Data science recently became a buzzword in tech. Its popularity made becoming a data scientist a coveted career. But not all are familiar with what a data scientist does and the skills you should have to become one.
This post provides you the answers to your most pressing questions about data scientists.
What Do Data Scientists Do?
Data scientists rely heavily on data. They extract meaning from, visualize, analyze, manage, and store data to give organizations insights for their decision-making processes. Some of the tasks expected of a data scientist include:
- Acquiring data to jumpstart the discovery process
- Processing and filtering data
- Integrating and storing data
- Investigating data and doing exploratory data analysis
- Using machine learning (ML), artificial intelligence (AI), and statistical modeling to solve organizational issues
- Measuring data and improving results
- Presenting data analyses to executives and stakeholders
- Analyzing trends and patterns and their relationships with acquired data
What Skills Are Needed to Be a Data Scientist?
Data scientists need various skills at work, such as:
- Programming skills: They need to know how to use statistical programming languages like Java, R, Python, and Structured Query Language (SQL).
- Statistical skills: All data scientists should have a working knowledge of statistics. They should be familiar with statistical tests, distribution, and estimation. They need to use statistics to validate techniques and evaluate experiments.
- ML skills: Working with both structured and unstructured data and using ML can help them accomplish tasks with ease. Familiarity with k-nearest neighbors (data classification approach depending on the nearest data points), ensemble methods (classifying data points that improve prediction), and random forests (method of classification using decision trees) would be useful for understanding key points.
- Mathematical skills: Mathematical concepts help improve algorithm optimization strategies that can bring about profitable wins for organizations. Calculus and algebra form the basis of algorithmic processes, especially for those who want to build in-house implementations.
- Data wrangling skills: Data scientists need to map and transform data from raw into a specific format to make it more useful for analytics, particularly when the dataset has imperfections.
- Data visualization skills: These are incredibly important when it comes to making data-driven decisions. Data scientists need to make sure that they can make sense of the data they are working on. They must be familiar with visualization tools such as ggplot, matplotlib, d3.js, or tableau.
- Software engineering skills: A strong background in software engineering can help data scientists handle data logging and develop products for data acquisition.
What Are the Requirements to Become a Data Scientist?
Here are the requirements for becoming a data scientist:
- A degree in computer science, mathematics, IT, or statistics
- Considerable experience working in a related field
- Strong problem-solving skills
- Capacity to work individually or with a team
- Familiarity with data collection and analysis
- Strong verbal and visual communication skills
What Projects Can Data Scientists Work On?
An impressive portfolio of projects can help boost your chances of landing a high-paying job as a data scientist. Some of the projects you can work on are:
1. Data Cleaning
Part of a data scientist’s responsibility is cleaning data or filtering data, depending on how it will be used. Taking on such projects can help improve your chances of getting hired. To start, you can take on data-cleaning projects by looking for messy datasets. A good start would be the data.gov website.
2. Exploratory Data Analysis
One of the things that can make your portfolio stand out is gaining experience in exploratory data analysis (EDA) to show that you can look beyond a hypothesis by summarizing major points. Your ability to show off your data investigation skills can make you a valuable part of any data science team. You can check out the IBM Analytics Community for some exploratory data analysis (EDA) datasets that you can work on.
3. Interactive Data Visualization
If you want to work for business-focused organizations, knowledge about dashboards is critical. By using dashboards, data teams can easily collaborate and develop insights for data visualization. One useful dashboard tool is Dash by Plotly.
Who Are Some Notable Data Scientists You Can Follow on Twitter?
Want to get inspiration from well-known data scientists? Here are some that you should follow:
- Dean Abbott (@deanabb): Data scientists who want to learn more about data analytics should follow Abbot, the founder and president of Abbot Analytics. For several years, Abbot worked on data mining and visualization methods to address business issues.
- Kenneth Cukier (@kncukier): Working as the data editor of The Economist, Cukier has also done significant research in AI. He also authored a book on big data and is often requested to speak on his knowledge of data science.
- Nando de Frietas (@NandoDF): Google DeepMind’s lead scientist for ML focusing on neural networks, deep learning, and Bayesian optimization can tell aspiring data scientists about data-driven robotics.
- John Elder (@johneleder4): Popular in the data mining field, Elder is the data scientist to follow if you want to learn about advanced analytics, biometrics, and text mining.
- Fei-Fei Li (@drfeifie): Serving as the co-director of Stanford University’s Human-Centered AI Institute, Dr. Li has done impressive work on deep learning and data analytics.
The field of data science is ever-growing. Along with this growth comes the increased demand for data scientists. Now that you know the answer to “What do data scientists do?,” you might want to brush up on your skills to enter the field.