Do you find working with data to be enjoyable? If your answer is yes, then a career as a data scientist could be the perfect fit for you.
A data scientist is a skilled professional who gathers, interprets, and presents data quickly and comprehensibly to others, much like an artist exhibits paintings or a mathematician elucidates their equations. As a data scientist, you will play a vital role in assisting clients and businesses in analyzing data, making informed decisions, comprehending new ideas, and effectively interpreting vast data sets.
Tally your yes/no answers on the following questions and find out if you have what it takes to be a data scientist!
TECHNICAL SKILLS
Have You Earned A Bachelor’s Degree In A Related Field?
A solid educational foundation is necessary to be eligible for a top data scientist position. To succeed in this field, one needs to be well-educated and have a solid background in data science, computer science, and social science.
A successful career in data science begins with an undergraduate degree. Here, you will learn a variety of computer and math courses, such as statistics, probability, and linear algebra. Also, data scientists are typically trained on how to model, analyze, and mine data, among others.
To be a data scientist, you should understand basic computer science principles, especially those related to algorithms and programming. You would also need knowledge of statistics and statistical software such as R and SAS.
Do You Have Database Knowledge?
To store and analyze data, database knowledge is necessary. This will facilitate your access, communication, and interaction with data. It can also be employed to aid in both data comprehension and data analysis. In addition, you must be proficient with database query languages (SQL, XQuery, OQL, etc.) and use databases to access, analyze, and store data.
Just as important, you must understand relational database management system (RDBMS) practices and work with tables defined in database software. It is also necessary for you to have a fundamental understanding of relational database system fundamentals such as data types, indexes, transactions, etc. Lastly, you must be knowledgeable about database operations, which are essential for effectively using a database.
These requirements are a bit overwhelming, but if you want to succeed in a data science career, you must master these skills. On the bright side, there’s always room for improvement. You can get better at these skills as you gain more experience.
Are You Comfortable With At Least One Programming Language?
Programming tools such as R and Python help meet data science demands. You can use R to find a solution for any data science issue you run into or use Python for its versatility makes it a flexible language.
However, one must understand you can’t rely solely on the tools that come with R or Python. The right tools may come from other available third-party libraries.
Are You Familiar With Machine Learning Concepts?
Given a set of data, machine learning combines data and algorithms to assist computers in analyzing and predicting future data or information. For example, a machine learning algorithm that assists in identifying city traffic patterns will help analyze trends and generate relevant predictions to aid in traffic-related problems.
You must have a solid knowledge of machine learning techniques to succeed as a data scientist. Two examples of machine learning techniques include supervised machine learning and logistic regression. You must be able to apply these techniques over time to systems to boost performance.
Today, thousands of companies and researchers are trying to identify and apply machine learning techniques to their systems. Many have dedicated departments for machine learning that utilize statistical knowledge and artificial neural networks.
Do You Know How To Visualize Data?
To succeed as a data scientist, you must be able to visualize data using programs like Ggplot, Tableau, and Matplotlib. Using these tools, you can transform complex data into an easily comprehendible format, especially when dealing with more complex data.
You must also be able to develop a data story or model to understand the created systems better. You may need to use design products like Adobe InDesign, Microsoft PowerPoint, and Apple’s iWork. Getting data across may require a lot of creativity.
Do You Know How To Do Data Wrangling?
Data Wrangling involves cleaning, manipulating, and organizing data. Popular tools for data wrangling include R, Python, and Flume.
R is primarily a computer language for statistics. Its powerful mathematical tools and high-level data analysis make it easy to work with data, present these results to a broader audience, and professionally communicate these results to peers.
On the other hand, Python is a high-level programming language for scientific computing. Its ability to express data through various libraries and its easy integration with many scientific and engineering applications make it a popular choice for data scientists for many types of work (e.g., machine learning, bioinformatics, data analysis, computer vision, etc.).
Finally, Flume is a data ingestion solution that helps you gather, load, and analyze data for any purpose with ease.
NON-TECHNICAL SKILLS
Does Intellectual Curiosity Drive You?
Most of a data scientist’s time is spent gathering and preparing data. To be successful in this field, one must be able to challenge the information at hand. Curiosity is also essential since data science is developing quickly, so you must learn to keep up.
Once you understand the data you are analyzing, you will have the skills to write data science reports, create statistical models, and discover patterns and trends. As a result, you and your team will have a deeper understanding of the problem you are investigating, its scope, and the tools you need to address it.
Do You Have Good Communication Skills?
Employers are in great need of data scientists that can effectively present their findings to a team. Data scientists need to be able to weave a narrative around the data to make the information accessible for everyone to comprehend. The report needs to be both descriptive and actionable. Most importantly, it should be simple enough for your customer to understand.
Furthermore, you should be able to use real-life examples to explain why something is happening and draw attention to the specific problem. This will help the team understand and look at the situation differently.
Are You A Team Player?
You’ll need to collaborate with your team to figure out what to do to fix difficulties. You’ll need to be able to work with other data scientists and various groups to discover the best solution to issues. In the same way, before releasing your work to the public, you will need to work closely with your team to ensure it is correct and relevant.
Furthermore, you may be asked to work with designers or collaborate with a third-party developer to get the desired outcome to visualize ideas. Likewise, you will work on projects involving multi-disciplinary teams: business strategy, sales, marketing, operations, engineering, technology, and finance. Therefore, working closely with people is vital to collaborate effectively and successfully. It can be challenging and even scary to collaborate. But the payoff is great when you have a good and effective partnership.