Let’s take a look at the inner workings of data science and what it takes to get started on this path.
What Is Data Science?
Data science is a generic term for a complicated web of skills and sub-domains. In a nutshell, it is the science of gaining actionable insights or value from data. The field encompasses all parts of the data life-cycle. This includes capture, pre-processing, storage, retrieval, post-processing, analysis, visualization, and so on. Data science uses a combination of scientific methods alongside automated tools to carry out each step in this life cycle. There are three basic classifications of data: structured, unstructured, and semi-structured. Data science deals with all three types. There are also no limitations on the volume and variety of data that can be processed. Data science is a very complex field, with a large variety of academic disciplines and technologies that are involved. It draws on an overlap of a number of disciplines. The core disciplines are mathematics, statistics, computer science, and programming. The field is also benefitting from emerging technologies that make it possible to gather and analyze data at a much larger scale and faster speeds. This includes artificial intelligence, natural language processing, visualization, predictive analytics, and so on. The potential applications of data science methods are endless. In fact, any field that has data that require analysis counts. There have been applications in social media, medicine, security, and health care. There have also been applications in social sciences, biological sciences, engineering, economics, finance, marketing, and many more.
What Do Data Scientists Do?
There are a variety of roles that branch out from the data science field. In fact, the term “data scientist” is used rather loosely to describe anyone from the spectrum of paths, from data analysts to business intelligence experts. In general, though, there are a few roles that all data scientists share regardless of their job title.
Management
Data scientists sometimes have to play the role of manager. This role would require the data scientist to either assist in or oversee the planning and execution of various projects. It may require them to research and create effective methods to gather and analyze data. It may also require the consolidation of the results of various projects into a single actionable plan.
Analytics
Data scientists also play a role in business analytics and have a variety of tasks that follow the data life cycle. They create methods to capture data, organize them, and then apply methods to analyze the data. Their main objective is to solve real-world problems using insights from the data that they analyze. For example, the task may be to identify the consumer behavior of teenagers for a particular brand of soft drinks. Data scientists also sometimes perform data mining and detailed analysis of “big data.” Big data, for those who don’t know, is used to describe modern data sets that have grown in terms of volume, variety, and speed of transmission. Here are examples of data analytics and tools that will transform the business world.
Designing Strategies
Data scientists are also sometimes directly involved in designing strategies for the organization. They apply statistical techniques to identify patterns and trends in existing data. These patterns and trends can gain actionable insights that the company can use to generate better strategies. Companies may also directly task data scientists with generating strategies based on those insights.
Collaboration
Data science is not an isolated profession. Data scientists need to have some social and communication skills on a daily basis. The common objective for collaboration is to work on problems at the organizational level and find solutions. There is a wide selection of teams that data scientists can work with. They can work with the data analytics team, data engineers, business analytics team, and so on.
What Does the Future of Data Science Look Like?
In our modern age, data has evolved from being a mere resource to a commodity akin to gold. Companies actively seek data to improve their services and processes. Whether that be the medical industry, engineering, entertainment, medicine, manufacturing, and other industry, data is a crucial and valuable commodity. In fact, the global daily data output is estimated to be at 2.5 quintillion bytes. Because of advanced technologies for data collection, there are fewer limitations to the volume of data that companies can gather. Cloud computing is also making it possible to store unlimited amounts of data at much faster rates than ever before. With all of this in mind, it’s not a stretch to imagine the data science industry expanding within the foreseeable future. As the volume of data grows, so will the demand for tools and technologies to handle them. This is accompanied by a demand for people trained in data science. As big data becomes the norm in organizations, there will be more demand for experts in data science. In fact, we may even reach a point where the demand for data scientists exceeds supply. Aside from the growth in demand for data scientists per se, the field is also expected to grow in terms of scope. Data-based technologies like artificial intelligence and machine learning will inevitably improve the possibilities for data science. The internet of things (IoT), edge computing, and other technologies will change the way data scientists process data. Eventually, these technologies might even replace traditional tools and approaches in order to meet the growing demands of big data.
What Skills Are Required in Data Science?
Now that you know that data science jobs are in demand, you are probably considering making a jump on the bandwagon. But before you do, you should know that data science is a demanding field with a steep learning curve. Becoming a true data scientist or data science practitioner requires years of technical training and inherent skills in certain areas. Here are some of the skills that you need to become an effective data scientist:
Technical Skills
An aspiring data scientist needs to have a solid foundation in programming, mathematics, and statistics. Data science is a highly technical field, and you need technical skills to fulfill your day-to-day work. Here are some of the skills that companies put at the top of their requirements:
1. Mathematical and Statistical Skills
There’s no other way around it; data scientists need to have at least decent math skills to survive in the field. There are just three areas that you need to master: linear algebra, statistics and probability, and calculus. Statistics and probability are crucial to a lot of data science tasks. You need to master the basic principles of statistics (central theorem, correlation, standard deviation) to extract meaningful information from data. You will also need statistics to present data in a meaningful way. A good grasp of linear algebra is also a great advantage. Data scientists often use matrixes to visualize machine learning models, so they need to master basic linear algebraic concepts to make them work. Linear algebra is also useful for different stages of data management including pre-processing, transformation, and the post-processing components. Calculus is another crucial math skill that you will need. Data scientists often use calculus to teach neural networks how to reach a particular outcome. This applies to both machine learning and deep learning algorithms.
2. Programming Skills
Data scientists are often immersed in programs and processes that are used to process data. It is thus essential for a data scientist to learn the “tools of the trade.” There are many programming languages, but the most common are Python and R. If you don’t have any background in Python, you can start with this easy step-by-step tutorial or Python machine learning. These are the basic programming that any data scientist should matter. It’s also to your advantage to learn how to navigate other programming languages like Tableau, Hadoop, SQL, and Spark.
3. Data Wrangling and Pre-Processing
Data is key for analysis in data science, regardless of the type of analysis you plan to conduct. Unfortunately, most of the data that lands on a data scientist’s desk will be messy and incomplete. A data scientist needs to know how to clean the data from imperfections to preserve its quality before the data is sent for processing. This is what’s known as data wrangling. Some examples of imperfections in data sets include missing values or inconsistent value formatting (e.g., USA versus U.S.A versus the United States of America) and date formatting (2010-03-21 versus 03/21/2010, etc.). Imperfect data are more common in companies where data is not the main product. It also happens when a company doesn’t have data cleaning procedures in place. Knowing how to clean or wrangle data will allow you to derive value from it in spite of its imperfections. You would also need to know how to deal with data before it’s processed. This includes dealing with missing data, handling categorical data, and encoding class labels for classification problems. It also helps to know techniques for feature transformation and dimensionality reduction.
4. Data Visualization and Communication
Visualizing and communicating data is a crucial component of data science. No matter what industry you are working for, there will always be some form of data reporting. Because of this, it’s really important for you to be familiar with some data visualization tools like Tableau, Power BI, Plotly, and Dash. Not only do you need to know how to use these tools, but you also need to understand the relevance of the data and its implications. You will also need to communicate all of this to stakeholders and teams.
5. Basic Machine Learning Skills
There are only a few people who are truly proficient in machine learning. While it’s not a basic requirement for data scientists, it is definitely an advantage. Machine learning involves the use of artificial intelligence and algorithms to reach specific outcomes. These machine learning algorithms are unique in that they have the capacity to “learn” from their mistakes and improve over time as more data is added. The technology has a variety of applications, but the most popular is facial recognition. Having knowledge in machine learning techniques will allow you to automate significant parts of data processing. For example, you can train an algorithm to identify redundancies in a data set and have it delete it automatically. A data scientist trained in machine learning should know the machine learning framework by heart. They should also be familiar with machine learning techniques such as supervised and unsupervised machine learning, decision trees, and logistic regression. Plus points if you also know advanced machine learning methods such as natural language processing, outlier detection, and recommendation engines.
Non-Technical Skills
These skills won’t require much technical training or certification, but they play an important role in how effective you will be at your job. These skills take years to develop and require a constant, conscious effort. Here are some of the non-technical skills that you’ll need to develop as a data scientist:
1.Critical Thinking
Every professional individual needs to have critical thinking skills. It’s even more important for data scientists since they need to make sense of many different types of data. Besides gaining valuable insights from data, you also need to frame your questions and find ways to get relevant data to your inquiry. A data scientist will also need to find the best-fit methods for analyzing data, which requires a lot of critical decision-making. It also means being able to see all angles to any given problem and finding the best ways to solve it. As a data scientist, you also need to keep an open mind and become aware of your own irrational tendencies.
2. Effective Communication
Data scientists spend a portion of their time working with other people, and this requires communication skills. Whether you are an entry-level data scientist or head of the department, the ability to connect with people is always essential. As a data scientist, you will most likely need to work within a team or with teams from other departments. Most companies also require their data scientists to report on their findings, be it through a written report or a presentation. In that case, you need to be able to explain your findings in a clear and cohesive manner even to non-technical audiences. The data will never speak for itself, and you need to explain it to others so they can take action.
3. Proactive Problem Solving
Data scientists solve problems all the time. It’s what the field is all about. However, effective problem solving is just as much as having the ability to explore the root cause of a problem in finding a way to solve it. Problem solvers are easily able to apply the scientific method to solving problems and then use their technical skills to find the appropriate solution. Data scientists are also expected to keep abreast of new developments and technologies and find ways to apply them to their daily work.
4. Intellectual Curiosity
A data scientist must have the intellectual curiosity and drive to find and answer the questions that are presented by the data. Most of the time, data provides multiple insights that can be interpreted differently. Data scientists must be able to cover all different interpretations and their implications. They must also go beyond the surface level to explore the hidden patterns and insights into the data. It’s never enough to say that something is “good enough” when it comes to exploring possibilities.
5. Business Sense
Data scientists are expected to know not only about their own field. They also need to have a good grasp of the inner workings of the industry that they are in. As mentioned, there are a lot of industries that need data scientists, and each industry has its own objectives. There are also sub-industries and individual companies to consider. To be an effective data scientist, you need to have a good grasp of the industry that you are in. This will allow you to look at problems not only within the context of your company but in the greater industry.
Final Thoughts on Data Science
This ends our basic exploration of the field of data science. We hope that this article has provided some insight into the field and the core competencies required. This is certainly a turning point for the industry, as we see emerging technologies that will help improve it. Not only will these technologies improve the data science industry, but their impact will also spill over to other fields as well. Even the demand for data science talent will expand, at least for the next decade. If you ticked most of the boxes above, or are willing to undergo technical training, then you may have a shot at this path. There has never been a better time to become a data scientist, and with this career, you can make an impact anywhere. If you enjoyed this article, you might be interested in this list of the top 5 most in-demand big data jobs.
