Computer science, modelling, mathematics, statistics, and analytics are all used in data science. These aspects are used by data scientists to analyze and understand large amounts of data in order to obtain meaningful insights. These insights can then be used by corporate management to make strategic decisions.
Data scientists must be able to do the following in order to interpret huge data:
- Clean and massage the data thoroughly, removing any unnecessary information and prepare it for preprocessing and modelling.
- Create statistical models to reveal important trends in massive datasets.
- Stakeholders should be informed of your predictions and findings.
Data scientists are the key to making objective, data-driven judgments for organizations striving to address complicated problems.
Netflix, for example, features a recommendation system that tracks a viewer’s previous viewing history in order to forecast what they might like to watch next. This is accomplished by comparing the viewer’s viewing history with “taste groups” – groupings of users who watch similar content — and proposing shows that are frequently seen in those closely related to theirs. Machine learning and algorithms were used to identify these taste groups, which were most likely created by teams of data scientists.
Roles and responsibilities of data scientists
In their day-to-day job, data scientists are responsible for a variety of responsibilities, including:
- Understanding business goals with non-technical stakeholders
- Consider how you can utilize data to help you achieve your objectives.
- Obtaining massive volumes of data from a variety of sources
- Data mining
- Database administration
- To ensure accuracy and consistency, the data is cleaned and processed.
- Analyzing data in an exploratory manner
- To mine the data, uncover trends, and extract actionable insights, you’ll need to design and implement algorithms and predictive models.
- Analyzing, assessing, and improving outcomes
- Providing non-technical peers and stakeholders with predictions and insights
- Models are being tweaked in response to feedback from stakeholders.
As you might expect, a data scientist’s responsibilities necessitate both a solid technical background and excellent communication abilities in order to clearly convey their findings.
What qualifications do you need to work as a data scientist?
A data scientist’s skill set often includes statistical analysis, machine learning, mathematics, programming, and data storytelling, among other things. Soft skills are also required of data scientists in order for them to think critically about business demands and explain their results to non-technical stakeholders.
Let’s look at each of these areas in greater detail to determine which abilities aspiring data scientists should acquire.
- Exceptional math abilities
In data science, strong math abilities are required. Calculus, linear algebra, and statistics are the three areas of math that are most generally considered as essential. However, statistics is the only branch of mathematics that you truly need to know for most data science jobs.
- Languages for programming
To clean, analyze, and construct models based on massive datasets, data scientists must write code. Python, R, and SQL are some of the most commonly used programming languages in data research. Apache Hadoop, an open-source software library, and Apache Spark, an analytics engine, are two more essential technologies.
Python is a user-friendly and developer-friendly object-oriented programming language. High code readability and a strong development community are two of its primary features. Data gathering, analysis, modelling, and visualization are all things that it excels at.
R is a free and open-source programming language and software environment for statistical and graphical applications such as clustering, linear and nonlinear modelling, time series analysis, and visualization. It is more commonly utilized in academic settings than in industry.
SQL is a programming language used to connect to and communicate with relational databases. It also makes data preprocessing easier by letting programmers to identify specific subsets of data and filter, sort, and summarize them according to predetermined criteria.
Apache Hadoop is an open-source software platform that enables the storage and concurrent processing of massive datasets in a distributed computing environment. In conjunction with an RDBMS system, data scientists frequently employ Hadoop as a file storage.
Apache Spark is an in-memory data analytics engine that is noted for its scalability, lightning-fast processing speeds, and advanced analytics capabilities. Map and reduce functions, SQL queries, data streaming, and complex machine learning and graph algorithms are all supported by Spark.
While you don’t have to be an expert on all of the above to get started, you should be able to code and have some experience with these technologies.
Machine learning is the study of computer algorithms that learn from large volumes of data to improve themselves automatically. These algorithms employ statistics to search for patterns in massive datasets. Machine learning techniques can be used by data scientists to create predictions based on data.
A big part of a data scientist’s job is explaining their findings to non-technical people. Data scientists must achieve this by extracting actionable insights that are relevant to the business challenge they’re assisting with.
Soft talents such as business knowledge, critical thinking, analytical thinking, and interpersonal skills are also required of data scientists.
Is data science a promising career path?
Data science is a field with a lot of options for advancement. Since 2012, data science has had a 650 percent surge in job growth, with the US Bureau of Labor Statistics forecasting 11.5 million new jobs in the field by 2026.
Job titles for data scientists that are commonly used
Data scientists can work in a range of roles, including:
- Data scientist
Data scientists create predictive models using data processes and algorithms to aid objective decision-making.
- Data analyst
To support corporate choices, data analysts investigate, alter, and analyse enormous data volumes. In comparison to data science, the procedure is usually less technical. They may also keep track of web analytics, do A/B testing, and generate management reports.
- Data engineer
Data engineers are in charge of processing stored data in real time or in batches. Cleaning, aggregating, and organising data from various sources, as well as transferring it to data warehouses, are all part of this procedure. Data engineers also create data pipelines to make it easier for data scientists to access data.
- Developer of business intelligence (BI)
BI developers create new apps or employ technologies to assist business users in finding and understanding the data they need to make objective, data-driven business choices.
How much do data scientists get paid?
According to Robert Half Technology’s 2020 Pay Guide, data scientists earn an average annual salary of $105,750 to $180,250. Compensation, on the other hand, might vary greatly based on location and job function.
Seniority affects compensation as well. For more senior data science roles, here are some compensation estimates:
- ▪ $138,226 for a senior data scientist
- ▪ $154,304 for a data science manager
- ▪ Director of data science: $164,716
What makes a data scientist different from a data analyst?
The data scientist’s function is frequently confused with that of the data analyst. Data scientists are in charge of creating data modelling techniques and algorithms in order to create predictive models. In comparison to a data analyst, their work is more technical and requires a greater level of seniority.
Data analysts, on the other hand, collect, organize, and analyze data in order to uncover crucial insights and develop conclusions. They may employ statistical or business intelligence technologies (such as Microstrategy) to aid in data interpretation and report preparation for stakeholders.
Getting a job in data science
Data science abilities are often built on a strong math and computer science basis. If you don’t already have the technical expertise required for an entry-level data science position, you can take one of three routes:
- Higher education
In the end, each road has its own set of advantages and disadvantages. Consider your personal learning style. You can chose which path to choose by answering a few key questions about your learning style. Do you, for example, learn better if you:
- Work in groups or alone?
- Meet in person or conduct business over the internet?
- Quickly or slowly?
- Read or do it yourself?
Path 1: Self-teaching
Self-education necessitates a tremendous lot of self-control. To guarantee that you’re focusing on the correct abilities, you should also conduct extensive study and evaluation. If you take this path, there are numerous books and online tools available to assist you.
Books and other materials
Introduction to Data Science from Alison
Data science techniques, beginning machine learning, and data models for data structure are covered in this free three-hour online course.
Learn R, Python, and SQL for Data Science with Dataquest
“Python for Data Science,” “Exploratory Data Visualization,” “Data Cleaning and Analysis,” “Fundamentals of SQL,” and other free data science learning tools are available on this online training site.
Johns Hopkins & Coursera: Data Science Specialization
Through Coursera, Johns Hopkins University faculty developed and taught a ten-course beginning specialty in data science. Classes like “R Programming,” “Exploratory Data Analysis,” “Regression Models,” and “Practical Machine Learning” are part of the specialisation.
IBM Data Science Professional Certificate
Python, SQL, databases, data visualisation, statistical analysis, machine learning techniques, and predictive modelling are all covered in this nine-course data science programme. The programme also allows you to develop a data science portfolio by include projects that use IBM Cloud, data science tools, and real-world data sets.
Free Trial in Data Science
The introductory and free trial in data science course at Singapore Coding Club covers Python and machine learning and is comprised of the first modules of our full-time data science program.
Self-teaching’s advantages and disadvantages
- Self-teaching is either free or affordable.
- You have the option of learning at your own pace.
- You can devote extra time to subjects in which you are having difficulty.
- You are free to utilize a wide range of materials from a variety of sources.
- You have the option of learning through the medium that best meets your needs and choices.
- It’s difficult to maintain self-control.
- It’s difficult to make sure you’re learning the correct skills.
- After you finish your schooling, there is no career guidance.
- There are no educational advisors available to you.
- Self-teaching may not be considered a valid education by hiring managers.
- The majority of self-teaching sites do not allow you to create a portfolio.
Path 2: Bootcamps
“How do I become a data scientist from the ground up?” you might wonder. Data science bootcamps are an alternative if you have no prior experience with data analysis.
A data science bootcamp is a concentrated, short-term training program that teaches the skills needed to be a successful data scientist.
In comparison to a standard degree program, bootcamps are often more hands-on, allowing you to work on projects. That way, you’ll have a complete portfolio to show off your abilities during employment interviews.
The advantages and disadvantages of bootcamps
- Bootcamps provide a hands-on learning experience.
- You can rest assured that you’re concentrating on the appropriate skills and material.
- Most university degrees are more expensive, and bootcamps can be completed part-time.
- After graduation, several bootcamps provide one-on-one career coaching.
- You can network with other data scientists who are interested in pursuing a career in the field.
- Instructors at bootcamps are up to date on market and employer demands.
- Bootcamp grads are preferred by hiring managers over self-taught data scientists.
- Bootcamps are notorious for their exorbitant upfront prices.
- Bootcamps, while shorter than a university degree, can require a lot of hard work and long hours.
- The content of bootcamps is usually less in-depth than those of computer science degree programs.
- Bootcamps may be a whirlwind of activity.
- Managers who prefer computer science degrees to bootcamp programs still exist.
Path 3: Higher education
The last alternative is to seek a formal data science education. A Master of Science in Data Science, Data Analytics, Business Analytics, or a comparable discipline is a common data science degree.
The benefits and drawbacks of pursuing a degree:
- You can rest assured that you’re concentrating on the appropriate skills and material.
- A degree program may be less rigorous and fast-paced than a bootcamp.
- In comparison to bootcamps, degree programs often give more in-depth content.
- Career fairs, career services, and other forms of job seeking support are available at universities.
- You can apply for financial aid from the federal government.
- Rather to coding bootcamps, many job employers prefer formal computer science or data science degrees.
- Degrees are far more expensive than bootcamps or self-teaching.
- Degree programs take far longer than bootcamps.
- Many degree programs need two years of full-time study.
- Formal academic institutions may be out of touch with current industrial trends and market demands.
- Degree programs are frequently more theoretical than practical.
Data science is a thriving, fast-growing discipline with a lot of room for expansion. And data science bootcamps are a great way to learn the skills you’ll need.
You can carve out a road to your first data science career if you’re prepared to put in the effort and develop the necessary skills. Singapore Coding Club can help you develop the abilities you’ll need to work as a data scientist. We offer full-time and part-time programs to fit your learning style, lifestyle, and schedule.
Enjoy your learning journey!