Tools for Data Science

Course Introduction

Welcome to the course! 

You've begun one of the most complete overviews on data science tooling that you’ll currently 

find on the internet. 

This doesn’t mean that we cover each and every tool, but later in the course we’ll 

introduce a comprehensive list of tasks a data scientist needs to perform and give you 

the top two or three open source and commercial tools available to complete them. 

We also explain how the tools overlap in functionality, what their pros and cons are, and how these 

tools can address the whole data science pipeline. 

Let’s start with data. 

Data is obviously central to data scientists. 

In this course, we’ll show you how to manage, extract, transform, analyze, and visualize 

data. 

Now, you might be able to survive data science without programming skills if you use the 

right set of tools. 

However, we highly recommend getting familiar with programming and the related programming 

frameworks for data science. 

To help you along, we’ll introduce you to the most commonly used programming languages 

and frameworks available for data science. 

That said, there is a lot of automation available in the latest tooling that a data scientist 

can use. 

In this course, we’ll explain how to make use of those tools to save time and uncover 

inspiration. 

Visual programming is available in many tools. 

In this course, you’ll learn how visual programming can be used to speed up development 

time and to help non-programmers enter the field of data science. 

Open source software is leading the field of data science, but its total costs of ownership, 

or "TCO," can be higher at times due to configuration, customization and maintenance costs. 

As a result, commercial software also has its place, especially since the new generation 

of commercial data science software leverages open source software and open standards. 

This makes it easy to migrate between tools and can reduce overall TCO. 

In this course, we’ll introduce you to both open source and commercial software and point 

out their strengths and weaknesses for data science. 

We'll also show you ways that you can take advantage of their respective strengths. 

Finally, we'll show you how cloud computing can be used to further speed up and facilitate 

data scientists' work. 

We'll introduce you to the most commonly used and newly emerging cloud tools for data science. 

In addition to lectures, this course, has numerous labs to make you more familiar with 

the material and get hands-on experience. 

There are also multiple quizzes to test your learning. 

Nothing more to say than we’re glad to have you in the course and happy learning. 

In case you have trouble in any way, please don’t hesitate to contact us in the discussion 

forum. 

There's nothing left but to begin! 

We're very happy to have you with us as you start your data science journey. 

If you have any trouble with any of the course material, please don’t hesitate to contact 

us in the discussion forum. 

Let's get started!

Languages of Data Science

The languages of Data Science 

For anyone just getting started on their data science journey, the range of technical options 

can be overwhelming. There is a dizzying amount of choice when it comes to programming languages. 

Each has it's own strengths and weaknesses and there is no one right answer to the question 

of which one you should learn first. The answer to that question depends largely on your needs, 

the problems you are trying to solve, and who you are solving them for. 

Python, R, and SQL are the languages that we recommend you consider first and foremost. 

But there are so many others that have their own strengths and features. 

Scala, Java, C++, and Julia are some of the most popular. 

Javascript, PHP, Go, Ruby, and Visual Basic all have their own unique use cases as well. 

The language you choose to learn will depend on the things you need to accomplish and the 

problems you need to solve. It will also depend on what company you work for, what role you 

have, and the age of your existing application. We’ll explore the answers to this question 

as we dive into the popular languages in the data science industry. 

There are many roles available for people who are interested in getting involved in 

data science. Business Analyst 

Database Engineer Data Analyst 

Data Engineer Data Scientist 

Research Scientist Software Engineer 

Statistician Product Manager 

Project Manager and many more. 

Let’s dive into what we will learn in Lesson 1. We will put most of our focus on the top 

three Data Science languages: Python, R, and SQL, which each have their own lessons. Then 

we will go on to highlight other noteworthy languages and what makes them special. Then 

we’ll finish with a short quiz to test your knowledge! 

● 1.1.1 - Python ● 1.1.2 - R 

● 1.1.3 - SQL ● 1.1.4 - Other Noteworthy Data Science 

Languages ● 1.1.5 - Practice Quiz

Machine Learning Models

Graded Quiz

Latest Submission Grade 100%

1.

Question 1

Which are the three most used languages for data science? (Select all that apply.)

1 / 1 point

Java

Scala

Python

Correct

R

Correct

SQL

Correct

2.

Question 2

Is it possible to use machine learning within a web browser with Javascript?

1 / 1 point

Yes

No

Correct

3.

Question 3

Comma Separated Values (CSV) is a commonly used format to store:

1 / 1 point

Hierarchical or network data

Tabular data

All of the above

Correct

4.

Question 4

Classification models can be used to determine whether:

1 / 1 point

An email is likely spam.

A video contains a specific sound.

An image contains a dog.

All of the above.

Correct

5.

Question 5

Fill in the blank: ________________ is the heart of every organization.

1 / 1 point

Integration

Open source

Data

The cloud

Correct

6.

Question 6

What does the “BI” in BI Tools stand for?

1 / 1 point

Build Information

Business Intelligence

Build Integration

Business Integration

Correct

Practice Quiz - RStudio IDE

Total points 3

1.

Question 1

Which of the following functions does RStudio unify? (Select all that apply.)

1 / 1 point

Storing of data.

Editing and execution of source code.

Correct

Display of the R Console.

Correct

Visualization of plots.

Correct

Visualization of data in table form.

Correct

2.

Question 2

Which statement is true about the RStudio IDE?

1 / 1 point

RStudio is a commercial product of IBM.

RStudio is free and open source.

Correct

3.

Question 3

Which statement about R packages is correct?

1 / 1 point

R doesn't require any packages to be installed since it contains all functionality necessary which a data scientists ever requires.

R currently supports more than 15,000 packages which can be installed to extend R's functionality.

Correct

Practice Quiz - GitHub

Total points 3

1.

Question 1

Which of the following statements are true? (Select all that apply.)

1 / 1 point

Git is a system for version control of source code.

Correct

Git is very useful for data science as well, since data science often involves a lot of source code to be written and managed.

Correct

Git is an integrated development environment for data science.

2.

Question 2

Which of the following statements about repositories are correct? (Select all that apply.)

1 / 1 point

The remote repository is only accessible by myself.

The local repository is only accessible by myself.

Correct

The staging is only accessible by myself.

Correct

The remote repository is accessible by all contributors.

Correct

The local repository is accessible by all contributors.

3.

Question 3

What is the best process for contributing a bugfix to a foreign repository?

1 / 1 point

Ask the repository owner for write access to the repository.

Fork the repository, update the fork and create a pull request.

Send the fix via email to the author.

Correct

Graded Quiz

Latest Submission Grade 100%

1.

Question 1

What tool do most Python developers use?

1 / 1 point

RStudio

Jupyter Notebooks / JupyterLab

Correct

2.

Question 2

True or false? Jupyter Notebooks / JupyterLab support development in R.

1 / 1 point

True

False

Correct

3.

Question 3

Which tool unifies documentation, source code and data visualizations into a single document?

1 / 1 point

VSCode

Notepad

Jupyter Notebooks / JupyterLab

Correct

4.

Question 4

Which command is used to install packages in R?

1 / 1 point

install.packages("package name")

install("package name")

package("package name")

install.package("package name")

Correct

5.

Question 5

True or False: The Jupyter Notebook kernel must be installed on a local server.

1 / 1 point

True

False

Correct

6.

Question 6

True or false? RStudio supports development in Python.

1 / 1 point

True

False

Correct

Final Exam

Latest Submission Grade 83.33%

1.

Question 1

SQL is what type of query language?

1 / 1 point

Cypher

Database

Contextual

Access

Correct

2.

Question 2

Is Keras a machine learning or deep learning library for Python?

1 / 1 point

Yes

No

Correct

3.

Question 3

Generally speaking, which type of model is used to predict a numerical value, such as the potential sales price of a used car?

1 / 1 point

Clustering model

Regression model

Classification model

Correct

4.

Question 4

What is data governance?

0 / 1 point

Deleting data stored within a central repository

Creating processes and controls around the access of data

An implementation of data processes and controls

Storing data in a central repository

Incorrect

5.

Question 5

Which are the two most used open source tools for data science?

1 / 1 point

Notepad

RStudio

Correct

Jupyter Notebooks / JupyterLab

Correct

Spyder

VSCode

6.

Question 6

What tool do most R developers use?

1 / 1 point

Jupyter Notebooks / JupyterLab

RStudio

Correct

7.

Question 7

What type of environment is RStudio?

1 / 1 point

API

Framework

Software Development Kit (SDK)

Integrated Development Environment (IDE)

Correct

8.

Question 8

Which of the following statements about Jupyter Notebook is correct?

0 / 1 point

Jupyter Notebook is only available if installed locally on your computer.

Jupyter Notebook provides storage of massive quantities of data in data lakes.

Jupyter Notebook supports the Visualization of data in charts.

Jupyter Notebook is a commercial product of IBM.

Incorrect

9.

Question 9

Which feature in Watson Studio helps to keep track of and discover relevant Machine Learning assets?

1 / 1 point

All of the above

AutoAI

Watson Knowledge Catalog

Modeler Flows

OpenScale

Correct

10.

Question 10

What are some of the Data Refinery abilities when working with data?

1 / 1 point

Analyzes data but takes longer to prepare

Analyzes data but cannot be saved for future use

Analyzes and transforms data quickly

Can only analyze small amounts of data at a time

Correct

11.

Question 11

What type of node is used to prepare the data for modeling in Modeler flows?

1 / 1 point

A data source node

All of the above

A modeling node

Auto data prep node

A type node

Correct

12.

Question 12

What does SPSS stand for?

1 / 1 point

Statistical Package for the Social Society

Static Package for the Social Sciences

Statistical Packing for the Social Sciences

Statistical Package for the Social Sciences

Correct