Data scientists are in huge demand recently, in this climate, more and more laymen are considering a career change. If you are interested in becoming a data scientist with zero tech background, Preface team is here to offer you the best solution!
What is data science?
Data science is in fact a full package, involving statistics, artificial intelligence (AI), data analysis, data value extraction and more.
Since data scientists are responsible for uncovering meaningful insights from vast volumes of data, they must master five different stages in the data science life cycle:
Data Science Life Cycle
Capture
Data Acquisition
Data Entry
Signal Reception
Data ExtractionMaintain
Data Warehousing
Data Cleansing
Data Staging
Data Processing
Data ArchitectureProcess
Data Mining
Clustering
Data Modeling
Data SummarizationAnalyze
Exploratory and Confirmatory
Predictive Analysis
Qualitative Analysis
Regression
Text MiningCommunicate
Data Reporting
Data Visualization
Business Intelligence
Decision Making
Source: Oracle, Berkeley School of Information
Why data science?
First of all, in today's data-heavy world, the ability to make sense of data is crucial to better decisions and strategic business moves. Data is unavoidable to every business. Through understanding statistics collected from websites, social media platforms and electronic payment bills, leaders know how to make smarter decisions about where to take their companies.
Moreover, data helps businesses to take accurate corrective steps, improving both efficiency and productivity. Historical data can reveal exactly the root cause beyond superficial problems, supplemented with concrete evaluation and real-time reports to keep track of the improvement progress, which is vital to the success of any business.
Source: AnalytiXLabs, Grow
Data science related jobs
To be honest, employees with a data science background are needed in literally every job sector — not limited to technology. Below are the most common careers:
- Data Scientist
Doubtless, data scientist is the most related position. Job duties include extracting data from multiple sources, sifting and analysing data from different angles so as to generate data-driven solutions.
- Machine Learning Engineer
The responsibilities include designing and developing machine learning models, thus, applicants must have exceptional knowledge in statistics and programming, relevant experience in data science and software engineering is also a must.
- Business Intelligence (BI) Developer
The BI Developer works closely with end users to structure a comprehensive reporting system, offering accessible information for future decision-making. Since the process involves use of warehouse data, candidates must be proficient in extracting data with retrieval and management tools.
Source: Target Jobs, Industry Connect, Springboard
H2: Data science course in Hong Kong
If you are a beginner to data science, the 80-hours part-time Data Science & A.I. with Python offered by Preface is the perfect choice for upskilling. The course consists of 5 modules. You will learn how to code in Python and work efficiently with big datasets, analyze and harvest clean data sets and create data frames to run basic analysis. Moreover, you will learn how to present data in informative and striking graphics. You will also be introduced to practical machine learning techniques so as to make predictions as well as deliver insights.
You can take your lessons either individually or with like-minded classmates, no matter what format you choose, there will always be a professional instructor. Most importantly, students never need to quit their jobs or give up other priorities, Preface allows participants to take full control over the lesson schedule and pace, maximizing the learning outcome.
Why is Data science related to Python and R?
Both Python and R are well suited for data science tasks.
Usually, Python is a general-purpose language that can be used whenever programmers want to delve into data analysis or apply statistical techniques, whereas R is applied mostly in exploratory data analysis.
Further Reading: Why Coding is important?
Source: IBM
What’s the difference between data science, machine learning and artificial intelligence?
Data Science
Definition: Include various data operations
Purpose: Create insights from data that deals with real world complexities
Application: Fraud detection, Healthcare analysis
Machine Learning
Definition: Subset of Artificial Intelligence
Purpose: Predict or classify outcomes for new data points by learning patterns from historical data
Application: Recommendation systems such as Spotify, Facial Recognition
Artificial Intelligence
Definition: Include Machine Learning
Purpose: Make a computer imitate human’s behavior and mindset to solve complex problems
Application: Chatbots, Voice assistants
Source: My Great Learning
Data Scientist vs Data Engineer vs Data Analyst: Job Role, Skills, and Salary
Data Scientist
Role: Senior
Requirements: Deep expertise in machine learning, statistics, and data handling
Annual Salary: ~ $790,000 HKD
Data Engineer
Role: Intermediary
Requirements: Experience in construction, development, and maintenance of the data architecture
Annual Salary: ~ $670,000 HKD
Data Analyst
Role: Entry
Requirements: Proficiency in programming languages, analytic tools, fundamentals of data handling, reporting, and modeling.
Annual Salary: ~ $380,000 HKD
Source: Simplilearn, Glassdoor
Original article: https://www.preface.ai/blog/others/data-science/