Data analysis is a broad term that covers many facets of data and its uses. It's an integral part of nearly every industry and business, from finance to medicine to government. If you can learn the skills in this top 9 list, then you will be able to find work anywhere.
Top 9 Data analysis skills to learn
in 2022
1. Learning Data Analysis
Data
analysis is the process of examining and interpreting raw data to describe and
understand its meaning.
In other
words, it's the act of taking information that you have about something (in
this case, people) and turning it into something else (a chart or graph). Data
analysis helps you make sense of your own data by showing how it relates to
other types of information or by breaking down large amounts of information
into smaller parts so you can see what's happening at each point in time.
2. SQL
SQL is the
standard language for data analysis. It's used to query, manipulate and analyze
data in relational database management systems (RDBMS). That means that if you
want your program to do anything with data stored in an RDBMS—like returning
all of the information from a table or calculating some value—you'll need to
know how SQL works.
SQL is
also a declarative language; this means that it doesn't require any programming
code whatsoever! You just tell it what questions you want answered and then
watch as your program runs through their answers using logic based on what they
found out about those questions.
SQL is one
of those things where learning how things work will make them easier for us
later on when we start working at companies that use it too often without
thinking about whether or not everything makes sense anymore either because
there are so many people who don't understand why certain things happen but instead
rely solely on experience alone which could lead them down wrong paths...
3. Data Cleaning
Data
cleaning is a skill that you'll need to learn if you want to be able to analyze
data. It's especially important when dealing with large amounts of raw data, as
this can cause a large number of mistakes in the analysis process and even lead
to incorrect conclusions.
Data
cleaning is basically the process of going through your collected information
and making sure it's ready for analysis before proceeding with any other steps.
You should always do some kind of data cleansing before using any machine
learning algorithms because they use large amounts of training data sets or
have complex models (such as neural networks).
There are
many different types of data cleaning methods available such as regular
expressions or Pandas functions (if available). In this article we will teach
you how simple standardizing datasets works:
4. Data Manipulation
There are
many aspects of data manipulation that you can learn. These include:
- Data cleaning (cleaning,
transforming, and aggregating)
- Data validation (checking for
errors)
- Data transformation (changing
the structure or format of your data)
Here are
some examples of what you might do as part of this skill set: - Cleaning up old
records from a database by removing duplicate values, whitespace characters and
other extraneous things; this is called "data cleansing". -
Transforming your raw numbers into useful information like percentages or
dollar amounts so that they're easier to understand when used in reports or
graphs; this is called "data transformation". - Validating the
accuracy of your calculations based on external standards such as government
regulations before using them in reports; this is called "data validation".
5. Data visualization
Data
visualization is the process of displaying data in a way that makes it more
accessible and easier to understand. This can be done through graphs, charts
and other visualizations that help you understand how certain information
relates to one another.
Data
visualization has become an important skill for data analysts because it allows
them to present their findings in compelling ways, helping people better
understand what they're looking at and how to use it.
There are
several different types of data visualizations: bar charts, line graphs (also
called scatter plots), pie slices or wedges (2D histograms), candlesticks (3D
histograms) etc.
6. Python coding
Python is
a general-purpose programming language that's perfect for beginners. It’s easy
to learn, and it has tons of great libraries and resources out there (like
Pandas).
Python can
be used in many different fields: data analysis, web development, AI… you name
it! That’s why I think learning Python will help you become more versatile when
it comes to your career in 2022.
7. Machine learning and AI
Machine
learning is a type of artificial intelligence that uses algorithms to make
predictions. It's used in many applications, including facial recognition and
speech recognition. Machine learning is a branch of artificial intelligence
(AI) that focuses on algorithms rather than human-like intelligence.
Machine
learning has become one of the most exciting fields because it can be used for
many things—from predicting what someone will buy next month based on their
behavior today, to identifying tumors based on X-rays taken from patients with
cancerous tumors—to name just two!
8. Web scraping / API skills
Web
scraping is a technique used to extract data from web pages. It’s frequently
used in the data science field, and it can be applied to many different fields
like business intelligence, machine learning and media analytics.
Web
crawling allows you to automatically retrieve information from a website or
database by following links from one resource (URL) on another resource (URL).
You can also use your own scripts or tools for crawling websites if you have
access rights!
In this
article I will show some examples of web scraping techniques that you can use
when learning about web scraping skills:
- Link extraction – This process
involves finding all links between two pieces of content on the same page
so that they appear together when viewed as part of an entire page; this
includes both images/links within text-based articles as well as meta tags
embedded within HTML headings such as title tags which may contain
relevant keywords related back behind their usage contextually speaking
where ever possible without having any impact whatsoever upon overall
aesthetic appeal factor score at least not yet anyways but maybe someday
soon maybe not right away perhaps instead maybe sometime down line through
future generations perhaps even centuries later into next millenniums
after centuries before millennia before millenniums before millennia
9. Knowledge of data analysis will make you indispensable in
any field.
Data
analysis skills are in demand across all industries, and it's not just because
Big Data is a thing. Businesses need to be able to analyze data from multiple
sources and make sense of it. If you want to work at Google or Amazon or
Microsoft—or even if you just want to get into a new industry—you'll need these
skills. The good news? They're transferable! You can apply them wherever your
career takes you (and hopefully far beyond).
Conclusion
Data analysis skills are vital in today’s world. Whether you want to be an entrepreneur or a data scientist, these skills will put you ahead of everyone else. The best way to learn is by doing it yourself, so get out there and start learning!