Think twice before becoming Data Scientist
Tags: datascience, data, dataengineering, career
Published: 2023-09-18 11:50:14



>  “All that glitters is not gold; often have you heard that told. Many a man his life hath sold, but my outside to behold.” — William Shakespeare

![Reality Check](https://cdn-images-1.medium.com/max/2800/1*HlAFFqc8oyhKM8S00DIJzA.png)

You know how they say data science is like a universe of infinite possibilities, with every problem simply waiting for a data-driven superhero to swoop in and rescue the day? That is somewhat correct, but here’s the deal: it isn’t always as glamorous as it seems.

But check out the reality: it's not all glitzy models and miraculous algorithms. In our reality, data isn’t always perfect, models don’t always behave as intended, and not every project ends triumphantly.

Fear not, It is not a gloom and doom. We are humans who have coded algorithms to go on Mars, the Generative AI’s, and certainly, there is a solution for our problems even though we have created them for ourselves: Evolution ‘huh’.

## what is discussed here:

 1. **Data Quality Issues**

 2. **Overhyped Expectations**

 3. **Continuous Learning**

 4. **Data Privacy and Ethics**

 5. **Documentation**

 6. **Project Failures**

 7. **Imposter Syndrome**

 8. **Model Deployment and Maintenance**

## Harsh Reality of Data Science

So, without going anywhere else, let’s get to the point and get you the reality checks and solutions for them.

### 1. Data Quality Issues

Garbage in, Garbage out. Due to insufficient, incorrect or inconsistent data sources, data scientists frequently spend a substantial amount of effort cleaning and preparing data. Most of the time data is duplicated, biased, and outdated. Tackling data quality concerns is critical in data science since the precision and dependability of modelling and conclusions created from that data are directly affected by the level of quality of the input data.

![Garbage Data](https://cdn-images-1.medium.com/max/2800/1*2mlv26DDTosyn6yRTAKRbg.png)

**Solution:** Spend time preparing and cleaning data to guarantee correctness and completeness. To optimise data pipelines, establish data quality standards and engage with data engineers.

### 2. Overhyped Expectations

Data science is frequently overhyped, leading to excessive expectations. Data cannot solve every problem, and not every data-driven endeavour will provide spectacular outcomes as there is no guarantee of definite accuracy and monetization from data-driven Predictions.

![](https://cdn-images-1.medium.com/max/2800/1*Oqi6oI6KQb9-uo7bRGyYSg.png)

**Solution:** Communicate with stakeholders on what data science can and cannot do. Project schedules and deliverables should be reasonable. Concentrate on small steps forward.

### 3. Continuous Learning:

Data science is an ever-changing discipline. Data scientists must commit to lifelong learning and staying current with new tools, methodologies, and technologies in order to remain relevant. Today, Some Algorithms are hyped like LLM(Large Language Models) may or may not be used as they are often upgraded, refined and improved.

![Continuous Learning](https://cdn-images-1.medium.com/max/2560/1*KaIh8j33i4D9Xp3P0KnNiw.jpeg)

**Solution:** Make time for continuous learning and professional growth. Attend seminars and keep up with industry developments. Choose a group of peers that make you learn new things.

### 4. Data Privacy and Ethics:

A double-edged sword without any doubt. Data privacy and ethical considerations grow increasingly important as data gets more lucrative, data is the currency of businesses. Navigating these challenges may be difficult. Sometimes data is extremely sensitive as it may hold people’s bank records, personal thoughts etc.

![Ethics](https://cdn-images-1.medium.com/max/2560/1*jbjF9PTKk2X7dRWGSxcojw.jpeg)

**Solution:** Stay up to date on data privacy rules and ethical norms. Use strong data confidentiality and encryption techniques. Keep stakeholders up-to-date on ethical issues.

### 5. Documentation:

Keeping up with code, models, and research and ensuring they are replicable and well-documented takes work, but it is vital for sustaining transparency and cooperation. What dependencies, experiments, results and Ethical Considerations have been used before and during the project?

**Solution:** Utilise version control systems and tools for documentation as a solution. Keep detailed records of all code, demonstrations, and model versions. To maintain openness, work closely with team members.

### 6. Project Failures:

Not every single data science project is a success. Certain might fail to deliver substantial outcomes, while some may be abandoned for a variety of reasons, including shifting corporate goals, Inaccurate Models, Data Quality Issues, Failure to meet deadlines and Insufficient resources.

![Failure](https://cdn-images-1.medium.com/max/2560/1*2tqkz7X9vuCaqU5ctz0ZdQ.jpeg)

**Solution:** Adopt an attitude of exploration and failure learning. Before committing significantly, do extensive project feasibility studies. Constantly assess the project’s growth and make necessary adjustments. Furthermore, Invest in proper project planning, stakeholder engagement, data quality assurance, and project management practices to reduce project failure risk.

### 7. Impostor Syndrome:

A lot of data scientists, including experienced ones, suffer from imposter syndrome, which makes them feel as though they don’t fully belong or aren’t as good as they should be. They downplay their achievements, overwork and seek validation constantly because of the feeling of not knowing everything like in web or software development fields.

![Imposter](https://cdn-images-1.medium.com/max/2560/1*Dt4eMDB4xv0EpDC-i1wddQ.jpeg)

**Solution: **Recognise that imposter syndrome is prevalent in data science, even in qualified data science individuals. Therefore, Seek out mentoring and support from your peers, if that can make you feel confident. Individuals suffering from imposter syndrome might benefit from developing self-confidence and adopting a growth mindset in order to prosper in their employment. Celebrate your accomplishments and recognise the skills you possess.

### 8. Model Deployment and Maintenance:

Building a model is only the first step. Model deployment and maintenance in a production setting can be complicated and difficult as it involves Hyperparameter tuning, data drifting, model updation, compliance changes and security concerns. The lifecycle management of models can be intricate and often overlooked due to its cumbersome work.

![Model maintenance](https://cdn-images-1.medium.com/max/2560/1*UcevP5C-4SPDszWtwA12aA.jpeg)

**Solution:** Create strong deployment pipelines and evaluation mechanisms. DevOps practices should be used to streamline model deployment and upgrades. Prioritise documentation for upkeep. Make data more refined for future endeavours, take enhanced security measures, regularly collaborate with stakeholders and use risk mitigation.

### Final Thoughts:

Regardless of these obstacles, data science is a lucrative and prominent discipline. Confronting these hard truths, according to several data scientists, is part of what makes the field both vital and intriguing. Data scientists may continue to contribute meaningful contributions to their organisations and society as a whole by recognising and overcoming these issues.

Don’t forget to follow. If You are still persistent about the data science field. Congratulations, I have got some presents for you. Here is the [Ultimate guide for data science books](https://dev.to/imadadrees/ultimate-guide-best-books-for-data-science-with-ratings-for-all-levels-42ol) for book wizards, otherwise, for comprehensive Roadmap and resources: [7-Stage Roadmap for Data Science](https://dev.to/imadadrees/7-stage-roadmap-for-data-science-451n). To know about [soft skills in Data Science](https://dev.to/imadadrees/the-secret-sauce-of-success-soft-skills-every-data-scientist-needs-3bl4).

Happy Learning!