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 the data ecosystem. 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 with other roles in the data ecosystem to extract and transform the data needed to make forecasts and predictions using machine learning and AI. Now, let’s consider what a data scientist does in more detail. Data scientists: Work with large amounts of structured and unstructured data. Clean and transform ingested data so that the data can be useful for applying machine learning, deep learning, and AI models. Build and apply machine learning, deep learning, and artificial intelligence (AI) models to solve business problems. By combining knowledge of statistics, business logic, and computer programming, data scientists design, create, and implement models that use disparate data sources. These can help in corporate goal planning and decision-making. Data scientists build their AI and machine-learning models to address important business questions using predictions and forecasts synthesized from various data sources. Some business questions where ML or AI may be applied are: 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? Data scientists need a solid foundation in statistics and probability, as well as machine learning, cloud computing, 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 of choice 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 the drive 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. 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.