How Data Science Works: Its Lifecycle, Applications, Prerequisites, and Tools
The Lifecycle of Data Science
The data science course malaysia lifecycle is comprised of five separate stages, each with its own set of responsibilities:
Data Capture: Data Gathering, Data Entry, Signal Reception, and Data Extraction. This stage entails the collection of unstructured and structured data.
Maintain: Data Warehousing, Cleansing, Staging, Processing, and Data Architecture. This stage entails transforming the raw data into a usable format.
Data mining, clustering/classification, data modelling, and data summarization are all processes. Data scientists assess the prepared data’s patterns, ranges, and biases to establish its predictive value.
Analyze using exploratory/confirmatory, predictive/predictive, regression, text mining, and qualitative analysis techniques. This is the true essence of the lifespan. This stage entails conducting a variety of analyses on the data.
Communicate: Reporting on Data, Visualization of Data, Business Intelligence, and Decision Making. Analysts present their findings in clearly understandable formats like as charts, graphs, and reports in this final step.
Data Science Prerequisites.
Here are a few technical terms you should be familiar with before diving into the world of data science in malaysia.
1. Artificial Intelligence
Data science is built on the backbone of machine learning. Data Scientists must have a firm grasp of machine learning in addition to a working knowledge of statistics.
2. Simulating
Mathematical models enable you to perform quick calculations and forecasts based on the data you already know. Modeling is also a subset of Machine Learning; it entails determining the optimal algorithm for solving a particular problem and how to train these models.
3. Statistical data
Statistics form the foundation of data science. A firm grasp of statistics can assist you in extracting additional intelligence and obtaining more relevant results.
4. Computer programming
A basic understanding of programming is essential to successfully complete a data science project. Python and R are the most frequently use programming languages. Python is particularly popular because it is simple to learn and supports a variety of data science and machine learning libraries.
5. Databases A competent data scientist should have a firm grasp on how databases work, how to manage them, and how to extract data from them.
What Is the Role of a Data Scientist?
A data scientist conducts analyses of corporate data in order to derive actionable insights. In other words, a data scientist approaches business challenges in a systematic manner, which includes the following steps:
Prior to collecting and analysing data, the data scientist establishes the problem by asking the appropriate questions and acquiring understanding.
Following that, the data scientist chooses the appropriate collection of variables and data sets.
The data scientist collects organised and unstructured data from a variety of sources—business data, public data, and so forth.
After the data is acquire, the data scientist analyses it and turns it to an analysis-ready format. This process entails cleansing and validating data to ensure its consistency, completeness, and accuracy.
After the data is transforme into a usable format, it is feed into the analytic system—whether it be a machine learning algorithm or a statistical model. This is where data scientists do analysis and uncover patterns and trends.
Once the data has been rendered completely, the data scientist examines it in order to uncover opportunities and solutions.
The data scientists complete the work by producing the results and insights for communication to the right stakeholders.
Now, we should be aware of certain machine learning algorithms that aid in the comprehension of data science.
Why Should You Consider Becoming a Data Scientist?
According to Glassdoor and Forbes, demand for data scientists will climb by 28% by 2026, demonstrating the profession’s resilience and endurance. If you’re looking for a stable career, data science offers that opportunity.
Additionally, data scientists were ranked second in the Best Jobs in America for 2021 report, earning an average base pay of USD 127,500.
Therefore, if you’re seeking for an exciting profession that gives stability and competitive salary, you’ve come to the right place!
What Position Do You Hold in Data Science?
Data science allows you to focus on and specialise in a certain component of the discipline. Here are a few examples of how you can get involved in this interesting, rapidly growing subject.
Scientist of Data
Determine the nature of the problem, the pertinent questions, and the appropriate data sources. Additionally, they mine, sanitise, and show pertinent data.
Programming capabilities (SAS, R, Python), storytelling and data visualisation, statistical and mathematical abilities, as well as familiarity with Hadoop, SQL, and machine learning are require.
Analysts serve as a link between data scientists and business analysts, organising and evaluating data to answer the organization’s inquiries. They take technical analyses and transform them into actionable items of a qualitative nature.
Statistical and mathematical abilities, programming capabilities (SAS, R, Python), and familiarity with data wrangling and visualisation are required.
Data Engineer Position Description: Data engineers are responsible for creating, deploying, managing, and optimising the organization’s data infrastructure and data pipelines. Engineers assist data scientists by assisting with the transmission and transformation of data in preparation for queries.
NoSQL databases (e.g., MongoDB, Cassandra DB), Java and Scala programming languages, and frameworks are require (Apache Hadoop).
