The Foundation: Understanding What Data Analysts Do
We’ve got a full
article exploring what data analysts do, which looks at
some of the roles and responsibilities expected in this role. Here, we’ll take
a brief look at some of the key things you need to know.
Role and
responsibilities of a data analyst
Data analysts
serve as the linchpin between raw data and actionable insights. They gather,
process, and analyze data to help organizations make informed decisions.
Data analysts frequently
collaborate with various departments, including marketing and finance, to
understand business objectives and translate them into data-driven solutions.
The data analysis process
The data analysis
process is a multi-step journey that starts with data collection and ends with
actionable insights. We cover the topic in greater detail in our guide, What is Data Analysis? As
shown in the infographic below, the process consists of several stages.
Each
step— from determining the outcome to data collection, cleaning, analysis,
visualization, and interpretation—is crucial for deriving accurate and
meaningful outcomes. Our Data
Analyst with Python career track covers everything you need to
know about the process and will teach you how to analyze data from scratch.
Essential Data Analyst Skills: Technical Skills
Technical skills
form the backbone of a data analyst's expertise. These skills enable you to
manipulate data, conduct complex analyses, and generate insights to drive
business decisions. Let’s take a look at the technical data analyst skills
you’ll need:
1.
Programming languages (Python, R, SQL)
In the realm of
data analytics, programming languages like Python, R, and SQL are
indispensable. These languages allow you to manipulate data, perform statistical
analyses, and create data visualizations.
·
Python.
Widely used for data manipulation and analysis, Python boasts a rich ecosystem
of libraries like Pandas and NumPy.
·
R.
Specialized for statistical analysis, R is another powerful tool often used in
academic research and data visualization.
·
SQL.
The go-to language for database management, SQL allows you to query, update,
and manipulate structured data.
2. Data
Visualization Tools (Tableau, Power BI)
Data
visualization is not just about creating charts; it's about telling a story
with data. Tools like Tableau and Power BI are widely used for this purpose,
allowing you to transform complex data into easily digestible visual formats.
·
Tableau.
Known for its user-friendly interface, Tableau allows you to create complex
visualizations without any coding. It's particularly useful for creating
interactive dashboards that can be easily shared across an organization.
·
BI Developed by Microsoft, Power BI
is another powerful tool for creating interactive reports and dashboards. It
integrates seamlessly with various Microsoft products and allows for real-time
data tracking, making it popular in corporate settings.
3. Statistical Analysis
Statistical
analysis is the backbone of data analytics, providing the methodologies for
making inferences from data. Understanding statistical methods allows you to
use the following:
·
Descriptive statistics.
Summarize and interpret data to provide a clear overview of what the data
shows.
·
Inferential statistics.
Make predictions and inferences about a population based on a sample.
·
Hypothesis testing.
Evaluate theories or hypotheses to solve practical problems.
4.
Data Wrangling and Cleaning
Before any data
analysis can occur, the data must be cleaned and transformed into a usable
format, a process known as data wrangling. This involves:
·
Data cleaning. Identifying and correcting
errors, inconsistencies, and inaccuracies in datasets.
·
Data transformation.
Converting data into a format that can be easily analyzed, may involve
aggregating, reshaping, or enriching the data.
·
Data integration.
Combining data from different sources and providing a unified view.
Essential Data Analyst Skills: Soft Skills
While technical
skills may get your foot in the door, soft skills ensure that you can
effectively communicate your findings and collaborate with others. These skills
are often what set great data analysts apart from good ones.
5. Communication skills
In the context of
data analytics, communication is not just about presenting findings; it's about
translating complex data into actionable insights that can be easily understood
by non-technical stakeholders. Effective communication involves:
·
Data storytelling. The ability to weave
data into a compelling narrative that drives business decisions.
·
Presentation skills.
Mastery of presentation tools and the ability to present data visually and
verbally are key.
·
Interpersonal
skills. Building relationships with team members and stakeholders is crucial
for collaborative projects.
6. Problem-solving
abilities
Data analysts are
often faced with ambiguous problems that require analytical thinking.
Problem-solving in data analytics involves:
·
Critical
thinking. The ability to objectively analyze and evaluate issues to form a
judgment.
·
Analytical
reasoning. Using logical reasoning to approach problems and make decisions
based on data.
·
Innovation.
Sometimes, traditional methods won't cut it, and you'll need to think outside
the box.
7. Attention to detail
According to a Gartner report,
poor data quality can cost organizations an average of $12.9 million every
year, highlighting the importance of attention to detail.
In data
analytics, even a small error can lead to incorrect conclusions. Attention to
detail is critical for:
·
Data cleaning.
Ensuring the data you work with is accurate and free from errors.
·
Quality
assurance. Double-check your analyses and visualizations for accuracy.
·
Documentation.
Keeping thorough records of your data sources, methodologies, and code.
Advanced-Data Analyst Skills: Taking Your Career to the
Next Level
As you gain
experience and mastery over foundational skills, you'll find that the world of
data analytics offers even more specialized areas to explore. These advanced
skills can set you apart in a competitive job market and offer new avenues for
career growth.
8. Machine learning
Machine learning
is an extension of data analytics that allows computers to learn from data.
According to a Gartner report, in 2022, 20% of analytic applications were
augmented with machine learning, a number that’s likely to increase thanks to
machine learning and AI’s growing importance.
Understanding the
basics can significantly broaden your capabilities as a data analyst. This
includes:
·
Supervised
learning. Techniques for building models that can make predictions
based on labeled data.
·
Unsupervised learning. Methods for finding
patterns in unlabeled data.
·
Natural Language Processing (NLP). A subfield
focusing on the interaction between computers and human language.
9.
Big data technologies
Data from Statista shows
that the big data market was expected to grow from $42 billion in 2018 to $103
billion by 2027, emphasizing the increasing need for skills in big data technologies.
As data continues
to grow in volume and complexity, big data technologies like Hadoop and Spark
are becoming increasingly important. These technologies allow you to work on:
·
Data storage.
Handle large datasets that are beyond the capacity of traditional databases.
·
Data processing. Perform complex
computations and analyses on big data.
·
Real-time analytics.
Analyze data in real-time to make immediate business decisions.
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