Data Science with Python Training in Kuala Lumpur, Malaysia

Get the ability to analyze data with Python using basic to advanced concepts

  • 40 hours of Instructor led Training
  • Interactive Statistical Learning with advanced Excel
  • Comprehensive Hands-on with Python
  • Covers Advanced Statistics and Predictive Modeling
  • Learn Supervised and Unsupervised Machine Learning Algorithms
Group Discount
HRDF Claimable

Description

Rapid technological advances in Data Science have been reshaping global businesses and putting performances on overdrive. As yet, companies are able to capture only a fraction of the potential locked in data, and data scientists who are able to reimagine business models by working with Python are in great demand.

Python is one of the most popular programming languages for high level data processing, due to its simple syntax, easy readability, and easy comprehension. Python’s learning curve is low, and due to its many data structures, classes, nested functions and iterators, besides the extensive libraries, this language is the first choice of data scientists for analysing, extracting information and making informed business decisions through big data.

This Data science for Python programming course is an umbrella course covering major Data Science concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression classification modeling techniques and machine learning algorithms.
Extensive hands-on labs and an interview prep will help you land lucrative jobs.


What You Will Learn

Prerequisites

There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.

3 Months FREE Access to all our E-learning courses when you buy any course with us

Who should Attend?

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

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Curriculum

Learning Objectives:

Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in  data science.

Topics Covered:

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

Hands-on:  No hands-on

Learning Objectives:

In this module you will learn how to install Python distribution - Anaconda,  basic data types, strings & regular expressions, data structures and loops and control statements that are used in Python. You will write user-defined functions in Python and learn about Lambda function and the object oriented way of writing classes & objects. Also learn how to import datasets into Python, how to write output into files from Python, manipulate & analyze data using Pandas library and generate insights from your data. You will learn to use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization and also have a hands-on session on a real-life case study.

Topics Covered:

  • Python Basics
  • Data Structures in Python
  • Control & Loop Statements in Python
  • Functions & Classes in Python
  • Working with Data
  • Analyze Data using Pandas
  • Visualize Data 
  • Case Study

Hands-on:

  • Know how to install Python distribution like Anaconda and other libraries.
  • Write python code for defining your own functions,and also learn to write object oriented way of writing classes and objects. 
  • Write python code to import dataset into python notebook.
  • Write Python code to implement Data Manipulation, Preparation & Exploratory Data Analysis in a dataset.

Learning Objectives: 

Visit basics like mean (expected value), median and mode. Understand distribution of data in terms of variance, standard deviation and interquartile range and the basic summaries about data and measures. Learn about simple graphics analysis, the basics of probability with daily life examples along with marginal probability and its importance with respective to data science. Also learn Baye's theorem and conditional probability and the alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value.

Topics Covered:

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

Hands-on:

Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario

Learning Objectives: 

In this module you will learn analysis of Variance and its practical use, Linear Regression with Ordinary Least Square Estimate to predict a continuous variable along with model building, evaluating model parameters, and measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization.

You will be introduced to a real Life Case Study with Linear Regression. You will learn the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It also covers techniques to find the optimum number of components/factors using screen plot, one-eigenvalue criterion and a real-Life case study with PCA & FA.

Topics Covered:

  • 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, you are required to build a regression model to predict the property prices.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling.

Learning Objectives: 

Learn Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Cuve, AUC, KS-Statistics, Kappa Value. Understand Binomial Logistic Regression with a real life case Study.

Learn about KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K. Understand KNN through a real life case study. Understand Decision Trees - for both regression & classification problem. Understand Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID. Use a real Life Case Study to understand Decision Tree.

Topics Covered:

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

Hands-on: 

  • With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
  • Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

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

Topics Covered:

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

Hands-on:  

  • Write python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Write 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.
  • Write Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.

Learning Objectives:

A mentor guided, real-life group project. You will go about it the same way you would execute a data science project in any business problem.

Topics Covered:

  • Industry relevant capstone project under experienced industry-expert mentor

Hands-on:

 Project to be selected by candidates.

Projects

Predict House Price using Linear Regression

With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.

Predict credit card defaulter using Logistic Regression

This project involves building a classification model.

Read More

Predict chronic kidney disease using KNN

Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

Predict quality of Wine using Decision Tree

Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Note:These were the projects undertaken by students from previous batches. 

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.

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

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Knowledgehut is the best training institution which I believe. The advanced concepts and tasks during the course given by the trainer helped me to step up in my career. He used to ask feedback every time and clear all the doubts.

Issy Basseri

Database Administrator
Attended PMP® Certification workshop in May 2018
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I had enrolled for the course last week. I liked the way KnowledgeHut framed the course structure. The trainer was really helpful and completed the syllabus on time and also provided live examples which helped me to remember the concepts.

York Bollani

Computer Systems Analyst.
Attended Agile and Scrum workshop in May 2018
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I liked the way KnowledgeHut course got structured. My trainer took really interesting sessions which helped me to understand the concepts clearly. I would like to thank my trainer for his guidance.

Barton Fonseka

Information Security Analyst.
Attended PMP® Certification workshop in May 2018
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Knowledgehut is the best training provider which I believe. They have the best trainers in the education industry. Highly knowledgeable trainers have covered all the topics with live examples.  Overall the training session was a great experience.

Garek Bavaro

Information Systems Manager
Attended Agile and Scrum workshop in May 2018
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The course material was designed very well. It was one of the best workshops I have ever seen in my career. Knowledgehut is a great place to learn and earn new skills. The certificate which I have received after my course helped me get a great job offer. Totally, the training session was worth investing.

Hillie Takata

Senior Systems Software Enginee
Attended Agile and Scrum workshop in May 2018
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Trainer really was helpful and completed the syllabus covering each and every concept with examples on time. Knowledgehut also got good customer support to handle people like me.

Sherm Rimbach

Senior Network Architect
Attended Certified ScrumMaster®(CSM) workshop in May 2018

FAQs

The Course

Python is a rapidly growing high-level programming language which enables clear programs on small and large scales. Its advantage over other programming languages such as R is in its smooth learning curve, easy readability and easy to understand syntax. With the right training Python can be mastered quick enough and in this age where there is a need to extract relevant information from tons of Big Data, learning to use Python for data extraction is a great career choice.

 Our course will introduce you to all the fundamentals of Python and on course completion you will know how to use it competently for data research and analysis. Payscale.com puts the median salary for a data scientist with Python skills at close to $100,000; a figure that is sure to grow in leaps and bounds in the next few years as demand for Python experts continues to rise.

  • Get advanced knowledge of data science and how to use them in real life business
  • Understand the statistics and probability of Data science
  • Get an understanding of data collection, data mining and machine learning
  • Learn tools like Python

By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as a data analyst.

Tools and Technologies used for this course are

  • Python
  • MS Excel

There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are Python and data science experts who have years of industry experience. 

Finance Related

Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of a written request for refund. Kindly go through our Refund Policy for more details.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

Have More Questions?

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.