Data scientists have led the way the world has dealt with the COVID-19 pandemic. Every day, we wake up to the number of case counts, the economic impact of the pandemic, how effective government measures have been to mitigate COVID-19’s effect in the country. Access to trusted, quality data has helped healthcare providers to forecast how many hospital beds, masks, and ventilators they will need. Grocery chains are using it to decide which items their customers need. Telecom companies are analyzing network traffic data to understand where they need to upgrade the capacity to support remote working.
Big Data is the backbone of the digital world today, and Data Science & Data Analytics are hot skills. Harvard Business Review says:
A Data Scientist is a high-ranking professional with the training & curiosity to make discoveries in the world of Big Data.
Very Strong Growth Prospects Projected in Data Science Careers
The 2016 McKinsey report forecasted that the US will see a shortage of 250,000 data scientists by 2024. The average wages for data scientists grew by 16% annually from 2012 to 2014 – much higher than <2% increase in the annual average salary of all occupations in the US at the time.
The Indeed Hiring Lab 2019 report shared that job opportunities for data scientists are rising rapidly in Australia for several years – with a 58% increase in 2018. 80% of these vacancies existed in New South Wales (NSW) and Victoria.
Overall, the field is on the rise and experts predict that it will only become hotter over the next decade.
How to Become a Data Scientist?
Data Science is one of the hottest courses today as the career opportunities to surf through this data for decision-making purposes are appearing in businesses of all sizes – from start-ups to industry giants. Apart from campus courses, many students are also opting for online degree courses or distance-learning courses in Data Science to upgrade their skills and prepare better for the future.
Essential Skills You Need to Learn
- Mathematics & Statistics: Data Scientists use Statistics & Probability for data visualization, model evaluation, feature engineering, and transformation, etc. Multivariable Calculus and Linear Algebra plays crucial roles in machine-learning models. Predictive modeling becomes possible by minimizing an objective function while testing data.
- Programming and Coding: Python and R are the two most popular programming languages in the field of Data Science. Excel, SQL, Spark, Hadoop, and Tableau are other programming skills that recruiters look for in data scientists.
- Data Wrangling & Pre-Processing: Extracting data from web pages, PDFs, tweets, files, and databases and cleaning it up to reveal critical insights is known as data wrangling. Pre-processing deals with missing data, categorizing data, data imputation, etc.
- Data Visualization: Different types of data (such as discrete data, categorical data, time-series data, etc.) can be visualized in different ways (such as line graphs, scatter plots, heat maps, etc.). Data professionals need to understand how to map, scale, and label components.
- Machine Learning (ML): The ML framework includes Problem Framing, Data Analysis, Model Building, Testing & Evaluation, and Model Application.
- Real-world Project Handling: To demonstrate their expertise in data science and ML processes, data scientists may need to prove their mettle through internships or Kaggle projects.
Data Science students in Australia and the rest of the world are struggling with the coursework and assignments on campus and off-campus. Many online assignment help providers are seeing a rapid surge in the number of students seeking their assistance in completing assignments related to Data Science. Those who are looking for assignment help Sydney services may use MyAssignmentAssistance or MyAssignmentHelp4u to get in touch with the data science experts nearby. GoAssignmentHelp employs both Australian and international experts to provide dissertation writing service to students doing post-graduation in this field.
A Glimpse of the Future of Data Science
The demand for Data Engineers, Business Analysts, Business Intelligence Experts, and Data Scientists is likely to accelerate in the foreseeable future. All industries and organizations are creating data every day and it needs to be stored and analyzed to create competitive advantages. Digital ecosystems are coming up and traditional industry borders are blurring. Artificial Intelligence (AI), machine learning, and deep learning thrive on this data explosion. Using this data to empower growth technologies will naturally lead to great opportunities for data professionals.
Here is how Data Science is changing our world:
- Rise of Digital Ecosystems: Digital organizations are not limited to one industry anymore. Amazon, for example, engages in e-commerce, logistics, computer electronics, and cloud computing. Tencent (from China) is a social media, gaming, and finance company. Rakuten Ichiba (from Japan) is an online retail marketplace that issues loyalty points, e-money, credit cards, mortgages, and securities brokerage. It also runs one of the largest online travel portals in Japan as well as an instant-messaging app called Viber.
These cross-sector dynamics of businesses require advanced data analytics. Companies need to set up enterprise-wide consumer data acquisition processes, integrate these databases, generate insights, and optimize their products and services accordingly. Data sets and sources have become the unifying factors in such an economy. Digital ecosystems are emerging to create a highly customer-centric marketplace with a single entry point for an end-to-end experience. Over the next decade, we can expect that Mobile Internet, advanced analytics, and AI applications will make it easier to deliver personalized solutions to customers in milliseconds, and predicting their needs before they express it.
- B2B Services: Today, small, medium, and micro-size companies struggle to exist independently. E-commerce platforms like Amazon and eBay are game-changers for them. One-stop shops on shared, cloud-based platforms make it easier for them to provide better services with more transparency. This increases the competition for these companies but it also provides them with opportunities to partner with other businesses and provides more sophisticated and tailored services to their customers.
- Consumer Marketplaces: Soon, the retail sector will show us recommendations according to our income and wealth and our choices. For example, those who buy healthy foods are more likely to buy physical-fitness equipment and services. Health and life insurance providers may target them for their affordable coverage plans. Businesses across the sectors will create a value chain for the end-user.
- Increased Mobility but Less Traffic: People are already becoming familiar with ridesharing, carpooling, vehicle connectivity, and traffic management systems. Purchasing and managing vehicles is also becoming easier and more streamlined. In the future, these systems will allow you to travel more for less while helping the environment and bringing down pollution levels.
While Data Science is a catch-all term, data roles are typically separated into different categories:
- Data Architect: sets up ways to capture, integrate, organize, maintain, and store data.
- Data Engineer: keeps data accessible and ready for analysts.
- Data Analyst: does data processing and interpretation to generate actionable insights for various stakeholders.
- Data Scientist: uses sophisticated technology to analyze Big Data.
The demand for data scientists with soft skills will increase to convince the management, do insightful marketing, and increase sales. Students hoping to make it big as a data leader in the future should specialize in one or more of these roles.
Enroll in the right course online or offline. Remote academic support services are readily available these days. Use them to your advantage and get ready for the future!
