Top 9 Mistakes Will Never You Get Success in Data Science Career

If you wish to begin a career in data science, you can save yourself days, weeks, or even months of frustration by avoiding these 9 costly beginner mistakes.

If you're not careful, these mistakes will eat away at your most valuable resources: your time, energy, and motivation.

We've broken them into three categories:

Mistakes while learning data science
Mistakes when applying for a job
Mistakes during job interviews

While Learning Data Science

The first set of mistakes are "undercover" and they're hard to spot. They slowly but surely drain your time and energy without giving you a warning, and they spawn from the misconceptions surrounding this field.

1. Spending too much time on theory.

Many beginners fall into the trap of spending too much time on theory, whether it be math related (linear algebra, statistics, etc.) or machine learning related (algorithms, derivations, etc.).

This approach is inefficient for 3 main reasons:

First, it's slow and daunting. If you've ever felt overwhelmed by all there is to learn, you've likely sunk into this trap.
Second, you won't retain the concepts as well. Data science is an applied field, and the best way to solidify skills is by practicing.
Finally, there's a greater risk that you'll become demotivated and give up if you don't see how what you're learning connects to the real world.

This theory-heavy approach is traditionally taught in academia, but most practitioners can benefit from a more results-oriented mindset.

To avoid this mistake:

Balance your studies with projects that provide you hands-on practice.
Learn to be comfortable with partial knowledge. You'll naturally fill in the gaps as you progress.
Learn how each piece fits into the big picture (covered in our Data Science Primer).

2. Coding too many algorithms from scratch.

This next mistake also causes students to miss the forest for the trees. At the start, you really don't need to code every algorithm from scratch.

While it's nice to implement a few just for learning purposes, the reality is that algorithms are becoming commodities. Thanks to mature machine learning libraries and cloud-based solutions, most practitioners actually never code algorithms from scratch.

Today, it's more important to understand how to apply the right algorithms in the right settings (and in the right way).

To avoid this mistake:

Pick up general-purpose machine learning libraries, such as Scikit-Learn (Python) or Caret (R).
If you do code an algorithm from scratch, do so with the intention of learning instead of perfecting your implementation.
Understand the landscape of modern machine learning algorithms and their strengths and weaknesses.

3. Jumping into the deep end.

Some people enter this field because they want to build the technology of the future: Self-Driving Cars, Advanced Robotics, Computer Vision, and so on. These are powered by techniques such as deep learning and natural language processing.

However, it's important to master the fundamentals. Every Olympic diver needed to learn how to swim first, and so should you.

To avoid this mistake:

First, master the techniques and algorithms of "classical" machine learning, which serve as building blocks for advanced topics.
Know that classical machine learning still has incredible untapped potential. While the algorithms are already mature, we are still in the early stages of discovering fruitful ways to use them.
Learn a systematic approach to solving problems with any form of machine learning (covered in our Data Science Primer).

When Applying for a Job

This next set of mistakes can cause you to miss some great opportunities during the job search process. Even if you're well qualified, you can maximize your results by avoiding these hiccups.

4. Having too much technical jargon in a resume.

The biggest mistake many applicants make when writing their resume is suffocating it with technical jargon.

Instead, your resume should paint a picture and your bullet points should tell a story. Your resume should advocate the impact you could bring to an organization, especially if you're applying for entry-level positions.

To avoid this mistake:

Do not simply list the programming languages or libraries you've used. Describe how you used them and explain the results.
Less is more. Think about the most important skills to emphasize and give them the space to shine by removing other distractions.
Make a resume master template so you can spin off different versions that are tailored to different roles. This keeps each version clean.

5. Overestimating the value of academic degrees.

Sometimes, graduates can overestimate the value of their education. While a strong degree in a related field can definitely boost your chances, it's neither sufficient nor is it usually the most important factor.

To be clear, we're not saying graduates are arrogant...

In most cases, what's taught in an academic setting is simply too different from the machine learning applied in businesses. Working with deadlines, clients, and technical roadblocks necessitate practical tradeoffs that are not as urgent in academia.

To avoid this mistake:

Supplement coursework with plenty of projects using real-world datasets.
Learn a systematic approach to solving problems with machine learning (covered in our Data Science Primer).
Take relevant internships, even if they are part-time.
Reach out to local data scientists on LinkedIn for coffee chats.

6. Searching too narrowly.

Data science is a relatively new field, and organizations are still evolving to accommodate the growing impact of data. You'd be limiting yourself if you only search for "Data Scientist" openings.

Many positions are not labeled as "data science," but they'll allow you to develop similar skills and function in a similar role.

To avoid this mistake:

Search by required skills (Machine Learning, Data Visualization, SQL, etc.).
Search by job responsibilities (Predictive Modeling, A/B Testing, Data Analytics, etc.).
Search by technologies used in the role (Python, R, Scikit-Learn, Keras, etc.).
Expand your searches by job title (Data Analyst, Quantitative Analyst, Machine Learning Engineer, etc.).

DURING THE INTERVIEW

The last set of mistakes are stumbling blocks during the interview. You've already done the hard work to get to this step, so now it's time to finish strong.

7. Being unprepared to discuss projects.

Having projects in your portfolio serves as a major safety net for "how would you" type interview questions. Instead of speaking in hypotheticals, you'll be able to point to concrete examples of how you handled certain situations.

In addition, many hiring managers will specifically look for your ability to be self-sufficient because data science roles naturally include elements of project management. That means you should understand the entire data science workflow and know how to piece everything together.

To avoid this mistake:

Complete end-to-end projects that allow you to practice every major step (i.e. Data Cleaning, Model Training, etc.).
Organize your methodology. Data science should be deliberate, not haphazard.
Review and practice describing past projects from any internships, jobs, or classes you've taken.

8. Underestimating the value of domain knowledge.

Technical skills and machine learning knowledge are the basic prerequisites for landing a data science position. However, to truly stand out above the competition, you should learn more about the specific industry you'll be applying your skills to.

Remember, data science never exists in a vacuum.

To avoid this mistake:

If you're interviewing for a position at a bank, brush up on some basic finance concepts.
If you're interviewing for a strategic position at a Fortune 500, practice a few case interviews and learn about drivers of profitability.
If you're interviewing for a startup, learn about its market and try to discern how it will gain a competitive edge.
In short, taking a little bit of extra initiative here can pay big dividends!

9. Neglecting communication skills.

Currently, in most organizations, data science teams are still very small compared to developer teams or analyst teams. So while an entry-level software engineer will often be managed a senior engineer, data scientists tend to work in more cross-functional settings.

Interviewers will look for your ability to communicate with colleagues of various technical and mathematical backgrounds.

To avoid this mistake:

Practice explaining technical concepts to non-technical audiences. For example, try explaining your favorite algorithm to a friend.
Prepare bullet-point responses to common interview questions and practice delivering your answers.
Practice analyzing various datasets, extracting key insights, and presenting your findings.

Conclusion

In this guide, you learned practical tips for avoiding the 9 costliest mistakes by data science beginners:

Spending too much time on theory.
Coding too many algorithms from scratch.
Jumping into advanced topics, e.g. deep learning, too quickly.
Having too much technical jargon in a resume.
Overestimating the value of academic degrees.
Searching too narrowly for jobs.
Being unprepared to discuss projects during interviews.
Underestimating the value of domain knowledge.
Neglecting communication skills.

For more over-the-shoulder guidance, we also offer a comprehensive Machine Learning Masterclass that will teach you data science while allowing you to build an impressive portfolio along the way.

Source: HOB

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