The Most Common Mistakes People Make with SAS Learning
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Published: 2022-08-06 06:27:47

People are more interested in SAS jobs as the field is so demanding and, in the spotlight, a professional in this field is admired for their outstanding work and, of course, the higher pay package. Although becoming a master in SAS requires a lot more effort and dedication, the result is worthwhile. Individuals who want to be SAS specialists should get the proper training that includes domain electives. As a SAS aspirant, you can learn and become skilled as much as you want, but if you do not apply the skill effectively, mistakes will occur.

Avoiding the following possible beginner mistakes can save you days, weeks, or even months of effort if you want to pursue a career in SAS. These mistakes, if not avoided, will deplete your most valuable resources like time, energy, and motivation. Learn the best SAS Training in Chennai to gain industry-standard skills for obtaining high pay SAS jobs.

We have classified the possible mistakes into three groups:
Mistakes made while learning SAS
Mistakes to avoid during applying for a job
Mistakes made during job interviews

**While Learning SAS**:
We define the first set of mistakes as "undercover," as they are difficult to detect. They gradually but consistently consume your time and energy without alert, and they are the result of misunderstandings about this field.

**Excessively spending time on theory**:
Many SAS newbies make the mistake of spending too much time on theory, whether it is basic procedures, data management, statistical analysis, graphics, operations research, or matrix language.

This method is ineffective for three primary reasons:
It will be slow and intimidating. 
You might not remember the concepts as well. As SAS is an applied field, experimental-based learning is the best way to strengthen SAS skills.

When you missed seeing how you can associate your learning of SAS with the real world, you're more likely to become discouraged and give it up.

To avoid making this mistake:
Integrate your learning with projects that will give you hands-on experience in SAS
Learn to be at ease with a little knowledge of SAS concepts
Discover how each component of SAS integrates with the larger picture.
**Code many algorithms from scratch**:
This next mistake is writing code for too many algorithms. You don't have to code every algorithm from scratch at first. It is also good to use a few for learning, the reality is that algorithms are becoming necessities. Most professionals never code algorithms from scratch anymore, thanks to mature and well-tested libraries and cloud-based solutions for SAS implementations.
 Nowadays, understanding how to implement the appropriate algorithms and libraries in the right settings is more important.
To avoid making this mistake
Get popularly used libraries like Sasuser, SasHelp, and so on.
When you decide to code any algorithm from scratch, do so to learn rather than perfect your implementation.
Understand the landscape of modern SAS implementations along with their pros and cons.

**Applying for SAS Jobs**:
These mistakes sometimes drive you to miss out on some great opportunities during your job search. Even if you are highly qualified, you can improve your results by avoiding these pitfalls.

**A resume that is overly technical**:
The most common mistake many applicants make when writing their resume is stuffing it full of technical jargon. Instead, your resume should be a picture with bullet points that tell a story. If you are applying for entry-level positions, your resume should emphasize the value you can bring to an organization.

**To avoid making this mistake**:
Don't just list the programming languages and libraries you have used. Define how you utilized them and also what happened as a result.

Less is actually more. Consider the most important skills to highlight and allow them to sparkle by expelling other diversions.
Create a resume master template from which you can keep turning off various versions customized for various roles. This ensures that each version is clean.

**Searching Narrowly**:
SAS is a relatively new field, and companies are still adapting to the increasing importance of data. You would be restricting your choices if you only look for "SAS Analyst" positions. Numerous jobs aren't categorized as "SAS," but they'll let you develop similar skills and work in a similar role.

**To avoid making this mistake**:
Look for required skills (Machine Learning, Data Visualization, Reporting, etc.).

Search by job responsibilities (for example, Data Sets Collection, Data Organization, Data Cleansing, Reports and Visualizations, and so on).

Look for technologies used in the role (Integration with ML or AI, VB, Java, etc.).

Enhance your search to include job titles (SAS Programmer, Statistical Programmer, Grid Computing Specialist, Data Analyst, etc.).

**WHILE INTERVIEWING**:
These are the mistakes that bring obstacles during the interview. you've already put in the effort to get to this point, it's time to conclude strong.

**Being unprepared to discuss projects**:
Having projects in your resume acts as a major safety hoop for "how would you" interview questions. Rather than speaking in generalities, you'll be capable of giving specific examples of how you managed difficult scenarios.

**To avoid making this mistake**:
Complete projects from start to finish that allow you to practice each major step (i.e. Data Cleaning, Model Training, etc.).
Prepare your methodology. SAS should be planned rather than unorganized.

Examine and practice describing previous projects from internships, jobs, or classes you've taken.
Ignoring communication abilities.
In most organizations, data science teams are still small in comparison to developer or analyst teams. As a result, while an entry-level software engineer is frequently managed by a senior engineer, data scientists work in more cross-functional settings.
To avoid making this mistake
Experiment with explaining technical concepts to non-technical people. Try explaining your favorite algorithm to a friend, for example.
Prepare bullet point answers to common interview questions and practice giving them.
Analyze different datasets, extract key insights, and present your findings.

**Conclusion**:
To summarise, spending too much time on theory, coding too many algorithms, using too much technical jargon in your resume, searching narrowly, being unprepared for projects, and ignoring communication skills are all highly possible mistakes that you must avoid to obtain high-paying SAS jobs. Enroll in our SAS Training in Chennai today to gain the field expertise that is most needed in the industry.
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