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.
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
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).
According to Glassdoor, Data Science was recorded to be the highest paying career in 2016. And since then the demand for data scientists has only grown, thanks to the technological innovations that have opened up new avenues for tech enthusiasts and software engineers to explore. Data has become the fulcrum around which the world revolves, it lays the foundation on which the World Wide Web operates. Big shot companies like Amazon, Facebook and Google are on the lookout for talented tech-savvy data scientists who can develop new and innovative ways to maximize their reach and earn more profits.
Singapore is one of the most advanced cities in the world. It offers education and a high standard of living due to the numerous institutions and leading companies such as AppDynamics, Paycom, Cisco Systems, Apple, NetApp, etc.
Here are some other reasons why data science has been such a popular career choice among tech graduates and engineers:
The bottom line is, data science has taken over practically every major industry sector and is hence becoming indispensable for companies. This is the best time to explore data science as a career option considering the massive investment, research opportunities and monetary incentives provided by corporate houses.
Learning data science can work wonders for your career. However, simply choosing data science doesn’t magically guarantee a high paying job offer. You need to master some technical skills and invest considerable time and effort to make a mark in the industry. The good news is that Singapore is home to some elite universities like National University of Singapore, Nanyang Technological University, School of Computer Science and Engineering, Singapore Management University etc. Here are a few skills that one needs to become a data scientist;
Data science is not everyone’s cup of tea. For even though a career in data science pays handsomely and is in demand, companies wouldn’t hire people who are incompetent in their job and unable to perform in the workspace. Other than the technical skills discussed above, you also need some behavioral skills and traits to become a credible data scientist. Listed below are a few character traits that every data scientist must possess;
Data science has a pretty big scope career-wise with almost every big corporation or company wanting a piece of the technology for themselves. And most of these organizations are willing to offer a hefty pay package to those who are qualified to work for them, however, money is not the only incentive. Being a data scientist comes with its fair share of perks. It is not called the “Sexiest Job of the 21st century” for no reason now, is it?!
Listed below are the benefits of data science and how it can change your life for the better:
Exposure: Data science gives you the opportunity to work with different sectors and brands, both national and international. You would get amazing opportunities to work with famous companies like Google, Apple, Amazon and Uber as a data scientist. These big shots often rely on the findings and insights of data scientists to figure out their marketing and brand promotional policies.
Flexibility: Your job as a data scientist wouldn’t confine you to your cubicle. For unlike other professions in the IT sector, you aren’t bound by a specific business. This gives you the freedom and flexibility to pick and choose your projects. You can do what interests you and derive the satisfaction of actually getting to see how it affects people at large.
Informative: A data scientist always has the opportunity to learn every day. You can always find new and innovative upgrades being introduced in the market. Oftentimes the organization you are collaborating with poses training programs and certificate courses that gives you an opportunity to update your knowledge. Also, contrary to popular belief, a career in data science is quite secure as there is no dearth of work.
Creative: Data science allows you to be creative as well, unlike other IT positions where you have to work with codes and exhausting programming. Yes, there will be a fair amount of code involved, but also a great deal of management, analysis, critical thinking and on the spot problem-solving. This adds a degree of excitement and challenge to your job, you will actually look forward to working every day!
Versatility: Data scientists have to solve real-time problems, deal with real-life data and figure out insights that actually determines market trends affecting real-life people. So, you are to be well-informed, practical and updated with the latest trends. Your work is not confined to one department or industry, every sector today feels the need for data science, machine learning, and other technologies.
Mobility: Data science gives one the opportunity to travel the world, collaborate with organizations worldwide and have a generally awesome life. There are new and better opportunities opening every day, giving you better mobility and scope for exploration. And if you are experienced enough, you can even establish your own business and work independently.
If you want to become a successful data scientist, you need to have these 4 business skills:
The 5 best ways to brush up your Data Science skills to get a job as a Data Scientist are:
Some companies collect data to sell to other companies while some collect it for their own benefit. Overall, this data is for improving the customer experience. In Singapore, there are several companies that are hiring data scientists including Apple, Michael Page, Sephora, Dell Technologies, Refinitiv, Agoda Company Pte. Ltd., Surbana Technologies Pte. Ltd., Randstad Pte Ltd., Pedro Group, DataRobot, Surbana Jurong Private Limited, Space Executive Pte Ltd., etc.
If you want to improve your Data Science skills, you need to keep practicing. Here, we have several problems categorized according to your expertise level:
If you want to become a top notch data scientist, you need to follow these steps:
Here is what you should do to jumpstart your career as a Data Scientist:
About 88% of data scientists have a Master's degree while about 46% have a Ph.D. degree. A degree is very important because of the following:
Singapore is home to leading universities offering data science courses including NanYang Technological University (NTU), Singapore Management University, National University Of Singapore, James Cook University, etc. Here is how you can decide if you need a Master’s degree or not. Given below is a scorecard. Grade yourself and if you score more than 6 points, you must get a Master’s degree:
Programming language is the most important skill required to become a data scientist. A data scientist has to deal with large datasets. Programming skills are required for the analysis of the dataset. Programming skills are also required for building frameworks suitable for the organization. This framework must be able to analyze the experiments, perform data visualization, and manage the data pipeline automatically.
The best learning path to get a job in Data Science includes the following steps:
If you are looking for a job as a Data Scientist, here are the 5 important steps that will help you prepare for it:
The most important part of the job of a data scientist is to analyze the raw data to look for patterns and inference information from it. This information is then used to promote the needs of the business. The data provided to a data scientist can be in structures or unstructured form. Today, data is generated every single second. With so much data, the job of a data scientist has become more difficult and important. They have to find out patterns and ideas that can help the business make a tremendous growth.
Data Scientist Roles & Responsibilities:
Data Scientist was termed as the ‘Sexiest job of the 21st century’ by the Harvard Business Review in 2012. Needless to say, data scientists are paid handsomely. In Singapore, a data scientist can earn about S$71,036 per year.
A regular day in the life of a data scientist is pretty interesting. Their job is not just limited to sitting in front of the computer and processing never-ending lines of code every day. A data scientist is supposed to do the role of a software engineer, a marketer, and a mathematician. As a data scientist, you will have to work with huge volumes of data set, often unstructured information, curate what’s relevant out of it and then gather insights that would help the organization predict customer trends and preferences with as much accuracy as possible. And it’s not easy work, get ready for jam-packed schedules, unrealistic deadlines, massive projects and more.
Getting into data science is no child’s play either, you will have to be qualified enough to get a really good position. but we have already discussed all that earlier, so let us move on to trace the career path of a data scientist. What is the growth potential for a data scientist you ask? Well, the sky's the limit! Here are some of the things you can be as a data scientist:
Data Analyst: As a data analyst you will be required to study the market trends, observe customer preferences and have a solid idea about the demographics that your company is targeting. This helps develop a clear plan of how exactly would you want to approach the market, the kind of business standards you would want to set up and the marketing strategies you will have to adopt.
Data Scientist: Data scientist has a more complicated job than just observing and recording marketing trends. As a data scientist, your job will entail tasks like analyzing massive volumes of data, figuring out patterns, develop a hypothesis and create algorithms based on the same. Data scientists also have to deal with some programming and hence you must have sharp coding skills.
Data Engineer: As a data engineer your job involves collecting data sets, examining the business requirements involved, interacting with third-parties, creating algorithms and curating data sets that would eventually help predict market trends with as much precision as possible. You would also have to design data related campaigns, think of innovative solutions and analyze the given information logically.
Data Architect: A data architect has to often collaborate with data scientists and engineers to create elaborate plans for the organization. The data architect is responsible for the technicalities of the plan. He has access to all the core codes and the data source which he integrates and adds on to the data for optimized results.
Singapore is one of the hottest destinations for data scientists. It offers freshers and experienced professional developers with a great environment for research. Plus, there is no dearth of organizations that are willing to hire the best data scientists and engineers offering them attractive pay packages and other perks. If you are a data scientist looking for a reliable and credible space to work, there are a few places you should definitely check out;
After you have completed your data science course and have equipped yourself with the necessary skills to make a mark in the industry, the next step is to make yourself visible to the big shots of the industry. Often your college or institution would organize job fairs and campus selection programs where you can connect with the top companies and corporate houses and showcase your work. Another way to get hired is via referrals. Here are some areas where you can expand your contacts and network with other data scientists as well
Data science opens up so many career prospects for you, you will be spoilt for choice. And especially in a country like Singapore, there are several opportunities that you can explore and apply for. Randstad, Sephora, Dell, Aryan Solutions, Experian etc., are some of the top organizations which offer data science jobs. Listed below are a few career options you can check out:
Data scientists are expected to know a hell of a lot of things. You’ll be required to be a lot of things- from mathematics to coding experts, managers, technicians, marketers and more. However, there are some core skills that every company wants. Singapore has some of the most technologically advanced organizations such as NetApp, Apple, Dell, Aryan Solutions, Sephora, Randstad, Experian, etc. They are willing to pay high salaries but also demand in-depth skills.
Let’s find out what these skills are:
General Skills: General skills involves education degree and theoretical knowledge. Data scientists need to have a Ph.D., a degree in Machine Learning and AI and a few research papers to their name
Technical Skills: Technical skills involve an in-depth knowledge of programming languages like Python, R Programming, SQL, Hadoop, Spark, JAVA, SAS, Hive, Jsp.net, C++, NSQL, AWL, Scala and more.
Practical Skills: Other than the technical skills and textbook knowledge, data scientists also need practical experience. Companies prefer candidates who have some experience in working on real-time projects and programs.
Data science involves dealing with a lot of data on a daily basis. And for this, you need to work with multiple programming tools and platforms for quick, effective and accurate results. Here are some of the top programming languages that every data scientist must master if he/she wants to carve a space in the industry and facilitate smooth operation:
R Programming: R is one of the most frequently used programming tools for data science. It is an open source software that allows users to compute huge data sets, get statistical insights, create custom graphics and more. The platform is a bit advanced for first-time users but extremely effective and accurate once you get the hang of it. It includes;
Python: Python is a very popular, dynamic and versatile data tool for analyzing, arranging and integrating data into complicated data sets and creating advanced algorithms. It is among the easiest programming languages and hence the most sought after platform by most data scientists. Some perks of using Python are;
SQL: SQL or structured query language is a mandatory tool that every data scientist must master. It is used for editing, customizing and arranging information in relational databases. SQL is used for storing databases, retrieving old data sets, and for gaining quick and immediate insights. Other perks include;
Java: JAVA is a well-known programming language that runs on the JVM or Java Virtual Machine Platform. Most MNCs and Corporations use Java to create backend systems and applications. Some advantages of using Java are:
Scala: Scala also runs on JVM and is an ideal choice for data scientists to run massive data sets. It also comes with a fully functional coding interface and a powerful static tape framework;
Here are a few simple and effective steps using which one can download and install Python 3 on your Windows platform;
Downloading Python 3: First, check whether your desktop is compatible with the new version of Python 3. Windows do not usually come with a Python program pre-installed. Visit the download page for Python, python.org and click on the link for the Latest Python 3 Release - Python 3.6.5. You then have to scroll to the GUI installer and select from either Windows x86-64 executable installerfor 64-bit or Windows x86 executable installerfor 32-bit.
One can also get the platform via Anaconda. Once you have downloaded the setup to the desktop, the next step is to install it. for that you need to update the setup tools and run the python -m pip install -U pip
Installing Python in Mac OS X devices is even easier, you simply have to go to the official website of Python and get the program through a .dmg package. We would also suggest the homebrew platform that is far more dependable and risk-free.
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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.
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.
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