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)
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
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40 hours of live instructor led training
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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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3 projects: a. Credit card fraud detection b. Customer Churn Prediction c. Model Development
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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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Quizzes and assignments
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15+ in-demand technologies and skills
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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
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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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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
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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 practitioner.
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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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Data Manipulation
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Data Mining
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Data Scrapping
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Data Cleaning
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Data Visualization
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Probability
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Bayesian Inference
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Regression Modelling
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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 : RM2000 (upon 60% discount)
Duration: 10 days/40 hours
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Module 1: Introduction to Data Science and Essential Libraries
Overview of Data Science
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Understanding data science and its applications
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Basic concepts in data analysis and visualization
NumPy Essentials
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Introduction to NumPy
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Creating and Indexing Arrays
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Array Slicing and Data Types
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Working with Array Shape and Reshape
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Array Iteration, Joining, and Splitting
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Searching, Sorting, and Filtering Arrays
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Using Random and Built-in Methods
Module 2: Mastering Pandas for Data Manipulation
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Getting Started with Pandas
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Introduction to Pandas
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Understanding Series and DataFrames
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Reading Data: CSV, JSON, Excel
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Data Analysis Techniques in Pandas
Module 3: Data Cleaning and Wrangling with Pandas
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Techniques for Data Cleaning
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Removing Unnecessary Columns and Rows
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Handling Missing Values
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Enhancing Data Readability
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Modifying DataFrame Index
Module 4: Data Visualization with Matplotlib
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Visualizing Data Using Matplotlib
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Introduction to Matplotlib and Pyplot
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Customizing Chart Properties and Styles
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Creating Various Charts: Box Plots, Heat Maps, Scatter Plots, Line Charts, Pie Charts, Bar Charts, Time Series, and Geographical Data
Module 5: Advanced Visualization with Seaborn
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Exploring Seaborn for Statistical Plots
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Introduction to Seaborn
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Line Plots, Dist Plots, Lmplot, Histograms, Bar Plots, Count Plots, Point Plots, Violin Plots, HeatmapsPlot Types:
Module 6: Fundamentals of Statistics
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Understanding Statistics
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Basic Statistical Terminology
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Types of Statistics: Descriptive and Inferential
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Measures of Central Tendency (Mean, Median, Mode)
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Measures of Dispersion (Variance, Standard Deviation, Range)
Module 7: Exploratory Data Analysis (EDA)
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Performing Exploratory Data Analysis
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Introduction to EDA
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Calculating Descriptive Statistics: Mean, Median, Mode, Quartiles
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Grouping Data for Visualization
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Techniques for Understanding Data Distribution and Relationships
Capstone Project 1: Credit Card Fraud Detection
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Project Overview
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Applying EDA in a Financial Context
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Developing Risk Analytics to Detect and Prevent Fraud
Capstone Project 2: Customer Churn Prediction
Project Overview
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Examining Customer Behavior and Churn
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Methods for Predicting and Analyzing Customer Attrition
Module 8: Model Development and Evaluation
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Constructing and Assessing Models
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Identifying Explanatory and Response Variables
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Simple vs. Multiple Linear Regression
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Model Evaluation: Visualization, Polynomial Regression, and Pipelines
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Understanding R-squared and Mean Square Error
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Making Predictions and Informed Decisions.
1. Understanding the Data
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Introduction to Data Types
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Numerical parameters to represent data
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Mean
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Mode
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Information Gain
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Entropy
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Median
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Sensitivity
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Statistical parameters to represent data
2. Probability and its uses
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Uses of probability
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Need of probability Bayesian Inference Density Concepts
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Normal Distribution Curve
3. Statistical Inference
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Point Estimation
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Hypothesis Testing Confidence Margin
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Levels of Hypothesis Testing
4. Data Clustering
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Association and Dependence Simpson’s Paradox Clustering Technique Covariance
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Causation and Correlation
5. Testing the Data
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Parametric Test Parametric Test Types Non- Parametric Test Experimental Designing A/B testing
6. Regression Modelling
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Logistic and Regression Techniques Problem of Collinearity
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WOE and IV
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Residual Analysis Heteroscedasticity Homoscedasticity
Module 1: Introduction to Business Intelligence and Tableau
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Overview of BI
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Overview of Tableau Environment
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Putting it all together
Module 2: Data Connections
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Getting to data from Tableau Desktop
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Learning the basics of visualizing data
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Visualizing business needs
Module 3. Transforming Data
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Why transform data?
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Data Blends
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Data Joins
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Simplifying and sorting your data
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Organizing your data
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Slicing your data by date
Module 4. Calculations in Tableau
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Data Aggregates
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Calculation Wizards
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Table Calculations
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Customized Calculations
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Using calculations in Tableau
Module 5. Advanced Calculations
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Strings
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Floor and Ceiling
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Dates
Module 6. Creating and using Parameters and Filters
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Basics of filtering
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Basics of Parameters
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Putting it all together
Module 7. Sorting
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Ascending and Descending Order manually
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Computes and Sorting
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Nested Sorting
Module 8. Grouping Techniques and formatting
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Sets
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Combining Fields
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Colors
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Custom Colors
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Highlighters
Module 9. Map Basics
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Layers
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Editing
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Customizing
Module 10. Visualizations:
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Understanding Charts
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Text Charts
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Visual Charts
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Time Charts
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Trend Charts
Module 11. Introduction to Dashboards
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Designing
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Actions
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Stories
Module 12. Server Deployment:
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What is the Tableau Server?
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Working with Users
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Working with Projects
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Working with Groups
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Publishing Data Sources
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Publishing Visualizations
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Securing Projects
Module 1:
Beginning with Microsoft Data Analytics
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Data Analytics and Microsoft
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Getting Started with Power BI
Module 2:
Data Preparation in Power BI
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Obtaining Data from Various Data Sources
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Optimizing Performance
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Resolving Data Errors
Module 3:
Clean, Transform, and Load Data in Power BI
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Data Shaping
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Data Structure Enhancement
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Data Profiling
Module 4:
Creating a Power BI Data Model
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Data Modeling Fundamentals
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Working with Tables
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Dimensions and Hierarchies
Module 5:
Create Measures in Power BI Using DAX
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Introduction to DAX
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DAX Content
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Advanced DAX
Module 6:
Optimize Model Performance
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Optimize data model for performance
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Optimize DirectQuery Models
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Create and maintain aggregations
Module 7:
Report Creation
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Report Design
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Report Enhancement
Module 8:
Dashboard Creation
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Create a Dashboard
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Real-time Dashboards
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Enhance a Dashboard
Module 9:
Create Paginated Reports in Power BI
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Overview of Paginated Reports
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Creating Paginated Reports
Module 10:
Advanced Analytics
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Advanced Analytics
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Data Insights Using AI Visuals
Module 11:
Create and Manage Workspaces
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Creating Workspaces
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Sharing and Managing Assets
Module 12:
Power BI Dataset Management
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Parameters
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Datasets
Module 13:
Row-level Security
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Power BI security
Introduction to Databases & SQL
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Introduction to relational databases
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SQL flavors
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Tables
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SQL data types
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Queries
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Aggregate functions
Joins and Subqueries
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Review Day 1
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Combining data with joins
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Working with subqueries
FAQ for Data Analytics
What is Nexperts Academy’s Certified Data Analytics Associate with Python course about ?
This course is designed by industry experts preparing you to be certified as a Data Analytics Associate by a globally recognised accreditation body, Python Institute and also transforms you into an expert Data Analytics professional. You will be mastering all data analysis process and acquiring new skills such Tableau, Python, Excel, Machine Learning, Python Libraries , Power BI.
Why should I enroll for hybrid Certified Data Analytics Associate with Python course?
Our course doesnt only give the students the necessary skills and knowledge on data analytics but also prepares you to become a renowned data scientist. Over and above that, Nexperts Academy has trained over 1000 fresh graduates in the year 2021 and 2022 and many of them have acquired good jobs as data analyst , digital marketers , junior programmers. We have also trained many corporates primarily from the banking and finance industry training their existing auditors , accountants, risk managers customised data analytics course , making our curriculum a solid and well structured syllabus incorporating latest industry best practices.
Are the Data Analytics Training Course Instructor Led or Self Paced?
It is combination of Live instructor led training that will held on a back to back basis and at the beginning of the training. You will then upon guidance of the trainer, you will be going through the self paced tutorials and would submit all assignments and projects respectively to the trainer.
How long will it take me to be a certified Data Analyst ?
Even upon taking the 40’hours live instructor led training , you can start preparing for the certification.
What are the topics that I should study orderly in this hybrid Certified Data Analytics Associate with Python course?
You will start with the 40 hours of Data Science topics and then move towards data visualisation and analysis with Excel, Tablaue , Power Bi and SQL self paced learning tutorial.
What kind of certification will I receive upon joining Nexperts Academy’s training?
You will receive certificate of completion and upon through preparation of the exam and passing of the exam, you will receive Certified Data Analyst Associate with Python certification.
Why should I consider beginning my career as a Data Analyst?
It is job role that is highly in demand but less in manpower. Not only the salary is higher but because there are less data analysts supply in the market it makes the salary demand much higher . On top of that, this job position can be acquired by any degree or diploma holders or working professionals in any field for as long as you have the problem solving and critical thinking skills.
What is programming knowledge necessary for this Data Analyst Certification Training?
Absolutely not . You will learn python programming basics during the training itself.
Is this Data Analyst Course recommended for beginners?
Yes
What are the prerequisites for Certified Data Analytics Associate with Python Course?
There isnt any prerequisites for this course. Beginners with zero knowledge are welcome to join this training
What is the average salary of a Data Analyst in Malaysia?
As per Glassdoor 2022 survey the basic salary of a data analyst starts from RM4,000 per month
WHY NEXPERTS ACADEMY
- CERTIFIED TRAINERS
- LIFETIME ACCESS TO
RECORDED SESSIONS
- HIGHLY EXPERIENCED
WITH INTERVIEW PREPARATION
- FLEXIBLE SCHEDULE
- 24/7 SUPPORT
