A Beginner’s Guide to Data Science in Malaysia

• The Analysis which is Predictive in Addition with the Decision- If you want to make a program for providing credit card money, then you can analyze the history of the customer, whether they have done the payments on time or not. This history helps you to predict the future payments which will come in time or be late.
• A Prescriptive Analysis- Prescriptive analysis is used when creating software that is intelligent and capable of making judgements on its own, with specific factors influencing such conclusions. Predictions are made as well as recommendations for specific actions and their results in the prescriptive analysis. Let's look at Google's autonomous driving vehicle as an illustration. Data is collected from vehicles and utilised to train these autos. In addition to the data, algorithms are employed to incorporate decision-making capabilities. It may decide to slow down, turn, choose a direct route, accelerate, and other actions.
• Using Machine Learning to Make Predictions- Machine Learning is generally used for making future predictions and determining trends and patterns based on recorded past data. This kind of program can be used for finance companies where transactions are revealed. Machines can be trained based on past data, and they can help to detect fraud transactions using past data records.
• Use of Machine Learning to Discover Patterns- When you don't have any established variables to base your forecasts on, you utilise this method. In situations like this, it's important to uncover any hidden patterns that may be present in the datasets. Clustering is the most used algorithm for this usage. For instance, when installing a public WiFi network, it's important to make sure that everyone in the area can access it and that the signal is strong.

The Need for Data Science

It has been shown that many people find it challenging to make decisions these days. We rely on statistics to make decisions for us because we lack the capacity to do so. Data science uses algorithms, machine learning, and artificial intelligence to forecast patterns and trends based on historical data. Many pieces of data are simplified, which facilitates decision-making. Making important decisions for your business is also aided by it.

A Beginner’s Guide to Data Science in Malaysia

Beginning Step

Knowing what Data Science is in its most basic form is the first step for a student on the path to becoming a data scientist. It combines a variety of abilities.

• Artificial Intelligence- This skill works on creating machines or robots which work like a human being. For example, Sophia is the first human-robot which is developed by a company which is Hong-Kong based. She acts and works just like a human.
• Machine Learning- Developing tools and methods to extract information from data falls under this category. Work with erratic and noisy datasets to train the machine learning models.
• Deep Learning- It includes the creation of multi-layer networks where fast or advanced analysis is required and it cannot be done using traditional learning.
• Big Data- It includes working with large and unstructured data sets. It includes design tools that have the ability to understand high loads.

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Step 1 - Mathematics and Statistics

Analytical maths is crucial while discussing data science. You must be well-versed in probability and statistics. Additionally, learning linear algebra is advisable. The foundation of machine learning is based on statistics, which involves laborious mathematical calculations.

Step 2 - Learn a Programming Language (Python)

Learning a programming language becomes essential when you study at a Data Science course. If you are not familiar with programming, then Python is the best available option for you. Python is the easiest programming language to learn as its syntax is relatively easier than other programming languages. It copes with the demands of specialists and it is also multifunctional.

Step 3 - Get Knowledge about Machine Learning

Machine learning assists in teaching computers to make their own judgements and act on their own. In order to provide robots the ability to make decisions, algorithms are used. By creating machine learning, we can eliminate the need for repetitively writing precise instructions.

Step 4 - Learning about Data Visualization and Data History

Data visualization refers to helping people to get the importance of data by visualizing the data in a context that is visible to people.

Data mining is about finding hidden patterns and trends of data according to categories of information; algorithms are used to facilitate low-cost business decisions. It also helps to increase revenue for the business.

Step 5 - Gain Practical Experience

Gaining experience in the real world is just as vital as mastering theories. To become an expert in data science, you need a lot of practise. You may participate in many hackathons to improve your technical and coding abilities. For practise, there are a lot of free data sets accessible. You can also gain knowledge through the accomplishments of others.

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Step 6 - Confirm Your Qualification

After you have followed these five steps, you can start applying for jobs. You can participate in competitions that may secure you a very charismatic opportunity for a job. This can also help you in checking your capabilities and skills. GitHub provides the platform to publish programs on data analysis. It can get you acquainted with your potential employers. By publishing your work, you can showcase your capabilities and skills.

These are some of the procedures that might help you succeed in the Data Science industry. One thing, nevertheless, should be kept in mind. In order to succeed in data science, you need to be committed and put in a lot of effort. Make sure learning data science is something you actually want to do. Learn it for the right reasons rather than merely because it is fashionable and a well-known field. The stages listed above can be used as a roadmap for your data science journey. Expecting to obtain everything in a month or a year is unrealistic. It will take some time to comprehend all of this because it is a step-by-step procedure. Learn the material with determination and drive, and finish the Data Science training successfully.