Data Science with Python Training in Singapore, Singapore

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; 

1. Must master Python Coding
2. Know about R Programming
3. Learn to operate Hadoop Platform
4. All about SQL database and coding
5. Machine Learning and Artificial Intelligence
6. Learn about Apache Spark
7. Learn Data Visualization
8. Deal with Unstructured data

• Python Coding: Python is one of the most common programming languages that every tech student has to learn. However, python coding is particularly useful in the field of data science because of its simplistic, flexible and intuitive framework. The language supports multiple formats, is compatible with almost every platform and is extremely user-friendly. It also saves a lot on time, making it easier for the coder to create and work on and optimize multiple datasets at once.
• R Programming: R Programming is an analytical platform that lets the coder make sense of the data procured from various sources and arrange it in a logical framework. As a data scientist, it is hence mandatory that you learn at least one analytical tool to solve the problem of arranging and compartmentalizing data in the right order to maximize results. 
• Hadoop Platform: Learning the Hadoop platform is an added advantage for data scientists, as several organizations prefer the platform for their research. Hadoop is hence the leading technical skill that every data scientist or aspiring engineer must have a solid hold on. 
• SQL Database Management and Coding: SQL is a data-based platform, allowing engineers and coders to collect, collaborate and customize data into well-structured tables and data sheets. Using SQL we can also get valuable insights into the core structure of the database, MySQL comes with a set of commands and queries for quick and effective database operations. 
• Machine Learning and AI: Machine Learning is another mandatory requirement for data scientists for it allows for a better in-depth understanding of the database structure and its formation. Artificial intelligence, on the other hand, enables users to develop new and unique platforms that would simplify the process of data management. AI includes the following aspects; 
• Reinforcement Learning
• Neural Network
• Adversarial learning 
• Decision trees
• Machine Learning algorithms
• Logistic regression etc.

• Apache Spark: Apache Spark is a huge data computational software that, even though it doesn’t have a wide scope as Hadoop, is comparatively faster and more effective than its counterpart. Apache creates caches in the system memory, unlike Hadoop where the computational data is read and written into the disk separately. Furthermore, Apache also facilitates faster dissemination of data and makes processing huge unstructured data sets easier. It even protects the data from any external and online threats and prevents data loss.

• Data Visualisation: There are several visualization tools via which a data scientist can visualize how to arrange and structure the given data systematically. Some of the top visualization tools are D3.js, Tableau, ggplot, matplotlib, etc. As a data scientist, you will be expected to know all these platforms and use them in your everyday work. The visualization tools allow the data scientist to process and convert the complex data into comprehensible formats. With data visualization, you can directly work with the given data, get immediate and in-depth insights and determine the outcome with more precision than before. 

• Unstructured Data: Unstructured Data or content is that which is not labeled or pre-organised by the corporation. It is but a jumble of information that needs to be structured in a set pattern and rationally processed to derive the desired outcome. Videos, blog posts, social media content, audio files, user reviews, etc are a few common examples of unstructured data that you would have to sort through. 

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; 

• Innovation: A data scientist must have a unique approach to work, as data science is a field that involves constant innovation and out-of-the-box approach. 
• Inquisitiveness: Curiosity to know more and an inherent thirst for information is a must for everyone working in the field of data science. Your job involves procuring, collecting and analyzing massive amounts of data every day hence an inquisitiveness is a must.
• Intuition: Another important trait that one must possess to become a competent data scientist is intuition. Although your job involves a lot of math and rationality, there are some cases when the data scientist gauges the accuracy or effectiveness of the dataset. You should be able to determine when the data model is ready or what set of combinations would suit your requirement 
• Intelligence: Intelligence doesn’t just mean technical skills or theoretical knowledge but also practically sound and logical mind. Data science involves a lot of pragmatic decision making, dealing with real-time issues, tackling clients and scenarios that involve intelligence and common sense. 
• Inventiveness: Last but not the least, every data scientist must possess the presence of mind that enables them to improvise upon a plan to boost performance. Inventiveness is an important trait that comes in handy especially when things are not going according to plan and hence one has to adapt according to the given situation.  

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:

1. Analytic Problem-Solving: This is the most important skill for a data scientist. If you don’t know what your problem is, you won’t be able to find a solution for it.
2. Communication Skills: A good data scientist must be able to communicate with the organization regarding customer analytics and deep business.
3. Intellectual Curiosity: One must be curious to get answers to be a successful data scientist. Without thirst and curiosity, you won’t be able to deliver results and produce value to the organization.
4. Industry Knowledge: You need to have a strong knowledge of the industry you are working in. You need to have an understanding of what should be taken care of and what needs to be ignored.

The 5 best ways to brush up your Data Science skills to get a job as a Data Scientist are:

• Boot camps: If you want to brush up on your programming skills, boot camp is the perfect way to go. They last for about 4-5 days and provide you with theoretical as well as practical knowledge.
• MOOC courses: These online courses will help you learn about the latest industry trends. These come along with assignments that will improve your implementation skills.
• Certifications: You can try getting some certifications that will not only improve your skills but also help enhance your CV.
• Projects: Projects are the best way to learn the implementation of your skills. You can try finding different solutions for problems. This will refine your skills and thought process.
• Competitions: Competitions like Kaggle will improve your problem-solving skills. You will have to create an optimum solution that satisfies the requirements.

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:

• Beginner Level
  • Iris Data Set: It is an easy, popular, and versatile dataset that is used for pattern recognition. This dataset contains 4 rows and 50 columns.Practice Problem: Predicting the class of the flowers.
  • Loan Prediction Data Set: For this problem, you will be working with concepts like variables used in banking and insurance domain, the strategies that are implemented and the challenges they face. This classification problem consists 16 columns and 615 rows.Practice Problem: Predicting whether the loan will be approved or not.
• Intermediate Level:
  • Black Friday Data Set: Collected from a retail store, this dataset, will help you explore your engineering skills and understand the shopping experience of millions of customers. This regression problem has 12 columns and 550,069 rows.
    Practice Problem: Predicting the total purchase amount.
  • Human Activity Recognition Data Set: To collect this dataset, inertial sensors were used for recording the smartphones of 30 human subjects. It contains 561 columns and 10,299 rows.
    Practice Problem: Predicting the category of human activity.
• Advanced Level:
  • Identify the digits data set: This is a dataset of 31MB containing 7000 images of 28X28 dimensions. You will study, analyze, and recognize the different elements present in the image.
    Practice Problem: Identifying the elements present in an image.
  • Vox Celebrity Data Set: Used for large scale speaker identification, this dataset contains words spoken by celebrities in YouTube videos. There are 100,000 words collected from 1,251 celebrities.
    Practice Problem: Identifying the celebrity’s voice.

If you want to become a top notch data scientist, you need to follow these steps:

1. Getting started: The first step is to select a programming language you are most comfortable in. The most preferred languages in the Data Science field are python and R
2. Mathematics and statistics: A basic understanding of algebra and statistics is a must. While dealing with data, you will have to work with data that is numerical, textual, or an image.
3. Data visualization: It is required to make the data simple and easy to understand. It helps in communicating better with the end users.
4.  ML and Deep learning: Machine Learning skills are a must. You need these to analyze the data and inference information.

 Here is what you should do to jumpstart your career as a Data Scientist:

1. Degree/certificate: Get a degree or a certification in Data Science. This can be an online or offline course that covers all the important aspects of Data Science. You will also be learning about the latest technology in the field of Data Science.
2. Unstructured data: A Data Scientist’s job is to analyze the data. This data is usually in an unstructured form. To structure this data, a lot of work is required which makes the job of a data scientist even more difficult.
3. Software and Frameworks: You need to learn how to use different frameworks and software. Also, you will need to learn a programming language like Python and R.
• For solving statistical analysis, R programming language is used.
• Hadoop is the most commonly used frameworks by data scientist. They are mostly used when the amount of data is too much to handle when compared to the available memory at hand.
• You will also need to learn about databases and SQL queries.
4. Machine learning and Deep Learning: After you have gathered the data and structure it, you can start applying machine learning algorithms to analyze it. Deep learning techniques are used to train the model and analyze the data.
5.  Data visualization: A data scientist has to visualize the data. This is done using charts and groups. Tools like matplotlib, ggplot2, etc. are used for this purpose.

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:

• Networking: When you are in college, you will make friends and acquaintances that will help in establishing your network.
•  Structured learning: During the course, you will be following a schedule and keeping up with the curriculum. This will result in effective studying.
• Internships: You will have to get an internship during college that will help you in getting practical hands-on experience.
• Recognized academic qualifications for your résumé: A degree from a reputed institute will help you improve your CV and get a head start in the race for a job as a data scientist.

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:

• A strong STEM (Science/Technology/Engineering/Management) background: 0 point
• A weak STEM background (biochemistry/biology/economics or another similar degree/diploma): 2 points
• A non-STEM background: 5 points
• Less than 1 year of experience in Python: 3 points
• No experience of a job that requires regular coding: 3 points
• Independent learning is not your cup of tea: 4 points
• Cannot understand that this scorecard is a regression algorithm: 1 point

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:

1. Getting started: Understand the roles and responsibilities of a data scientist. Select a programming language that you know and are comfortable with. Python and R are the most used programming languages in Data Science.
2. Mathematics: Mathematics and Statistics are required for finding patterns in the data. You need to have an in-depth knowledge of probability, linear algebra, descriptive and inferential statistics.
3. Libraries: You need to have an understanding of libraries for performing different processes in Data Science like data preprocessing, plotting the data, and applying ML algorithms. The most commonly used libraries in data science are Pandas, Matplotlib, SciPy, Scikit-learn, NumPy, gglpot2, etc.
4. Data visualization: It is an important step in the learning path of Data Science. It is the job of a data scientist to find a way to visualize the data in a form that can be understood by the non-technical members of the team as well. Graphs and charts are used for this. Libraries like matplotlib, ggplot2, etc. are used for this purpose.
5. Data preprocessing: Data preprocessing is required to make the unstructured data ready for analysis. 
6. ML and Deep learning: Machine learning and deep learning skills are the most important skills required to be a data scientists. You need to have an in-depth knowledge of topics like Neural networks, CNN, RNN, etc.
7. Natural Language processing: Natural language processing is required for classification and processing of textual data.
8. Polishing skills: Participate in online competitions like Kaggle to exhibit and polish your data science skills. You can also try developing your own projects and experiment with other projects.

If you are looking for a job as a Data Scientist, here are the 5 important steps that will help you prepare for it:

• Study: Study before the interview and brush up on important topics like probability, statistics, neural networks, statistical models, machine learning, etc.
• Meetups and conferences: You need to expand your network and start building professional connections. You can visit meetups, conferences, tech talk, etc. to get in touch with other data scientists.
• Competitions: Participate in online competitions like Kaggle to implement, test, and polish your data science skills.
• Referral: Update your LinkedIn profile. This will help you with referrals as they are the primary source of interviews.
• Interview: If you think you are ready, give the interview. It might not work out for a couple of interviews. Learn from the mistakes you made during the interviews. Make sure that you study the questions you could not answer during the interview.

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:

• Separate the relevant data from the huge amount of structured and unstructured data provided to them.
• Organize and analyze the separated data.
• Create machine learning tools, programs, and techniques that can be used in identifying the patterns in the data and making sense out of it.
• Perform statistical analysis for predicting future outcomes.

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; 

• Data Science Association, Singapore 
• DataScience SG
• Data Science Bootcamp | Singapore
• Data Science Dojo Singapore
• BigDataX (DataEngineering+)
• Singapore Data Science #ODSC
• Experimental Data Science
• DataCouncil.ai - Singapore Data Engineering & Science

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 

• Attend data science conferences, you can usually read out your research papers, connecting with fellow data scientists in the process. 
• There are also online platforms like LinkedIn and other professional websites where you can post your CV. Corporate houses usually look for data scientists in these web platforms. 
• There are also social gatherings, trade fairs and inter-college programs organized by your institute for widening your contact circle. 

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:

• Business Intelligence Developer 
• Data Mining Engineer 
• Senior Data Scientist 
• Data Architect 
• Data Analyst 
• Machine Learning Scientist 
• Data Statistician 
• Infrastructure Architect 

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.

• Python is a multipurpose, flexible and intuitive platform that allows users to develop and design data sets effectively- a feature that is very important for data scientists who are working with massive amounts of data. Python is an object-oriented language that comes with extensive libraries, query codes and databases useful for structuring and arranging data in a logical sequence. 
• Python is a very user-friendly platform that doesn’t require a lot of technical expertise, almost anyone with a basic understanding of OOPS and coding can work with Python without any hassles. It is an inherently simple and comprehensible platform that allows the user to create specific data libraries, integrate algorithms and write extensive code with equal ease. 
• Python also offers users a wide range of resources and technical features for working out complicated data sets, arranging unstructured data, finding useful insights and other advanced analytical tools for effective results.
• And in case you still manage to get stuck in your work, there is a huge community of programmers, experts and technical people who are ready to help you out. Python comes with a powerful customer service group that offers quick and reliable solutions for any complication that you might have to tackle. 

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; 

• Top-notch data packages, statistical analysis models, optimized templates,
• Functionalities such as public R package which is connected to over 8000 networks, Microsoft RStudio and more
• Viva GGPLOT, Visual tools and a great interface for smooth matrix handling  

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; 

• Open source platform is easy to customize 
• Optimized for most devices, and compatible with almost every operating system hence easy to access 
• Comes with special features like Scikit learn, sensor flow and Pandas for quick and effective data analysis 

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; 

• A user-friendly interface that comes with a comprehensive syntax 
• Quick and time-saving, it's very easy to sort, create tables, curate data, manipulate queries and more

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:

• Java is an extremely compatible and comprehensive platform which runs on OOPS framework and hence easy to customize. 
• Users can edit and design codes for both frontend and backend applications 
• Plus, it is easy to compile data using Java 

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; 

• Scala supports Java and other OOPS platforms 
• It is also used along Apache Spark and other high-performance programming languages. 

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. 

• To install python u need to install brew and need the Apple Xcode package which can be procured using the $ Xcode-select –install command. 
• An alternative way to Install brew package using /usr/bin/ruby -e "$ (curl -fsS https://raw.githubusercontent.com/Homebrew/install/master/install)"
• Install the latest version of the program and ten confirm the version, 
• We would also recommend installing the virtual.env which will help create separate programs and framework for different versions.