The interpreted programming language Python has surged in popularity in recent years. Long beloved by system administrators and others who had good use for the way it made routine tasks easy to automate, it has gained traction in other sectors as well. In particular, it has become one of the most-used tools in the discipline of numerical computing and analysis. Being put to use for such heavy lifting has endowed the language with a great selection of powerful libraries and other tools that make it even more flexible. One upshot of this development has been that sophisticated business analysts have also come to see the language as a valuable tool for those own data analysis needs.

Greatly appreciated for its simplicity and elegance of syntax, Python makes an excellent first programming language for previously non-technical people. Many business analysts, in fact, have had success growing their skill sets in this way thanks to the language's tractability. Long beloved by specialized data scientists, the iPython interactive computing environment has also attracted great attention within the business analyst’s community. Its instant feedback and visualization options have made it easy for many analysts to become skilled Python programmers while doing valuable work along the way.

Using iPython and appropriate notebooks for it, for example, business analysts can easily make interactive use of such tools as cohort analysis and pivot tables. iPython makes it easy to benefit from real-time, interactive researches which produce immediately visible results, including charts and graphs suitable for use in other contexts. Through becoming familiar with this powerful interactive application, business analysts are also exposing themselves in a natural and productive way to the Python programming language itself.

Gaining proficiency with this language opens up further possibilities. While interactive analytic techniques are of great use to many business analysts, being able to create fully functioning, independent programs is of similar value. Becoming comfortable with Python allows analysts to tackle and plumb even larger data sets than would be possible through an interactive approach, as results can be allowed to accumulate over hours and days of processing time.

This ability can sometime allow business analysts to address the so-called "Big Data" questions that can otherwise seem the sole province of specialized data scientists. More important than this higher level of independence, perhaps, is the fact that this increased facility with data analysis and handling allows analysts to communicate more effectively with such stakeholders. Through learning a programming language which allows them to begin making independent inroads into such areas, business analysts gain a better perspective on these specialized domains, and this allows them to function as even more effective intermediaries.

Related:

Who Are the Main Players in Big Data?

There are normally two sides to the story when it comes to employment. On one hand, employers hold the view that the right candidate is a hard find; while on the other, job hunters think that it’s a tasking affair to land a decent job out there.

Regardless of which side of the divide you lay, landing good work or workers is a tedious endeavor. For those looking to hire, a single job opening could attract hundreds or thousands of applicants. Sifting through the lot in hope of finding the right fit is no doubt time consuming. Conversely, a job seeker may hold the opinion that he or she is submitting resumes into the big black hole of the Internet, never really anticipating a response, but nevertheless sending them out rather than sit back doing nothing.

A recruitment agency normally keeps an internal database of applicants and resumes for current and future opportunities. They first do a database search to try and identify qualified and screened candidates from their existing crop of talent. Most often the case, they’ll also post open positions online through industry websites and job boards so as to net other possible applicants.

When it comes to IT staffing needs, HR managers even find a more challenging process in their hands. This is because the IT department is one of the most sensitive in any given organization where a single slip-up could be disastrous for the company (think data security, think finances when the IT guys are working in tandem with accounts). You get the picture, right?

In the ever changing landscape of software programming, it is not surprising that developers and employees have a different set of preferences for desired skills.  However the number one language that developers want to learn according to a survey of developers by technical recruiter, Hacker Rank is Python. This is not a surprise considering that Python has been in demand for several years and programmers tend to really enjoy this language for clear syntax, good OOP support and great shortcuts. Python, named “the language of the year” in 2007 and 2010 in the TIOBE Index and has climbed to #4 status in May of 2018.

According to the study, employers want developers who:

-  Have problem-solving skills, such as the ability to break down large, complex problems.
- Are proficient in their programming language and debugging.
- Can design systems.
- Can optimize performance.
- Have experience in reviewing and testing code.
- Are proficient in database design

Surprisingly, formal education is not the deciding factor when it comes to what companies care about the most. People with computer degrees or certifications on a resume are not necessarily a first choice for hiring managers. Others that have years of experience even if those individuals are partially self-taught in the field stand to be taken seriously in the field.   For those individuals with a passion to learn and master a skill, there are ample opportunities with smaller to mid-sized companies.

Some interesting FAQ’s from the study:

    On average, developers know 4 languages, and they aspire to learn 4 more.
    Younger developers between 18 and 24 plan to learn 6 languages.
    Folks older than 35 only plan to learn and additional 3 languages.
    The top languages developers said they will learn were, Go, Python, Scala, Kotlin, and Ruby.
    There is a large gap between employers seeking developers that know React than there are folks that can do it.

So, Why Learn Python?
It is now the most popular introductory teaching language in U.S. universities.  Python is easy to use, powerful, and versatile, making it a great choice for beginners and experts alike. It allows you to think like a programmer and not waste time understanding difficult syntax that other programming languages can command. And, because of its rapid growth, many developers contribute to the Python community and share Python libraries making creativity that much more a reality

I’ll get straight to the point.  Why should companies invest more in management training?  Here are 10 simple reasons.

1) An employee’s relationship with his or her direct manager is the most important single factor in employee engagement.

2) Engaged employees are happier and more productive.  Disengaged employees are frustrated and more disruptive.

3) Because there’s no widely agreed-on skillset for management (good managers come in all shapes and sizes), there’s an assumption everyone knows how to do it.  This is akin to someone who’s never driven before being given keys to a car and told: “Drive.”  (Many many years ago, this is how I first learned to manage.  I blundered my way through it.  Trial and error.  It wasn’t pretty.)