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Gain hands-on Python coding skills for practical business applications. No prior programming knowledge is required.


Proposed Training Date*
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1 JUNE 2022


5 days, On-site Training in our office or your company site

5 days (6 hours per Day)


RM 3000 or USD700

*Limited time until 1st August 2022

Apply before 21 July 2022 and avail early bird tuition assistance of RM 500.

What will you get out from this Training Program?

Following successful completion of the Certified Data Scientist programme, participants will be able to:
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Apply NumPy and Pandas for Data Preprocessing.
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Transform data using Encoders.
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Explore relationships in data.
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Learn common machine learning methods to perform forecasting and predictions.
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Apply feature engineering to improve forecasting and prediction performance.
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Evaluate machine learning methods.
  • Data Transformation
  • Descriptive Analytics
  • Predictive Analytics
  • Clustering
  • Classification
  • Neural Networks
  • Feature Engineering
  • Performance Evaluation
  • Text Analytics
  • Real Data Science Use Cases

Training Modules

This is a complete course that provides you detailed understanding of data science, encompasses basic statistical concepts to advanced analytics and predictive modeling techniques, along with data acquisition, analysis, statistical methods and machine learning. The objective of the course is to learn statistical analysis techniques and tool to solve business problems that help you to emerge as ‘Industry Ready’ professional in the field of Data Science. You will be learning the Data Science skills using Python and its numerical, machine learning, and NLP libraries, the most popular and leading programming language, analytical tools and libraries widely used across industries.

The training is divided into multiple modules that would you to explore the basics of the Python programming language, and once you become comfortable with the basics, we will gradually introduce the step-by-steps the features of the Python analytics libraries.

Module 1: Python Fundamentals
  • Arithmetic and String Operations in Python
  • Identifiers, Lists, and Dictionaries
  • Operators, Control Structures and Loops
  • Data Input and Output
  • Data Structure and Data Types
  • File Handling: Import and Export Data
Module 2: Numerical Processing with NumPy
  • NumPy Standard Data Types
  • NumPy Arrays
  • Aggregations: Max, Min, Sum, and Average
  • Mathematical Functions
  • Array Manipulation: Sorting and Filtering
Module 3: Data Manipulation and Filtering with Pandas
  • Series, Dataframe
  • Data Selection
  • Indexing, Reindexing,
  • Iteration
  • Sorting
  • Statistical Functions
  • Aggregations
  • Missing Data
  • Grouping
  • Sparse Data
  • Merging/Joining
  • Concatenation
Module 4: Numerical Processing with NumPy
  • Arrays
  • Indexing and Slicing
  • Reshape and Resize
Module 5: Data Transformation with Encoders
  • Categorical Data
  • Ordinal Encoder
  • OneHot Encoder
Module 6: Data Visualization
  • Line, Scatter, and Density Plots
  • Multiple Subplots
  • Visualising Errors
  • Customize Legends, Ticks, Colourbars, and Labels
Module 7: Exploring Variable Relationships
  • Correlation
  • Covariance
  • Pearson's correlation
  • Spearman's rank correlation
Module 8: Classification Methods
  • Correlation
  • Linear Regression
  • Logistic Regression
  • Support Vector Machines
  • K-Nearest Neighbors
  • Decision Trees
  • Random Forests
  • Gradient Boosting Trees
  • Ensemble Learning
Module 9: Clustering Methods
  • K-means
  • Expectation-Maximization (EM) + GMM
  • Kernel Density Estimation
  • Hierarchical Trees
  • Density-based Spatial
  • Distance Measurements
Module 10: Neural Networks
  • Multilayer Perceptron
  • Multi-class classification task
  • Network Topology and Configuration
Module 11: Feature Engineering
  • Principle Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
Module 12: Performance Evaluation
  • Accuracy
  • Confusion Matrix
  • Area Under ROC Curve
  • Mean Absolute Error
  • R Squared
Module 13: Text Analytics
  • NLP & Text Processing Libraries
  • Text Normalization
  • Text Representation Model
  • Word Embedding
  • Text Classification
  • Text Similarity
Module 14: PROJECTS – Real Use Cases
  • Sentiment Analysis
  • Supermarket Sales Prediction
  • Insurance Prediction


Learn from a skilled instructor with extensive professional experience in data science and dashboard analytics.
Lead Instructor
Amril Nurman, PhD
Lead Instructor

Dr. Amril currently works as the Chief Architect at a leading telecom analytics solutions provider. He has 15 years of experience in managing Artificial Intelligence and Big Data related projects from both academic and industry.

As the Chief Architect, he has led and managed USD$150M+ data science & big data technical project delivery and operational teams for the Company’s Telecommunication Traffic Monitoring and Tax Revenue Assurance system, which processes 20+ billion of CDR (Call Detail Records) transactions with an average of 4 Terabytes of binary and text data on a daily basis from telecommunication operators in India, Ghana, Guinea, and Sierra Leon.

He has conducted extensive data science training classes to students for the last 5 years in the Middle East. He earned his PhD in Computer Science from the University College London (UCL), UK.

Lead Instructor

Lujaini currently works as Lead Solution Analyst. He has vast experience implementing various Machine Learning algorithms using PyTorch, Sci-kit Learn, Tensorflow, YOLO and Darts.

In his previous work, he worked on various data analytics projects such as the Malaysian political sentiment analysis, image processing analysis, and exploratory covid-19 analysis. Currently, he is working on speech-to-text data modelling project.

Who Is This Training For?

Professionals from various quantitative backgrounds like Mathematics,Statistics, Business, etc.
Someone who aspires to spearhead his/her career in the field.
Students/Professors who want to have a good grasp of data analysis.
Data Analytic or Data Analyst Trainee.
Software Developer who has no prior Python and Analytics Experience.
Proven Track Records

No. of Students Taught


Student Feedback

Average Instructor and Course Rating 4.8/5 based on 807 reviews

Post-Training Support

25+ of our past participants came back for continuous support and skills development throughout their careers. Some become our friends to work on joint software development projects.

Taught by Real Professionals

Both the Lead Instructor and Instructor are real professionals, experienced software developers with real and substantial experience in the industry, who are also great teachers. They have taught students at various colleges and universities too.
Get Answers
Have questions? We’ve got the answers. Get the details on how you can grow in this course.
Why is this course relevant today?
Given the prevalence of technologies and the amount of data available in the online world about users, products, and the content that we generate, businesses can be making so much more well-informed decisions if this vast amount of data was more deeply analyzed through the use of data science. The data science course provides the tools, methods, and practical experience to enable you to make accurate predictions about data, which ultimately leads to better decision-making in business, and the use of smarter technology (think recommendation systems or targeted ads).
What practical skill sets can I expect to have upon completion of the course?
This course will provide you with technical skills in machine learning, algorithms, and data modeling which will allow you to make accurate predictions about your data. You will be creating your models using Python so you will gain a good grasp of this programming language. Furthermore, you will learn how to parse and clean your data which can take up to 70% of your time as a data scientist.
Whom will I be sitting next to in this course?
Individuals who have a strong interest in manipulating large data sets, finding patterns in data, and making predictions.
Are there any prerequisites?
Basic knowledge in computing / statistics is recommended for this course. Knowledge in Python programming will be useful but not necessary.


Upon successful completion of the programme, participants will be awarded a verified digital certificate by AnalytiСray.
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