Welcome to Understanding the Role of a Data Scientist. After watching this video, you will be able to: Summarize the role and tasks of data scientists Identify some of the main skills a data scientist needs And describe some possible career paths for data scientists. In the industry, opinions differ about the skills and responsibilities of different roles in data science. Moreover, many job titles and descriptions in data science appear to have overlapping responsibilities. For comparison, let’s look at some major roles in the data ecosystem. Data analysts translate data and numbers into plain language so organizations can make decisions. Data analysts identify correlations, find patterns, and apply statistical methods to analyze and mine data. They also visualize data to interpret and present their findings. Data scientists analyze data for actionable insights and build machine learning or deep learning models that can train on past data to create predictive models. Data engineers extract and organize data from different sources, clean and prepare it, and then store and manage it in data repositories so that other data professionals can access it. There are other data professionals in the ecosystem too, such as business analysts and business intelligence analysts, who make decisions based on the data results. Regardless of the roles and job titles, data science professionals generally work collaboratively to extract the data needed to identify trends or correlations and make predictions. Now let’s consider what a data scientist does in more detail. Data scientists: Perform data mining of large sets of structured and unstructured data Evaluate vast volumes of data using statistical methods and seek actionable insights Build machine learning and deep learning, artificial intelligence (AI) models by applying algorithms to data And use machine learning models to automate tedious data manipulation tasks and predict future results. By combining knowledge of statistics, business logic, and computer programming, data scientists design, create, and implement models that make sense of complex data to inform corporate goal planning and decision-making. Data scientists build their AI models and evaluate data to answer important business questions and make predictions and projections about data-related behavior. Some of these questions might include: How many new social media followers am I likely to get next month? What percentage of my customers am I likely to lose to the competition in the next quarter? And is this financial transaction unusual for this customer? Answering these questions requires in-depth work with massive data sets, complex mathematics, and algorithms. Data scientists focus on building sound models that other data professionals can use to answer many other questions. Data scientists need a solid foundation in statistics and probability, as well as machine learning, cloud computing, and other data science disciplines. Higher mathematics such as calculus and linear algebra aren’t strictly necessary but can be useful assets. Data-related computer skills such as knowledge of programming languages, databases, and data modeling are necessary. And domain-specific knowledge and skills in the field you want to enter are also helpful. For example, if you are interested in medical fields, some knowledge of research methods and biology will make you a stronger candidate. Python is a vital tool for data scientists. A survey published by Kaggle showed that 79.9% of data science professionals advise those entering the field to learn Python first. When asked which programming tool was used the most at work, 86.7 percent answered that Python was the preferred tool at their workplace. Which other tools are in high demand? Someone entering the data science field might want to develop skills in: SQL, R, C++, and Java Big data manipulation using Hadoop and Spark Dashboard design and creation using Power BI And data visualization and storytelling with Tableau. In addition to technical skills, data scientists need an array of important soft skills: Communication and presentation skills, so that they can effectively convey the insights they derive from data sets. Critical thinking skills, so that they can analyze and solve problems that arise Creative thinking skills to develop innovative ways to approach the data and create useful models The ability to convert data into a story that the company can understand and apply to their goals A collaborative approach, because they will always be working with other data professionals to glean the most meaning from data And diligence and tenacity to stick with projects and see them through when they become complex or something unexpected happens. If you are new to the field of data science, your first job in any organization is likely to be in an entry-level role, but with a background from a different field, consider all of what you have learned as an important asset. Many skills are transferable, especially from other technical work. Even your hobbies and personal interests provide some experience and level of expertise that may boost your marketability, depending on the company or industry you decide to pursue. So be sure to examine your background and other activities for capabilities you can use. From the beginning or as you gain experience as a data scientist, you may advance your career in several different directions. Depending on your goals and interests, you may progress into becoming a specialist in some aspect of data science like machine learning, advancing your technical skills in programming or deeper data analysis, becoming a manager for a data science team, or going into business as a consultant, to name only a few options. Let’s take a closer look. Sometimes a career path takes you deeper into the specialized knowledge of a specific discipline of data science, such as: Big data Machine learning And business analytics. If you have a love or aptitude for one of these areas, it can be a great idea to dive into it and become the go-to professional in that discipline. You may choose to progress into a senior role, building your toolkit as time goes by. This requires gaining more skills and developing the skills you have, including: Mastering programming skills Learning more advanced mathematics And developing an understanding of many aspects of data science, such as AI and machine learning, big data work, and others. Rather than diving deeper as a specialist, the senior data scientist works to broaden and grow many skills. If you are interested in business, you may advance into a management position. This requires business aptitude and people skills as well as data science skills. If this is your goal, you may want to: Focus on developing leadership and other people skills Acquire project management skills And consider acquiring a graduate degree or certifications in project management. Management isn’t for everyone, but it may be a good fit for you if you like the required mix of skills and work. If you become a consultant, you can choose exactly what type of data science expertise you want to develop. Since you are likely to be an independent worker, you may have some freedom about the situations you choose to work in. Consultants may want to: Develop experience working in the data-driven aspects of specific industries Acquire knowledge of policies and regulations that affect data management, such as customer privacy and data-collection protocols And understand the basics of running your own business. Consultancy can be a great path if you want to design your own career, with all the freedom and risk that may entail. In this video, you learned that: Data scientists apply their specific skills and expertise to draw patterns and make predictions from large data sets A career in data science requires specialized knowledge and technical skills And there are several possible career paths that data scientists can follow as they progress. Data science is one of the fastest-growing professions in the world today, so if you set your sights on entering the field, the sky’s the limit for how far you can go.
