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Data Science can seem overwhelming because of its vastness. But if you map out a learning path, acquiring the necessary skills is not a difficult task. In this video, we'll build a learning path through Data Science, starting with programming and math, moving on to machine learning and data foundations, and rounding things out with domain expertise.
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Welcome to How to Learn Data Science.
0:00
In this workshop, we'll build a learning
path through data science with treehouse.
0:03
Let's get started.
0:08
Data science is the field of study
that combines programming, math,
0:10
and domain expertise to extract
meaningful insights from data.
0:15
The phrase domain expertise refers to
having knowledge in a particular field,
0:20
for example, medicine,
finance, or agriculture.
0:26
The AI revolution is changing
the way we interact with technology.
0:30
Experts believe that as the world trains
its focus on creating intelligent
0:34
machines.
0:39
but ways that humans interact with
data science will rapidly evolve.
0:39
This means that as a developer,
understanding data science can provide you
0:44
with an array of possibilities for
your career.
0:48
It's an extraordinary time to
start learning data science,
0:51
but you may be asking, where do I start?
0:56
Data science can seem overwhelming
because of its vastness.
1:00
But if you map out a learning path,
acquiring the necessary skills is not
1:03
a difficult task will present
one of many possible paths.
1:08
Remember, your learning
path is personal to you.
1:12
Everyone is unique and
takes a different approach to learning.
1:15
It doesn't matter where you start.
1:19
The important thing is that you
actually start the journey.
1:21
A smart path to learning data
science begins with programming.
1:26
Learning programming languages is an
essential step in learning data science.
1:31
Programming equips data scientists with
the tools needed to perform practical
1:35
data science work.
1:40
Those new to programming can get started
with two relatively easy programming
1:42
languages.
1:46
Python's simplicity and readability
makes it a great choice for beginners.
1:47
Its rich set of libraries provide data
scientists with a powerful toolkit for
1:52
performing tasks like data manipulation,
analysis, and visualization.
1:57
Sequel is a domain-specific
language designed for
2:02
managing and manipulating data.
2:05
Almost all organizations
store data in databases,
2:07
making SQL a critical language for
data scientists.
2:11
Starting with these two versatile and
2:15
easy-to-learn languages is a great way
to begin your journey in data science.
2:17
The next step on our path is mathematics.
2:23
Math serves as a fundamental pillar
in the field of data science,
2:27
providing essential tools and concepts
that underpin the entire discipline.
2:31
Linear Algebra is used in machine learning
for tasks such as image processing and
2:36
natural language processing.
2:41
Key concepts to learn include matrix
manipulation and vector spaces.
2:42
Calculus is used in data science to
optimize machine learning models and
2:47
to calculate probabilities and statistics.
2:52
Calculus involves two main concepts,
derivatives and integrals.
2:54
Start by understanding these two concepts.
2:59
Probability and
statistics are used to analyze data and
3:02
to make decisions based on that data.
3:06
Learn the fundamental concepts of mean,
median,
3:09
and mode, variance, and
standard deviation.
3:12
These disciplines make up the foundation
of many data science techniques and
3:16
algorithms.
3:21
Without a strong mathematical foundation,
it's difficult to fully
3:22
understand how these techniques work and
to apply them in real world scenarios.
3:27
Machine learning is
the next stop on our path.
3:32
Machine learning is a subset of
artificial intelligence that focuses on
3:37
developing algorithms and
3:41
models that enable computers to learn from
data without being explicitly programmed.
3:43
Start by learning the basic concepts of
machine learning, such as unsupervised and
3:48
supervised learning, clustering,
and neural networks.
3:53
Machine learning algorithms have
a backbone of any machine learning
3:57
application.
4:00
They're used to analyze data,
identify patterns, and make predictions.
4:01
By using algorithms like linear and
logistic regression, and decision trees.
4:06
You can quickly identify patterns and
trends and
4:12
data that might not be
visible to the naked eye.
4:15
Then gain experience of machine
learning tools and libraries.
4:18
A few popular options are scikit-learn,
a Python library that includes
4:22
well-designed tools for performing common
machine learning tasks and PyTorch.
4:27
A Python based machine learning framework
that simplifies the development and
4:32
training of deep learning models with
its ease of use and flexibility.
4:37
Making it a preferred choice for
researchers and
4:41
practitioners in the field
of Artificial Intelligence.
4:44
Machine learning can be leveraged
to predict customer churn,
4:48
forecast demand for
a product and identify fraud and
4:51
financial transactions
in all of these fields.
4:55
And many more professionals with
the skills are in high demand.
4:58
Data foundations is a good next
step in learning data science.
5:03
Working with data is a fundamental
skill in data science,
5:09
allowing data scientists to extract
insights from data, identify patterns and
5:13
relationships, and
represent data in visual form.
5:17
Data scientists spend significant time
preparing data before analyzing it.
5:21
And this is where data wrangling and
manipulation become critical.
5:26
This involves techniques like cleaning,
filtering, and
5:30
sorting and transforming raw data
that's often messy and unstructured,
5:33
which can affect the accuracy and
validity of the analysis.
5:38
Once the data has been transformed into
a suitable format, it can be analyzed to
5:41
extract insights and identify patterns,
trends, and relationships.
5:46
Data visualization is the process of
representing data in a visual form.
5:51
As a beginner, the easiest way to learn
data visualization is to start with
5:55
basic charts and
graphs using tools like Excel.
6:00
A popular spreadsheet program that
includes basic data visualization
6:03
features.
6:08
And Tableau, tool that allows users
to connect to various data sources,
6:08
create interactive dashboards,
and share insights with others.
6:13
Additionally, it can be helpful to learn
some basic principles of visual design,
6:18
such as color theory,
typography, and layout.
6:23
By understanding these principles,
you can create visualizations that
6:26
are both aesthetically pleasing and
effective at conveying information.
6:31
Having a solid understanding of
the methods and tools used to analyze and
6:35
derive insights from data will set you up
for success in your data science journey.
6:40
Gaining domain knowledge is
an important step on the path to
6:46
a career in data science.
6:50
Domain knowledge is important when
studying data science because it provides
6:52
context of a specific industry or field,
but the data science work applies to.
6:57
Here are four ways to gain
experience in a particular domain.
7:02
Identify the specific domain or
industry you want to learn about.
7:06
This will help you focus your efforts and
resources.
7:10
Working in the industry is the best
way to gain domain knowledge.
7:13
Look for internships, entry level jobs, or
7:17
volunteer opportunities in
your field of interest.
7:20
Read industry publications,
blogs, and newsletters.
7:23
This will help you stay up to
date with the latest trends and
7:27
developments in your industry.
7:30
Attend conferences and
events relevant to your industry.
7:32
Look for opportunities to network
with other professionals and
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learn from experts in your field.
7:40
Without domain knowledge, a data
scientists may not understand the nuances
7:43
and intricacies of the data
they're working with.
7:47
Leading to inaccurate or
incomplete analysis and conclusions.
7:50
And there you have it,
a smart learning path for you to follow.
7:56
Many people have built their data science
skills along the same path we just
8:00
explored and launched new careers
in data science on their own.
8:05
If a career in data science
sounds right for you,
8:09
you can start building your
skills with treehouse.
8:11
The demand for skilled
professionals is growing every day.
8:14
And tech companies around the world are
removing formal degree requirements for
8:18
tech positions to access
a bigger pool of job candidates.
8:22
What does this mean for you?
8:27
You can start a career in data
science right here at treehouse,
8:30
no diploma required.
8:35
In addition to individual courses perfect
for beginning coders, treehouse offers for
8:36
Python Techdegree.
8:41
A self paced interactive bootcamp
where you will learn to build apps and
8:42
work with data while building
a portfolio and getting certified.
8:46
The Techdegrees
project-based curriculum and
8:51
incredible student support is a great
place to start your data science journey.
8:53
We also offer the beginning Python track,
a guided curriculum
8:58
that includes 14 hours of courses and
workshops, starting with the basics.
9:02
Tracks cover all relevant courses and
workshops necessary to master a subject.
9:06
So you'll be coding
with Python in no time.
9:11
To learn the math needed
to work in data science,
9:15
consider starting with basic
statistics for data analysis.
9:19
This course teaches students how
to calculate basic statistics,
9:23
solidify understanding of the terminology.
9:26
And determine which graphs might
be most useful in displaying data.
9:29
In our machine learning basics course you
can explore the fundamental concepts in
9:34
machine learning.
9:39
For instance supervised versus
unsupervised learning as well as write
9:40
a bit of code in Python.
9:45
So that you can make
intelligent predictions.
9:46
To establish a solid foundation in all
things data, check out our library for
9:49
many courses and
workshops covering data cleaning,
9:55
visualization, analysis, and much more.
10:00
Better yet, dive into the data analysis
Techdegree where you'll learn how to
10:03
use data to answer questions,
gather actionable insights, and
10:08
tell a story using spreadsheets,
databases, and Python.
10:12
You'll graduate with a data analysis
certification through a credible and
10:16
a well-rounded portfolio of projects
that demonstrates your newly acquired
10:20
expertise.
10:24
The industries of the future will be built
on the foundations of digital technology,
10:28
artificial intelligence, and data science.
10:33
These fields are rapidly expanding and
the demand for
10:35
skilled professionals
is growing every day.
10:39
Organizations everywhere are actively
searching for data science experts to help
10:42
them improve their products,
tools, and business operations.
10:46
By learning data science,
10:50
you can increase your chances of being
hired in a rapidly growing field.
10:52
Learning data science can be
a game changer for your career.
10:57
We encourage you to take the next step.
11:02
Whatever data science path you choose,
remember, treehouse is here to help.
11:05
Thanks for watching.
11:12
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