Foundation Course in
Data Science for Beginners
- 24 Hours Classroom & Online Sessions
- 40+ Hours Assignments & Real-Time Projects
- 100% HRD Corp Claimable!
- 6 months Learning Management System access
- Blockchain enabled tamper-proof security certificate
Academic Partners & International Accreditations
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 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 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.
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.
Block Your Time
Classroom & Online Sessions
Assignments & Real-Time Projects
Who Should Sign Up?
- Business Analysts and Data Analyst
- Data Scientist and Data Engineer
- Project Managers for Data Stream projects
- Beginners who are planning to entry into Data Stream jobs
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.
Get about, CRISP - ML(Q) the perfect Project Management Methodology used for handling Data Mining projects. Understand the entire process flow including Business Problem definition, Data Collection, Data Cleansing, Feature Engineering, Feature Selection, Model Building, Deployment and Maintenance. Get introduced to the principles of big data and learn about the opportunities being created. Understand about how Data is generation and explosion of data, Innovations in the space of analytics. Learn how to distinguish between data types, Exploratory data analysis, the Various moments of Business decisions and various Graphical techniques. Learn about probability and probability distribution namely Z distribution and Student's t-distribution.
Learn about Hypothesis testing, the many Hypothesis testing Statistics, work with the Null Hypothesis & Alternative hypothesis and Types of hypothesis testing. Interpret the results of Hypothesis test and probabilities of Alpha error, understand Type I and Type II errors. Get introduced to Linear regression, various components of Linear regression viz regression line, Linear regression equation, the concept of Ordinary Least Square. Get introduced to Linear regression analysis, and Linear regression examples
Understand the Linear regression in a multivariate scenario, understand collinearity and how to deal with it. Get introduced to the analysis of Attribute Data, understand the principles of Logistic regression, Binary Logistic regression analysis. Learn about the Multiple Logistic regression, Probability measures, and its interpretation. Get clarity on the confusion matrix and its elements. Get introduced to “Cut off value” estimation using AUC and ROC curve, understand False Positive Rate, False Negative Rate, Sensitivity, Specificity. Gain a birds-eye view to various advanced regression techniques and analysis of count data namely Poisson regression, Negative binomial regression. Learn when to use Poisson regression and negative binomial regression for predicting count data.
Data Science Foundation Course Trends in Malaysia
Many of the South-Eastern nations including Malaysia are rich in minerals, natural gas, and petroleum. The natural resources of these regions give them an advantage over most nations of similar size, making them an economic power. This affluence led to the rapid development and industrialization of Malaysia making it a hub for investment by many global MNC's. The use of Data-driven decision making is on the rise in most countries. Therefore there is a huge demand for courses that can teach the basics of data-driven decision making.
Both middle and top management derive benefit from courses on “ Fundamentals of Data Science ”. These courses enable them to employ Data Analytics in decision making. Nowadays fraud is the main issue in banking sectors. AI will be the solution for these problems which is cost-effective and generates revenues. Many apps are built with AI to give their clients professional fashion advice. There will be rapid advancements in the implications of chatbots to improve customer service.
Practical Data Analytics: Work-Integrated Learning Course
Dive deep into analytics and transform your career in just 6 months.
Elevate your data insights and seamlessly transition from learning to working.
- Work-Integrated Learning: Transition from learning to working in 6 Months - 30 days
- Tackle 3 Industry-specific real-time projects to refine and showcase your skills
- Secure 100 hours of credible working experience in data analytics
- HRDC claimable and 6 months instalments available
Offer ends:9th Dec,2023
How We Prepare You
Additional Assignments of over 60-80 hours
Live Free Webinars
Resume and LinkedIn Review Sessions
6 Months Access to LMS
Job Assistance in Data Science Fields
Unlimited Mock Interview and Quiz Session
Hands-on Experience in a Live Project
Life Time Free access to Industry Webinars
Call us Today!