Introduction to Data and Data Science

4.8/5

(5724)

Introducing you to the field of data science and building your understanding of the key data science terms and processes.

22 lessons 2h

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Course Overview

Working with data is an essential part of maintaining a healthy business. This course will introduce you to the field of data science, help you understand the various processes and distinguish between terms such as: ‘traditional data,’ ‘big data,’ ‘business intelligence,’ ‘business analytics,’ ‘data analytics,’ ‘data science,’ and ‘machine learning.’

22 High Quality Lessons

17 Practical Tasks

2 Hours of Video

Certificate of Achievement

Skills you will gain

Career developmentdata analysisProgrammingTheory

What You'll Learn

You are interested in starting a career in data science? Begin with the very basics by receiving the proper introduction to the world of data with this course!

Distinguish between various data science related fields

Understand the terms traditional and big data

Integrate common data science techniques

Use specific data science tools

What are the most common data science programming languages

Become familiar with data science job positions and alternatives

Curriculum

The Different Data Science Fields Free

6 Lessons 32 Min

For a novice, the data science field can be rather confusing. It takes a while to make sense of all the buzz words and different areas of data science. In this section, you will learn how to distinguish between business analytics, data analytics, business intelligence, machine learning, and artificial intelligence. We will discuss all of this with the help of a specially-designed infographic, and by the end of the section, you will know exactly where data science fits into today’s society.
Course Introduction Free Why are there so many business and data science buzzwords? Free Analysis vs Analytics Free Intro to Business Analytics, Data Analytics, and Data Science Free Adding Business Intelligence (BI), Machine Learning (ML), and Artificial Intelligence (AI) to the picture Free An Overview of our Data Science Infographic Free
The Relationship between Different Data Science Fields Free

1 Lesson 7 Min

In this section, you will learn how data science fields relate to each other and which ones leverage on traditional and big data, business intelligence, or traditional data science methods and machine learning.
When are Traditional data, Big Data, BI, Traditional Data Science and ML applied? Free
What is the Purpose of each Data Science field Free

1 Lesson 4 Min

Here, you will learn not only which are the various data science disciplines, but also what each discipline is used for in practice. This is really valuable for you, as it will allow you to gain an idea of the practical application of the different methods you will learn later on in our program.
Why do we Need each of these Disciplines? Free
Common Data Science Techniques

11 Lessons 54 Min

There are different ways to approach traditional data, big data, business intelligence, traditional data science methods, and machine learning. In this part of the course, we will introduce you to some of the most common techniques to do that, and we will provide several practical examples that will make things easier and more relatable.
Traditional Data: Techniques Traditional Data: Real-life Examples Big Data: Techniques Big Data: Real-life Examples Business Intelligence (BI): Techniques Business Intelligence (BI): Real-life Examples Traditional Methods: Techniques Traditional Methods: Real-life Examples Machine Learning (ML): Techniques Machine Learning (ML): Types of Machine Learning Machine Learning (ML): Real-life Examples
Common Data Science Tools

1 Lesson 6 Min

Before we dive in to studying the different types of tools used in data science, we will provide a quick overview for you, so you can have a good idea of why we are studying different tools and how they interact with each other. This will greatly facilitate your learning process, as you will already know what to expect and which tools will be necessary for a specific task.
Programming Languages & Software Employed in Data Science - All the Tools You Need
Data Science Job Positions: What do they Involve and What to Look out for?

1 Lesson 3 Min

In this section, we will discuss several job positions related to the fields of data and data science, including what responsibilities they’re comprised of and what to look out for when choosing your path.
Data Science Job Positions: What do they Involve and What to Look out for?
Dispelling common Misconceptions

1 Lesson 4 Min

We will conclude our Intro to Data and Data Science training with a lesson that dispels the most common misconceptions about the field of data science.
Dispelling Common Misconceptions

Student feedback

4.8/5

5724 ratings

5 stars

4835 (84%)

4 stars

758 (13%)

3 stars

107 (2%)

2 stars

15 (0%)

1 star

9 (0%)

Daniel

12.07.2022

Much of the information seemed overwhelming and not important at the same time. Personally, I felt the semantics of the definitions (data scientist vs data analyst) to be very boring and not particularly useful, and therefore could not remember the difference between the terms. The structure I like. The videos and explanations were clear. I enjoyed the course more when it went more into the graphs and process of machine learning. The questions just had to do mainly with memorization. The brief videos were not enough to engrain the terms and subtle differences into my memory. I failed almost all the quizzes the first time and had to retake them to get a passing grade. Maybe an aniki flashcard setup to prep for the quizzes would be a solution to get the buzzwords to stick in memory. I think I'll like the rest of the course better when the topics are more interesting and get more into specifics.

Element Zeroth (ElementZeroth)

02.11.2022

I really like the course, but maybe it is just the choppy audio (the voice is fine just the sudden start and start) or maybe it is just me and I am not in learning mood as (I am kinda tired) I am a little bored. Though, this is the introductory course so it isn’t to the interactive part of it yet, so that might be a thing which is fine. Without a foundation, how can you build a multistory house? I say 4 stars for several reasons. Not because of the course, though. Here is one example: Some people will not look at 5 stars and because of possible bias (and kinda the same with 1 stars) they will look at 2-4 star reviews first. (I am not saying this is a majority, just some). The course’s info-graph itself is going to be extremely helpful for remembering the terms, that doesn’t include the breakdown of the info graph and other information in the course.

lnp

07.11.2022

In some questions the context was not immediately obvious to me - are talking about this info-graphic or the previous one? why is sales forecasting not ML? etc.. I have a feeling, that I would need more quiz questions to really solidify the new knowledge - for example I can repeat the examples about qualitative and quantitative analysis from part one, but I'm not really sure I got the principle right. It's really good that the course points out several times, that in many places these terms are being used differently - they really were in my previous company and it pops out in random places... I'm happy to be show other perspective.

Noor-ul-Ain Razzaq

09.11.2022

Good course for understanding data analysis 👍

leclere

04.11.2022

+: the content is well structured. - : the explanation is based on the flow of the data. the table brings several dimensions to this flow. all dimensions are not as important. it would be clearer with a graph with data associated to it than with a table: there is a lot of type of relations that are not very specific and fit in a table, and that are a lot clearer with another presentation. maybe it's a question of taste.