Lessons learnt early as a Data Engineer

 

There is a lot to learn in the field of data engineering. The demand for data scientists is continuously expanding, but there is a scarcity of people with data science abilities. As a Data Scientist, the most satisfactory solutions emerge from thinking from the ground up. Exploring, manipulating, analyzing, and developing algorithms to provide answers to questions or provide valuable recommendations is what Data Science is all about.

Curiosity is a crucial talent in data science.

Other abilities, such as mathematics and programming, can be learned through experience and various online courses. A Data Scientist's curiosity, growth mentality, and self-starter attitude are equally crucial as their technical skillset. Curiosity, on the other hand, should be the essential trait of a data scientist. The breadth and depth of the data science area, on the other hand, can be daunting at times.

·         Curiosity is about deducing meaning from facts and discovering answers to questions—curiosity about getting fresh insights from raw data because measuring something is the only way to improve it.

·         Diversify your data science learning curriculum to take advantage of this paradigm.

·         Curiosity was the sole thing that prompted me to obtain an IBM Data Science Professional Certification in Data Science.

·         The only thing standing in your way is your own time and dedication.

If I hadn't been interested in data science, I would not have begun this programme and, as a result, would not have learned additional abilities. When you're exposed to the same concepts across several mediums and have the correct balance of information consumption and hands-on learning by doing, your mind forms the most profound connections. This is why curiosity is essential for anyone interested in pursuing a career in data science. Most crucial, seek advice from mentors, coworkers, or friends.

Concentrate on the fundamental abilities.

To be a data scientist, you must be knowledgeable with various tools and technologies, particularly open-source ones such as Hadoop, Java, Python, C++, and database systems such as SQL, NoSQL, HBase, and others.  We must first master the fundamentals before moving on to the more advanced technology we will face later. The data varies depending on the industry.

·         Learning everything at once will not get you very far, but having a solid foundation will.

·         As a result, interpreting corporate data necessitates knowledge, which can only be gained by working in a particular data area.

·         Whatever new tools are released, you must first master the fundamentals to maximize the value of the latest technologies.

·         Organizations amass massive volumes of data through a variety of methods.

As new, more optimized tools are introduced, the most recent technology frequently changes. So, rather than pursuing the latest device, focus on developing a deep understanding of transferable issues from the outset of your career. It's better to learn new technologies quickly and with a solid foundation than to know one single tool that will be out of date in a few years.

Many companies are increasingly campaigning for a simpler, more efficient IT stack. A data scientist must comprehend large-scale machine learning algorithms and programming while also being a skilled statistician. For example, instead of developing sophisticated Hadoop systems, Databricks can be used as a "one-size-fits-all" solution. Apart from computer languages, this necessitates knowledge of other scientific and mathematical areas.

However, you should learn about the tech stack you're employing at work!

Don't try to remember everything also; understand it.

It's a mistake to put too much emphasis on being able to code from memory. It's quite impressive, but yet it's simply not feasible.

·         Every project you start work on will most likely be substantially different from the last, necessitating the application of new methodologies.

·         The idea is to improve your critical thinking and problem-solving abilities.

·         We are compensated in part for our capacity to adapt to issues and solve them.

·         You're not assisting the team if you can solve the problem using code you've written before.

 The point is that they are technically not able to recall the syntax. They are unable to reflect their values and beliefs. They are having some trouble remembering important and needy information. They have trouble remembering how to accomplish things, such as developing code and applying the data science concept.

Maintain good contact with some stakeholders.

Be adaptable while communicating with stakeholders. Because projects change and new solutions are invariably discovered during the process, it's vital to keep lines of communication open. To stay aligned and be efficient with my time, I always keep in touch with the stakeholders. They are usually thinking about the topic differently than you because they have so much more context. You'll have to push back at times, and you'll have to adapt to others.

·         Working effectively with stakeholders is one of the most crucial skills for a data scientist.

·         Every model you technically create, whatever the prediction you make, and advice, there's quite a reasonable probability you'll be wrong.

·         Your ability to brainstorm with product managers, interact with engineers, and persuade executives will determine your effectiveness.

·         As a result, collaborate and bring them along for the ride so that your stakeholders have a pleasant experience and learn more about you.

·         This is an exciting element of the work, but it's challenging to get good instruction on how to do it properly.

When working on a project, it's also a good idea to keep the stakeholder updated frequently, rather than waiting until you've mastered your job, which is a common mistake made by rookie programmers. Other employees in your organization benefit greatly from knowing what you're working on to avoid duplication of effort and uncover opportunities to collaborate. Remember, it's your responsibility, not theirs, to improve.

Learn how to prioritize your tasks.

It's difficult to strike a balance between what's most important and what can wait, especially when dealing with multiple demands for assessments. There's no doubt that you'll be a valuable member of your team, and everyone will want a piece of the action (your time).

·         This creates a climate where the projects you work on have a significant impact on the business, yet you are limited in your options.

·         You'll need to learn to prioritise chores based on their importance and urgency, as well as learn to say no to others.

·         Furthermore, answering one query with data frequently leads to new inquiries; thus, fulfilling requests to the amount of labour that is quite already on the table rather than reducing it.

·         It's better to state that a piece of work isn't possible right now but that you'll let them know as soon as you have time than to say that you can do it but then under deliver.

Prioritize projects that impact the firm and are innovative as much as feasible (the work we all wish we were doing). The 80–20 rule applies in this case, with only 20% of your efforts yielding 80% of your results. It would be best to focus on the aspects of your job that add the most value rather than spending too much time refining the last 20% of a project. However, concentrate on the non-innovative but vital work of providing proof to people who can help the company advance. You'll finish what's important and spend the majority of your time on it this way.

Get a good mentor for better mentorship.

Data scientists are in higher demand than ever before in history. And, given the tight labour market, finding a desirable position for data scientists has been difficult. An essential part of working as a Data Engineer is being open to learning. This includes building both your technical and soft skills. Often, data science aspirants plan their careers by reading online articles, talking to their peers or following other professionals.

·         A mentor will fill the knowledge gap and help aspirants understand the core of the industry.

·         Again, if you find a good data science mentor, then respect this relationship, and with that, you'll profit from it in one way or another, for sure!

·         If you have no idea where you'll find your first data science mentor, I recommend going to a few local data science meetups or conferences first.

·         Getting a mentor allows you to learn from those that have already walked the path you are on.

·         In short, a good mentorship would assist applicants in improving their performance by providing ongoing feedback and constructive criticism.

Many mishaps and mistakes can be prevented if you spend time learning from a mentor. Such appropriate guidance can ideally assist newbies in their data science journey.

 

Understand your company's maturity model.

When it comes to acceptance and execution, you must be practical. It's worthless to develop a model if your organisation isn't ready to put it into action. The transition from simple business reporting to business intelligence is significant, but companies beyond typical BI disrupt markets. They've invested in modern data teams, drawn from their business operations to improve insights across the organization, resulting in greater revenue and ROI.

 

·         Nothing is more frustrating than spending 80 hours building a deep learning model only to discover that your company lacks the process and resources to implement it.

·         Going from zero to significant machine learning expenditures may or may not be the most excellent road forward for your company; nevertheless, taking a good hard look at the maturity curve is the best way to know.

·         The reality is that not every company is ready to deploy and expand analytics systems quickly and efficiently.

·         Understanding what constitutes a modern data team is critical in this endeavour.

Also, keep in mind that a machine learning method isn't necessary in all cases. Simple rules-based heuristics are also acceptable at times. Firms build the infrastructure and experience needed to produce new insights and then communicate them efficiently and correctly by focusing on those early phases.

Take pleasure and have an interest in your task.

According to a new study, most data scientists nowadays believe they've landed the sexiest job of the century. You probably don't want to spend a significant portion of your waking hours working if you don't have to. According to the study, more than 90% of data scientists polled indicated they were satisfied with their careers, with nearly half saying they were overjoyed.

·         Concentrate on finding the positive aspects of your work, improving it, and enjoying it.

·         According to the findings, data scientists spend an abnormal amount of time on things they despise and very little time on activities they enjoy.

·         Working in data engineering is extremely rewarding. You have the technical ability to persevere in the face of hardship and have the potential to have a significant influence on the company.

·         Data scientists claim that generating and modelling data, mining data for trends, and developing algorithms are their favourite tasks.

When your code stops producing errors and runs to completion for the first time, the relief you feel should be treasured.

Final lines

It's all about curiosity and a desire to learn more and assist others in obtaining the correct data! Inquire about everything; your intellectual curiosity will set you apart. You will begin to improve the assignment and provide your opinions, which will benefit your career and help the team with your experience.