Data Science with Python Training in Kuala Lumpur, Malaysia

Get hands-on Python skills and accelerate your data science career

  • Learn Python, analyze and visualize data with Pandas, Matplotlib and Scikit.
  • Create robust predictive models with advanced statistics.
  • Leverage hypothesis testing and inferential statistics for sound decision-making.
  • 220,000 + Professionals Trained
  • 250 + Workshops every month
  • 70 + Countries and counting

Grow your Data Science skills

This comprehensive hands-on course takes you from the fundamentals of Data Science to an advanced level in weeks. Get hands-on programming experience in Python that you'll be able to immediately apply in the real world. Equip yourself with the skills you need to work with large data sets, build predictive models and tell a compelling story to stakeholders.

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Highlights

  • 42 Hours of Live Instructor-Led Sessions

  • 60 Hours of Assignments and MCQs

  • 36 Hours of Hands-On Practice

  • 6 Real-World Live Projects

  • Fundamentals to an Advanced Level

  • Code Reviews by Professionals

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Why Become a Data Scientist?

Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire in-demand data science and Python skills and meet that need.

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The KnowledgeHut Edge

Learn by Doing

Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on.

Real-World Focus

Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

Industry Experts

Get trained by leading practitioners who share best practices from their experience across industries.

Curriculum Designed by the Best

Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

Continual Learning Support

Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!

Exclusive Post-Training Sessions

Six months of post-training mentor guidance to overcome challenges in your Data Science career.

Prerequisites

Prerequisites for the Data Science with Python training program

  • There are no prerequisites to attend this course.
  • Elementary programming knowledge will be of advantage.

Who should attend this course?

Professionals in the field of data science

Professionals looking for a robust, structured Python learning program

Professionals working with large datasets

Software or data engineers interested in quantitative analysis

Data analysts, economists, researchers

Data Science with Python Course Schedules

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What you will learn in the Data Science with Python course

1

Python Distribution

Anaconda, basic data types, strings, regular expressions, data structures, loops, and control statements.

2

User-defined functions in Python

Lambda function and the object-oriented way of writing classes and objects.

3

Datasets and manipulation

Importing datasets into Python, writing outputs and data analysis using Pandas library.

4

Probability and Statistics

Data values, data distribution, conditional probability, and hypothesis testing.

5

Advanced Statistics

Analysis of variance, linear regression, model building, dimensionality reduction techniques.

6

Predictive Modelling

Evaluation of model parameters, model performance, and classification problems.

7

Time Series Forecasting

Time Series data, its components and tools.

Skill you will gain with the Data Science with Python course

Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Data distribution: variance, standard deviation, more

Calculating conditional probability via hypothesis testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Binomial Logistic Regression models

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for regression and classification

Visualizing Time Series data and components

Exponential smoothing

Evaluating model parameters

Measuring performance metrics

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Data Science with Python Course Curriculum

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Learning objectives
Understand the basics of Data Science and gauge the current landscape and opportunities. Get acquainted with various analysis and visualization tools used in data science.


Topics

  • What is Data Science?
  • Data Analytics Landscape
  • Life Cycle of a Data Science Project
  • Data Science Tools and Technologies 

Learning objectives
The Python module will equip you with a wide range of Python skills. You will learn to:

  • To Install Python Distribution - Anaconda, basic data types, strings, and regular expressions, data structures and loops, and control statements that are used in Python
  • To write user-defined functions in Python
  • About Lambda function and the object-oriented way of writing classes and objects 
  • How to import datasets into Python
  • How to write output into files from Python, manipulate and analyse data using Pandas library
  • Use Python libraries like Matplotlib, Seaborn, and ggplot for data visualization

Topics

  • Python Basics
  • Data Structures in Python 
  • Control and Loop Statements in Python
  • Functions and Classes in Python
  • Working with Data
  • Data Analysis using Pandas
  • Data Visualisation
  • Case Study

Hands-on

  • How to install Python distribution such as Anaconda and other libraries
  • To write python code for defining as well as executing your own functions
  • The object-oriented way of writing classes and objects
  • How to write python code to import dataset into python notebook
  • How to write Python code to implement Data Manipulation, Preparation, and Exploratory Data Analysis in a dataset

Learning objectives
In the Probability and Statistics module you will learn:

  • Basics of data-driven values - mean, median, and mode
  • Distribution of data in terms of variance, standard deviation, interquartile range
  • Basic summaries of data and measures and simple graphical analysis
  • Basics of probability with real-time examples
  • Marginal probability, and its crucial role in data science
  • Bayes’ theorem and how to use it to calculate conditional probability via Hypothesis Testing
  • Alternate and Null hypothesis - Type1 error, Type2 error, Statistical Power, and p-value

Topics

  • Measures of Central Tendency
  • Measures of Dispersion 
  • Descriptive Statistics 
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing

Hands-on

  • How to write Python code to formulate Hypothesis
  • How to perform Hypothesis Testing on an existent production plant scenario

Learning objectives
Explore the various approaches to predictive modelling and dive deep into advanced statistics:

  • Analysis of Variance (ANOVA) and its practicality
  • Linear Regression with Ordinary Least Square Estimate to predict a continuous variable
  • Model building, evaluating model parameters, and measuring performance metrics on Test and Validation set
  • How to enhance model performance by means of various steps via processes such as feature engineering, and regularisation
  • Linear Regression through a real-life case study
  • Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis
  • Various techniques to find the optimum number of components or factors using screen plot and one-eigenvalue criterion, in addition to a real-Life case study with PCA and FA.

Topics

  • Analysis of Variance (ANOVA)
  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA

Hands-on

  • With attributes describing various aspect of residential homes for which you are required to build a regression model to predict the property prices
  • Reducing Dimensionality of a House Attribute Dataset to achieve more insights and better modelling

Learning objectives
Take your advanced statistics and predictive modelling skills to the next level in this advanced module covering:

  • Binomial Logistic Regression for Binomial Classification Problems
  • Evaluation of model parameters
  • Model performance using various metrics like sensitivity, specificity, precision, recall, ROC Curve, AUC, KS-Statistics, and Kappa Value
  • Binomial Logistic Regression with a real-life case Study
  • KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K
  • KNN through a real-life case study
  • Decision Trees - for both regression & classification problem
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID
  • Using Decision Tree with real-life Case Study

Topics

  • Logistic Regression
  • Case Study: Logistic Regression
  • K-Nearest Neighbour Algorithm
  • Case Study: K-Nearest Neighbour Algorithm
  • Decision Tree
  • Case Study: Decision Tree

Hands-on

  • Building a classification model to predict which customer is likely to default a credit card payment next month, based on various customer attributes describing customer characteristics
  • Predicting if a patient is likely to get any chronic kidney disease depending on the health metrics
  • Building a model to predict the Wine Quality using Decision Tree based on the ingredients’ composition

Learning objectives
All you need to know to work with time series data with practical case studies and hands-on exercises. You will:

  • Understand Time Series Data and its components - Level Data, Trend Data, and Seasonal Data
  • Work on a real-life Case Study with ARIMA.

Topics

  • Understand Time Series Data
  • Visualizing Time Series Components
  • Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • ARIMA
  • Case Study: Time Series Modelling on Stock Price

Hands-on

  • Writing python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Writing python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smoothing constants.
  • Writing Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Use ARIMA to predict the stock prices based on the dataset including features such as symbol, date, close, adjusted closing, and volume of a stock.

Learning objectives
This industry-relevant capstone project under the experienced guidance of an industry expert is the cornerstone of this Data Science with Python course. In this immersive learning mentor-guided live group project, you will go about executing the data science project as you would any business problem in the real-world.


Hands-on

  • Project to be selected by candidates.

FAQs on the Data Science with Python Course

Data Science with Python Training

The Data Science with Python course has been thoughtfully designed to make you a dependable Data Scientist ready to take on significant roles in top tech companies. At the end of the course, you will be able to:

  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Data visualization with Python libraries: Matplotlib, Seaborn, and ggplot
  • Distribution of data: variance, standard deviation, interquartile range
  • Calculating conditional probability via Hypothesis Testing
  • Analysis of Variance (ANOVA)
  • Building linear regression models, evaluating model parameters, and measuring performance metrics
  • Using Dimensionality Reduction Technique
  • Building Binomial Logistic Regression models, evaluating model parameters, and measuring performance metrics
  • Building KNN algorithm models to find the optimum value of K
  • Building Decision Tree models for both regression and classification problems
  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot
  • Build data distribution models: variance, standard deviation, interquartile range
  • Calculate conditional probability via Hypothesis Testing
  • Perform analysis of variance (ANOVA)
  • Build linear regression models, evaluate model parameters, and measure performance metrics
  • Use Dimensionality Reduction
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics
  • Perform K-means Clustering and Hierarchical Clustering
  • Build KNN algorithm models to find the optimum value of K
  • Build Decision Tree models for both regression and classification problems
  • Build data visualization models for Time Series data and components
  • Perform exponential smoothing

The program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the course covers everything you need to know, whether you’re a novice or an expert. To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities.

Yes, our Data Science with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job.

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Data Science with Python course is ideal for:

  • Anyone Interested in the field of data science
  • Anyone looking for a more robust, structured Python learning program
  • Anyone looking to use Python for effective analysis of large datasets
  • Software or Data Engineers interested in quantitative analysis with Python
  • Data Analysts, Economists or Researcher

There are no prerequisites for attending this course, however prior knowledge of elementary programming, preferably using Python, would prove to be handy.

To attend the Data Science with Python training program, the basic hardware and software requirements are as mentioned below -

Hardware requirements

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors
  • 4 GB RAM
  • 10 GB of free space

Software Requirements

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox

System Requirements

  • 32 or 64-bit Operating System
  • 8 GB of RAM

On adequately completing all aspects of the Data Science with Python course, you will be offered a course completion certificate from KnowledgeHut.

In addition, you will get to showcase your newly acquired data-handling and programming skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects.

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a data science project in the real business world.

Data Science with Python Workshop

The Data Science with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.

The Data Science with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Data Science. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects.

Our Date Science course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Data Science with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it.

Schedules for our upcoming workshops in Data Science with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing.

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger.

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us at support@knowledgehut.com and we will be happy to get back to you.

What Learners Are Saying

O

Ong Chu Feng

Data Analyst

4

The content was sufficient and the trainer was well-versed in the subject. Not only did he ensure that we understood the logic behind every step, he always used real-life examples to make it easier for us to understand. Moreover, he spent additional time to let us consult him on Data Science-related matters outside the curriculum. He gave us advice and extra study materials to enhance our understanding. Thanks, Knowledgehut!

Attended Data Science with Python Certification workshop in January 2020

L

Lauritz Behan

Computer Network Architect.

5

Overall, the training session at KnowledgeHut was a great experience. I learnt many things. I especially appreciate the fact that KnowledgeHut offers so many modes of learning and I was able to choose what suited me best. My trainer covered all the topics with live examples. I'm glad that I invested in this training.

Attended PMP® Certification workshop in May 2020

A

Astrid Corduas

Senior Web Administrator

5

The skills I gained from KnowledgeHut's training session has helped me become a better manager. I learned not just technical skills but even people skills. I must say the course helped in my overall development. Thank you KnowledgeHut.

Attended PMP® Certification workshop in April 2020

A

Alexandr Waldroop

Data Architect.

5

The workshop held at KnowledgeHut last week was very interesting. I have never come across such workshops in my career. The course materials were designed very well with all the instructions were precise and comprehenisve. Thanks to KnowledgeHut. Looking forward to more such workshops.

Attended Certified ScrumMaster (CSM)® workshop in January 2020

G

Garek Bavaro

Information Systems Manager

5

Knowledgehut is among the best training providers in the market with highly qualified and experienced trainers. The course covered all the topics with live examples. Overall the training session was a great experience.

Attended Agile and Scrum workshop in February 2020

B

Barton Fonseka

Information Security Analyst.

5

This is a great course to invest in. The trainers are experienced, conduct the sessions with enthusiasm and ensure that participants are well prepared for the industry. I would like to thank my trainer for his guidance.

Attended PMP® Certification workshop in July 2020

E

Estelle Dowling

Computer Network Architect.

5

I was impressed by the way the trainer explained advanced concepts so well with examples. Everything was well organized. The customer support was very interactive.

Attended Agile and Scrum workshop in February 2020

F

Felicio Kettenring

Computer Systems Analyst.

5

KnowledgeHut has excellent instructors. The training session gave me a lot of exposure to test my skills and helped me grow in my career. The Trainer was very helpful and completed the syllabus covering each and every concept with examples on time.

Attended PMP® Certification workshop in May 2020

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Data Science with Python

What is Data Science

The profession of Data Scientist is considered as the sexiest job of the 21st century. User data is being collected by major corporations and sold to advertisement agencies. Whenever you are online, you will be recommended products and services based on your interests. How is that possible? The answer is data. Some reasons behind the popularity of data science include:

  • Increase in demand for data-driven decisions
  • Lack of expert data scientists, resulting in high pay for professionals in the field
  • Companies need data scientists to make the most out of raw data and take crucial marketing decisions accordingly.

Kuala Lumpur is home to many of the prominent organizations in Malaysia. It also has numerous universities which offer advanced courses in Data Science.Hence, skilled Data Scientists are in high demand, from the perspective of both employees and companies. 

Kuala Lumpur, Malaysia is home to many prominent universities like University of Malaya, HELP University, Tunku Abdul Rahman University College, Universiti Sains etc. which offer Data Science courses. Following are the top skills needed to become a data scientist:

  1. Python: Being a highly versatile programming language, Python is frequently used among data scientists. It helps in processing data in numerous formats. With the help of Python, data scientists can create and perform different operations on datasets.
  2. Coding in R: In order to become a master data scientist, it is necessary to have a thorough knowledge of at least one analytical tool. R programming helps in solving data science problems more easily, which is why it is preferred by data scientists.
  3. Hadoop: A study of 3490 LinkedIn jobs shows Hadoop as a leading skill requirement for data science engineer jobs. This shows the importance of the skill, even though it isn’t a must-have requirement. 
  4. Database and coding in SQL: With the help of SQL, accessing, communicating and working on data is possible. The concise commands of MySQL allow operations to be performed on databases in reduced time without requirement of high level of technical skills. 
  5. Machine Learning and AI: If you want to pursue a career in the field of data science, you need to be proficient in areas of Machine Learning and AI. Certain concepts of these areas that data scientists need to be familiar with include:
    • Reinforcement Learning
    • Neural Network
    • Decision trees
    • Machine Learning algorithms
    • Adversarial learning 
    • Logistic regression 
  6. Apache Spark: Apache Spark is a tool used for big data computation, just like Hadoop. Apache Spark and Hadoop are dissimilar in terms of speed. Hadoop reads and writes to the disk while Spark makes computation caches in system memory, which is why it is faster. The use of Apache Spark allows faster running of data science algorithms. Data scientists use the tool for disseminating large data sets and processing unstructured complex datasets. It also helps in the prevention of data loss. Other benefits of Apache Spark include its speed and ease of operation, allowing data scientists to carry out projects easily
  7. Data Visualization: Visualization tools such as ggplot, d3.js, matplotlib and Tableau are used by data scientists for visualizing data. Data scientists are expected to know how to obtain results from processing data sets, and to convert those results into a comprehensible format. Data visualization also allows organizations to directly work with data. Through it, data scientists can also gain quick insights from a specific data and its outcome.
  8. Unstructured Data: Data scientists need to be capable of working with unstructured data as well. Unstructured data are not organized and don’t have labels, with videos, audio samples, customer reviews, etc. being examples. 

Following are the top behavioural traits of a successful data scientist: 

  • Curiosity: Data scientists are required to deal with huge amounts of data every day. So, they need to be curious enough to enquire about things around them.
  • Clarity: Data Scientists need to know exactly what they are doing and the reason behind it. 
  • Creativity: Creativity in the field of data science can be development of new tools or finding innovative ways of visualization of data. Data scientists need to be able to get results by figuring out what’s missing and what has to be included. 
  • Scepticism: Scepticism serves as a reality check for the creative minds of data scientists.

For a job to be as popular as that of a data scientist, there has to be some exceptional benefits such as:

  1. High income: Given the high qualification requirement, anyone would expect the job to pay well. Data scientist jobs are amongst the highest paying jobs in the industry. The average pay for a Data Scientist in Kuala Lumpur, Malaysia is RM 96,000 per year.
  2. Bonuses: Often, impressive bonuses are included as a part of a data scientist' salary along with other perks.
  3. Education: The educational requirement for data science jobs are high. With a high level of qualification, you can even be offered work as a researcher for private and governmental institutions. 
  4. Mobility: There is a high chance of being hired by a company located in a developed country with high living standards. 
  5. Network: You can expand your network of data scientists by being involved in the tech field. This can be helpful with referrals as well.

Data Scientist Skills and Qualifications

Some of the business skills that data scientists must have are as below. One must also keep in mind that these skills are applicable everywhere and not just limited to Kuala Lumpur, Malaysia.

  • Analytical approach for solving problem: Finding an ideal solution is not possible without proper analysis or understanding of the problem first.
  • Ability to be communicative: Data scientists also have the responsibility of communicating results to companies in a proper and effective manner.
  • Curiosity of the intellect: You need to be curious to find answers to problems. Curiosity and ability to deliver results is always valued by businesses.
  • Industry knowledge: Knowing the industry thoroughly is as important a business skill as any other because it provides an idea of what to ignore and what to pay attention to.

You can brush up your skills in Data Science through:

  • Boot camps: This is an excellent way of brushing up the basics. Over the course of 4 to 5 days, you get the opportunity to gain some hands-on experience along with theoretical knowledge.
  • MOOC courses: You can find online courses taught by experts in the field on trendy topics in data science. Assignments further help improve implementation skills.
  • Certifications: With the right certification, you improve your resume and add important skills to your skill set. Some famous certifications in data science include:
    • IBM Watson IoT Data Science Certificate
    • Applied AI with Deep Learning
    • Cloudera Certified Professional: CCP Data Engineer
    • Cloudera Certified Associate - Data Analyst
  • Projects: With the help of projects, you can improve your thinking ability, skills and explore solutions all at the same time.
  • Competitions: Participating in online competitions including Kaggle is also helpful for improvement of problem-solving skills to find optimal solutions as per requirements.

The nature of the modern world is such that almost everything can be said to involve datasets, be it a medical diagnosis, stock market investment or even browsing history. Companies benefit from gathering data and user experience is improved as well. 

Kuala Lumpur, Malaysia is a city that has several leading companies which deal with data science professionals such as Dell, iPrice, BNY MELLON, Argyll Scott, Sephora, Randstad etc. The kind of data science jobs a company offers is an indicator of the kind of company it is.

  • Big companies usually look for data scientists with particular specializations like Data Visualization and Machine Learning. This is because they already have a team of data scientists in place.
  • Medium size companies look for data science experts for applying Machine Learning techniques on their data
  • Small companies usually make use of Google Analytics for data analysis, which suffices their data needs and resource availability

To become an expert in Data Science, you need to practice problems and work to solve them. Given below are some data science problems based on the level of difficulty. You should practice these, depending on your level of skills, and try to improve to the next level:

Beginner Level

  • Iris Data Set: This data set is one of the easiest ones for incorporating while learning different classification techniques. It is said to be the most resourceful and versatile data set when it comes to pattern recognition. There are just 50 rows and 4 columns in the Iris Data Set.Practice Problem:  Prediction of the class of a flower depending on the given parameters.
  • Bigmart Sales Data Set: The Retail Sector uses analytics heavily for optimizing business processes. Business Analytics and Data Science allow efficient handling of operations. The data set contains 8523 rows and 12 variables and is used in Regression problems.Problem to Practice: Predicting a retail store’s sales
  • Loan Prediction Data Set: As compared to all other industries, the banking field uses data science and analytics most significantly. This data set can help a learner by providing an idea of the concepts in the field of insurance and banking, along with the strategies, challenges and variables influencing outcomes. It contains 615 rows and 13 columns.
    Problem to Practice: Predicting whether a given loan would be approved by the bank or not.

Intermediate Level:

  • Black Friday Data Set: This Data Set consists of a retail store’s sales transaction and it can be used for exploring and expanding engineering skills. It is a regression problem and contains 550,609 rows and 12 columns.
    Problem to practice: Predicting the total purchase amount
  • Text Mining Data Set: This data set contains safety reports describing problems encountered on flights. It is a multi-classification and high-dimensional problem and contains 21,519 columns and 30,438 rows.
    Problem to practice: Classifying documents depending on their labels.
  • Human Activity Recognition Data Set: This Data Set consists of 30 human subjects collected through smartphone recordings. It consists of 10,299 rows and 561 columns.
    Problem to practice: Predicting the category of human activity.

Advanced Level:

  • Urban Sound Classification: Most beginner Machine Learning problems do not deal with real world scenarios. The Urban Sound Classification introduces ML concepts for implementing solutions to real world problems. The data set contains 8,732 urban sound clipping classified in 10 categories. The problem introduces concepts of real-world audio processing.
    Problem to practice: Classifying the type of sound from a specific audio.
  • Identify the digits: This data set contains 31 MB of 7000 images in total, each having 28x28 dimension. It promotes study, analysis and recognition of image elements.
    Problem to practice: Identifying the digits in an image.

  • Vox Celebrity Data Set: Audio processing is an important field in the domain of Deep Learning. This data set contains words spoken by celebrities and is used for speaker identification on a large scale. It consists of 100,000 words from 1,251 celebrities from across the world.
    Problem to practice: Identifying the celebrity based on a given voice.

How to Become a Data Scientist in Kuala Lumpur, Malaysia

Given below are the steps needed to become a top data scientist:

  1. Select an appropriate programming language to begin with. R and Python are usually recommended
  2. Dealing with data involves making patterns and finding relationship between data. A good knowledge of statistics and basic algebra is a must.
  3. Learning data visualization is one of the most crucial steps. You need to learn to make data as simple as possible for the non-technical audience.
  4. Having the necessary skills in Machine Learning and Deep Learning is necessary for all data scientists.

To prepare for a data science career, you need to follow the given steps and incorporate the appropriate skills:

  1. Certification: You can start with a fundamental course to cover the basics. Thereafter, you can grow your career by learning application of modern tools. Also, most Data Scientists have PhDs, so you would also be needed to have the right qualifications.
  2. Unstructured data: Raw data is not used in the database as it is unstructured. Data scientists have to understand the data and manipulate it to make it structured and useful.
  3. Frameworks and Software: Data scientists need to know how to use the major frameworks and software along with the appropriate programming language.
    • R programming is preferred because it is widely used for solving statistical programs. Even though it has a steep learning curve, 43% of data scientists use R for data analysis.
    • When the amount of data is much more than the available memory, a framework like Hadoop and Spark is used.
    • Apart from the knowledge of framework and programming language, having an understanding of databases is required as well. Data scientists should know SQL queries well enough.
  4. Deep Learning and Machine Learning: Deep Learning is used to deal with data that has been gathered and prepared for better analysis.
  5. Data Visualization: Data Scientists have the responsibility or helping business take informed decisions through analysis and visualization of data. Tools like ggplot2, matplotlib, etc. can be used to make sense of huge amounts of data.

It is a matter of fact that 46% of data scientists have a PhD, with 88% of all data scientists having a Master’s degree. There are many leading universities in Kuala Lumpur, Malaysia, such as Universiti Sains, HELP University, University of Malaya, Tunku Abdul Rahman University College, etc. which offers Data Science courses.

  • Networking: Networking is important in all fields and it can be developed while pursuing degrees.
  • Structured education: Having a structured curriculum and a schedule to follow is always beneficial.
  • Internships: These allow much needed practical experience.
  • Qualification for CVs: Earning a degree from a reputed institution is always helpful for your career.

University of Malaya, HELP University, Tunku Abdul Rahman University College, Universiti Sains etc. are situated in Kuala Lumpur and these are among the best institutes in Malaysia which offer advanced courses in Data Science. The given scorecard can help you determine whether you should get a Master’s degree. You should get the degree if you get over 6 points in total:

  • A strong background in STEM (Science/Technology/Engineering/Management)- 0 point.
  • Weak STEM background, such as biochemistry, biology, economics, etc.- 2 points
  • Non-STEM background- 5 points
  • Python programming experience less than 1 year in total- 3 points
  • No job experience in coding- 3 points
  • Lack of capability to learn independently- 4 points
  • Not understanding that this scorecard follows a regression algorithm- 1 point.

Programming knowledge is a must for any aspiring data scientist irrespective of which city he or she is in. This is because:

  • Analysing data sets: Programming helps data scientists to analyse large amounts of data sets.
  • Statistics: Just knowledge of statistics is not enough. Knowing programming is required to implement the statistical knowledge.
  • Framework: The ability to code allows data scientists to efficiently perform data science operations. It also allows them to build frameworks that organizations can use for visualizing data, analysing experiments and managing data pipelines.

Data Scientist Jobs in Kuala Lumpur, Malaysia

Logically, the following step sequence needs to be followed for getting a Data Scientist job:

  1. Initial Step: Start by knowing the fundamentals of data science along with the role of a data scientist. Select a programming language, preferably R or Python.
  2. Mathematical understanding: Since data science involves making sense of data by finding patterns and relationships between them, you need to have a good grasp of statistics and mathematics, particularly topics like:
    • Descriptive statistics
    • Linear algebra
    • Probability
    • Inferential statistics
  3. Libraries: The process of data science involves tasks like pre-processing data, plotting structured data and application of ML algorithms. The popular libraries include:
    • SciPy
    • Scikit-learn
    • Pandas
    • NumPy
    • Matplotlib
    • ggplot2
  4. Visualizing data: Data scientists need to find patterns in data and make it simple for making sense out of it. Data visualization is popularly done through graphs and libraries used for that include ggplot2 and matplotlib.
  5. Data pre-processing: Pre-processing of data is date with the help of variable selection and feature engineering to convert the data into a structured form so that it can be analysed by ML tools.
  6. Deep Learning and ML: Along with ML, knowledge of deep learning is preferable since these algorithms help in dealing with huge data sets. You should take time learning topics such as neural networks, RNN and CNN.
  7. NLP: All data scientists are required to have expertise in Natural Language Processing, which involves processing and classification of text data form.
  8. Brushing up skills: You can take your skills to the next level by taking part in competitions such as Kaggle. You can also work on your own projects to polish your skills.

The steps given below can help you improve your chances of landing a data scientist job:

  • As a part of interview preparation cover the important topics such as:
    • Statistics
    • Probability
    • Statistical models
    • Understanding of neural networks
    • Machine Learning
  • You can build and expand your network and connections through data science meetups and conferences
  • Participation in online competitions can help you test your own skills
  • Referrals can be helpful for getting data science interviews, so you should keep your LinkedIn profile updated.
  • Finally, once you think you are ready, go for the interview.

The profession of data scientist involves discovery of patterns and inference of information from huge amounts of data, for meeting the goals of a business.

Nowadays, data is being generated at a rapid rate, which has made the data scientist job even more important. The data can be used for discovering ideas and patterns that can potentially help advance businesses. A data scientist has to extract information out of data and make relevant sense out of it for benefitting the business.

Roles and responsibilities of data scientists:

  • Fetching relevant data from structured and unstructured data
  • Organizing and analyzing the extracted data
  • Making sense of data through ML techniques, tools and programs
  • Statistically analyzing data and predicting future outcomes

As compared to other professionals in predictive analytics, data scientists have 36% higher base salary. Kuala Lumpur is home to several leading companies, such as Dell, BNY MELLON, Argyll Scott, Sephora, Randstad etc. The average pay for a Data Scientist in Kuala Lumpur, Malaysia is RM 96,000 per year.

A data scientist can spot trends and use mathematics and computer science skills. Data scientists have to decipher and analyse big data and make future predictions accordingly.

A data science career path can be explained through:

  • Business Intelligence Analyst: This role requires figuring out the trends in the business and the market. It is done through data analysis.
  • Data Mining Engineer: The job of a Data Mining Engineer is to examine data for business as well as a third party. He/she also has to create algorithms for aiding the data analysis.
  • Data Architect: Data Architects work with users and system designers and developers for creating blueprints used by DBMS for integrating, protecting, centralizing and maintaining data sources.
  • Data Scientist: Data Scientists perform analysis of data and develop a hypothesis by understanding data and exploring its patterns. Thereafter, they develop systems and algorithms for productive use of data for the interest of business.
  • Senior Data Scientist: The role of Senior Data Scientists is anticipating future business needs and accordingly shaping the present project, data analyses and systems.

Following are the top professional organizations for data scientists:

  • Big Data (Employment and Skill Hacks)
  • DataCouncil.ai - Kuala Lumpur Data Engineering & Science
  • KL Data Science
  • KL Healthcare AI & Data Science

Apart from referrals, other effective ways of networking with data scientists include:

  • Online platforms such as LinkedIn
  • Data Science Conferences
  • Meetups and other social gatherings

There are numerous career options in the field of data science in  Kuala Lumpur, Malaysia, including:

  • Data Scientist
  • Data Analyst
  • Data Architect
  • Marketing Analyst
  • Business Analyst
  • Data Administrator
  • Business Intelligence Manager
  • Data/Analytics Manager

Dell, iPrice, BNY MELLON, Argyll Scott, Sephora, Randstad etc. are some of the companies in Kuala Lumpur, Malaysia looking for data science professionals. They demand mastery in the field of data science for the high salary they offer.
Some key points that employers look for while employing data scientists include:

  •   Qualification and Certification: Having high qualification is a must and certain certifications also help.
  •   Python: Python programming is highly used and is usually preferred by companies.
  •   Machine Learning: It is an absolute must to possess ML skills.
  •   Projects: Working on real world projects not only helps you learn data science but also build your portfolio.

Data Science with Python Kuala Lumpur, Malaysia

  • Multi-paradigm programming language: Python is an OOP and structure language containing numerous packages and libraries suited for Data Science purposes.
  • Simple and Readable: It is highly preferred by data scientists over other programming languages due to its simplicity and the dedicated packages and libraries made particularly for data science use.
  • Diverse resources: Python gives data scientists access to a broad range of resources, which helps them solve problems that may come up during the development of a Python program or Data Science model.
  • Vast community: The community for Python is one of its biggest advantages. Numerous developers us Python every day. So, a developer can get help from other developers for resolving his/her own problems as the community is highly active and generally helpful.

The field of data science is huge, involving numerous libraries, and it is important to choose a relevant programming language.

  • R: It offers various advantages, even though the learning curve of the language is steep.
    • Huge open source community with high quality packages
    • Availability of statistical functions and smooth handling of matrix operations
    • Data visualization tool through ggplot2
  • Python: It is one of the most popular languages in data science, even though it has fewer packages in comparison to R.
    • Easier learning and implementation
    • Huge open-source community
    • Libraries required for the purpose of data science are provided through Panda, tensorflow and scikit-learn
  • SQL: This structured query language works on relational database
    • The syntax is readable
    • Allows efficient updation, manipulation and querying of data.
  • Java: It doesn’t have too many libraries for the purpose of data science. Even though its potential is limited, it offers benefits like:
    • Integrating data science projects is easier since the systems are already coded in Java
    • It is a compiled and general-purpose language offering high performance
  • Scala: Running on JVM, Scala has complex syntax, yet it has certain uses in the field of data science.
    • Since it runs on JVM, programs written in Scala are compatible with Java too
    • High performance cluster computer is achieved when Apache Spark is used with Scala.

Python 3 can be installed on Windows by following the given steps:

  • Visit the download page to download the GUI installer and setup python on Windows. During installation, you need to select the bottom checkbox for adding Python 3.x to PATH. This is your classpath that will allow using of functionalities of python via terminal.
  • Python can also be alternatively installed via Anaconda. The given command can be used to check the version of any existing installation: 

python –version

  • The following command can be used for installing and updating two of the most crucial third party libraries:

python -m pip install -U pip

Virtualenv can also be used for creation of isolated python environments and python dependency manager called pipeny.

Python 3 can be installed from its official website via a .dmg package. However, Homebrew is recommended for installation of python and its dependencies. The following steps will aid installation of Python 3 on Mac OS X:

  1. Xcode Installation: Apple’s Xcode package is required for installation of brew. So, you need to start by executing the given command:$ xcode-select –install
  2. Brew Installation: Homebrew can be installed with the help of given command:
    /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
    The installation can be confirmed by using: brew doctor
  3. Python 3 installation: Use the given command for installing latest version of Python:
    brew install python
    Confirm the python version using: python –version

Installation of virtualenv will allow running different projects

Data Science with Python Certification Course in Kuala Lumpur

The Petronas towers are probably the most iconic buildings of Kuala Lumpur and very much reflect the ideology of the city?to reach for the skies. Ever since its metamorphosis into the financial capital of Malaysia, Kuala Lumpur has been on the road to progress and is today among the best places to do business in. But amidst all this industrialization lies a city with a rich cultural heritage reflected in its colourfully adorned mosques, temples and ancient architecture. Food is of course an important part of Malay culture and can be experienced everywhere from road side eateries to glitzy restaurants that crowd malls and other hip shopping places. A perfect blend of modern and tradition, Kuala Lumpur is an ideal place to begin a career and KnowledgeHut will help you along the way with courses such as PRINCE2, PMP, PMI-ACP, CSM, CEH, CSPO, Scrum & Agile, MS courses, Big Data Analysis, Apache Hadoop, SAFe Practitioner, Agile User Stories, CASQ, CMMI-DEV, and many more. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.

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