Big Data Analysis Courses Online in Malaysia

✓ Data cleaning and preparation.
Data analysis and exploration.
✓ Creating dashboards and reports.
✓ Writing and communication.
✓ SQL 
✓ 
Identifying problems and solving problems
✓ Building models to perform prediction

✓ Creating data visualizations with power bi , tablaue, microsoft excel and python libraries

Certified Data Analytics Associate with Python (hybrid)

Course Price

RM 3,500

RM 2,000

(Duration: 10 days/40 hours)

If you are looking for complete Full-Time Instructor Led training , our course duration would be 120 hours / 30 days. Contact our course advisor for consultancy.

Course Preview

Sign up for Python Fundamentals Course for $10

Overview

Gain the career-building Python skills you need to succeed as a data analyst. No coding experience required. In this course, you’ll learn how to import, clean, manipulate, and visualize data—all integral skills for any aspiring data professional or researcher. Through interactive exercises, you’ll get hands-on with some of the most popular Python libraries, including pandas, NumPy, Seaborn, and many more. You’ll also gain experience working with real-world datasets, including data from banking industry and , to grow your data manipulation and exploratory data analysis skills. Start this course, grow your Python skills, and begin your journey to becoming a confident data analyst. This is one of the Big Data Analytics Course someone could get with more in-depth of each and every topic covered in Data Analysis Courses.

Highlights

  • 40 hours of live instructor led training

  • 10 hours of python fundamentals self paced learning tutorial video

  • 4 hours of self paced learning on statistical essentials

  • 4 hours SQL fundamentals self paced learning tutorial video

  • 3 projects: a. Credit card fraud detection b. Customer Churn Prediction c. Model Development

  • Comprehensive Blended Learning program

  • Flexible access to online classes

  • Instructions carried out through industry experienced trainers

  • Quizzes and assignments

  • 15+ in-demand technologies and skills

  • Preparation to sit for Certified Data Analytics Associste with Python by Python Institute

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This course is a 40 Hours curriculum intended for those who have a basic knowledge of python programming. In this course, we will learn the basics of conducting data science, how to perform data analysis in python and then create some beautiful visualizations using Python. This data science course also summarizes many concepts, techniques, and algorithms in machine learning, beginning with topics such as linear regression and ending up with three projects.

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  • How to wrangle data, or Data wrangling

  • Learning to explore data or Data exploration

  • Visualising data

  • Learning how to scrap data from various sources or datasets

  • Fundamentals of Python programming 

  • Data Science libraries 

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  • Software Professionals

  • IT Professionals

  • Analytics professionals

  • Data Scientist

  • Data Analyst

  • Fresh Graduates

  • Anyone with a genuine interest in Data Science

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  • Our aim is to provide everyone vital hands-on experience so that you are well-prepared for job interviews alongside an exhibition at their positions

  • Learn from pioneers in Data Science, both in research and industry.

  • Learn the tricks of the trade from seasoned Python Developer practitioner.

  • Work on hands-on projects that develop your ability to solve real-world problems.

  • Practice your skills on our hands-on projects that simulate real-world problems

  • How to wrangle data

  • Learning to explore data

  • Visualizing data

  • Learning how data is scrapped from various datasets or sources

  • Fundamentals of Python programming

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  1. Data Manipulation

  2. Data Mining

  3. Data Scrapping

  4. Data Cleaning

  5. Data Visualization

  6. Probability

  7. Bayesian Inference

  8. Regression Modelling

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  • 10 hours of python fundamentals self paced learning tutorial video

  • 4 hours of self paced learning on statistical essentials

  • 4 hours SQL fundamentals self paced learning tutorial video

  • Comprehensive Blended Learning program

  • flexible access to online classes

  • instructions carried out through industry experienced trainers

  • Interactive Quizzes

  • 15+ in-demand technologies and skills

  • Get hands-on experience with four industry-related projects

  • 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)

  • On-site Company Training (Malaysia)

  • Online Training via Microsoft Team (Malaysia and International)

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Training Fee : RM2000 (upon 60% discount)

Duration: 10 days/40 hours

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Live instructor led training : Tablaue

The Analyze and Visualize Data using Tableau Certification Training Course is designed to those without any programming or analysts background who wants to learn more on data analytics and visualization with Tableau. 
This course prepares students with mastering the foundational knowledge of Tablaue to achieve visually interactive analyses and insights. 
This course also helps students to improve their business decision-making skills which is the key role of any data analysts 
by teaching the students to utilise the predictive analysis feature of Tableau to present reports that consist of information that are accurate. 
This course is highly recommended to those who want to start a career in data analysis and business intelligence.

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Module 1 – Introduction To Data Science And Data Science Libraries

Data science is the field of applying advanced analytics techniques and scientific principles to extract valuable information from data for business decision-making, strategic planning and other uses. Through this module, you will learn the basics, how to analyze data, and then create some beautiful visualizations using Python.

Numpy

It’s a general-purpose array-processing package that provides high-performance multidimensional objects called arrays and tools for working with them. NumPy also addresses the slowness problem partly by providing these multidimensional arrays as well as providing functions and operators that operate efficiently on these arrays.

 

  • NumPy Getting Started

  • NumPy Creating Arrays

  • NumPy Array Indexing

  • NumPy Array Slicing

  • NumPy Data Types

  • NumPy Copy vs View

  • NumPy Array Shape

  • NumPy Array Reshape

  • NumPy Array Iterating

  • NumPy Array Join

  • NumPy Array Split

  • NumPy Array Search

  • NumPy Array Sort

  • NumPy Array Filter

  • NumPy Random

  • NumPy Inbuilt Methods


Module 2 – Pandas

Pandas is an important library in Python for Data Science. It is used for data manipulation and analysis.  It is well suited for different data such as tabular, ordered and unordered time series, matrix data, etc.

 

  • Pandas Getting Started

  • Pandas Series

  • Pandas DataFrames

  • Pandas Read CSV

  • Pandas Read JSON

  • Pandas Read Excel

  • Pandas Analyzing Data


Module 3 – Data Cleaning And Data Wrangling Using Python Pandas

Data scientists spend a large amount of their time cleaning datasets and getting them down to a form with which they can work. In fact, a lot of data scientists argue that the initial steps of obtaining and cleaning data constitute 80% of the job. Therefore, if you are just stepping into this field or planning to step into this field, it is important to be able to deal with messy data, whether that means missing values, inconsistent formatting, malformed records, or nonsensical outliers. In this module, we’ll leverage Python’s Pandas to clean data.

 

  • Cleaning a DataFrame

  • Removing Columns

  • Removing Rows

  • Filling Missing Values

  • Improving Readability

  • Dropping Columns in a DataFrame

  • Changing the Index of a DataFrame

 

Module 4 – Matplotlib Visualization with Python

Matplotlib is a python library used to create 2D graphs and plots by using python scripts. It has a module named pyplot which makes things easy for plotting by providing feature to control line styles, font properties, formatting axes etc. It supports a very wide variety of graphs and plots namely - histogram, bar charts, power spectra, error charts etc

 

  • Python Data Visualization

  • Python Chart Properties

  • Python Chart Styling

  • Python Box Plots

  • Python Heat Maps

  • Python Scatter Plots

  • Python Line Charts

  • Python Pie Charts

  • Python Bar Charts

  • Python Time Series

  • Python Geographical Data


Module 5 – Python seaborn Library

Seaborn is one of an amazing library for visualization of the graphical statistical plotting in Python. Seaborn provides many color palettes and defaults beautiful styles to make the creation of many statistical plots in Python more attractive. Seaborn library aims to make a more attractive visualization of the central part of understanding and exploring data. It is built on the core of the matplotlib library and also provides dataset-oriented APIs.

 

  • Plotting Chart Using seaborn Library

  • Line plot

  • Dist plot

  • Lmplot

  • Histogram

  • Bar Plot

  • Count Plot

  • Point Plot

  • Violin Plot

  • Heatmap

 

Module 6 –  Statistics

  • What is statistics?

  • Basic terminology of statistics

  • Types of statistics

  • Descriptive statistics

  • Measure of Central Tendency ( Mean, median, mode )

  • Measures of Dispersion ( Variance, Standard Deviation, Range-its derivation )

  • Inferential statistics

Module 6 – Exploratory Data Analysis

In this module, you will learn what is meant by exploratory data analysis, and you will learn how to perform computations on the data to calculate basic descriptive statistical information, such as mean, median, mode, and quartile values, and use that information to better understand the distribution of the data. You will learn about putting your data into groups to help you visualize the data better. Exploratory data analysis (EDA) is an especially important activity in the routine of a data analyst or scientist. It enables an in depth understanding of the dataset, define or discard hypotheses and create predictive models on a solid basis. It uses data manipulation techniques and several statistical tools to describe and understand the relationship between variables and how these can impact business.

 

Capstone Project 1 : Credit Card Fraud Detection Case Study

Overview : Lots of financial losses are caused every year due to credit card fraud transactions, the financial industry has switched from a posterior investigation approach to an a priori predictive approach with the design of fraud detection algorithms to warn and help fraud investigators.

This case study is focused to give you an idea of applying Exploratory Data Analysis (EDA) in a real business scenario. In this case study, apart from applying the various Exploratory Data Analysis (EDA) techniques, you will also develop a basic understanding of risk analytics and understand how data can be utilized in order to minimize the risk of losing money while lending to customers.


Capstone Project 2 :  Customer Churn Prediction

Overview : When clients stop doing business with a company, this is known as customer churn or customer attrition.

Because the cost of getting a new customer is usually higher than keeping an existing one, understanding customer churn is critical to a company’s success. As a result, churn analysis is the first step in gaining a better understanding of your clients.

In this , we tried to analyze customer behaviour.  


Module 7 – Model Development

In this module, you will learn how to define the explanatory variable and the response variable and understand the differences between the simple linear regression and multiple linear regression models. You will learn how to evaluate a model using visualization and learn about polynomial regression and pipelines. You will also learn how to interpret and use the R-squared and the mean square error measures to perform in-sample evaluations to numerically evaluate our model. And lastly, you will learn about prediction and decision making when determining if our model is correct.

 

1. Understanding the Data

  • Introduction to Data Types

  • Numerical parameters to represent data 

  • Mean

  • Mode

  • Information Gain

  • Entropy

  • Median

  • Sensitivity

  • Statistical parameters to represent data


2. Probability and its uses

  • Uses of probability

  • Need of probability Bayesian Inference Density Concepts

  • Normal Distribution Curve

3. Statistical Inference

  • Point Estimation

  • Hypothesis Testing Confidence Margin

  • Levels of Hypothesis Testing

4. Data Clustering

  • Association and Dependence Simpson’s Paradox Clustering Technique Covariance

  • Causation and Correlation

5. Testing the Data

  • Parametric Test Parametric Test Types Non- Parametric Test Experimental Designing A/B testing

6. Regression Modelling

  • Logistic and Regression Techniques Problem of Collinearity

  • WOE and IV

  • Residual Analysis Heteroscedasticity Homoscedasticity

 

Module 1: Introduction to Business Intelligence and Tableau

  • Overview of BI

  • Overview of Tableau Environment

  • Putting it all together


Module 2: Data Connections