The ability to unveil the trends and insights in data has been in talks since prehistoric times. But have you ever wondered what exactly this data is for? Where does all this data go? Why is there a need to extract such data? While these questions surround the domain of data science, have you ever thought about how a person could be qualified to be a data scientist? To be a data scientist, you’ll find a variety of Data Science Courses. Let’s discuss different aspects of Data Science Courses.
What is Data Science?
There is no standardized definition of data science. But to simply put, Data science is an interdisciplinary field of study that combines scientific processes, algorithms, methods, expertise, programming skills, and systems to extract insights and trends from numerous unstructured and structured data.
Generally, the life cycle of data science has five-stage consisting of:
- Capture- Data entry, data extraction, data acquisition
- Maintain- Data cleansing, data processing, data architecture, data warehousing
- Process- Data modeling, data mining, data classification, data summarization
- Communication- Business intelligence, decision making, data reporting, data visualization
- Analysis- Predictive analysis, qualitative analysis, regression, text mining
How to learn Data Science?
The data scientists usually belong to diverse work and educational backgrounds, but must be strong in the four fundamental areas:
- Mathematics, including probability and statistics
- Computer Science, for instance, engineering, data/software architecture, etc.
- Business/Domain
- Written and Verbal Communication
As mentioned earlier, Data Science is an interdisciplinary field of study. As a result, there are numerous Data Science Courses available, covering the diverse study under Data Science. In the field of Data Science courses, there are 3 main components to learn:
- Big Data: The unstructured data in the form of videos, images, clicks, comments, articles, etc. is called Big Data. Under this, data scientists convert the unstructured data into structured data with the use of Big Data tools and techniques.
- Machine Learning: Mathematical models and algorithms is a part of Machine Learning. Under this, data scientists prepare the machines to adapt to everyday advancements. As a result, machines predict the outcomes for future perspectives.
- Business Intelligence: There is too much data available in business every day. Under this, data scientists analyze the data and carefully present it in visual reports like graphs for the company to make future decisions.
For these 3 components, the Data Science courses cover techniques, tools, and business understanding.
Read: Why Should I Choose Career in Data Science As Data Scientist
What do Data Science Courses offer?
The Data Science Courses have numerous courses covering techniques, tools, and business understanding for the field.
Under Data Science courses, for tools, the courses would cover in-depth knowledge of the following:
- R Programming: R is a programming language used for effective data analysis and data visualization. The course would cover programming in R, accessing R packages, profiling R code, writing R functions, etc.
- MS Excel: Every data entry job requires a basic skill in MS Excel. The course would cover tips and tricks for effective data entry out of unstructured data.
- Python coding: Python coding is majorly used to implement mathematical concepts and models. The course would cover lessons for python coding and its tips and tricks.
- SQL coding: SQL is primarily a tool used for the preparation and extraction of datasets.
- Hadoop Platform: Hadoop Platform is an open-source for processing framework. Generally, it manages, stores, and processes big data applications.
- Technology Knowledge: The unstructured data is available on numerous web servers, APIs, etc. These data can be extracted with proper knowledge of technology.
Data Science courses for techniques cover an in-depth knowledge of the following:
- Mathematical Expertise: Data scientists work with machine learning algorithms like time series, clustering, regression, etc. that require a depth of mathematical knowledge for mathematical algorithms. Hence, the course covers the understanding and usage of mathematical algorithms.
- Working with unstructured data: The unstructured data comes in numerous forms of images, tweets, search history, comments, etc. Hence, the course covers techniques to convert unstructured data into structured data.
Data Science courses for business understanding cover the knowledge of Business Acumen. Business knowledge for analytics professionals is a big requirement as they generally work in middle and high management.
Reasons to take these Courses?
The field of data science is evolving, and companies realize the importance of data science, machine learning, and AI. Regardless of the industry or sector or size, organizations that wish to have an edge in the market are in need of data scientists. As a result, job portals have reported an increase in job requirements for data scientists by 75% in the last three years.
- The first and the foremost reason to pursue Data Science Courses is the growth in career.
- Data scientist have freedom, flexibility, and numerous job options
- The data scientists are in demand in the industry, and this will only evolve further.
After Course Career Opportunities
A world where 2.5 quintillion bytes of data is produced on an everyday basis, there are multiple job opening. In 2019, approximately 2.9 million data science job openings were available across the world, and in 2020, despite the pandemic, the job opportunities after data science courses are still increasing.
To name a few career options after Data Science Courses are as follows:
- Data Analyst
- Data Engineers
- Database Administrator
- Machine Learning Engineer
- Data Scientist
- Data Architect
- Statistician
- Business Analyst
- Data and Analytics Manager
Read: How To Prepare for Data Science Career
Where to seek opportunities after these Courses?
Data science has been an effective tool for coping with real-world problems. The world is increasing the use of intelligent machines for a better working pattern. This brings data scientists and data analysts into the picture. Moreover, there are numerous industries that have day-to-day requirements of data science. The industries are as follows:
- Retail
- Healthcare
- Banking and Finance
- Transportation
- Construction
- Communications, Media, and Entertainment
- Education
- Manufacturing and Natural Resources
- Government sector
- Outsourcing Industry
- Energy and Utilities
To conclude, Data science is a new phenomenon in numerous industries. Consequently, the industry is evolving and continues to evolve to be adaptable with every possible change.