Can I learn Data Science in 6 Months ?… 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?
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.
Data science is one of the fastest growing careers of the 21st century. Every industry has pressing questions answered by Big Data, from businesses to non-profit organizations to government institutions. There is a seemingly-infinite amount of information that can be sorted, interpreted, and applied for a wide range of purposes.
How can a business sort through purchasing data to create a marketing plan? How can government departments use patterns of behavior to create engaging community activities? How can a non-profit best use their available marketing budget to further enhance their potential operations?
It all comes down to the work of data scientists.
Can I learn Data Science in 6 Months ?
Data scientists are trained to gather, organize, and analyze data, helping people from every corner of industry and every segment of the population.
Data scientists come from a wide range of educational backgrounds, and the majority will have technical schooling of some kind. Data science degrees include a wide range of computer-related majors, plus areas of math and statistics. Training in business or human behavior is also common, which bolsters more accurate conclusions in data science work.
Data Science in the Real World
Let’s look at a fairly typical example of a data scientist in action. Perhaps a cell phone company wants to find out which current customers are most likely to switch services to their competitor. The company could hire a data analyst, who would look at millions of different data points (or more specifically, create an algorithm to look at millions of data points) related to former customers. That data analyst (or, scientist) may discover that customers who use a certain amount of bandwidth are more likely to leave, or that customers who are married and between the ages of 35 and 45 are the most likely to switch carriers. The cell phone company can then change their business plan or marketing efforts to engage and retain these customers.
Netflix users see a real-world example of data management in action every time they access their account. The video streaming service has a program designed to give you suggestions that will best fit your preferences. Using information from your past viewing history, an algorithm gives you recommendations for shows you may enjoy. This is also seen in services like Pandora with their thumbs-up and thumbs-down buttons, and from Amazon, with their shopping recommendations.
Data Science vs Statistics
Data Science should not be mistaken for statistics. Although these two areas combine similar skills and share common goals (such as using a large amount of data to reach conclusions), they are unique in one clear aspect.
Data science, which is a newer field, is heavily based on the use of computers and technology. It accesses information from large databases, uses code to manipulate data, and visualizes numbers in a digital format.
Statistics, on the other hand, generally uses established theories and focuses more on hypothesis testing. It is a more traditional discipline that has, from a broad perspective, changed little over the last 100 years or more, while data science has essentially evolved with the rising use of computers.
How Long Does it Take to Learn Data Science ? Can I learn Data Science in 6 Months ?
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 are 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.
Conclusion
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.
