by Javaid Iqbal
Learning outcomes for graduates in Kashmir is intractable low. Quality concerns around education are seldom seen as a political priority, too many engineers and diverse professionals are waving around degrees that are relatively worthless, but we cannot ignore these for much longer, especially considering the changing job market because of Covid-19. India will have the highest number of secondary school graduates in South Asia by 2020, but nearly half of them will lack the skills to enter the job market.
This is the time we need to focus our time and energy on how to make our student’s data literate. Demand for skilled data scientists continues to be sky-high, with IBM recently predicting that there will be a 28% increase in the number of used data scientists in the next two years. Employers are waking up to the evidence that employees with an understanding of how to handle data and analytics to solve business problems are increasingly valuable, whatever their education or position in an organization.
To be data literate, the first step involves learning languages such as Python and R. Python is a language suggested to people who choose to work in the Big Data or data science fields. As an open-source programming language, Python is free, which makes it tempting to startups and smaller businesses. Students can learn Python from Corey Schafer free on YouTube and can also learn it on Kaggle. Automate the boring stuff with python book is accessible freely on the internet and would support students in learning the complexities of the language efficiently.
Why Our Education System Must Change?
Anyone who wishes to work as a data analyst also requires to learn Statistics and Khan Academy is one of the best sources for it. There is no better programming language for a statistician to learn than R. It is a universal skill among Big Data analysts, and data scientists experienced in R are pursued after by some of the biggest brands, including Google, Facebook, and Reliance. Also, R’s financial applications increase by the minute, and companies appreciate its adaptability. Students can learn R from Dr Rai who is an authority in the field and has free videos on YouTube.
Students can also complete a Data Science Specialization course on Coursera which provides one of the longest-established online data science disciplines, through Johns Hopkins University, they waive the fee for students who don’t have the monetary resources possible. It contains 10 courses which include statistical programming in R, cluster analysis, natural language processing, and practical applications of machine learning. To complete the program, students make a data product that can solve a real-world problem.
Data-Driven Decision Making is another course that graduates should complete, this course is provided by PwC so unsurprisingly focuses more on business operations than theory. It covers the range of tools and systems which are being used by organizations today to deal with data demands and the positions that data specialists can fill in contemporary businesses. The four-week course concludes with a task involving deploying a data solution in a simulated business environment.
Another language the graduates should learn is the Structured Query Language. It is a programming language specifically created to work with databases. It allows you to set up, manoeuvre, and distribute data, specifically data from relational database management systems. Despite being comparatively modest in its design, SQL can be powerful while computing very complex tasks. SQL is easy to learn and students should learn it to improve the chances of getting a good job.
Once students learn all these languages and are ready to take the next step, they should learn machine learning. Machine Learning is an application of Artificial Intelligence. It allows software applications to become perfect in anticipating results. Machine Learning focuses on the development of computer programs, and the principal purpose is to support computers to learn automatically without human interference. Machine learning emerges from artificial intelligence and the application of pattern recognition. Today, when excessively huge amounts of data are being dealt with every day, pattern recognition is something that benefits large businesses and websites work splendidly with the users. Learning Machine learning basics will help students in understanding how complex technological organizations work.
Reading blogs such as towards data science and machine learning on the medium will help augment knowledge of data among students.
These courses will help students gain problem-solving skills. The capability to reflect analytically and approach questions in the appropriate way is an expertise that is constantly useful, not just in the professional world, but in normal life as well. With a looming skills shortage on the horizon as more and more corporations and sectors start engaging with big data, this value is only going to increase. In pragmatic terms, this means graduates with analytics skills will be able to command greater incomes and enjoy their choice of vacant jobs. These competencies are an indispensable part of any career, and with the extra benefit of being a significant part of an organization’s decision-making processes, analytics experts often pick up strong leadership techniques efficiently.
This was the concluding part of the two-part series. Here is the first part
(The author Javaid Iqbal is a Global Fellow at Brandeis University and also a Fellow at Institute for Economics and Peace)
Great Information. Thanks for sharing