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Do you want to become a data analyst that employers seek out? If so, you need to focus on mastering the skills necessary for the job.
There are many different data analyst tips out there that people suggest, but I have compiled 11 of the most important ones here.
Each of these tips will help you improve your skills and become a more sought-after data analyst.
Read on for more info!
What are Some Data Analyst Tips?
1. Understand Data Types and Data Structures
One of the most important data analyst tips is to understand data types and data structures. This means being able to identify different types of data, such as numerical data, categorical data, and ordinal data.
These are the core concepts of data as you’ll be running into many problems with data types as a data analyst, and you’ll need to convert them accordingly.
You should also be familiar with different data structures, such as arrays, lists, and dictionaries. Knowing how to work with these data types and structures is essential for any data analyst.
In Python, these data structures can help you wrangle data easily and efficiently, and most importantly, to write neater and more readable code which will be a big help when you pick it up later on.
2. Become Proficient in Excel
Excel is a data analyst’s best friend. As a data analyst, you will be working with data in Excel spreadsheets on a daily basis. This is so especially if you work in a small company, or receive Excel files from stakeholders who aren’t necessarily data-trained.
Therefore, it is important that you become proficient in Excel and know how to use all the features and formulas available.
Examples of things you should start learning include:
- Pivot tables
- Data validation
- Conditional formatting
These are just some of the basics, and once you have a good understanding of them, you can start exploring more complex features in Excel. There are many online resources and tutorials available that can help you learn Excel, so make use of them!
I find that Excel has helped me to solve lots of quick problems through small analyses. If datasets aren’t large and the analysis is a one-time ad-hoc analysis, Excel is perfect for it!
Make sure you learn it well and cover all the advanced functions such as conditional formatting too.
However, I wouldn’t recommend going into too much detail like learning how to write your own macros.
For programmatic analyses, use programming languages like R and Python instead. They are way more robust.
3. Develop Your SQL Skills
SQL (Structured Query Language) is a programming language that data analysts use to query databases.
This means that if you want to extract data from a database for analysis, you will need to know SQL.
SQL skills are essential for data analysts as it allows them to manipulate data easily and quickly. Most importantly, it helps to ensure that the data analyst can get accurate and reliable data.
There are many online resources available that can help you learn SQL, so make use of them! I would also recommend joining online forums or taking an online course to further develop your skills.
Some companies may also require data analysts to have a certain level of proficiency in SQL before they are hired, so it is definitely a skill worth learning.
Once you have developed your SQL skills, you can start applying for data analyst jobs that require SQL proficiency.
You can also use SQL to create data visualizations, which will be very impressive to employers!
4. Learn Data Visualization Techniques
Data visualization is another important skill for data analysts.
This is because data visualizations help to make data easier to understand and interpret, especially for non-technical people.
There are many different data visualization techniques that data analysts can use, such as bar charts, line graphs, scatter plots, and heatmaps.
Each data visualization technique has its own strengths and weaknesses, so it is important to learn about all of them and know when to use each one.
Data visualizations can be created using Excel, Tableau, or programming languages like R and Python.
If you want to create data visualizations that are truly impressive, I would recommend using R or Python. This is because of their high customizability, but I do admit that it will take a longer time to go through it because of how technical it can get.
For quicker and more beautiful analyses, use Tableau instead. Learning Tableau can be a great skill at work, as I know many of my colleagues struggle with it. I personally learned it in my free time and it has paid its dividends well at work!
There are many online resources and tutorials available that can help you learn data visualization techniques, so make use of them!
Some companies may also require data analysts to have a certain level of proficiency in data visualization before they are hired, so it is definitely a skill worth learning.
Once you have learned data visualization techniques, you can start creating data visualizations that will be very impressive to employers!
At this point, doing a simple data analytics project and making it available online through a blog, Rmarkdown, or simply Tableau Public will allow you to slowly develop your own digital data portfolio.
5. Understand Business Analytics
Business analytics is a branch of data science that deals with the application of data and statistical methods to business problems.
This means that if you want to be a data analyst, you need to understand business analytics.
There are many different types of business analytics, such as predictive analytics, prescriptive analytics, and marketing mix modeling.
Each type of business analytics has its own strengths and weaknesses, so it is important to learn about all of them and know when to use each one.
Business analytics can be learned through online courses, books, or by working with a data analyst who already knows how to do it.
If you want to learn business analytics, I would recommend taking an online course or working with a data analyst who already knows how to do it.
This is because you will need to understand the theory behind business analytics before you can start applying it to real-world problems.
Once you have learned about business analytics, you can start using it to solve business problems that employers are facing.
This will show employers that you have the skills and knowledge that they are looking for in a data analyst.
I hope these tips have helped you understand what data analysts do and what skills you need to become one! If you want to learn more about data science, check out the other posts on my blog!
6. Take Data Mining Courses
Data mining is the process of extracting data from large data sets.
This is a very important skill for data analysts, as they often need to work with large data sets.
There are many different data mining techniques that data analysts can use, such as association rules, clustering, and decision trees.
Each data mining technique has its own strengths and weaknesses, so it is important to learn about all of them and know when to use each one.
Data mining can be learned through online courses, books, or by working with a data analyst who already knows how to do it.
If you want to learn data mining, I would recommend taking an online course or working with a data analyst who already knows how to do it.
This is because you will need to understand the theory behind data mining before you can start applying it to real-world problems.
Once you have learned about data mining, you can start using it to solve business problems that employers are facing.
This will show employers that you have the skills and knowledge that they are looking for in a data analyst.
I recommend completing data analytics projects on your own so you can understand the data analytics process and employ the data analysis techniques you’ve learned.
There’s nothing as satisfying as going on your own exploratory data analysis, finding a problem, conducting your own data wrangling, and producing data visualizations for it!
7. Learn Python or R Programming Language
Python and R are two of the most popular programming languages for data science.
If you want to be a data analyst, you need to learn at least one of these programming languages.
Both Python and R have their own strengths and weaknesses, so it is important to learn about both of them and know when to use each one.
Python is a general-purpose programming language that data analysts can use for data wrangling, data visualization, and machine learning.
R is a statistical programming language that data analysts can use for data wrangling, data visualization, and statistical analysis.
Both Python and R are easy to learn, but i find that R is more difficult to learn than Python.
Python has very readable code and is very easily deployable to other applications. R is more rigid, but can feel very comfortable if you’re using it for well-contained industries such as research, bioinformatics, or the healthcare industry.
If you want to learn Python or R, I would recommend taking an online course or working with a data analyst who already knows how to do it.
This is because you will need to understand the theory behind these programming languages before you can start applying them to real-world problems.
To be honest, most data professionals can code in both languages as being able to pick up any new tech is one of the technical skills that anyone looking for a data analyst job should have.
At this point, you should also invest in a good data science/data analytics laptop because it can go a long way in helping to process some of the larger datasets that you’ll encounter!
8. Understand Data Lakes and Data Warehouses
Data lakes and data warehouses are two important concepts that data analysts need to understand.
Data lakes are large data repositories that data analysts can use for data storage and data processing.
Data warehouses are data repositories that data analysts can use for data warehousing, which is a process of storing data in a central location.
You should also develop technical skills in how to handle them. Many data analyst jobs will require you to have experience working with data lakes and data warehouses.
9. Use Big Data Technologies
Big data is a term that describes data sets that are too large and complex to be processed by traditional data processing applications.
Big data technologies are tools and technologies that data analysts can use to process big data.
Some of the most popular big data technologies include Hadoop, Spark, and MapReduce.
Also, increasingly, many companies are looking to expand and move into what’s known as a modern data stack. In this modern data stack, there are a number of technologies that you’ll need to know such as:
- data ingestion tools
- data warehouse tools
- BI tools
- reverse ELT tools
- data science tools
10. Stay Up to Date on the Latest Trends in Data Analytics
Data analytics is an ever-changing field. New technologies and methodologies are constantly being developed.
As a data analyst, it is important to stay up to date on the latest trends in data analytics.
The data analytics process is always being updated with new ways of analysis. It’s not always a simple data analytics process all the time.
Some of the latest trends in data analytics include machine learning, data visualization, and big data.
Traditionally, data analysts are expected to know more about visualization, but increasingly, we are expected to know be knowledgeable about AI trends too.
I will recommend taking an AI course and obtaining certification if you’re deep into learning data science.
11. Join a Data-centric Community
Make sure you’re taking time to connect with other data professionals to exchange useful advice on how to grow your skillset better and faster.
Data analysis is a community effort, and data analysts need to be active members of the data community in order to be successful.
Some of the best ways to connect with other data professionals are through online forums, data meetups, and data conferences.
To become a successful data analyst, you need to know how to make connections with others. This way, you’ll get access to opportunities that you would previously would not have if you did not network at all.
What Do Data Analysts Do?
Data analysts are responsible for turning data into insights that can be used to make business decisions.
They collect data from a variety of sources, including surveys, interviews, observations, and experiments.
Once they have collected data, data analysts use statistical techniques to clean and organize it.
After the data is cleaned and organized, data analysts use math and statistics to find trends and patterns.
Finally, data analysts communicate their findings to decision-makers in the form of reports, presentations, and data visualizations.
There you have it! These are 11 tips to become a skillful data analyst that employers seek.
If you follow these tips, you’ll be on your way to becoming a data analyst that employers will want to hire.
Data analytics is a growing field with many opportunities. So make sure you’re prepared by having the right skills, knowledge, and data analytics process outlined ahead of you.
Do you have any tips on becoming a data analyst? Please share them with me too!
Thanks for reading and all the best in becoming a better data analyst!
Justin is the founder and author of Justjooz. He is a Nanyang Technological University (NTU) alumni and a former data analyst.
Now, Justin runs the Justjooz blog full-time, hoping to share his deep knowledge of business, tech, web3, and analytics with others.
To unwind, Justin enjoys gaming and reading.