Data Science has made a massive impact on our ever-changing world since the beginning of the digital era. But, with this being such an incredibly complex profession requiring a stupendous amount of skills and expertise, just how long does it take to learn Data Science?
You can learn Data Science fundamentals in approximately 6 – 9 months by committing 6 – 7 hours a day. However, becoming a ‘good data scientist’ that can add value to a company within a high responsibility role will take years.
With the capacity to predict threatening events like natural disasters, to improve technology for the betterment of humanity, and to drastically alter success and profits for organizations across all industries, the potential within this profession is endless.
It’s not surprising that the demand for data scientists is on the rise, and many people have shifted their career focus as a result. Stick around to find out just how long it will take to get into this innovative professional field.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
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How Long Does it Take to Learn Data Science?
Within the field of Data Science, there are three primary occupations that make up the field, namely ‘Data Analyst’, ‘Data Engineer’, and ‘Data Scientist’. Each of these occupations require foundational education in data science, and each has a particular focus on various aspects within the field, with the most desirable, sought after and the astoundingly complex position being that of a Data Scientist.
While it’s true that you can learn the fundamentals of Data Science within around 6 – 9 months by dedicating around 6 – 7 hours every day, the journey to becoming a good data scientist that could operate effectively within a business is much longer.
If you are simply flirting with the illusion of data science for the sake of landing a flexible, high paying job, then odds are you are going to hit a wall and burnout before you’ve even gotten that far. There are plenty of online data science related mini-courses and advertisements which create unreasonable expectations and false perceptions surrounding the occupation.
The truth is that it’s a long, challenging, and bumpy road that requires an astonishing amount of perseverance, dedication, focus, and hard work. While it’s possible to learn data science by simply buckling down and getting stuck into it, the only thing that is really going to prevent you from giving up is passion and a realistic view of data science within the bigger picture.
Don’t get discouraged when you are told that you need to learn absolutely everything within the field and then some, as there is no rush to mastery. From the fundamentals to programming, machine learning, statistics, database technologies, and several other domain-specific technologies – all of these elements will be necessary, and you cannot skip forward in the learning process.
The world changes incredibly quickly, and it’s crucial for any data scientist to be constantly aware of this reality. According to research, 65% of present grade schoolers will hold jobs that do not exist yet, and 50% of current IT practices will be outdated in about 4 years. The job of a data scientist is to assess past and current data and solve complex problems in the present with a forward-thinking approach.
This means that the skills, wisdom, and expertise that you gain throughout your learning process is far more valuable than the actual information you learn. It’s more about improving your coding skills, mathematical/statistical skills, business skills, as well as data visualization, presentation, communication, and other soft skills. This will enable a necessary ability to hold a functioning balance between the present and future, and fostering adaptability within this regard is a key trait of a good data scientist.
Of course, all basic abilities and relative information are crucial in building a solid foundation and is necessary to develop these skills. But, what makes the difference is that learning this information will allow you to understand the bigger picture – Why do tools work the way they do? Where is the underlying logic? How do functions work with comparable tools?
Once this sort of mindset begins to form, adaption, flexibility, problem-solving, and switching between tools or programming languages will become far easier – as opposed to attempting to simply memorize every single thing.
There is a massive difference between what it takes to ‘learn data science’ and ‘become a good data scientist’. Your ability to learn quickly, adapt, and your individual orientation for this field will make the most impact on your data science learning potential.
For example, someone who has recently begun developing an interest in this field due to how much attention and praise it’s gotten over the past few years will take much longer to learn data science, as opposed to someone who has also never studied it, but has shown an interest in data science or related subjects throughout his/her life.
When there is an innate predisposition for the field, there is a natural tendency to search for deep and intrinsic understandings of its many aspects, and an internal drive to persevere whenever things get tough. Coupled with educational support systems, the consistency of this trait is what will ultimately amount to becoming a good data scientist over time.
Can You Learn Data Science on Your Own?
Can you learn the ins and outs of this insanely multifaceted and fascinating field all on your own? No, definitely not. You need a solid support system and guidance from experienced professionals. However, if the underlying question is more – Can you learn data science in the privacy of your own home, without ever physically attending classes? Then, yes! You can definitely learn data science ‘on your own’ without attending a school.
Free online classes are a great way to start your data science educational journey within the privacy and comfort of your home. It’s a fantastic option for you to practice, make mistakes, and learn from them at your own pace. From technical skills like coding and programming to data analysis and machine learning, it’s a viable option to get a good feel for the industry.
That being said, as much as free online material is helpful for gaining insight, you may need to invest in officially accredited courses when deciding to really buckle down and get into it. Courses that offer certification are a great option as you will get a physical representation of your hard work in addition to all of the knowledge and skills gained, which will assist the job hunting process.
The practice is a crucial component when learning data science privately, and there are open source projects and hackathons that you can use as a strong stepping stone in addition to your coursework. Be sure to check reviews and accreditation of any acclaimed educational service before enrolling to ensure quality and thorough learning experience.
What Should You Learn First in Data Science?
The most influential factors involved in learning data science is your outlook on the occupational field, the quality of your education or learning approach, as well as a frequent and consistent practice. That being said, here are some of the basic data science fundamentals that you should focus on when beginning your educational journey.
Before inventing any solution one must understand the problem, which requires a deep, holistic, and intricate understanding of the domain. While it can take you years to become an expert in the data science domain, the basics should be learned at a reasonable pace throughout your educational journey.
Python and R Programming
Establishing good coding skills is one of the most crucial factors when learning data science. For someone who has no experience, it will take 6 – 9 months to learn, and practicing on some open source projects will be advantageous.
This is another must-have for any data scientist. If you have some experience with coding and programming, it can take up to 3 weeks. But, if you are starting from scratch, it will likely take a few months.
Statistics and Probability
Must know skills within this area include probability distributions, sampling and simulation techniques, calculation and time series knowledge, accuracy measures and functions, and understanding of Regression and Bayesian models.
Data Cleaning, Data Visualization, Data Formatting, and Data Automation
A common joke amongst data scientists is that 80% of data science is data cleaning, and the other 20% is complaining about data cleaning. While this is not statistically accurate, it’s relevant to the fact that unlike common perceptions of the profession, a large majority of the job entails prerequisite steps.
This includes the collection, transforming, understanding, running other data analytics projects, and so on. These are basic yet necessary steps that are performed before running any proper ML algorithm.
While having a thorough grasp on these aspects may aid in developing a holistic understanding of the occupational field and its role within industries, these are only the basic data science fundamentals that are required to reach the junior level. This is only the beginning of your journey towards becoming an esteemed data scientist.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Learning data science is not easy – it will take time, hard work, and a plethora of rookie mistakes before you start to get into the swing of things. But, if you’re passionate about this field, and have a desire that will keep you motivated in growing your wisdom and skills day by day, then this learning period will be one of your best short-term and long-term investments.
On average, it takes approximately 6 to 7 months for an individual to become moderately proficient in the field of data science. However, by having a well-structured and thought through plan, and by committing yourself to it, you can considerably expedite this learning process and timeline.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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