Forget these 10 common data science myths

Despite the recent buzz around data science, for many technologists, data science is complex, unclear, and involves too many unknowns compared to other technology careers. At the same time, the few who venture into the field continue to hear some discouraging data science myths and ideas.

However, it seems to me that most of these stories are common misconceptions. In fact, data science is not as scary as people think. So, in this article, we’ll debunk 10 of the most popular data science myths.

Myth 1: Data science is only for math geniuses

While data science does have its mathematical elements, there is no rule that says you have to be a math guru. In addition to standard statistics and probability, the field includes many other non-strict mathematical aspects.

Even in areas involving mathematics, you don't need to deeply relearn abstract theories and formulas. Of course, this is not to completely eliminate the need for mathematics in data science.

Like most analytics career paths, data science requires basic knowledge in certain areas of mathematics. These areas include statistics, algebra, and calculus. So while math isn't the main focus of data science, numbers can't be avoided entirely.

Myth 2: No one needs a data scientist

Unlike more established technical majors like software development and UI/UX design, data science is still growing in popularity . However, the demand for data scientists continues to rise steadily.

For example, the U.S. Bureau of Labor Statistics estimates that the demand for data scientists will grow 2,031% by 2021. This estimate is not surprising as many industries including civil service, finance and healthcare have started to see the need for data scientists due to the increase in data volumes.

For many companies without data scientists, big data makes it difficult to publish accurate information. So while your skill set may not be as sought-after as other technical fields, it's just as necessary.

Myth 3: Artificial Intelligence will reduce the need for data science

Today, artificial intelligence seems to solve every need. Artificial intelligence is used in medicine, the military, self-driving cars, programming, essay writing, and even homework. Nowadays, every professional fears that one day robots will take over their jobs.

But this fear is not true for data science. AI may reduce the need for some basic work, but it still requires the decision-making and critical thinking skills of a data scientist.

Artificial intelligence can generate information, collect and process larger data, but it has not replaced data science. This is because most artificial intelligence and machine learning algorithms rely on data, which This creates a need for data scientists.

Myth 4: Data Science Only Contains Predictive Modeling

Data science may involve building models that predict the future based on events that occurred in the past, but is it only built around predictions? mold? of course not!

Training data for prediction purposes may seem like the fancy and fun part of data science. Even so, the behind-the-scenes chores like cleanup and data transformation are just as important.

After collecting large data sets, data scientists must sift necessary data from the collection to maintain data quality, so predictive modeling is a mission-critical and integral part of the field.

Myth 5: Every data scientist is a computer science graduate

This is one of the biggest data science myths. Regardless of your college major, with the right knowledge base, courses, and mentors, you can become a great data scientist. Whether you are a computer science or philosophy graduate, data science is within your grasp.

However, there are a few things you should know. While this career path is open to anyone with the interest and drive, your course of study will determine how easily and quickly you can learn. For example, computer science or mathematics graduates are more likely to master data science concepts faster than those from unrelated fields.

Myth 6: Data scientists only write code

Any experienced data scientist will tell you that the concept of data scientists only writing code is completely wrong. Although most data scientists write some code along the way, depending on the nature of the job, coding is just the tip of the data science iceberg.

Writing code only gets part of the job done. However, code is used to build programs, algorithms that data scientists use for predictive modeling, analysis, or prototyping. Coding only facilitates workflow, so calling it your main job is a misleading data science myth.

Myth 7: Power BI is the only tool needed for data science

Microsoft’s Power BI is a star data science and analysis tool with powerful functions and analytical capabilities. But, contrary to popular belief, learning to use Power BI is only part of what it takes to succeed in data science; it involves much more than this single tool.

For example, while writing code is not the central focus of data science, you will need to learn some programming languages, usually Python and R. You will also need to understand software packages such as Excel and work closely with databases to extract and organize data from them. Feel free to get courses to help you master Power BI, but remember; this is not the end of the road.

Myth 8: Data science is only necessary for big companies

When learning data science, the general impression is that you can only find it from big companies in any industry Work. In other words, failing to get hired by a company like Amazon or Meta equates to being unavailable for any data scientist job.

However, there are many job opportunities for qualified data scientists, especially today. Any business that directly handles consumer data, whether a startup or a multi-million dollar company, needs data scientists for optimal performance.

That said, put together your resume and see what your data science skills can bring to the companies around you.

Myth 9: Bigger data equals more accurate results and predictions

While this statement is often valid, it is still half-truth of. Large data sets can reduce the margin of error compared to smaller data sets, but accuracy depends on more than just data size.

First of all, data quality is important. Large data sets are only helpful if the data collected are suitable for solving the problem. Additionally, using artificial intelligence tools, up to a certain level, more volume is beneficial. After that, more data doesn't add any value.

Myth 10: It’s impossible to teach yourself data science

It’s impossible to teach yourself data science. This is one of the biggest data science myths. Similar to other technical paths, teaching yourself data science is very possible, especially with the abundance of resources currently available to us. Platforms like Coursera, Udemy, LinkedIn Learning, and other resourceful tutorial sites have courses to fast track your data science growth.

Of course, it doesn’t matter what level you are currently at, novice, intermediate or professional; there is a course or certification for you. So, while data science can be a bit complicated, that doesn’t make teaching yourself data science far-fetched or impossible.

Data science is much more than that

Despite the interest in this field, the above data science myths and more keep some tech enthusiasts from avoiding Opened this role. Now that you have the right information, what are you waiting for? Explore numerous detailed courses to start your data science journey today.

Original title: 10 Common Data Science Myths You Should Unlearn Now

##Original author: JOSHUA ADEGOKE

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