reat work reinforcing your learning with a thoughtful self-reflection!

A good
reflection on this topic would consider the phases that data goes through in its
life cycle and how this impacts the data analysis process.

While the data analysis process will drive your projects and help you reach your
business goals, you must understand the life cycle of your data in order to use
that process. To analyze your data well, you need to have a thorough understanding
of it. Similarly, you can collect all the data you want, but the data is only
useful to you if you have a plan for analyzing it.

The Plan and Ask phases both involve planning and asking questions, but they tackle
different subjects. The Ask phase in the data analysis process focuses on big-
picture strategic thinking about business goals. However, the Plan phase focuses on
the fundamentals of the project, such as what data you have access to, what data
you need, and where you’re going to get it.

Cleaning data is an important part of the data analysis process. If data analysis
is based on bad or dirty data, it may be biased, erroneous, and uninformed. Sorting
and filtering are essential skills for every data analyst, and are also very useful
for cleaning data. In upcoming activities, you will continue to learn more about
the most effective and efficient ways to clean data.

Data analysts use many forms of data throughout day-to-day work. For instance,
an analyst might use a spreadsheet for one project but use a database for another.
A company might use a spreadsheet to track internal revenue data, but might use a
database to store dynamic consumer info. Understanding which type is appropriate to
use in a specific situation is crucial
to being an effective data analyst. In upcoming activities, you will learn more
about databases
and how they differ from spreadsheets.

Hey again. Today, a lot of us spend a lot of time connecting with people online. We
stay in touch with family and friends we can't see everyday, or post about what
we're doing, eating, and watching on social media. But our presence online goes
beyond the personal. A consistent and professional online presence is an important
tool in building a career in data analytics. A professional online presence is
important for a few key reasons. First, it can help potential employers find you.
Second, it lets you make connections with other data analysts in your field, learn
and share data findings, and maybe even participate in community events. Keep in
mind that a lot of networking happens online now. If you aren't keeping up your
online presence, you might be missing out on great opportunities without even
knowing it. There are lots of different professional sites that you can take
advantage of as you start building your own online presence. For now though, we'll
focus on LinkedIn and GitHub. LinkedIn is specifically designed to help people make
connections with other people in their field. It's a great way to follow trends in
your industry, learn from industry leaders, and stay engaged with the wider
professional community. And if you're actively looking for a new job, LinkedIn has
job boards that you can search. You can even narrow down your location to see who's
hiring near you. Plus, job recruiters frequently use LinkedIn to find potential
data analysts for new projects. It's always a good idea to keep your LinkedIn
profile up to date with your resume. You might find yourself being recruited.
LinkedIn also lets you connect with people and build a network. You can share
exciting things happening in your professional life and keep up with where your
connections go. You never know when you might end up working with someone again.
With LinkedIn, you can be endorsed for having job skills or endorse other people.
If you impress someone at a previous job, they can let other people know just how
awesome you are to work with. GitHub, the other website I mentioned earlier, is a
little different. GitHub is part code-sharing site, part social media. It has an
active community collaborating and sharing insights to build resources. You can
talk with other GitHub users on the forum, use the community-driven wikis, or even
use it to manage team projects. GitHub also hosts community events where you can
meet other people in the field and learn some new things. GitHub has a lot of
features for you to check out. The best way to learn more about it is to check it
out for yourself. We'll also be talking more about GitHub later in the program.
Sometimes if you're looking for a new career, finding someone who has something in
common with you, like shared interests or the same hometown, and reaching out to
them, can help a lot. Just a 15-minute conversation with someone could set you on
the path to a new career, whether that's on a professional networking site like
LinkedIn, or at a community event hosted by GitHub. LinkedIn has become one of the
standard professional social media sites, so it's a good starting place for
building your online presence. GitHub offers a lot of really great tools for data
analysts in the community. If you don't already have accounts on these sites,
challenge yourself to set them up now. Connect with other people. Share some
updates about what you're working on right now. If you're already using LinkedIn
and GitHub, great news: we're going to talk more about how to enhance your existing
social media presence next time. See you soon.

Taking charge of your online presence and establishing a record of your hard work
is crucial to honing your skills and getting a job as a data analyst. Going
forward, you can read through discussion posts that interest you on Kaggle or
another data science forum. Engage with them by adding your thoughts
or asking follow-up questions to improve your online presence and learn new data
skills.

Cleaning data is an important part of the data analysis process. If data analysis
is based on bad or “dirty” data, it may be biased, erroneous, and uninformed.
Knowing how to effectively use spreadsheet functions to work with data is an
essential skill for every data analyst. In upcoming activities, you will continue
to learn more about spreadsheet functions and how they can help you analyze your
data.

A changelog should capture any of the following changes to the dataset while
cleaning:

Treated missing data

Changed formatting

Changed values or cases for data

Adding code chunks to your R Markdown notebook can give other users an interactive
way to understand your data analysis process and test out your code in their own
RStudio console space. These can be useful for documenting your code and giving
stakeholders a chance to explore the data.

Data analysts share their work in a variety of formats, such as pdfs, html files,
and R Markdown notebooks. Understanding how to export or convert your work into any
of these formats will help you be flexible in how you share it. As html files and
pdfs, your work can be attached to emails or uploaded to a cloud-based file sharing
platform like Google Drive. This also allows you to share your analysis with
potential employers when you search for a job as a data analyst, which you will
learn more about in a future course.
ou can use R Markdown templates for lots of different purposes. Exploring a variety
of packages with unique templates will help you figure out which ones you might use
to document your own analysis and conclusions. You can use R templates as starting
points for reports, portfolio pieces, and other documents you will create in your
career as a data analyst.

You have made some of these changes while cleaning data in previous activities. If
you had kept a changelog during those activities, you would have described and
categorized each change. When in doubt about the significance of a change, you
should enter it into the changelog.

As you learn more about how to craft effective presentations, you will get better
at identifying why they are effective. Earlier, you learned about the
McCandless method and how to bring your presentation from general ideas to
specific information. Now, you will be able to identify examples of this
in real presentations. Going forward, you can apply the principles
you’ve learned to create your own great presentations as a data analyst.
If you don’t address the objection, your stakeholders may not appreciate or respect
the work you’ve done in your analysis.
By communicating respectfully with your stakeholders, you establish a positive
relationship with them. You also can use their feedback to improve your analytical
approach for future presentations.

The R console is a simple environment in which you can write single lines of R
code.
It won’t save your code beyond a single session, but it is very valuable for
running simple functions.
In upcoming activities, you will use RStudio, an interactive development
environment that builds on the simplicity of the R console.

summarize(max_b1=max(flipper_length_mm),mean_b1=mean(flipper_length_mm))

trimmed_flavors_df %>% summarize(max(Rating))

install.packages('tidyverse')
library(tidyverse)

geom_bar(mapping = aes(x = Company.Location)) to create a bar chart with the

A well-written business task provides you with a clear purpose when cleaning and
analyzing data. It also helps you prioritize the types of visualizations to create
and fine-tune the content of your final presentation.
As a junior data analyst, the credibility of your data has a big impact on your
credibility with stakeholders. Information about your data should be readily
available and clearly communicated so stakeholders know you are using high-quality
data.
Data analysts are always thinking about how to improve their data analysis
documentation skills. Reviewing your documentation sparks new ways of thinking and
can highlight where there might be a mistake. This ensures your data-driven
recommendations are sound.
A compelling summary of your data analysis demonstrates that you understand the
data, can identify how it relates to the business case, and are sharing what you’ve
found in the best possible way.
As a data analyst, it’s important that your visualization skills keep evolving with
the changes in technology and design. Reviewing and critiquing your own work
provides many opportunities for growth and development.
To benefit from your efforts, consider printing this webpage as a PDF file before
submitting this assessment. You can then refer to it in order to further improve
your case study report.