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Daily, Weekly, Monthly, and Yearly Goal Tips to Guide a Self-Taught Data Scientist in 2023

These goals will help you set up and organize your upcoming year of becoming a data scientist

If you’re anything like me, December is the month that is dedicated to preparing for the upcoming year. Whether it’s cleaning up my computer files, donating old items I didn’t use in the past year, or purchasing the books I’ll be reading for the upcoming year, I believe that December should be set aside for preparation so that you can enter January organized, focused, and without any baggage from the previous year.

Part of this preparation also involves setting my resolutions or goals for the upcoming year and preparing all of the things I’ll need to achieve them. But why do I need an entire month to prepare my resolutions for the upcoming year?

The data suggests that even if you manage to complete your resolution or goal through the entire month of January, 80% of resolutions will still fail by the middle of February.

Why?

Because setting resolutions or goals in January is too late for them to be successful.

Have you ever considered why you see a sudden influx of gym-goers in the first week of January and then see them significantly taper off over the following weeks until by the end of the month it’s back to just the regulars? That’s because those people who resolved to "lose weight" or "hit the gym" in a giddy-induced stupor on New Year’s Eve didn’t set themselves up for success to achieve that goal through adequate pre-planning.

So what does all this talk about resolutions have to do with Data Science?

Well, if I’ve noticed anything about self-teaching yourself data science it’s that more often than not, you fail, at least once. The beginning of a new year is a great time to set the goal of teaching yourself data science, but only if you put in the appropriate amount of preparation beforehand. Therefore, December is the perfect time to begin preparing how you’ll achieve your goal of becoming a data scientist in 2023. Not only does it give you enough time to get prepared, but it also gets you in the right frame of mind to enter January driven to succeed.


Daily Goals

Daily goals for learning data science should be all about getting that 1% better each day. These goals shouldn’t be monsters that need an entire day to be tackled, but should instead be bite-sized endeavors that add up to a big win at the end of the year. Naturally, your daily goals will be guided by your overarching goal for the year, but they shouldn’t be anything too big or overwhelming.

For example, you could look for one menial task to automate, working for just 10 minutes on developing a new data science skill (think visualizations or mastering a new library), making progress on a passion or portfolio project (add a new feature every day, or take a few minutes to clean up your code and create documentation), listening to a data science podcast (or really just consuming any type of informal data science-related content), or sharing what you’ve learned with a community such as Twitter or through a newsletter.

Daily goals are not where you conquer the world. Instead, they’re much smaller, similar to taking your vitamins or walking the dog every day.

These daily goals should also be something that stretches your abilities without frustrating you. Daily goals should be something that you look forward to each day without becoming discouraged. The minute you skip a day you become easily susceptible to the whole goal framework for the year crashing down. That’s why these goals need to be manageable and reasonable, something that will be different for each person’s circumstances.

For example, if you’re working a job while also trying to transition into data science, your daily goal may be as simple as listening to a data science podcast during your commute. This can then be scaled down for times such as vacations or days when you’re not feeling well to ensure that you don’t break your daily goal streak without over-taxing yourself.

One of my daily goals for the last year has been to keep up with advances in data science by reading literature shared on sites like Towards Data Science. This takes less than 10 minutes each day as I skim what’s new and make a few notes of what I find interesting or relevant to my goals. This goal gets easily accomplished every day when I’m waiting for my tea to brew or when I’m needing a social media break from my regular work.

Remember, this goal has to be easy – something you can accomplish every day for the entire year without getting burnt out. You can have as many daily goals as you want but they should all be small, and easily attainable with just a few minutes each day.

You should be reminded of your daily goals every day at the same time. My favorite tool for this is a simple to-do app that sends me a notification at the same time every day. It’s extremely satisfying getting to check the boxes of each of my daily goals and also helps me set aside time to do them. Depending on how you work best, you may find that doing your goals at varying times every day works or you may prefer having a scheduled allotment of time every day in which to do them. Regardless, you need to be reminded of your goals every day and have a physical way of "checking" them off the list. Bonus points if you write some code to have your daily goals pop up on your computer as your daily reminder.

Weekly Goals

Weekly goals should leave you feeling like you’ve accomplished something at the end of the week.

When it comes to self-learning data science your weekly goal should have you moving one step forward in your learning curriculum having knocked some of the learning concepts off your list.

Depending on how much time you have to dedicate to your goal of becoming a data scientist in 2023, your weekly goals will range from learning single concepts to entire units of study. For example, if you have several other commitments on top of learning data science, your weekly goal could be "mastering variables and functions in Python". Alternatively, if most of your time is dedicated to learning data science, one of your weekly goals could be to "develop AI code that implements predictive capabilities in determining which customers in this data set will return their order".

Begin setting your weekly goals by taking out a calendar and writing on it the goals you want to accomplish each week. These goals should be closely aligned with your learning curriculum. For example, each concept or unit in your curriculum should be a weekly goal. This tactic helps you continuously move forward through your curriculum. I used this tactic during my bachelor’s degree which I completed online in an entirely self-taught method. By organizing my work for the week based on units, I was able to fly through courses by completing a unit or two each week as my weekly goal.

I prefer setting weekly goals up a little more liberally than daily goals. During your week you’ll have more productive days and less productive days. Therefore, I find it unhelpful to schedule tasks for certain days (unless of course they have specific deadlines). Instead, I set up a master to-do list for the entire week where I list everything I have to accomplish. Then, as I go through the list throughout the week, I check things off. No more "I have to get this whole list of things done on Monday". Instead, I just go with the flow. Sometimes I’ll get tonnes of work done on Monday and not much work done on Tuesday because something came up. Regardless, I have an idea of all the work that needs to be accomplished by the end of the week instead of tying myself down to having to accomplish a certain number of tasks each day – this also helps me achieve a better work-life balance.

Monthly Goals

Monthly goals should be substantial. They should be 12 milestones that give you big tangible benefits or advancements in your journey toward becoming a data scientist.

Monthly goals should be challenging without being unattainable. These goals could each be a portfolio project, a certification completed, or a tangible skill that was mastered. Regardless of what they are, you should be able to look back at the end of the year and see 12 big accomplishments that all helped add up to you achieving your yearly goal.

Monthly goals should first and foremost align themselves with your weekly goals. For example, if your weekly goals for January primarily reside around learning Python, your monthly goal should be something along the lines of creating a project in Python where you can create, update, delete, and search contacts in a digital address book (for example). Or, if your weekly goals for April are about putting together your programming, math, and visualization skills to build a full data analysis, your monthly goal could be to create a simple analysis of World Bank data that determines the amount of political participation in a given region. Alternatively, if your weekly goals for October were centered around career prep, your monthly goal could be applying to 10 viable data science jobs.

Monthly goals should culminate all of the things you worked on with your weekly goals and push them to the next level.

I like to work on monthly goals throughout the month instead of leaving them to the last minute. Organization is essential to ensure that you’re balancing your daily and weekly goals on top of completing your monthly goal. Monthly goals should be chunked into manageable portions so you’re not left rushing to a deadline (unless that’s your thing of course). For example, if your monthly goal is to apply for 10 viable data science jobs (which involves developing unique resumes and cover letters for each if you want a good shot at it), you could pick 2–3 jobs to work on applications for each weekend. Alternatively, if your monthly goal is to put together an entire data analysis, you could work on the data acquisition and cleaning the first weekend of the month, do the analysis the second and third weekend, and complete your visualization and project conclusion on the fourth weekend.

I think the success of monthly data science goals is to make them enjoyable or exciting to work on. Daily and weekly goals are easy to get done because you get in a rhythm and it just becomes part of your daily grind. However, monthly goals are always looming over your head and can feel like an unnatural burden to have to accomplish on top of all the other work you have to do. You can mitigate this feeling by making your monthly goals involve something you enjoy. For example, your monthly goal could be to predict the upcoming World Cup winner or help a small business in your community with pro bono data science work. Whatever it is, your monthly goal should incorporate data science with something you love to make it something you look forward to completing. Not only that, but monthly goals that become portfolio projects will leave you with a bunch of projects that you’ll be able to describe to future employers passionately.

Yearly Goals

Your goal for the year is quite simple: to become (or at the very least get closer to becoming) a data scientist.

However, this yearly goal isn’t specific enough to be attained.

When you set your goal for the upcoming year this December, you need to set some parameters for it first. While I’ve always hated the SMART acronym, I think it gives us a good foundation on which to lay this specific goal.

Your goal of becoming a data scientist (or getting closer to becoming one) by the end of 2023 has to be specific, measurable, attainable, relevant, and timely (yes, I also cringe every time I use this acronym, but bear with me). The point of SMART goals is to set better goals that have a higher chance of being achieved. When you set specific goals, you ensure that you have a clear picture of what needs to be achieved. The goal must be measurable so you can measure how well you’re doing in accomplishing it. Your goal also needs to be realistically attainable in the time you’ve allotted for yourself (which is why your goal may be to become a data scientist by the end of 2023, or to just get closer to becoming one). Relevancy will help you stay committed to your goal because it aligns itself with your broader aspirations. Finally, your goal must have time constraints placed upon it otherwise (and trust me here) you’ll never accomplish it.

Therefore, your overarching goal for 2023 could be to:

Become a data scientist by December 31st, 2023, by acquiring a data science job at a company after preparing for the interview throughout the year by honing my data science skills using my self-learning data science curriculum. This will lead me closer to my overall aspiration of starting my own data science consulting company.

Why does this goal work? Because it’s specific (become a data scientist), measurable (by acquiring a data science job at a company), attainable (after preparing for the interview throughout the year by honing my data science skills using my self-learning data science curriculum), relevant (this will lead me closer to my overall aspiration of starting my own data science consulting company), and timely (by December 31st, 2023).

Another example of a great yearly goal for 2023 could be:

I will become proficient in customer marketing data analysis using Python, Excel, SQL, and Tableau by December 31st, 2023, by completing my project on which apps are more likely to attract users, which will be added to my data science portfolio that will be used to help me attain a job in data science.

This goal is also specific (become proficient in customer marketing data analysis using Python, Excel, SQL, and Tableau), measurable (the project will be added to my data science portfolio that will help me attain a job in data science), attainable (by completing my project on which apps are more likely to attract users), relevant (to help me attain a hob in data science), and timely (by December 31st, 2023).

While these goal attributes are verifiably cringe-worthy, they can help give you a direction that will guide you, as well as your daily, weekly, and monthly goals, throughout the coming year.

Your yearly goal needs to be posted prominently in your workspace where you will see it every day. I’m a big fan of the humble sticky note for this. Look at this goal every day, even when you’re not lacking ambition, to remind yourself why you’re working so hard on your daily, weekly, and monthly goals.


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