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“Short, Smart and Precise learning that makes you job-ready within a few days.”
The Data Science Job Market
The big data market is projected to reach a whopping $229.4 billion by 2025, representing a CAGR of 10.6%. This rapid growth warrants the need for trained data experts. According to the Jobs of Tomorrow report published by the World Economic Forum in 2020, data and AI exhibited the highest annual growth, at 41%, among the top seven emerging professions.
Several industries have come to value data analysis and are facing an acute shortage of trained data scientists. A study conducted by QuantHub showed that there was three times the number of data science job postings than the number of job searches in 2020. Around 35% of the respondents of the study also said that they faced the most difficulty finding people with the right data science and analytics skills, making it the second most challenging area for hiring, after cybersecurity.
Cost of Data Science Bootcamps
An in-person data science program can be quite expensive, starting at around ₹11 lakhs. An online course will be more cost-effective. You can also ask your company to pick up the cost of the course, in case they have unmet demand for data scientists. If cost is an issue, you can choose a program that allows you to pay in easy monthly instalments. This way, you can make small payments every month, rather than organising a lump sum before beginning the course.
If you are interested in learning data science but are worried about the cost, consider GeekLurn, which has partnered with NASSCOM and IBM to offer world-class training. The Data Science Architect Program offers a research project scholarship of up to ₹2 lakhs. EMI options are available for making fee payments. These range from 12 to 48 months, and you have the option of beginning the EMIs after securing your placement at the end of the course. You get:
- Guidance from corporate specialists who are industry experts
- Cohesive learning network with workmates and mentors
- Placement guarantee with a one-of-a-kind learning program
These features make it the best bootcamp for data science. At the end of the 24-month program, you will have expertise in Statistics and Probability, Machine Learning and Python, Time Series Analysis, Model Selection and Boosting, Dimensionality Reduction and Data Manipulation.
Can I Calculate My Data Science Bootcamp ROI?
A data science programming bootcamp is a great choice for those who wish to kickstart their career. Here’s a look at how to check the ROI (return on investment) to help you make a more informed decision regarding whether this is the right career choice for you.
To calculate the ROI, first consider your current financial status. Make a note of your current monthly income and deduct your monthly expenses and taxes. Now look at the total time and money you will spend on completing the program. Take into account all the costs. For instance, if you decide to pursue an online course, you may need a more powerful internet connection and a laptop. Finally, consider your salary expectations after completing the program. If you choose a course that does not offer 100% job placement, then you need to consider the time you would take to find a job.
Common Technologies Taught at Data Science Programming Bootcamp
The technologies taught at the best bootcamp for data science are different from those you would learn pursuing a traditional coding program. Here’s a look at the most common technologies taught at a data science programming bootcamp.
SQL: Structured Query Language (SQL) is commonly used for processing traditional databases. It mainly involves extracting data for analytics and reporting.
Hadoop: This is an open-source framework from Apache and is used to process and analyse massive amounts of data. It is a suite of technologies for distributed processing of large datasets across clusters of computers. It includes the Hadoop Distributed File System (HDFS), where the files are broken into blocks and stored in nodes over the distributed architecture. It also includes Map Reduce for running programs in parallel and the Hive database for querying data in a cluster.
Spark: This has also been developed by Apache and is designed for the distributed processing of big data workloads. It uses in-memory caching and optimised query execution for fast processing. Compared to Map Reduce, Spark has proved to have higher efficiency on several problems. It includes a machine learning library known as mllib. Spark can be used with R, which is why it is very popular among data scientists.
Python and R: Both these are standard languages used by data scientists. Python is used in computer science and R is popular for statistics. Both these domains are critical for data scientists.Machine Learning: This encompasses sets of algorithms that are capable of analysing massive volumes of data to make predictions of future events. The algorithms are programmed to continue learning from past data, such that the performance or results keep improving over time.