What is a Data Scientist?
Data scientists are guys who specialize in analyzing and interpreting data to let business owners make better-informed decisions and improve the company operation based on patterns, like user preferences, demand trends, and analysis of turnover fluctuations.
Data scientists usually have extensive experience in mathematics, statistics, and computer science. Do not confuse data scientists and data engineers; the second can develop new ways to collect and store data, while the first is mainly work as the analyst, doing sampling and evaluation from the ready storage.
Strong analytical and mathematical skills would greatly help succeed in a junior data scientist position. You should be able to understand complex datasets and work with them. In addition, it’s helpful to familiarize yourself with some statistical software packages. And to become a middle-level data scientist, being familiar with Python would be a strong plus, while R programming language is becoming less popular.
Prerequisites and Soft Skills
Try to gain a certificate for machine learning courses where you work with tools such as TensorFlow or no-code ML applications.
- Here are some valuable tips that can increase your chances of success. A good education: a solid foundation in mathematics and basic computer science knowledge. In particular, you should be well-versed in statistical methods and algorithms.
- Experience working with datasets. Specialists should be able to analyze data sets effectively.
- Strong communication skills. Data scientists should be able to communicate their findings effectively to others.
- Desire to learn. The field of science is constantly evolving, which means that specialists must be ready to master new methods.
Data Science in Action
Have you heard that the media provider Netflix extensively uses data science? The company measures user engagement and retention, including:
- What time of the day you are viewing content
- When and why you leave contentedly
- Where in the world you are viewing
- What device you are viewing and scrolling you are watching
Netflix has more than 120 million users worldwide! To process all this information, Netflix uses advanced data science metrics. The company knew where people stopped when fast-forwarding and where they stopped watching. The analysis of this data allowed Netflix to create an exciting show.
Now let’s look at some important skills of a data scientist that a person should have.
Hard skills required to become a data scientist
- Learn how to work with database software such as Oracle Database, MySQL, and Microsoft SQL Server.
- Study statistics and mathematical analysis. Statistics is a science that develops and studies methods for collecting, analyzing, interpreting, and presenting empirical data.
- Master one programming language. Knowledge of Python and its frameworks and libraries are essential for progress within this profession.
- Python ready frameworks can help extract, modify, manage data from various sources, and perform statistical data analysis.
- Learn data processing, which includes cleaning, manipulating, and organizing data. Popular data processing tools include R, Python, Flume, and Scoop.
- Master machine learning concepts. Allowing systems to learn and improve automatically based on experience without explicit programming. Machine learning can be achieved using various algorithms such as regressions, naive Bayes, SVM, K-means clustering, KNN, and decision tree algorithms, to name just a few.
- Proficiency in big data processing tools such as Apache Spark, Hadoop, and Tableau, which work with large and complex arrays that cannot be estimated using traditional data processing software.
- Have the ability to visualize results. Visualization of various data sets and creating a visual display of results using charts, graphs, and diagrams.
How to become a data scientist?
Data science is a field of research that involves extracting knowledge from all collected data. As a data scientist, you will support the business growth by analyzing the data, gaining patterns, and predicting trends.
Learn The Appropriate Programming Languages
Although a bachelor’s degree can give you a theoretical understanding of the subject, it is important to refresh your knowledge of the relevant programming languages such as Python, R, SQL, and SAS. These are the most important languages when it comes to working with large datasets.
Master The Appropriate Skills
In addition to the various languages, a data scientist should also know about working with several tools for data visualization, machine learning, and big data. Understanding how to handle large datasets and clean, sort, and analyze the row data is essential. Get some basic understanding of these technologies from Serokell’s blog, I really like it.
Certificates related to the particular tools and general AI technology algorithms are a great way to confirm your knowledge in the field. Here are some great certifications to help you pave the way:
- Tableau Certification Training Course
- Power BI Certification Course
These two most popular tools used by data scientists will be the perfect complement to the beginning of your career path.
Internships are a way to get into companies that hire data scientists. Look for jobs that include keywords such as data analyst, business analyst, statistician, or data processing engineer. Internships are also a great way to learn in practice what exactly working with us entails.
Entry-level assignments in data science
Once your internship is over, you can either join the same company (if they are hiring) or start looking for entry-level positions for data scientists, data analysts, and data processing engineers. From there, you will be able to gain experience and move up the career ladder as you expand your knowledge and skills. As a good start, check these lections on YouTube.