This is kind of a special blog post, because here I’m sharing 3 videos from my brand new O’Reilly Video course – called Data Science Fundamentals for Marketing and Business Professionals. It has been published on Safari Books Online. There is a free 10-day trial period, so if this is your first time there, you can watch my 60-min course practically for free. (Then you can decide if you want to stay with them or not.)

I’ve decided to share 3 videos on as well, to help you decide whether you are interested or not. I created this course to clarify the basics of data science and analytics. I targeted it specifically to marketing and business professionals, but it’s general enough to be helpful to aspiring data scientists from any field.


VID#1 – Data Scientist or Data Analyst?


Statistics and mathematics are indispensable for Data Science and Analytics.
However it’s also true, that different data projects need different levels of statistical knowledge.

One of the most common questions I get from aspiring data professionals is:
“What’s the difference between a Data Analyst and a Data Scientist?”

First of all – you have to know that the term Data Science recently became a buzzword. Thus you can find a wide variety of data topics under the umbrella of Data Science – especially in online articles. Because of that, unfortunately there is no clear definition on how data science and analytics are different.

However you can see a clear pattern in job descriptions, that at least illustrates the small differences between Data Scientists and Data Analysts.

For instance when companies are hiring a Data Analyst, they are usually looking for a person who will be working on research projects, on optimization and on reporting. This person will help the company to understand their customer base and flag possible issues and future opportunities.

When a company is looking for a Data Scientist, it usually wants someone on board who’s good at predictive analytics and who has experience with machine learning and similar advanced methodologies. This knowledge can be useful for managing risk, for building recommendation systems, for optimising resources, for face recognition and many-many more things – depending on the profile of the given company.

As I see it, Data Analytics is usually mentioned as the conservative part of the data projects and it has a big effect on the business side – while Data Science is more progressive and it can even have an effect on the product itself.
Note that – in my opinion at least – both of these roles are equally important.

I’ll give you a concrete example. Let’s say, we have a video sharing portal. Our Data Analytics team will create reports on how many video uploads we received, and who are our best users; they might also do A/B tests to find the best placement of the UPLOAD button.
On the other hand the Data Science Team will build the algorithms behind the recommendation system that autostarts the next video – and they might do churn predictions too.

As you can see the line is blurry, but this is the high-level concept.

In terms of required coding skills, business skills and domain knowledge, the difference between a Data Analyst and a Data Scientist is not substantial. It’s good to know though, that a Data Analyst – as it’s closer to the business part – might need better business skills, while a Data Scientist, who has to implement complex methods, might need better coding skills.
However the biggest contrast between the two is mostly in the statistical and mathematical methods that they apply during their data projects.

Let’s check out that what statistical tools Data Analysts and Data Scientists need!

VID#2 – SQL demo


If you want to be a data analyst or a data scientist: SQL is a must.
Almost every company is using it.
Let’s take a look at a simple SQL query:

SELECT * FROM zoo WHERE animal = ‘zebra’;

This query is almost fully understandable if you simply follow common sense.

We want to SELECT – everything – FROM – the data table that is called zoo – and we want to see every row WHERE the animal that we are talking about is a ZEBRA.

Alright. Let’s see, what working with SQL looks like.
I’m using a free SQL query tool called SQL Workbench.

The first thing I’ll do is query my whole data table:


Again: the asterisk refers to “everything”. You can see all the rows and columns of my data table. There are different animals like zebras, elephants or tigers in it. Each of the animals has a unique identifier and one additional piece of information: water need.

If we want to filter for zebras only, on the top of our base query we have to type:
SELECT * FROM zoo WHERE animal = ‘zebra’;

With that we made sure that only those rows are showing up, where the field in the animal column is ‘zebra’.

But let’s say, that we don’t want to select all the columns anymore! We need only the water needs. For that, I am going to change the asterisk to the name of the column: water_need

SELECT water_need FROM zoo WHERE animal = ‘zebra’;

Note that as the WHERE clause is still there, I got back the water_need information only for my zebras. Perfect!

At last let’s summarize the water needs for all the zebras.

SELECT SUM(water_need) FROM zoo WHERE animal = ‘zebra’;

There you go! 1720 is the water need of all the zebras in my zoo.

And this is how SQL works: you simply select a piece of information from a table. It can be raw data like unique id or water need. And it can be an aggregate, like SUM or AVERAGE – in these cases SQL does the calculation for you. You can also filter for specific subsets of your data.

You might have noticed that SQL has some very specific syntax rules too! Did you see the semicolon at the end of my queries? That’s a must in SQL. If you miss it, your query won’t run properly!
This semicolon gives you freedom as well. For instance: you can break your query into more lines without any trouble. You can also add as many spaces as you want to. These will help you make your code more readable and easier to change.

For instance my previous query can look something like this too:

animal = ‘zebra’;

If I re-run it: the results won’t change.

Of course this is only the tip of the iceberg – and there is much more in SQL, but at least now you got a taste of how the syntax looks. Not scary at all, right?

(Note: learn SQL for Data Analysis through my tutorial articles!)

VID#3 – Intro


I’ll cover four topics in this course. The first three – coding, statistics and business thinking – are the 3 pillars of data science and analytics. Then I’ll have an extra chapter focusing on how to go further on the road you have just started on by taking this course.

I’ll show you demos, examples based on real life scenarios and will share some of my best practices that I’ve collected during my years working as a data analyst. Additionally – for each chapter I’ll give you a comprehensive list of specific resources for further learning.

By the end of this course you will understand:

    1. what data scientists and analysts do, how they work, and how they think
    1. what tools and data languages are essential for data science and analytics
    1. what soft skills are essential
    1. how to communicate better with data professionals
  1. and how to determine the first steps toward becoming a data professional

I hope you will enjoy it!

Watch the full course here: Data Science Fundamentals for Marketing and Business Professionals

Read more here: the 4 untold truths of learning data science.

Tomi Mester