Myths around data science

Data science is a competitive weapon for organizations globally. Like other technologies and processes that can change the way businesses operate, there are a lot of contradictory information and myths around data science on social media, blogs, and case studies that causes considerable confusion.

While most business leaders are aware of the fact that people adept at data science can enhance operational efficiency and customer relationships, they do not have right guidance in place and take the wrong steps by considering myths as facts. Below are six myths around data science, which are good to know in order to to position yourself better in this realm.

Let’s get started!

1)  Data science applies to unstructured data only

IBM has explained the four Vs of data science. These important elements state that you don’t require terabytes of data for predictive analytics.

According to IBM, the four Vs are: volume, velocity, variety, and veracity.

  • Velocity-based data: Data size is not the only thing that matters in data science. The velocity at which it data is generated also matters. Data developed in fragments but at a faster pace would help you more to make the right decision on time.
  • Variety-based data: Data science can be implemented on different types of data from CRM systems, social media, and call logs, but the data should be structured. Organizations can develop better insights on their customer profiles, buying pattern, thus leading to efficient decision making.
  • Veracity-based data: A large volume of data is important, but more so is the accuracy of the data. When data is integrated, current, and confirmed, you can make decisions that are likely to have positive outcomes.
  • Volume-based data: Big data involves large amounts of data. Earlier, employees created this data. Today, data is generated by networks, machines, and human interaction through various sources like social media. This results high data volumes to be analyzed to generate insights.

Analytics can be performed on structured data, typically easier than unstructured data. That being said there are platforms out there that can help to turn your unstructured data into an easier data set to perform analytics on.

Whether you have experience in this or not, data science can be implemented in many ways. More important than the structure of your data, setting a new benchmark in the field of data science depends on a thorough understanding of big data modules. As a result, a big data certification course has become important, so you can master these modules and apply your insights to real world problems.

2)  Data scientists are innumerable

Though the discussions about data scientists are numerous, there is actually a scarcity of individuals with the skills to perform the work. A report by Fast Company has already indicated that there will be a shortage of 250,000 data scientists in the U.S. alone by 2024. Most companies hunt for skillful data scientists or unicorns. A unicorn refers to data scientists who have a graduate or masters degree in statistics or math with expertise in programing skills. As the candidates with this domain experience are hard to find, most organizations have begun to develop a data science practice that integrates the skills of several people.

Usually, organizations make the common mistake of hiring specialized expertise, such as a Ph.D.-level statistician before they really require one. Most decision makers believe that a company needs such a person to stay ahead of competitors but the responsibilities of that person are still not clear. Make sure you evaluate your requirements well and understand your data driven goals to understand whether you require a proficient data scientist or not.

 3 ) You need to have a Ph.D. degree

Investing in academics for periods of long time is appreciated, but a Ph.D. in this field is far from mandatory. A Ph.D. degree in String Theory or Electrical Engineering does not make you efficient at solving complex partial differential equations, using hidden Markov models or developing a deep learning algorithm. Similarly, students do not get better at performing matrix algebra while moving ahead with their specialized education. In cases like these, field experience matters more than just theoretical knowledge.

4) Analysts take care of algorithms only

A notion prevails that analysts consider only algorithms and program and do not show interest in understanding business and its problems. To some extent, this is right because of rapid changes in technology that make analysts a busy bee who spend most of their time in solving technological or algorithmic issues, and less time in understanding the business problems. But they need to have a basic knowledge of a company’s tools and trade, which can improve data analytics and problem-solving.

An absence of knowledge about a company’s policy hinders an analyst’s ability to make a valid target variable, conclude the right set of predictors, or locate the appropriate success base for the model. Moreover, they won’t be able to create a product that results in measurable benefits.

When analysts and the business side leads do not work in tandem, it results in the development of corporate laboratories working on research and science rather than improved data-driven decision making.

5) Learning a tool means knowing data science

A professional who has taken a data science course is more than a programmer. Therefore, understanding a tool or a programming language alone won’t make you an efficient data scientist. You need to look beyond tools and algorithms by understanding the applications of different predictive modeling techniques.

Businesses don’t seek data scientists who only have tool expertise, but look for someone who understands varying data science concepts like programming, statistics, and most importantly business. Make sure you learn a business platform along with a tool. Get deeper into analytics and refine the skills on various tools to be a data scientist that businesses can rely on.

6) Data Science needs high Computing Power

Due to the hype of AI and big data, many people believe that they need parallel GPU-accelerated machines or huge clusters. Deep learning and neural networks sometimes need high computing power, but in most cases, this is not true. In most cases, you would require a PC with 64GB or 128GB of RAM for problems that can be solved with simple models. If that does not solve the problem, you could always invest a few hours on cloud systems every day to build and test a model.

A cloud environment is required if data processing or data cleansing exceeds the capacity of a single node. Experts recommend it’s affordable to scale computing resources when required than to over-engineer a computing landscape that consumes time and money as compared to problem needs.

Final thoughts:

Being an entrepreneur, your main concern is how to outshine your competitors by providing better services by predicting customer buying patterns.

Data science has many benefits to offer and, as demonstrated above, the myths around data science aren’t always true. The critical step of implementing data science is to construct a methodological way to developing data driven actionable insights. With sorted data science requirements, the next step would be to use data for decision-making, by considering organizational cases as data problems.

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