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.
Get acquainted with various analysis and visualization tools such as Matplotlib and Seaborn
Understand the behavior of data;build significant models using concepts of Statistics Fundamentals
Learn the various Python libraries to manipulate data, like Numpy, Pandas, Scikit-Learn, Statsmodel
Use Python libraries and work on data manipulation, data preparation and data explorations
Use of Python graphics libraries like Matplotlib, Seaborn etc.
ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees
There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.
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Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.
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Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Get reviews and feedback on your final projects from professional developers.
Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in data science.
Hands-on: No hands-on
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.
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.
Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario
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.
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.
Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
Work on a real- life Case Study with ARIMA.
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.
Project to be selected by candidates.
With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
This project involves building a classification model.
Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
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).
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:
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:
Following are the top behavioural traits of a successful data scientist:
For a job to be as popular as that of a data scientist, there has to be some exceptional benefits such as:
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.
You can brush up your skills in Data Science through:
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.
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:
Given below are the steps needed to become a top data scientist:
To prepare for a data science career, you need to follow the given steps and incorporate the appropriate skills:
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.
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:
Programming knowledge is a must for any aspiring data scientist irrespective of which city he or she is in. This is because:
Logically, the following step sequence needs to be followed for getting a Data Scientist job:
The steps given below can help you improve your chances of landing a data scientist job:
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:
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:
Following are the top professional organizations for data scientists:
Apart from referrals, other effective ways of networking with data scientists include:
There are numerous career options in the field of data science in Kuala Lumpur, Malaysia, including:
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:
The field of data science is huge, involving numerous libraries, and it is important to choose a relevant programming language.
Python 3 can be installed on Windows by following the given steps:
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:
Installation of virtualenv will allow running different projects
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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.
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
There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.
Yes, KnowledgeHut offers virtual training.
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.
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Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor
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.