Do spreadsheets and vast amounts of data come to mind when someone mentions “data science”? Data science enables us to make a judgment about the data that we already have or we gather and put it to use. In the coming years, millions of positions will open up because the data science field is expanding like never before.
Companies now more than ever need data analytic abilities to expand their businesses in this data-driven world. Additionally, thousands of data scientists are getting training annually through multiple data science boot camps, which are continuously developing to address the current market vacuum. If you want to know more about data science and want to complete a crash lesson in big data, data engineering, and data science, keep reading. Below are our top picks for data science boot camps.
Key Takeaways
- Data science combines computer science and statistics to extract practical knowledge from large datasets.
- The demand for data science professionals is increasing, with a projected 28% growth by the end of the decade.
- Data scientists earn an average salary of $80,265 per year, while advanced data scientists and data engineers earn $105,909.
- Data science boot camps offer training in technologies like SQL, Hadoop, Spark, Python, R, and Machine Learning.
- Data science, data engineering, and data analytics are distinct fields with different focuses and requirements.
Data science: What is it?
Computer science and statistics when combined in the multidisciplinary domain make up data science. The basic goal of data science is to extract insightful and practical knowledge from datasets that are sometimes too big to evaluate using standard statistics. Moreover, this may comprise anything from deciphering handwriting to perfecting a marketing plan to studying tricky biological structures. A data scientist is “someone better at statistics than any software engineer and better at software engineering than any statistician,” says Josh Wills, director of the NYC Data Science Academy.
The Market for Data Science Jobs
The number of roles in data analysis will increase from 364,000 to 2.7 million by 2020. According to IBM, there is going to be a 28 percent increase in the workforce by the end of the decade. Not only this but the demand will increase to 61,800 for data science and other advanced data positions. From the government sector to the private sector, one can make informed decisions because of the availability of data.
What is a Data Scientist’s typical salary?
Data science analysts typically earn $80,265 per year, while advanced data scientists and data engineers often earn $105,909.
Manhattan Institute of Management (MIM)
Manhattan Institute of Management (MIM) offers on-site/online 3M, 6M, and 9M Business Administration programs, as well as a 3M Data Science Bootcamp and coding courses. Start studying in New York City and launch your career here. All MIM programs follow up to 12-month paid internships in New York City. MIM will provide you with the opportunity to network and build connections with sharp-minded people who will provide you with a much-needed different perspective. Living and studying in New York City is an adventure in itself that very few people get to experience.
Data Science vs. Data Engineering vs. Data Analytics
The terms Data Science, Data Engineering, and Data Analytics sound the same. However, they all are different. Let’s see how:
Data science is a multidisciplinary field that calls for expertise in math, statistics, and computer science (machine learning). Candidates often need to have a Ph.D. in a STEM subject (such as Science, Technology, Engineering, Mathematics, or Statistics) and a solid grasp of complex ideas. R and/or Python are the key tools that the majority of data scientists use.
With only a basic understanding of data science, data engineering leans more toward software engineering and computer science. Hadoop, Spark, Python, Java, and Scala are the key topics that are present in the subject. To infuse and extract data from massive data warehouses requires script development as well as tool familiarity. The focus of data analytics is more entry-level as it concentrates more on business intelligence.
Its main objective is to derive business insights from popular data types. Also, it comprises rudimentary modeling, such as linear regression, data cleansing, and data visualization. Excel and SQL are some data tools that are very common.
Most used Data science technologies
In contrast to standard coding boot camps, data science boot camps frequently develop other technologies. Let’s examine the most widely utilized technologies in the area and their applications:
- Structured Query Language is what SQL stands for.
- Hadoop is a collection of technologies for handling data and running programs together.
- Spark is a solution for creating parallel applications that can operate in clusters.
- Python and R are two widely used programming languages among data scientists.
- Machine Learning is a new-age technology that can analyze a huge set of data.
Conclusion
Data science is a rapidly growing field that has become essential for businesses to make informed decisions and expand their reach. With the increasing demand for data science professionals, it’s an exciting time to consider a career in this field. Data science boot camps offer a great way to gain the necessary skills and knowledge to succeed. By understanding the difference between data science, data engineering, and data analytics, and familiarizing yourself with the most used technologies in the field, you can set yourself up for success in this exciting and rewarding career. Whether you’re looking to start a new career or upskill, data science is definitely worth exploring.
