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Welcome to How to Learn Data Science.

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In this workshop, we'll build a learning
path through data science with treehouse.

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Let's get started.

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Data science is the field of study
that combines programming, math,

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and domain expertise to extract
meaningful insights from data.

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The phrase domain expertise refers to
having knowledge in a particular field,

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for example, medicine,
finance, or agriculture.

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The AI revolution is changing
the way we interact with technology.

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Experts believe that as the world trains
its focus on creating intelligent

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machines.

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but ways that humans interact with
data science will rapidly evolve.

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This means that as a developer,
understanding data science can provide you

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with an array of possibilities for
your career.

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It's an extraordinary time to
start learning data science,

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but you may be asking, where do I start?

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Data science can seem overwhelming
because of its vastness.

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But if you map out a learning path,
acquiring the necessary skills is not

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a difficult task will present
one of many possible paths.

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Remember, your learning
path is personal to you.

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Everyone is unique and
takes a different approach to learning.

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It doesn't matter where you start.

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The important thing is that you
actually start the journey.

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A smart path to learning data
science begins with programming.

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Learning programming languages is an
essential step in learning data science.

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Programming equips data scientists with
the tools needed to perform practical

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data science work.

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Those new to programming can get started
with two relatively easy programming

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languages.

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Python's simplicity and readability
makes it a great choice for beginners.

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Its rich set of libraries provide data
scientists with a powerful toolkit for

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performing tasks like data manipulation,
analysis, and visualization.

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Sequel is a domain-specific
language designed for

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managing and manipulating data.

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Almost all organizations
store data in databases,

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making SQL a critical language for
data scientists.

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Starting with these two versatile and

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easy-to-learn languages is a great way
to begin your journey in data science.

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The next step on our path is mathematics.

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Math serves as a fundamental pillar
in the field of data science,

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providing essential tools and concepts
that underpin the entire discipline.

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Linear Algebra is used in machine learning
for tasks such as image processing and

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natural language processing.

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Key concepts to learn include matrix
manipulation and vector spaces.

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Calculus is used in data science to
optimize machine learning models and

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to calculate probabilities and statistics.

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Calculus involves two main concepts,
derivatives and integrals.

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Start by understanding these two concepts.

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Probability and
statistics are used to analyze data and

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to make decisions based on that data.

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Learn the fundamental concepts of mean,
median,

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and mode, variance, and
standard deviation.

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These disciplines make up the foundation
of many data science techniques and

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algorithms.

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Without a strong mathematical foundation,
it's difficult to fully

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understand how these techniques work and
to apply them in real world scenarios.

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Machine learning is
the next stop on our path.

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Machine learning is a subset of
artificial intelligence that focuses on

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developing algorithms and

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models that enable computers to learn from
data without being explicitly programmed.

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Start by learning the basic concepts of
machine learning, such as unsupervised and

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supervised learning, clustering,
and neural networks.

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Machine learning algorithms have
a backbone of any machine learning

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application.

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They're used to analyze data,
identify patterns, and make predictions.

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By using algorithms like linear and
logistic regression, and decision trees.

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You can quickly identify patterns and
trends and

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data that might not be
visible to the naked eye.

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Then gain experience of machine
learning tools and libraries.

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A few popular options are scikit-learn,
a Python library that includes

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well-designed tools for performing common
machine learning tasks and PyTorch.

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A Python based machine learning framework
that simplifies the development and

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training of deep learning models with
its ease of use and flexibility.

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Making it a preferred choice for
researchers and

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practitioners in the field
of Artificial Intelligence.

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Machine learning can be leveraged
to predict customer churn,

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forecast demand for
a product and identify fraud and

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financial transactions
in all of these fields.

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And many more professionals with
the skills are in high demand.

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Data foundations is a good next
step in learning data science.

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Working with data is a fundamental
skill in data science,

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allowing data scientists to extract
insights from data, identify patterns and

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relationships, and
represent data in visual form.

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Data scientists spend significant time
preparing data before analyzing it.

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And this is where data wrangling and
manipulation become critical.

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This involves techniques like cleaning,
filtering, and

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sorting and transforming raw data
that's often messy and unstructured,

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which can affect the accuracy and
validity of the analysis.

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Once the data has been transformed into
a suitable format, it can be analyzed to

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extract insights and identify patterns,
trends, and relationships.

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Data visualization is the process of
representing data in a visual form.

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As a beginner, the easiest way to learn
data visualization is to start with

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basic charts and
graphs using tools like Excel.

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A popular spreadsheet program that
includes basic data visualization

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features.

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And Tableau, tool that allows users
to connect to various data sources,

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create interactive dashboards,
and share insights with others.

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Additionally, it can be helpful to learn
some basic principles of visual design,

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such as color theory,
typography, and layout.

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By understanding these principles,
you can create visualizations that

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are both aesthetically pleasing and
effective at conveying information.

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Having a solid understanding of
the methods and tools used to analyze and

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derive insights from data will set you up
for success in your data science journey.

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Gaining domain knowledge is
an important step on the path to

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a career in data science.

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Domain knowledge is important when
studying data science because it provides

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context of a specific industry or field,
but the data science work applies to.

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Here are four ways to gain
experience in a particular domain.

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Identify the specific domain or
industry you want to learn about.

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This will help you focus your efforts and
resources.

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Working in the industry is the best
way to gain domain knowledge.

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Look for internships, entry level jobs, or

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volunteer opportunities in
your field of interest.

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Read industry publications,
blogs, and newsletters.

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This will help you stay up to
date with the latest trends and

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developments in your industry.

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Attend conferences and
events relevant to your industry.

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Look for opportunities to network
with other professionals and

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learn from experts in your field.

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Without domain knowledge, a data
scientists may not understand the nuances

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and intricacies of the data
they're working with.

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Leading to inaccurate or
incomplete analysis and conclusions.

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And there you have it,
a smart learning path for you to follow.

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Many people have built their data science
skills along the same path we just

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explored and launched new careers
in data science on their own.

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If a career in data science
sounds right for you,

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you can start building your
skills with treehouse.

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The demand for skilled
professionals is growing every day.

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And tech companies around the world are
removing formal degree requirements for

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tech positions to access
a bigger pool of job candidates.

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What does this mean for you?

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You can start a career in data
science right here at treehouse,

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no diploma required.

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In addition to individual courses perfect
for beginning coders, treehouse offers for

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Python Techdegree.

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A self paced interactive bootcamp
where you will learn to build apps and

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work with data while building
a portfolio and getting certified.

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The Techdegrees
project-based curriculum and

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incredible student support is a great
place to start your data science journey.

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We also offer the beginning Python track,
a guided curriculum

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that includes 14 hours of courses and
workshops, starting with the basics.

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Tracks cover all relevant courses and
workshops necessary to master a subject.

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So you'll be coding
with Python in no time.

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To learn the math needed
to work in data science,

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consider starting with basic
statistics for data analysis.

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This course teaches students how
to calculate basic statistics,

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solidify understanding of the terminology.

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And determine which graphs might
be most useful in displaying data.

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In our machine learning basics course you
can explore the fundamental concepts in

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machine learning.

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For instance supervised versus
unsupervised learning as well as write

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a bit of code in Python.

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So that you can make
intelligent predictions.

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To establish a solid foundation in all
things data, check out our library for

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many courses and
workshops covering data cleaning,

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visualization, analysis, and much more.

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Better yet, dive into the data analysis
Techdegree where you'll learn how to

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use data to answer questions,
gather actionable insights, and

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tell a story using spreadsheets,
databases, and Python.

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You'll graduate with a data analysis
certification through a credible and

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a well-rounded portfolio of projects
that demonstrates your newly acquired

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expertise.

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The industries of the future will be built
on the foundations of digital technology,

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artificial intelligence, and data science.

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These fields are rapidly expanding and
the demand for

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skilled professionals
is growing every day.

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Organizations everywhere are actively
searching for data science experts to help

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them improve their products,
tools, and business operations.

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By learning data science,

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you can increase your chances of being
hired in a rapidly growing field.

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Learning data science can be
a game changer for your career.

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We encourage you to take the next step.

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Whatever data science path you choose,
remember, treehouse is here to help.

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Thanks for watching.