Choose Online Data Science Course in Malaysia
✓ Learning python for data science
✓ Prediction, Manipulating Data, and Data Analysis
✓ Learning and implementing various Machine Learning Algorithms
✓ Validation of data
✓ Supervised and Unsupervised Learning
✓ Statistics
Data Science with Python
Best Data Science Course in Malaysia
Course Price
RM 4,500
RM 990
(Duration: 10 days/40 hours). Limited time Offer.
Overview
Get ready to exploit large datasets for insightful analysis and data-based prediction. In this training, you will master the technique of how Python is deployed for Data Science, working with Pandas library for Data Science, data cleaning, data visualization, Machine Learning, advanced numeric analysis, and many more. In addition, you'll experience building real-world-like projects that could help your resume stand out amongst your peers. Our Data Science with Python training course is an entire circle course that covers all aspects of data analysis processes and data science. Data Science Course Malaysia is a program offered by the Nexperts Academy that aims to provide students with knowledge and skills in data science and analytics. The program is designed to equip students with the necessary skills to work in various industries where data analysis is critical. Students enrolled in this program will receive training in both theoretical and practical aspects of data science and analytics.
Highlights
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40 Hours of Live, Interactive, Trainer-Led Training
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Assignments and Quizzes
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10 Hours Self Paced learning with Python with trainer support
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10 hours of SQL self paced learning
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10 hours power bi self paced learning
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5 hours excel self paced learning
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10 hours of Tableau self paced learning
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5 hours of statistical essentials self paced learning
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2 projects , including Credit Card Fraud Detection analysis and Bank Churn Prediction using popular classification algorithms
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1 capstone project ; building model for prediction analysis
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Science, data cleaning, data visualization, Machine Learning, advanced numeric analysis, etc.
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How to wrangle data, or Data wrangling
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Learning to explore data or Data exploration
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Visualising data
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Learning how to scrap data from various sources or datasets
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Fundamentals of Python programming
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Data Science libraries
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Software Professionals
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IT Professionals
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Analytics professionals
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Data Scientist
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Data Analyst
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Fresh Graduates
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Anyone with a genuine interest in Data Science
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We aim is to provide everyone with vital hands-on experience so that you are well-prepared for job interviews alongside an exhibition of their positions.
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Learn from pioneers in Data Science, both in research and industry.
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Learn the tricks of the trade from seasoned Python Developer practitioners.
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Work on hands-on projects that develop your ability to solve real-world problems.
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Practice your skills on our hands-on projects that simulate real-world problems
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How to wrangle data
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Learning to explore data
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Visualizing data
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Learning how data is scrapped from various datasets or sources
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Fundamentals of Python programming
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Ability to use data with operators and functions
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Ability to access, index, and slice strings and other data
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Using various data structures in different contexts
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Implement decision making and flow control
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Understand the functionality of functions and modules
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Extracting relevant data
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Utilise Pandas and DataFrames to organise & data filteration
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Ability to produce statistical inferences using Pandas and NumPy
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Ability to utilise NumPy for numerical and mathematical computations
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Expose to various analytics techniques with Pandas
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Data Visualization with matplotlib and seaborn
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10 hours of python fundamentals self paced learning tutorial video
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4 hours of self paced learning on statistical essentials
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4 hours SQL fundamentals self paced learning tutorial video
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Comprehensive Blended Learning program
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flexible access to online classes
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instructions carried out through industry experienced trainers
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Interactive Quizzes
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15+ in-demand technologies and skills
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Get hands-on experience with four industry-related projects
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24x7 learner assistance and support
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The inquiry process comprises three simple steps.
STEP 1 Submit Inquiry- Tell us a bit about yourself and the questions you want to enquire
STEP 2 Reviewing–Your questions will be processed and answered within a day or two
STEP 3 Response–Answers will typically be sent through email. However, you may tell us the option you prefer us to contact you in
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Physical Classroom Training (Malaysia)
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On-site Company Training (Malaysia)
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Online Training via Microsoft Team (Malaysia and International)
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Training Fee : RM2200 (upon 52% discount)
Duration: 15 days/60 hours
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Module 1: Foundations of Data Science and Key Libraries
Introduction to Data Science
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Overview of Data Science Applications
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Basics of Data Analysis and Visualization
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NumPy Fundamentals
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Getting Started with NumPy
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Creating and Indexing Arrays
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Array Slicing and Data Types
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Managing Array Shape and Reshaping
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Iterating, Joining, and Splitting Arrays
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Searching, Sorting, and Filtering Arrays
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Random Number Generation and Built-in Methods
Module 2: Advanced Data Handling with Pandas
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Introduction to Pandas
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Overview of the Pandas Library
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Working with Series and DataFrames
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Importing Data: CSV, JSON, Excel
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Techniques for Data Analysis and Manipulation
Module 3: Data Cleaning and Wrangling
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Data Preparation with Pandas
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Techniques for Cleaning DataFrames
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Removing and Handling Columns and Rows
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Addressing Missing Values
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Enhancing Data Readability and Index Management
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Module 4: Data Visualization with Matplotlib
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Creating Visualizations with Matplotlib
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Introduction to Matplotlib and Pyplot
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Customizing Chart Properties and Styles
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Building Various Plots: Box Plots, Heatmaps, Scatter Plots, Line Charts, Pie Charts, Bar Charts
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Visualizing Time Series and Geographical Data
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Module 5: Enhanced Visualization with Seaborn
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Exploring Seaborn for Statistical Graphics
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Overview of the Seaborn Library
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Creating Visuals: Line Plots, Dist Plots, Lmplot, Histograms, Bar Plots, Count Plots, Point Plots, Violin Plots, Heatmaps
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Module 6: Statistical Fundamentals
Understanding Statistics
Basic Statistical Concepts
Types of Statistics: Descriptive and Inferential
Measures of Central Tendency: Mean, Median, Mode
Measures of Dispersion: Variance, Standard Deviation, Range
Module 7: Exploratory Data Analysis (EDA)
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Performing EDA
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Introduction to EDA and Its Importance
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Calculating Descriptive Statistics: Mean, Median, Mode, Quartiles
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Grouping Data for Better Visualization
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Techniques for Understanding Data Distribution and Relationships
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Capstone Project 1: Credit Card Fraud Detection
Project Overview
Application of Exploratory Data Analysis (EDA) in Fraud Detection
Developing Risk Analytics for Fraud Prevention
Module 8: Introduction to Machine Learning
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Basics of Machine Learning
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Overview and Types of Machine Learning
Module 9: Supervised Learning – Regression
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Regression Techniques
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Fundamentals of Linear Regression
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Implementing Linear Regression with Scikit-Learn
Capstone Project 2: Predicting Student Grades
Project Overview
Using Simple Linear Regression to Predict Academic Performance
Introduction to Polynomial Regression
Capstone Project 3: Predicting Startup Profits
Project Overview
Applying Multiple Linear Regression to Forecast Profits for Startups
Module 10: Supervised Learning – Classification
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Classification Techniques
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Overview of Logistic Regression
Capstone Project 4: Diabetes Prediction in Healthcare
Project Overview
Using Logistic Regression to Predict Diabetes Risk Based on Medical Data
Module 11: Unsupervised Learning
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Exploring Unsupervised Learning
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Types and Applications of Unsupervised Learning
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Introduction to Clustering Algorithms
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Understanding K-Means Clustering and Apriori Algorithms
Capstone Project 5: Market Basket Analysis
Project Overview
Implementing the Apriori Algorithm to Analyze Product Relationships
Practical Applications in Product Recommendation Systems
Module 12: Applying Machine Learning Algorithms
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Application and Evaluation of Algorithms
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Comprehensive Overview of Machine Learning Algorithms
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Implementing and Evaluating Various Algorithms
Capstone Project 6: Customer Churn Prediction
Project Overview
Analyzing Customer Retention and Churn Rates
Applying Classification Algorithms to Predict Customer Churn
Comparing and Optimizing Models for Performance