, Statistical Modelling of ML. Application of ML
    3. Machine Learning Algorithms
    4. Popular ML algorithms, Clustering, Classification and Regression, Supervised vs Unsupervised.
    5. Choice of ML
    6. Supervised Learning
    7. Simple and Multiple Linear Regression, KNN, and more
    8. Linear Regression and Logistic Regression
    9. Theory of Linear regression, hands on with use cases
    10. K-Nearest Neighbour (KNN)
    11. Decision Tree
    12. Naïve Bayes Classifier
    13. Unsupervised Learning: K-Means Clustering
    Module 7 - Machine Learning Expert

    Machine Learning Expert

    1. Advanced Machine Learning Concepts
    2. Tuning with Hyper parameters
    3. Random Forest – Ensemble
    4. Ensemble Theory, Random Forest Tuning
    5. Support Vector Machine (SVM)
    6. Simple and Multiple Linear Regression, KNN
    7. Natural Language Processing (NLP)
    8. Text Processing with Vectorization, Sentiment Analysis with Text Blob, Twitter Sentiment Analysis.
    9. Naïve Bayes Classifier
    10. Naïve Bayes for Text Classification, New Articles Tagging
    11. Artificial Neural Network (ANN)
    12. Basic ANN network for Regression and Classification
    13. TensorFlow Overview
    14. Deep Learning Intro
    Module 8 - Time Series Foundation

    Time Series Foundation

    1. What is a Time-Series?
    2. Trend, Seasonality, Cyclical and Random
    3. White Noise
    4. Auto Regressive Model (AR)
    5. Moving Average Model (MA)
    6. ARMA Model
    7. Stationarity of Time Series
    8. ARIMA Model – Prediction Concepts
    9. ARIMA Model Hands on with Python
    10. Case Study Assignment on ARIMA
    Module 9 - Model Deployment

    Model Deployment

    1. Basics of Application Program Interface (API)
    2. API basics, loosely Coupled Architecture
    3. Installing Flask
    4. Installation and configuring Flask and cross domain authentication.
    5. End to End ML project with API Deployment
    6. Complete Project Flow with API Deployment and assessing through the website
    Module 10 - Deep Learning Foundation

    Deep Learning Foundation

    1. Introduction to Deep learning
    2. What is Deep Learning?
    3. Various Deep Learning models in practice and applications.
    4. Convolutional Neural Network CNN Intro
    5. Case Study: Keras–TensorFlow Image Classification
    6. CNN hands on application for classification of images of Cats and Dogs
    Our Training Methodology

    Practical Assignments

    We provide hands-on assignments that requires practical implementation.

    Virtual Coaching Sessions

    Online coaching sessions that happen over the phone, via video, or on a web platform.

    1 Year Access to LMS

    Get access to learning resources upto 1 year of class completion.

    Live Project Experience

    Hands-on learning and training gives participants the opportunity to experience real world situations.

    Online Assessments

    Participants can assess reflect on their own learning and their level/skills.

    Free Industry Webinars

    Stay current on market research trends, learn best practices through our webinar sessions.
    Program Key Highlights
    online-learning-2
    40 hours of Remote Online Learning
    learning-hours
    Additional Coaching Hours
    hands-on
    Live Hands-on Projects
    certification
    Certified by International Body
    mentor
    Mentorship with Industry Experts
    industry
    Designed for Beginners & Professionals
    Request For Information

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      Program Benefits

      International Credential

      IABAC® is a widely recognized credentialing framework based on European commission funded EDISON Data Science body of knowledge. This credential provides distinction as high potential certified Data Science Professionals enabling better career prospects.

      Global Opportunities

      IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

      Specialization

      IABAC Certification designed to cater to the job requirements of all experience levels and specializations, which suits roles aligned with the industry standards.

      Relevant and updated

      IABAC® CPD (Continuing Professional Development) program enables credential holders to update their skills and stay relevant to the industry requirements.

      Higher Salaries

      On an average, a certified professional earns 30-40% more than their non-certified as per recent study by Forbes.

      Summits & Webinars

      In addition, IABAC members will have exclusive access to seminars and Data Science summits organised by IABAC partners across the globe.
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      Get Professionally Certified
      Upon successfully completing this program, participants will be awarded the Data Science Developer Certification by International Association of Business Analytics Certification (IABAC).
      This award is a validation to the efforts taken to master the domain expertise that will set you apart from your competition.
      Be a part of the global network of data science professionals and join the community across sectors.
      Get in Touch With Us Today!

      This training program is suitable for anyone who intends to enter into the field of Data Science. This program is being conducted in Malaysia and can be joined by anyone, anywhere in the world remotely.
      Program Fee

      MYR 6800 per pax.
      Funding price MYR 3700 per pax .
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      Funding Schemes for Companies who are claiming from their HRDF levy or from the MDEC MyWiT scheme.

      Limited scholarships available for early self applying individual applicants.

      Find out how you can qualify for a scholarship.

      Enquire NOW on the various funding options available.

      One-time fee. One year access to course materials and resources.
       
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      Thulija Academy is a HRDF registered training provider. Our panel of expert trainers provide technology training for some of the biggest organizations in Asia.

       
       
       



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