Top Skills You’ll Learn in an MS in Applied Data Science

Want to learn more about data science but not sure where to start? Read more in this comprehensive guide on the skills and education needed to be a successful Data Scientist.

Data science is a complex and diverse subject with multiple applications across industries. It combines several concepts across mathematics, science, programming, and statistics. With the advent of artificial intelligence (AI) and machine learning (ML), these have also become an integral part of the discipline. The data science lifecycle includes four stages: data ingestion, storage and processing, analysis, and finally, communication of the data analyzed. In India, the market is predicted to grow to USD 1.391 Billion by 2028. Professionals who wish to make a career in this domain can consider a master's in Applied Data Science from the USA to add a global perspective to their learning. 

Applications of data science are across industries right, from technology and retail to healthcare, public governance, aerospace, and more. Most professionals in their own domain choose to specialize in data science to help them grow as specialists in their fields. In finance, for example, data scientists can use their skills in math, statistics, and programming to build applications for algorithmic trading and credit scoring. Healthcare industries can also use data science for disease prediction and drug discovery. The applications are endless across functions like operations, marketing, and even human resources.

Building a career in Data Science:

There are several pathways to building a career in data science. You can consider the following steps:

• Build your foundation: If you already have a degree in data science or a related field, you should be able to keep up with the changes in the sector through your job role. However, if you are not from the same industry, you can consider getting an MS in Applied Data Science. It will help you build the relevant skills and act as a signal to potential recruiters as well.
• Develop skill sets: Most data scientists need to acquire skills in data analysis, programming, predictive modeling, machine learning, predictive modeling, and cloud computing. You can gain the required skills through practice and professional education.
• Gain practical skills: Work on real-life projects either through your current work or via consulting projects. Several small-scale businesses require data science experts to advance their products and customer service. You can also participate in data science competitions and contribute to open-source projects. This will also help you build a strong portfolio for future roles.
• Develop soft skills: While hard skills like analysis, programming, and predictive modeling are essential, an important part is also data visualization, communication, and storytelling.
• Network and update your skills: Apart from the proper skill set, data scientists should consider effective networking through active collaboration. This helps in career progression and diversifying into new industries.

Data scientists can work in large corporations, with governments, in research laboratories, or as professional consultants. You can progress to mid-senior data scientist positions from a fresher data analyst. As late-career professionals, some roles to consider are – Data Architect, Data Manager, and even Chief Data Science Officer. After completing an MS in Applied Data Science, you can also consider a doctorate degree in a niche data science field to improve your career prospects.

Also Read: Learn How to Leverage USD's Online Programs in India to Build Your Career?

What are the core skills needed to become a Data Scientist?

Most organizations look for specific skills and experience in data science professionals. Some of these are listed below. Consider upskilling yourself in a niche or building a working knowledge of all the skills.

• Probability and Statistics for Data Science: These two skills form the base for all other skills in data science. Concepts like probability theory, understanding of variables, data, sampling distributions, descriptive statistics, and hypothesis testing are essential for understanding future data science concepts and applications.
• Advanced data analysis: Simply put, data analysis involves understanding large datasets to uncover patterns, trends, and insights. Unlike regular data analysis that involves charts and graphs, advanced analysis can be used to discover deep trends and predict probable outcomes.
• Data science programming: Programming is a critical part of the data science skill set. The online MSc in applied data science from USD explores a wide range of topics, covering the basics of Python and R. It also delves into data acquisition and integration, transformation, problem understanding, data preparation, standardization, and exploratory data analysis. A good grasp of these key skills helps data scientists to solve critical problems for complex projects.
• Applied data mining: The foundational studies for AI and ML, data mining covers concepts and practical algorithms governing both supervised and unsupervised learning methods. This skill allows data scientists to apply their data mining principles to problems like anomaly detection, trend analysis, and more.
• Predictive modeling: Predictive modeling uses machine learning and data mining to predict and forecast outcomes across industries. Skills like model training and development are relevant to diverse applications and real-world challenges. One of the tools you should be able to use is RStudio for R package management. Predictive modeling is beneficial for sales forecasting, predicting risks in the credit card industry, and even disease prediction. Prediction models like classification, clustering, forecasting, outliers, and time series are essential concepts to master to become a successful data scientist. The MSc in Applied Data Science from USD India Online covers this skillset in detail through a dedicated course.
• Machine learning and deep learning for data science: Data scientists use several techniques like supervised, unsupervised, and reinforcement learning. These techniques can be used for labeled and unlabeled data to create algorithms to find patterns and make predictions. Machine learning is a core skill for all data scientists. It forms the base of many advanced computational applications that they work on in their projects.
• Data visualization and effective storytelling: Apart from the technical skills needed, data scientists also need to understand the art of effective data visualization. While there are several tools that can visualize the data effectively, data scientists also need to answer how to present the data in a manner that business professionals can understand. Courses like the MSc in Applied Data Science from USD delve deeper into this skill through tools like Tableau.
• Practical data engineering: While data scientists may or may not work directly on data engineering, they will work closely with such teams. Understanding data architecture, relational databases, SQL, data pipelines (both ETL and ELT), ethical engineering, and best practices in data engineering is hence an essential skill for data scientists.
• Applied Text Mining: Data scientists who work with extensive, unstructured data, especially qualitative data, need to understand Natural Language Processing (NLP). Critical components of this skill are text processing, feature extraction, sentiment analysis, topic modeling, and text classification.

There are many skills to master to become a data scientist. An online MSc in applied data science can help you learn and master these skills. A structured course with practical projects will help you gain expertise and apply your skills in the real world.

How to Choose the Right Course

If you are thinking of doing a course in data science, here’s a simple guide to help you decide:

• Analyze your current skills. Your current work performance can guide you on whether you need upskilling in the field. If you are looking to start your career, it is a good idea to reflect on the skills learned in your bachelor’s degree.
• Understand the level of learning you might need. If you are already well-versed in data science, you might need a couple of workshops to stay updated with the latest advancements in the industry. On the other hand, if you are not from a data analysis background, a structured MS in applied data science will help you learn all the basics as well.
• Finally, consider your budget and time constraints. An online degree is more beneficial for professionals who do not wish to quit their jobs. 

References: https://codegnan.com/future-scope-of-data-science-career-in-india