Tips for Setting Daily, Weekly, Monthly, and Yearly Goals for an Autodidactic Data Scientist in the Year 2023
In the quieter months of December, as the year draws to a close, it presents an opportune moment for individuals aiming to advance their data science skills. This period aligns well with reflection and planning cycles, making it an ideal time to set clear, structured objectives for the upcoming year.
One of the key advantages of December is the chance for reflection and goal setting. Many people and organisations review annual achievements during this month, making it a strategic time to establish ambitious yet achievable learning goals for data science. This cognitive reset enables clearer planning, setting the stage for a successful learning journey.
Another advantage is the availability of structured programs starting early in the new year. Comprehensive data science bootcamps, such as 31-week immersive programs that combine self-paced and instructor-led modules, often commence early in the calendar year. Preparing in December allows learners to build basic skills in programming, statistics, and data handling, smoothing the transition into intensive courses.
December also offers the opportunity to focus on critical data science skills. Learners can hone their abilities in Python, R, SQL programming, machine learning frameworks, and data visualization tools. Engaging in self-study during this quiet period supports mastering the fundamentals required for practical and project-based learning during the year.
Preparing in December also creates commitment and motivation, leveraging the mindset of a “new year, new goals” mentality. This temporal benchmark often boosts engagement and discipline compared to starting mid-year, when work and responsibilities can be heavier.
Monthly goals should be substantial, offering big tangible benefits or advancements in the journey towards becoming a data scientist. A yearly goal 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 data science skills using a self-learning data science curriculum.
Monthly goals should also align with weekly goals. For example, if weekly goals are about learning Python, a monthly goal could be creating a project in Python. These goals should be enjoyable or exciting to work on to avoid feeling like an unnatural burden.
Daily goals for learning data science should stretch your abilities without frustrating you. They should be small, manageable, and easily attainable with just a few minutes each day. A simple to-do app can be used to remind you of your daily goals every day at the same time.
It's important to note that setting resolutions or goals in January is too late for them to be successful. By starting in December, learners can leverage the advantages of reflection, program cycles, and the opportunity to start foundational learning before structured courses begin in the new year, ultimately enhancing readiness to tackle complex data science challenges and attain career goals effectively.
However, daily goals for learning data science may vary depending on personal circumstances. The key is to find a routine that works for you and stick to it. By doing so, you'll be well on your way to achieving your data science learning goals in the new year.
- Utilizing the quieter months of December offers an excellent opportunity for individuals aiming for personal growth and self-development, specifically in the field of data science, to review their annual achievements and establish structured, achievable learning goals for the upcoming year.
- By embarking on a data science learning journey in December, one can leverage the "new year, new goals" mentality, setting the stage for a successful learning experience, and ensuring readiness to tackle complex data science challenges and attain career goals effectively.
