We are not all equally motivated. Some people have more self-drive than other people. This is why we find that some people always end up at the top even when the odds are against them. An employee, with this realization, through the Human Resource department, should be able to design efficient career development systems. For this system to work, the employer must understand the nature of the business environment in which they are operating.

Why Train Employees?

The purpose of training employees is to enable them to grow with time and increase their efficiency. The business world is quite dynamic, nothing stays the same for long. Training one’s employees allows them to keep abreast with the ever changing technological advancements and many other factors that are relevant to his/her line of work. Employees cannot be expected to solve all their employer’s expectations with static skills and techniques. Even the most updated technology becomes obsolete at some point.
People are the biggest assets in organizations. For an establishment to flourish, it is important that the employer understands certain key things that help spur their development.

In a report from the Harvard Business Review, “The Impact of Employee Engagement of Performance,” the most impactful employee drivers are:

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?

A string in Python is enclosed in either single or double quotes.  Therefore, either one does the trick.  A common practice is to place single words with no characters that can be interpolated in single quotes and multi-word strings that contain interpolated characters in double quotes.  This may be a carry over from Perl where interpolated characters are in double quotes. 

If you do not want to interpolate a string, use a raw string ... r"\n".  With the exception of the last print statement, each of the print statements prints hello on a separate line from how are you?.  They are great for regular expressions.

Finally, triple double quotes """ some message about a function or class ... """ are used for docstrings.

print "hello \n how are you?"
print 'hello \n how are you?'
print r"hello \n how are you?"

Let’s face it, fad or not, companies are starting to ask themselves how they could possibly use machine learning and AI technologies in their organization. Many are being lured by the promise of profits by discovering winning patterns with algorithms that will enable solid predictions… The reality is that most technology and business professionals do not have sufficient understanding of how machine learning works and where it can be applied.  For a lot of firms, the focus still tends to be on small-scale changes instead of focusing on what really matters…tackling their approach to machine learning.

In the recent Wall Street Journal article, Machine Learning at Scale Remains Elusive for Many Firms, Steven Norton captures interesting comments from the industry’s data science experts. In the article, he quotes panelists from the MIT Digital Economy Conference in NYC, on businesses current practices with AI and machine learning. All agree on the fact that, for all the talk of Machine Learning and AI’s potential in the enterprise, many firms aren’t yet equipped to take advantage of it fully.

Panelist,  Michael Chui, partner at McKinsey Global Institute states that “If a company just mechanically says OK, I’ll automate this little activity here and this little activity there, rather than re-thinking the entire process and how it can be enabled by technology, they usually get very little value out of it. “Few companies have deployed these technologies in a core business process or at scale.”

Panelist, Hilary Mason, general manager at Cloudera Inc., had this to say, “With very few exceptions, every company we work with wants to start with a cost-savings application of automation.” “Most organizations are not set up to do this well.”