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Foundation Course in

Data Science for Students

Grasp the fundamental concepts of Data Science and learn Statistical Analysis, Machine learning, Predictive Analytics many more.
  • 24 Hours Classroom & Online Sessions
  • 40+ Hours Assignments & Real-Time Projects
  • IBM Digital Certificates and Badges
  • Complimentary Python and R Programming Beginners Course
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Calendar-On-Campus Classes

Data Science

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Total Duration

3 Months

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Prerequisites

  • Computer Skills
  • Basic Programming Knowledge
  • Analytical Mindset

"The data science market to witness the fastest growth in the Asia-Pacific region. It is projected to reach $1,95,676.5 million by 2023, growing at a CAGR of 39.3%." - (Source). Malaysia has massive opportunities, as it is located in the heart of Southeast Asia. It has got all the accessories to evolve tremendously by using Digital technology. But there is a lack of supply for talent in the fields of Data Science, Machine learning, etc. The Government of Malaysia understood the importance of application of Data Science in various sectors like Education, Health care, Banking Agriculture, Environment, etc and launched Big Data Analytics Digital Government Lab (BDA-DGL). By this lab, it has tasted success in resolving the issues and getting profitable insights. Many entities in Malaysia are providing training to the aspirants, at the same time industries are getting associated with training institutes to obtain the right talent for the industry and looking forward to hiring foreign talent.

Data Science Foundation Programme Overview

This course will enable you to understand Data Science and its applications to leverage the Data. Perform statistical computation on the Data to estimate results for business decisions. Gain knowledge on the most popular CRISP-DM project management methodology for Data Mining projects. You will understand the difference between Descriptive and Predictive analytics and learn to develop Prediction models. By the end of the foundation course in Data Science, students will have great insights on the application of programming tools R, Python, and will be confident enough to grab opportunities in Data Science and have a flourishing career.

Data Science

Data Science is one of the most promising and in-demand courses for skilled professionals. It is all about analyzing huge amounts of data, Data mining, and programming. Data science is still evolving and has abundant opportunities.

Data Scientist

Data Scientists are a group of analytical experts who inscribe data and find out the patterns in the data. A Data Scientist has efficient skills in handling raw, structured, and unstructured data, analyzing it, implementing in various statistical procedures, visualizing, and generating insights from the data.

Data Scientists should have adequate knowledge about tools like R, Python, Tableau, Hadoop, SAS, etc. Ten years ago there was no noise about Data Science, but all of a sudden the hype about Data Science has increased immensely. A Data Science professional should be able to balance business-related work and IT-work. A Data Scientist should be in a position to handle the situations and resolve the issues which might arise in the future.

Course Details

Data Science Foundation Learning Outcomes

Malaysia is adopting and witnessing a great advancement in Data Science. By this foundation of the Data Science course, the students will learn the main programming tools R and Python and its applicabilities. Students will learn about the statistical calculations. They will be able to predict and build models with Descriptive and Predictive analysis. They will also gain insights on how to extract data from large datasets and develop models using Supervised learning and Unsupervised learning techniques. These modules are designed in response to the changing business trends and prepare the students well to advance and face the challenges. Participants will also gain insights into the applications of Artificial Intelligence, Machine learning, and many trending topics.

Begin the journey with High-level programming languages R and Python
Perform Descriptive analytics using statistical calculations
Develop an understanding of Descriptive and Predictive Analytics
Develop Predictive analytical solutions to enable data-driven decisions
Peek into the Project life cycle for Data Analytics projects
Develop Models to Predict a variable of interest using Supervised Learning techniques
Derive insights from the business Data using Unsupervised Learning techniques
Block Your Time
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24 hours

Classroom Sessions

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40 hours

Assignments &
e-Learning

data science foundation course in malaysia - 360digitmg

40 hours

Live Projects

Who Should Sign Up?
  • Graduates
  • Freshers, Junior/Mid-Level Executives
  • Employees of Organizations
  • Managers
  • Business Analytst
  • IT Engineers
  • Data Analyst

Modules for Fundamentals of Data Science Course

The Data Science Foundation course will be your first stepping stone towards Data Science. You will learn the art of churning the data for finding out patterns. In this module, you will be introduced to the analytics programming languages. R is a statistical programming language and Python is a general-purpose programming language. These are the most popular tools currently being looked upon to churn the data for deriving insights. Learn about the most commonly used packages/libraries for Data Science. Along with the tools you will be exposed to various kinds of Data Analytics and its applications in different fields of Business. EDA also is known as Exploratory Data Analysis is performed to understand what has happened with the business. In this tutorial, you will learn how statistical computing is applied to perform Descriptive Analytics. In this tutorial, you will learn how to make a Hypothesis for a business condition. What are the rules in making such assumptions? Learn about the conditions that may arise while inference of a solution for business problems. The CRISP-DM process is applied in general for Data Analytics projects, learning about CRISP-DM, and the stages of the project life cycle. Understand the Descriptive Analytics strategies performed by mining the data using mathematical techniques. Learn to derive insights by segregating the data into homogeneous groups and heterogeneous groups of observations. Understand the relationship between entities to help explain correlations between attributes. Learn to handle high dimensional data to help achieve better performance on the models. Ultimately you will gain knowledge and confidence by learning all the topics in the course and will be prepared to get lucrative jobs.

The Data Science Foundation course will be your first stepping stone towards Data Science. You will learn the art of churning the data for finding out patterns. In this module, you will be introduced to the analytics programming languages. R is a statistical programming language and Python is a general-purpose programming language. These are the most popular tools currently being looked upon to churn the data for deriving insights. Learn about the most commonly used packages/libraries for Data Science.

 
  • Introduction to R & R Studio
    • Installation
      • Intro
      • What is R programming
      • Installation of R software
      • Basic code execution in R shell
      • R packages
    • R Studio
      • Intro
      • Installation of R Studio
      • Basic code execution in R Studio
      • Python packages
  • Introduction to Python and IDE for python
    • Installation
      • Intro
      • What is Python programming
      • Installation of Python software
      • Basic code execution in Python shell
    • IDE for Python
      • Various IDE options for Python
      • Introduction to Anaconda
      • Multiple options of IDE within Anaconda
      • Introduction and code execution in Spyder and Jupyter

Data Analytics can be explained in 4 stages: Descriptive, Diagnostic, Predictive and Prescriptive.To resolve a business problem, we need to understand the cause of the problem. Business statistics is the stage of Data analysis where we dig into the raw data to find out the patterns. EDA also known as Exploratory Data Analysis is performed to understand what has happened with the business. In this tutorial, you will learn how statistical computing is applied to perform Descriptive Analytics.

 
  • Business Statistics and Foundation for Analytics
    • Data Analytics
    • Type of Analytics
      • Descriptive Analytics
      • Diagnostic Analytics
      • Predictive Analytics
      • Prescriptive Analytics
    • Introduction to data
    • Data types
    • Measuring data
      • Nominal
      • Ordinal
      • Interval
      • Ratio
    • Probability
    • Probability applications with examples
    • Probability Distribution
    • Types of Probability Distribution
    • Examples of Probability Distribution
    • Inferential Statistics
    • Sampling Technique
    • SRS technique
    • Measure of Central Tendency

      1st Moment business decision
      Measure of centrality

      • Mean
      • Median
      • Mode

      Measure of Dispersion

      2nd Moment business decision
      Measure of spread

      • Variance
      • Standard Deviation
      • Range

      Shape Statistics
       

      3rd Moment business decision
      4th Moment business decision

  • Graphical Representation in Python and R

    Visualization

    • Bar Plot
    • Histogram
    • Box Plot
    • Normal Q-Q plot
    • Scatter Plot

Predictive Analytics is performed to estimate an unknown value for a business. Learn the science of predicting a parameter using a sample set of data. You will also learn how to build confidence while estimating with quantifiable value.


Probability Distribution
  • Continuous Probability Distribution
  • Introduction to Normal Distribution
  • Properties of Normal Distribution
  • Standard Normal Distribution (Z-distribution)
  • Normal Q-Q plot and its Interpretation
  • Inferential Statistics
  • Sampling Variation
  • Central Limit Theorem
  • Confidence Interval
  • Case Study for Estimating a Population value using Sample data

How about mocking a business condition and making inferences before you take action to ascertain a business condition. In this tutorial, you will learn how to make a Hypothesis for a business condition. What are the rules in making such assumptions? Learn about the conditions that may arise while inference of a solution for business problems.


Hypothesis Testing using Python and R
  • Introduction to Hypothesis
  • Null Hypothesis and its conditions
  • Alternate Hypothesis and its conditions
  • Type I error
  • Type II error
  • Framing Hypothesis statement
  • Types of Hypothesis testing
    • 2-Sample T-test
    • One-Way ANOVA
    • 2-Proportion test
    • Chi-Square test

Understand how a project is undertaken using analytics. Learn about the life cycle and the detailed understanding of each step while project accomplishment. The CRISP-DM process is applied in general for Data Analytics projects, learning about CRISP-DM and the stages of the project life cycle.


Data Mining
  • Introduction to Data Mining
  • Types of Data Mining
    • Supervised learning
    • Unsupervised learning
    • Cocktail models
  • Flow chart of Learning steps
  • Stages of Data Science Project
    • Understand the Business Problem
    • Data Collection
    • Exploratory Data Analysis / Descriptive Analytics
    • Data Preparation
    • Partition of Data
    • Building Training Models
    • Evaluation of Models using Test Data
    • Apply Final Model on for New Data

Mining the data to derive interesting insights is a process called Data Mining. Supervised learning is a branch of study to perform Predictive Analytics. In this module, you will learn to predict the dependent variable based on the features which influence it. You will also learn different techniques used to predict numeric and non-numeric values.



Multiple Linear Regression using Python and R
  • Real Time problems in Regression Analysis
  • Prerequisites of Regression
    • Heteroscedasticity
    • Collinearity
    • Variance Inflation Factor (VIF)
  • Types of Linear Regression
    • Simple Linear Regression
    • Multiple Linear Regression
  • Validate the Regression Model Output for Challenges
  • Predict Dependent variable using multiple Independent variables
  • Model Building using R and Python
  • Evaluate the Model for Pre-requisite and Post-requisites
    • Heteroscedasticity
    • Collinearity
    • Variance Inflation Factor (VIF)
  • Measure of Accuracy
  • Model Improvement Techniques

Understand the Descriptive Analytics strategies performed by mining the data using mathematical techniques. Learn to derive insights by segregating the data into homogeneous groups and heterogeneous groups of observations. Understand the relationship between entities to help explain correlations between attributes. Learn to handle high dimensional data to help achieve better performance on the models.

 
  • Discrete Probability Distribution
    • Binomial Distribution
  • Sigmoid Curve / Logistic Curve
  • Probability Function
  • Logit Function
  • Principles of Logistic regression
    • Types of Logistic regression
    • Assumption & Steps in Logistic regression
    • Analysis of Simple logistic regression results
  • Model Evaluation using Confusion Matrix
    • Accuracy
    • Error
    • False Negative
    • Sensitivity
    • Specificity
    • Precision
  • Measure of Accuracy of Models using Cutoff value
  • Receiver Operating Characteristic curve
  • Lift & Gain chart
How We Prepare You
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    Additional Assignments of over 60-80 hours
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    Live Free Webinars
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    Resume and LinkedIn Review Sessions
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    3 Month Access to LMS
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    24/7 support
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    Job Assistance in Data Science Fields
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    Complimentary Courses
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    Unlimited Mock Interview and Quiz Session
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    Hands-on Experience in a Live Project
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    Life Time Free access to Industry Webinars
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Data Science for Beginners Panel of Coaches

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Bharani Kumar Depuru

  • Areas of expertise: Data Analytics, Digital Transformation, Industrial Revolution 4.0.
  • Over 14+ years of professional experience.
  • Trained over 2,500 professionals from eight countries.
  • Corporate clients include Hewlett Packard Enterprise, Computer Science Corporation, Akamai, IBS Software, Litmus7, Personiv, Ebreeze, Alshaya, Synchrony Financials, Deloitte.
  • Professional certifications - PMP, PMI-ACP, PMI-RMP from Project Management Institute, Lean Six Sigma Master Black Belt, Tableau Certified Associate, Certified Scrum Practitioner, AgilePM (DSDM Atern).
  • Alumnus of Indian Institute of Technology, Hyderabad and Indian School of Business.
Read More >
 
data science foundation certification trainers - 360digitmg

Sharat Chandra Kumar

  • Areas of expertise: Data Science, Machine Learning, Business Intelligence and Data Visualisation.
  • Trained over 1,500 professional across 12 countries.
  • Worked as a Data Scientist for 14+ years across several industry domains.
  • Professional certifications: Lean Six Sigma Green and Black Belt, Information Technology, Infrastructure Library.
  • Experienced in Big Data Hadoop, Spark, NoSQL, NewSQL, MongoDB, R, RStudio, Python, Tableau, Cognos.
  • Corporate clients include DuPont, All-Scripts, Girnarsoft (College-dekho, Car-dekho) and many more.
Read More >
 
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Nitin Mishra

  • Areas of expertise: Data Science, Machine Learning, Business Intelligence and Data Visualisation.
  • Over 20+ years of industry experience in Data Science and Business Intelligence.
  • Trained professionals from Fortune 500 companies and students from prestigious colleges.
  • Experienced in Cognos, Tableau, Big Data, NoSQL, NewSQL.
  • Corporate clients include Time Inc., Hewlett Packard Enterprise, Dell, Metric Fox (Champions Group), TCS and many more.
Read More >
 
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data science foundation certificate course - 360digitmg

Certificate

Earn a certificate and demonstrate your commitment to the profession. Use it to distinguish yourself in the job market, get recognised at the workplace and boost your confidence. The Data Science Certificate is your passport to an accelerated career path.

Recommended Programmes

FAQs for Foundation Programme in Data Science

Python and R are open source programming languages in pursuit of robust Data Science. Both languages can be used for Big Data Analytics Python is a general-purpose programming language whereas R language was developed for statisticians. It is advised that a Data Scientist should learn both Python and R language so that strengths of both complement each other while handling complex Big Data. Both are useful for Data Mining.

Data Scientist training can be taken from all sectors and people from all experiences. The foundation program in Data Science will help you update your skills for an exciting Data Scientist career. You can also become a Data Engineer. You bring in the Industry experience, which will give you an idea on what a Data Scientist needs to achieve and how to build strategies keeping in mind the business end goals.

To become a successful data scientist one should have a basic understanding of mathematics, statistics & probability. However, 360DigiTMG provides all the basic introductory learning on Probability and Statistics that will help you prepare the foundation.

The entire course is lab-based. The class is a mixture of theory along with hands-on exercises.

Our faculty are industry experts with around 15+ years of experience. Our trainers work on real-time projects with various clients and bring that experience into their teaching methodology. This way students get rich real-time exposure on the most trendy concepts in the industry today.

Data Science is the sexiest job of the 21st century. Companies worldwide are putting all efforts to analyze their data and are falling short of quality resources. All the job portals have many data science positions open worldwide to fill in these shortages.

If you miss a class, we will arrange for a recording of the session. You can then access it through the online Learning Management System.

The most popular tools in Data Science field including R and Python will be covered in this Foundation Program on Data Science.

Each student is assigned a mentor during the course of this program. If the mentor feels that additional support is needed to help the student, we may refer you to another trainer or mentor.

Jobs in field of Foundation programme in data science in malaysia

Jobs in the Field of Data Science in Malaysia

Demand for Data Science professionals is burgeoning as many industries are leaping towards data-driven insights. The Job roles for Data Science professionals in Malaysia are Data Scientist, Senior Data Scientist, Project Manager- Big data, Business Analytics consultant, Data Engineer, and so on.

Salaries in Malaysia for Data Scientist

Salaries for Data Science In Malaysia

The average salary for a Data Science Expert in Malaysia is RM 61,353. The salary range for entry-level is RM 3k - RM 88k, for middle-level RM 6k - RM 179k and experienced RM 114k - RM 171k. This varies as per the job roles.

Foundation programme in data science Projects in Malaysia

Data Science Projects in Malaysia

Data Mining, Predictive Analysis, and advanced algorithms are among the supreme projects for Industries that are imbibing Artificial Intelligence and Machine Learning. As we are shifting towards 5G technology and many other enhancements in the field of computer science we can significantly observe greater innovations and achievements in Data Science.

Role of Open Source Tools in Analytics

Role of Open Source Tools in Data Science

Programming tools like R and Python are considered to be the most essential tools for learning Data Science. In this course, you will be exposed to the basics and applications of R, Python, Tableau, and R studio.

Modes of training for Foundation programme in data science with Python

Modes of Training of Data Science Course

360DigiTMG offers students the option of both classroom and online learning. We also support e-learning as part of our curriculum. Individual attention is guaranteed to all the participants. The foundation program in Data Science in Malaysia is designed based on the requirements of students and working professionals.

Industry Application of Foundation programme in data science

Industry Applications of Data Science in Malaysia

Artificial Intelligence of things is booming in Malaysia and many companies are inculcating it for their better development. Prime sectors like Education, Banking, Manufacturing, Health care, Agriculture, and e-commerce are depending upon Data Science.

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