Why should I learn Data Science ? The Importance of Data Science in the Modern World.
Data science has emerged as a pivotal field in the modern era, where data is often likened to a new form of currency. The significance of data science spans across various industries, including healthcare, education, travel, and government, influencing decisions and enhancing customer experiences. In today’s data-driven world, the ability to analyze large and complex datasets to extract valuable insights is not just a skill but a necessity for organizations aiming to make informed decisions, solve intricate problems, and foster innovation.
Data scientists extrapolate the data they collect to uncover trends in every area of the business. They help leaders and C-suite executives make decisions backed by data to continue growing their company and make the best decisions for their consumers.
Businesses are all about analytics and metrics. Without those, you don’t have a clear picture of the health of your business. Data science is essential for every business because of the value and insight it provides.
Why Study Data Science? 5 Top Reasons
1. Demand: Data Scientists are in High Demand
According to the Bureau of Labor Statistics (BLS), employment of Data Scientists is projected to grow 36% from 2021 to 2031, much faster than the average for all occupations.1 With the utter importance of this career, data scientists are highly sought after, making for a great potential career choice.
2. Growth: Data Science Careers Have High Earning Potential
According to the BLS, the median annual wage for data scientists was $100,910 in may 2021.1 A career in data science may not only be lucrative but can be fulfilling, rewarding, and challenging.
3. Job Security: Data Science is a Fast-Growing Field
This year, LinkedIn released its annual jobs on the rise report — and Machine Learning Engineers came out on top at 4th place. While it didn’t rank as the #1 fastest-growing field, it certainly cropped up pretty quickly.
4. Opportunity: Data Science Has a Range of Potential Job Opportunities
According to BuiltIn, studying data science can give you access to a diverse range of job opportunities. Some of these employment options may include:
- Data Scientist
- Data Analyst
- Data Engineer
- Data Architect
- Business Intelligence Analyst
- Database Administrator
- Machine Learning Engineer
5. Flexibility: Data Scientists are Needed in Various Sectors
Data scientists are in demand across a range of industries and sectors. The need will only grow as businesses integrate more data environments into their operations.
Some of the popular industries for data science professionals may include:
- Healthcare
- Financial Industry
- Manufacturing
- Logistics
- Retail Industry
- Telecommunications
- Automotive Industry
Data science is essential in the robotics industry
Data science is essential in the robotics industry for several reasons. It enables robots to learn and adapt to changing environments and tasks through machine learning algorithms, including reinforcement learning, supervised learning, and unsupervised learning. These algorithms allow robots to analyze data, identify patterns, and optimize their behavior over time, leading to more efficient and intelligent systems.
Data science also contributes to data-driven perception, allowing robots to interpret sensory information more accurately. This is crucial for tasks like navigation, object recognition, and interaction with humans or other robots. Additionally, predictive maintenance and fault detection are made possible through data science, ensuring that robots operate reliably and reduce downtime.
Moreover, data science fosters improved human-robot interaction and collaboration, creating more intuitive and responsive robots that can work alongside humans in various settings. Autonomous systems and swarm robotics, which require coordination among multiple robots, also rely heavily on data science for synchronization and collective decision-making.
Where do I learn data science?
Skillshare is an online learning platform that offers a diverse range of courses, including data science. It provides learners with access to video lessons, projects, and community discussions led by industry professionals. On Skillshare, you can find courses covering essential data science topics such as Python programming, data visualization, machine learning, and statistical analysis. These courses are designed to be accessible for both beginners and more advanced practitioners, allowing users to learn at their own pace and apply new skills through hands-on projects. Whether you’re looking to start a career in data science or enhance your existing knowledge, Skillshare offers flexible and practical learning opportunities.
Data Science vs Data Analytics: What’s the Difference?
Data Science
Data science focuses mainly on raw, unstructured data. Generally, the job of a data scientist is to organize, store, and analyze data, creating modeling and uncovering trends based on the extracted data. They look to find meaningful connections between large amounts of data from an organization.
Data Analytics
Data analytics is a branch of data science. Data analysts dive deeper and look at more specific answers to questions brought forth by data scientists.
Top 10 Essential Skills for Data Science
Data Science Fundamentals
The fundamentals of data science are mathematics, computer science, and domain expertise. Coding Dojo offers a part-time data science bootcamp to teach you the foundations of data science (and beyond!), so you can begin your career path in this expansive field.
1. Data Modeling and Analytics
Data modeling requires the visualization and plotting of data to better understand the data you’re working with. It’s also imperative to know how to distill this information down in a way that can be shared with–and communicated to–important stakeholders of the business.
Analytics are an inherent part of data science, as well. Analytics involves defining a goal for discovery, collecting the proper data to reach this goal, prepping it for interpretation, and finally, plotting and presenting it.
2. Programming Knowledge
Data scientists need to know Python, and other languages such as SQL, JavaScript, Scala, and C are also helpful. Our data scientist bootcamp begins by teaching the foundations of Python that are needed to succeed as a data scientist.
3. Statistics and Probability
Learning more about probability as it relates to data science will help you better explore and understand data. Probability can also help you make further, more accurate estimates and analyses. Statistics are interwoven with probability, making these two skills go hand-in-hand.
4. Advanced Mathematics
Aside from statistics and probability, linear algebra and calculus are valuable advanced math skills to have, as they’ll make it easier to work with data.
Linear algebra techniques in data science allow you to transform and manipulate data, and calculus is often used in machine learning. Calculus techniques are also used in almost every data model, so understanding this math — which involves studying the rates of change — is integral.
5. Data Visualization
Arguably one of the most important technical skills of any Data Scientist, data visualization is all about translating and visually communicating data, usually through graphs or charts.
A Data Scientist needs to be able to easily share findings and data from their work to high-level decision-makers in the organization, so knowing how to plot data and create straightforward visuals is an exceptionally important skill.
6. Machine Learning
Machine learning helps data scientists make quality predictions. Understanding how to use machine learning will benefit an organization greatly, as it can adequately output more real estimates without human intervention–it’s all A.I.-based, giving you more time and freedom to work on other things.
7. Deep Learning
A type of machine learning, deep learning also uses A.I. to help develop statistics and predictive modeling, which are incredibly important for a data scientist.
8. Data Wrangling
Essentially, data wrangling is the process of cleaning up messy data sets. Data is complex and tangled in its rawest form, so data scientists must wrangle the data, making sure it’s easy to access and analyze.
9. Big Data Processing
Especially in large organizations, data scientists work with large data sets. They need to extrapolate raw data and distill large data sets into smaller ones that are easier to review and work with.
10. Database Management
A database holds a structured set of data. They are used to store, retrieve, access, and update data, allowing data scientists to keep their information secured in one place.
Learn Data Science at Coding Dojo
At Coding Dojo, we’re all about learning. Our bootcamps are specifically designed to help you prepare for a career path of your choice — including data science — in a short amount of time. They’re intensive but are designed to provide you with all the foundations and concepts you’ll need to know to make a career path out of your chosen field.
In the first four weeks alone, our data science bootcamp you’ll study foundations in Python, understanding and manipulating data, data modeling, machine learning, and statistics. After 16 weeks of part-time learning, you’ll be ready to enter the data science field.
