Handling knowledge overload as a data scientist
4 Ways to Keep Yourself Going…when it feels like too much.
Ever look at all the data science content on Medium, Linkedin, and other sites and feel like you’ll never learn it all?
Me too. We’re all in the same boat.
It can get very overwhelming, even for experienced data professionals. Sometimes you even question the point of it all. Fortunately, you don’t need to do that.
The secret is learning to focus your efforts, and be realistic about your interests, passions, and how you approach your learning.
Here are four ways to not get overwhelmed while breaking into the data science space.
1. Set a Clear Career Vision
Getting overwhelmed comes from taking on too much at once. It also comes from not budgeting your time well. Vision help break this down in to small manageable goals, and allow you to asses the results of your efforts. Of all five ways, setting career vision is the biggest one- it enables the other four to be successful.
Specific and measurable goals will help direct your efforts. They help you decide how much time to spend studying and what path to follow. They keep you from burning out, and help you to be consistent. Once you set you goals, break them up into: 2 week goals, 6 month goals, and long term goals — 2 years max. Always start from the long term goals then break it down.
This will help you keep track of your data science goals, and keep you from looking to far ahead — a common source of feeling overwhelmed. You also get a view of the larger picture.
You begin see how each goal you complete is a part of a larger effort, and the progression to it. When you know how your effort fits in the bigger picture? Its not so overwhelming.
2. Stop Following Trends
Avery quick way to get overwhelmed is to start following trends. This means that every time that someone posts a new technique, code snippet, or statistical article or post, you end up worrying about it. After enough of these, you feel overwhelmed and unmotivated to pursue your goals. Chasing trends relentlessly destroys your ability to be skilled at anything.
When you feel tempted to follow a trend:
Evaluate whether or not that trend is relevant to your goals. Does it move the needle forward to achieving your immediate goals? If not, then set aside for later. You can always return to it.
Distinguish between the hype and reality. If its mostly hype, search for facts that make it useful to your learning. If it is pure hype, toss it out. If it is reality, break it down into pieces you learn one step at a time.
Research. Discover if fundamentals exist. If none exist, it’s most likely hype. If they do exist, learn them well. It is easy to learn bad habits, and but it much harder to unlearn them.
The bottom line?
Stick to what motivates you in data science and analytics. These are your passions. Those passions will act as a magnifier for what you do. If you are always chasing random trends, it will make it impossible to learn or find your career niche.
Sticking to a trend without your long-term vision? Can tie you to path dependence. I’ve seen data scientists try to learn the trendiest topics - only to find that they lack critical foundations. Don’t do this.
If your passion happens to coincide with a trend? Then strive to be the innovator: learn the basics and aim for the highest quality. It might seem pointless in the beginning, but if you learn these well, the insights will flow like water.
3. Focus on Your Journey
Another frequent way to get overwhelmed in focusing on other people’s journey and not yours.
When you focus on trying to compete with others, you end up feeling overwhelmed. Especially if seem they are very knowledgeable in the data science space.
To focus on your journey, focus on quality of your efforts. Always check to making sure they are aligning with your goals and career vision.
If you want to focus on your journey, focus on your deep interests. Make your projects and learning an extension and expression of who you are. These show up many times in your life. You’ve probably seen them before.
Do:
Assess what your passionate about, and do regular projects related to it.
Strive to do small things well in your projects. These add up in the long run.
Experiment. You must tinker in order to understand the strengths and limitations of your tools.
Compete against yourself. Ask of any project or work you did, How can it be done better? Competition against yourself has clear benchmarks - and even clearer iteration.
Evaluate things logically. If you must analyze at other people, assess the facts of what they do well and don’t do well.
Don’t:
Copy the journey of other people. Unless you lived their life experiences from birth, you will never be able to copy them.
Try to learn the same way as someone you admire. The way they learn and perceive knowledge is unique to them and you. Be proud of your learning style and the unique niche you occupy.
Get discouraged by seeing someone’s achievements. You don’t know the circumstances or effort it took them to get there. If you’re discouraged, use that as a guide. Ask what bothers you. It often points to what you want to learn and develop.
Your journey is your own. Neither I, nor anyone in data science can tell you how to walk it. We are only signs, pointing you to were you want to go. You have to make the choice. No number of books or mentors can do that.
What you want out of it, and how you walk it? That’s up to you. Your data science path is made by walking on it.
When you focus on your own journey and compete against yourself, you’ll get overwhelmed less. Work will become play, and other accomplishments and achievements will be data that guides you.
4. Be Consistent About Daily Effort
Think about learning to play an instrument. At first, your fingers fumble and the music sounds off-key. But you practice every day, one agonizing piece at a time. One day, someone listens to you. They say you sound much better - much to your surprise. You didn't notice the improvement, since you focused only on each small practice.
Consistency in data science follows the same principle. It’s not just about pushing yourself until you break but about making incremental improvements while keeping an eye on the big picture. Take time to compare your progress with your goals.
Your goals relevance depends on successes and mistakes - they give context to the other. You get more successful because learn from your mistakes, and you make mistakes because you are trying new ways to be successful.
When you completely give up because of a bad day or because you didn’t live up to your expectations? This breaks the accumulated knowledge.
Accumulating knowledge means knowing when to start and stop. Take breaks or take them all at once. So, your study and relaxation must have a rhythm — you have to eventually switch from one to the other. Committing to a break or vacation a consistency lesson itself.
Through the process of consistent quality work you will refine your knowledge. And being so focused on this, you will not have time to feel overwhelmed.
Conclusion
Data science can be a formidable field to break into, no matter what background you come from. Feeling overwhelmed is a normal and natural thing, and is a part of the process — what’s more important is how you handle it.
In summary:
Set Goals
Don’t always follow trends
Focus on Your Journey
Be Consistent about Daily Effort.
Remember, its not about falling down. Its about whether you get up every time.
Thanks for reading! I’ll be doing occasional thought mentality and pragmatic thought pieces. Getting the right attitude and actionable steps is half the battle to being an effective data scientist. I’ll still have technical and more job related stuff in the next few weeks.
Before then, if you have any questions or comments, feel free to comment or reach out to me on:
Thanks for reading!
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