This is a quick crash course video where we’ll talk about business, data science, and how

the two go hand-in-hand.

Welcome!

So…

"Business basics for data scientists?

What do I need that for?"

Let me explain.

Imagine the following: You’re a data scientist.

You thrive on maths and statistics, you’re confident in using SQL and Python, and have

some experience in data cleaning and visualization.

Plus, you’re no stranger to machine and deep learning, which, in your opinion, makes

you the perfect candidate for any high-paying data scientist job.

Maybe you’re a seasoned data scientist trying to break new ground.

Or you’re a novice who just completed an online certificate course to land an internship

at a prestigious data science consultancy.

Either way, you go to the interview, feeling like a winner.

You boast about all of your skills, explaining how you know 19 programming languages and

want to use them all and how you can apply the latest MFCC algorithm, with the enthusiasm

of a girl scout determined to sell all her boxes of Samoas cookies right then and there.

Judging by the impressed look on the interviewer’s face - you got the job!

But, in reality, here’s what the employer is thinking: “Awesome, but I don’t have

a job for another run-of -the mill data scientist.

I need somebody who understands that data is business, who knows how to solve complex

data problems, and share their insights with the management.”

And that’s exactly why you should watch this video.

We’re giving you 5 key business basics that will show you how to work with data to reach

practical business solutions.

Because today, dealing well with data is table stakes for any company to stay in the game.

It means innovation, productivity growth, and richer customer insight.

And helping a company ensure these will make you successful as a data scientist.

So, here they are!

Starting with Number 1: Understanding business objectives.

Data scientists must understand the strategic goals of the company and use them as guidance

for the whole data collection and interpretation process.

This guarantees that the analytics you provide will ensure the competitive edge of your company.

Nota bene - always keep in mind your audience.

Is the data information for internal use by the board of directors or the sales managers?

Or is it for external use by capital markets or suppliers?

Each audience has different needs, even if the overall strategic objective is the same.

Once you’ve identified your audience, make sure you provide the answers to their performance-related

problems.

Ok.

But how do I do that?

Well, make yourself familiar with the concepts of Key Performance Questions (KPQs) and Key

Analytics Questions (KAQs).

Both allow you to contextualize performance data and derive actionable knowledge form

it.

KPQs revolve around how well your company is performing in achieving certain goals.

For example: “How well are we promoting our services?”.

Or “To what extent are we attracting new profitable customers?”.

KAQs, on the other hand, aim to narrow down the strategic choices for achieving a goal.

For instance, “How do customers click through our website?” or “Who are going to be

our most profitable customers?”

“What about business intelligence tools and other IT systems?” the nay-sayers might

ask.

Unfortunately, in most companies, BI tools are driven more by the information on hand,

than by the information that will actually lead to the best business decisions.

This could put any company at a major disadvantage.

That’s why it’s important to discover what knowledge the recipient needs first and

use the tools accordingly, as opposed to applying the tools and then deciding on the information

needs they could possibly fulfill.

Alright!

Moving on to Number 2: Collecting the right data.

A senior data scientist must ensure the team under their lead collects and organizes relevant

and useful data.

So, it’s crucial to know if the necessary data is already stored in the organization

and in what formats– numerical or non-numerical, such as images, text, or sound.

That will help you establish the company’s methodologies for collecting additional data

- quantitative for numerical data or qualitative for non-numerical data.

Quantitative data is collected automatically from operations, or via surveys and questionnaires.

It’s easy to analyze and represent visually.

However, to provide more richness and context, a company can’t do without qualitative data.

Its analysis uncovers the factors influencing certain behavior, like customer satisfaction

or customer churn.

Qualitative surveys, focus groups, and peer-to-peer evaluation are some of the methods for collecting

qualitative data.

Other ways include analysis of click-through rate and engagement in social media

So, you have the data.

Now it’s time to interpret and contextualize it to extract valuable information.

Number 3: Analyzing the data.

Meaningful analysis is crucial for effective decision-making.

As we already mentioned, BI tools are not sufficient for a great analysis per se.

Still, they can play an important role in various types of other analyses.

For example, Online Analytical processing, a.k.a. OLAP, which provides numerous dimensions

to look at data.

Or data mining which correlates various factors.

And, of course, text mining, used to extract, analyze, and summarize information from large

text datasets.

BI software also provides data scientists with interactive drill-down and rich graphic

capabilities, and the ability to perform root-cause analysis.

What if you need to view data from different perspectives?

Then multidimensional technology comes into play.

Using data models, it helps to make decisions based on consolidated summarized business

information from various sources.

Basically, we can say that all is fair in love, war, and data analysis.

So, don't shy away from taking advantage all tools available, as long as you use them smart

to reach relevant and actionable insights.

Number 4: Communicating the data effectively.

To prepare a clear and compelling presentation of your insights, you need to use different

types of charts and graphs, such as tally charts, histograms, scatter plots, etc.

However, for truly informative and engaging data storytelling, use graphs and narrative

together.

This will help your audience see the big picture and derive business value from the collected

data.

How to make sure that the valuable insights won’t be overlooked?

Bernard Marr, a renowned strategic performance consultant”, suggests 4-steps to powerful

and strategically relevant reports: • Frame the report with KAQs and KPQs;

• Support the KAQs and KPQs with suitable and informative graphs and charts;

• Use headings to capture the key insights; • and narratives to provide context for

the visuals.

If you opt for a dashboard representation, be mindful of some common design mistakes,

such as supplying inadequate context for the data, cluttering the display with useless

decorations, or arranging the data poorly.

You believe a data scientist’s job ends with packaging the information and presenting

it to stakeholders?

Think again.

Truth is, if you’re serious about your career, you should be aware of Number 5.

Understanding how evidence-based decisions are made.

The best data scientists make sure that their insights will become the basis for actionable

steps.

As a data scientist, you can have a strong impact on the company’s desire to learn

and improve.

And, sometimes, it will be up to you to inspire accommodation of analytical capabilities throughout

the organization.

Or initiate implementing an appropriate IT infrastructure.

So, embrace your data science power and use it for good!

That wraps up our list of 5 business basics that will help you on your data science career

path.

You can use them as a stepping stone to build up on your business know-how or expand your

knowledge with some relevant books.

Or take an online business course to improve your skill set and make your resume stand

out.

Well, if you happen to be watching this video while waiting to be called in your next interview,

don’t despair.

You still have a few minutes to repeat the basics 5 times as a data science mantra, take

a deep breath, exhale, and enter with the unshakeable confidence of a master data scientist

who truly knows their stuff.

Good luck!