Joseph Nicholas Wambugu

Breaking into Data Science without Any Experience In early 2012, the Harvard Business Review referred to Data Science “the sexiest job of the 21st century”. Even today, the hype is very much alive and in 2016, Glassdoor referred to Data Science as the “best job of the year.” In the Oscar-winning film, Hidden Figures, mathematician Katherine Johnson verifies landing coordinates for the spaceship, Friendship 7. Similarly, modern day technology churns out copious volumes of data every single minute. And with it comes the need for data scientists in analyzing and obtaining actionable insights for use in myriads of different ways. Isn’t it laughable that in 1961, data science depended on human computers to solve complex equations? Back then computing was in its infancy. Today, sophisticated tools for data collection and analysis are available. These tools equip virtually every sector with tremendous computing and data processing capabilities. However, just like in Hidden Figures humans are required to sift through the results of data processing to extract hidden truths. In turn, we are witnessing the emergence of a cult-like following around data science. As Data Science is a relatively new and emerging profession, the Average Joe is naturally curious. Perhaps, you have asked yourself is Data Science for Me? Do I have the requisite skills? If not, can I learn them? And if the answer to these questions is Yes, how can I do so? Clarifying these questions is essential, as numerous misconceptions exist regarding Data Science. Many articles portray data science as a sure path to riches, fame, and professional acclaim. In contrast, data science is nothing like these assumptions. In reality, the profession requires conversancy with complex and often confusing concepts and technical adeptness. This post will discuss the misconceptions, importance, and offer suggestions on how to pursue a career as a Data Scientist. Misconceptions Today, data science continues to intrigue and perplex many people. As a result, most of us have different perceptions of what this field entails. Accordingly, this section dispels Data Science’s most common myths. Myth # 1: Data Scientists Are Proficient Coders Often, this myth asserts that a Data Scientist is better in coding than a statistician and better in statistics than an engineer. A relatively unfair fallacy is difficult to dispel. As such, it portrays data scientists as unicorns- fantastical creatures fashioned by less knowledgeable minds in the past. So too is the data scientist in the minds of overworked business managers and executives. Insight #1: Though advanced coding skills are desirable as a Data Scientist, they are not necessary. What’s most important is your ability to formulate business problems into concrete and executable insights. Typically, this involves the collection of relevant data and its correct interpretation. Proficiency in programming is just one skill in the data scientist’s arsenal. A majority of data scientists survive with beginner and intermediate coding skills. Myth #2: Data Scientists are Tech Wizards Most people revere data scientists as technical wizards. Arguably, the reverse is true. Not all data scientists can fix slow network connections, troubleshoot laptops, office printers, or a projector. Insight # 2: Though these skills are a plus in the field, they are not core requisites for becoming a data scientist. If you enjoy working with data, exploring, and finding answers to challenging questions, interested in quantitative problem-solving approaches, then Data Science is for you! In this way, data scientists are similar to artists, inventors, and innovators working to improve both the present and the future of humankind. Myth # 3: Data Science is Entirely about Data Collection Data collection activities play an essential role in data science. In addition, your ability to collect, collate, and analyze large volumes of data are critical skills in this field. Besides that, data scientists require sophisticated equipment for visualizing, predicting, modeling, and presenting their findings. Insight # 3: Typically, data scientists spend the greater part of the day analyzing data for bias, solving complex problems using insufficient data, validating results, collaborating with team members, and communicating findings. This is why you should avoid fallacies and hyperbole instead you should focus on developing skills such as teamwork and project sustainability. Why is Data Science Important? Data science combines several fields in solving complex analytical problems. Among these disciplines are algorithm development, data interference, and information technology. The discipline’s essence involves the application of different processes and systems related to the fields mentioned above for extracting knowledge, identifying actionable insights, and information from structured or unstructured data sources. Similar to other types of sciences, data science doesn’t focus on any specific theories or methodologies in any of its constituent fields. Instead, data science leverages techniques and theories from five key areas: statistics, operations research, information science, computer science, and mathematics. Besides that, it relies heavily on techniques widely used in Big Data analytics, a field it shares similarities with. Today, data science finds practical and theoretical application in areas such as business, digital economy, economics, finance, machine translation, medical informatics, healthcare, robotics, social sciences, search engines, and speech recognition. Below are a few examples that demonstrate the flexibility, versatility, and impact of data science in various industries. I. Agriculture It is expected that the use of predictive analytics will transform the entire agricultural sector as well as help millions of farmers across the globe. Accurate predictions regarding factors such as highest productivity seasons, expected rainfall amounts, the location of crop processing, fertilizer quantities, crop prices, and market forces can help farmers make informed decisions. By relying on data science, farmers can control crop damage and increase productivity leading to higher revenues as well. II.Sales and Marketing All businesses rely on the creation of value and specific targeting of their customer base to drive revenue upwards. By depending on data science and data scientists, businesses can identify product or service demand trends, customer demographics, and success rates of past consumer transactions as well as the effectiveness of marketing campaigns. Using this knowledge, companies can determine which clients to focus on and the required improvements to achieve their mission objectives. III. Finance The financial services sector generates vast amounts of data each day. Due to that, it is challenging managing scattered data repositories, detecting as well as preventing fraud, customer analytics, and trade visibility. Combining data science and big data analytics can provide actionable insights for preventing money laundering, fraud mitigation as well as improving enterprise risk management practices. IV. Education Data science can help measure the effectiveness of education policies, teachers, students, and resource allocation processes. Insights obtained from data analysis can help predict which courses suit students based on their past performance. In addition to that, big data can provide accurate assessments of student interests or school dropout rates. These examples demonstrate the way in which data science will transform different sectors, the global economy, and infrastructure. Only technology limits the impact of data science in the above sectors as well as in others. Nonetheless, it is currently impossible to know how development in one field affects outcomes in other fields and the applicability of any single skill in a different area. Given that, Galileo Galilei who had competencies in several disciplines is an example in the Innovators book. This is why; when it comes to data science, you should consider challenges as opportunities to exhibit fortitude and creativity. What Do I Need to Be a Successful Data Scientist? Despite data science’s steep learning curve, here are a few steps to help you get started on your journey as a Data Scientist. Read on to find out how to get a head start in this discipline. Nurture a Passion for Data Science As you prepare to get into this field, remember that data science is an emerging discipline. So plan accordingly for feelings of excitement and frustration. Doing so will help you resolve any challenges and obstacles that you encounter along the way. In addition, depending on the path you follow, definitions and concepts are bound to change making the learning process a little bit intimidating. Adopt a Hands-on Learning Approach During your research, you will come across well-meaning recommendations about the need of having a good grasp of concepts such as image recognition, machine learning, and neural networks. Initially, these concepts will fascinate you, but if you begin here, you will never achieve your goal of becoming a data scientist. Instead, you should begin with its basics: learn how to clean data for use by programs and computers. While doing so, focus on gaining mastery of a few algorithms along the way. Enhance Your Communication Skills As a data scientist, you will spend most of your time telling others in non-technical terms about the insights your data implies for them. Try conversing with less tech proficient friends, create and maintain a blog as well as look for a platform to help you share your insights. The more you practice, the better you can express yourself to ordinary people. Learn from Data Science Industry Leaders Find online forums, chat rooms, or websites for networking with experts, students, and data science enthusiasts. Perhaps, your local university has a data science program, inquire about any scheduled conferences, seminars, lectures, or panels and plan to attend. During these events get to know a few data science inclined individuals or those already working as data scientists. Continue Testing Yourself Keep testing yourself to see how well you have progressed in data analysis and data collection. Don’t tire challenging yourself. Increase the difficulty levels of your challenges. If they seem too easy, use more extensive data sets while tweaking and scaling algorithms for use by multiple processors and get a good understanding of theories behind emerging algorithms. Remember the further you explore along the path, the more comfortable you become as a data scientist. Wrapping up In wrapping up, data science is here to stay. Nonetheless, remember that data science is a young field that is expected to impact on various industries in different ways. In addition, this post highlighted various roles that data science plays in different fields. In addition, this post covered the importance of overcoming misconceptions that can hinder you from achieving your goal of becoming a data scientist. Besides, we have outlined the necessary steps you need to take to get started and developed a strong foundation for a career in data science. Following these steps will ensure that you eventually attain your dream. So, what are you waiting for? Get started on this journey today!