How to become a Data Scientist – A complete career guide
Data science as a field is new, and it’s growing exponentially. According to research by IBM in 2015, 90 percent of the world’s data was created in just the two years prior to the study. Then, in 2016, 90 percent more data was created in one year alone.
The need for systems to collect and manage this massive amount of data is growing across all sectors of the economy and industry. Companies, research organizations, and governmental agencies are all scrambling to find well qualified people to help build and scale their data operations.
This guide provides a basic overview of some of the opportunities in this emerging field and shows the steps required to become a data scientist. For reference a detailed job description, salary information, and the future data science job outlook are included.
Six steps to launching a successful Data Scientist career
Step 1: Preparation: Future data scientists can start preparing before even arriving on a college campus. Becoming familiar and proficient with a programming skill (i.e. HTML, Java, Objective C) and taking classes in applied math and statistics is also recommended. Entering college with these skills will help accelerate learning and open more opportunities.
Step 2: Undergraduate studies: Major in statistics, computer science, information technologies, mathematics, or data science (if available) while minoring in another related field. If possible, learn to write code and understand databases such as MySQL. Start building professional networks by looking for connections within college communities, look for internship opportunities, and ask professors and advisors for guidance.
Step 3: Entry level job: Companies are eager to fill entry level data science jobs. Looking for positions with statistic assistant, technician, or data analyst are good places to begin job searches. System-specific training or certifications in data-related fields might help when looking for entry level data jobs.
Step 4: Master’s Degree: Data science is a field where career opportunities track with advanced degrees. In-demand data science master’s degree specializations include: munging (or wrangling) data visualization, and reporting. Most advanced degree programs also require becoming proficient with big data programs (i.e. Hadoop, Mapreduce, Python, R, and Spark). Check out the masters in data science programs.
Step 5: Get promoted: More education and experience leads to promotion. Businesses value results. Coupling strong technical skills with project management and leadership experience will generally lead to bigger opportunities and greater compensation.
Step 6: Keep learning: Staying current and relevant are crucial to the ever-evolving field of data science. A career-oriented data scientist is always learning and evolving with the industry. Continue to network and look for educational and professional development opportunities through bootcamps and conferences.
What is a Data Scientist?
Data scientists are experts in collecting and analyzing all kinds of data. These individuals possess the technical computer skills needed to solve complex problems. Using data analysis, they can explore and anticipate problems and figure out how to solve them.
Data scientists are part mathematician and part computer scientist. Their skill set encompasses both the business and information technology sectors. They are highly sought-after for their valuable skillset.
Data science is deep knowledge discovery through data inference and exploration. This discipline often involves using mathematical and algorithmic techniques to solve some of the most analytically complex business problems. In doing so, they leverage troves of raw information to figure out hidden insight that lies beneath the surface. The core of the field centers around evidence-based analytical accuracy and building strong decision capabilities.
What does a Data Scientist do?
A data scientist extracts meaning from data, which requires both tools and methods from statistics and machine learning. A data scientist works on collecting, cleaning and munging data. Managing data pipelines requires an abundance of human persistence, as well as a knowledge of statistics and software engineering skills. These skills are also necessary for understanding biases in the data and for debugging logging output from code.
After the data scientist shapes the data, an exploratory data analysis takes place which combines visualization and data awareness. They will find patterns, build models and algorithms with the intention of understanding product usage and the overall health of the product.
Data Scientist job description
The variety of projects a data scientist is involved in can be incredibly broad and might vary from company to company and from day to day. Here are a few examples of data science tasks:
- Collecting large amounts of data and converting it into a user-friendly format.
- Problem solving business-related challenges while using data-driven techniques and tools.
- Using a variety of programming languages, as well as programs for data analysis and collection.
- Having a wealth of knowledge with analytical techniques and tools.
- Communicating findings and offering advice through effective data visualizations and comprehensive reports.
- Identifying patterns and trends in data; providing a plan to implement improvements.
- Predictive analytics – anticipate future demands, events, etc.
- Contribute to data mining architectures, modeling standards, reporting and data analysis methodologies.
- Invent new algorithms to solve problems and build tools.
- Recommend cost-effective changes to existing procedures and strategies.
Data Scientist Skill Set
- Experience and Fluency in many of these computer/coding programs: SAS and/or R, Python Coding, Java, Perl, or C/C++, Hadoop Platform, SQL Database and coding.
- Business Savviness: Data scientists need to understand the business they are working in and come up with solutions to complex problems using data analysis.
- Communication skills: A data scientist can clearly and fluently translate their technical analytical findings to a non-technical department. A data scientist must empower the business to make decisions by presenting them with quantified data. They must also be able to understand the needs of their non-technical departments (such as business development or marketing teams) in order to analyze the data correctly.
- Expert Technical skills in the following:
- Math (e.g. linear algebra, and probability)
- Machine learning tools and techniques
- Software engineering skills
- Data mining
- Data cleaning and munging
- Data visualization and reporting techniques
- Unstructured data techniques
Data Scientist salary
According to Payscale.com the “median annual data scientist salary is $120,714, as of March 31, 2017, with a range usually between $105,187 to $135,304, however this can vary widely depending on a variety of factors.”
The factors include educational level, years of experience, location, certifications, and the involvement one has in professional organizations.
Data Scientist Job Outlook
More and more companies are looking for data scientists to help them process and understand data. There is a vast shortage of qualified candidates for this growing industry. New educational programs in big data, data science and data analysis are helping the companies fill these positions.
As the data science profession continues to grow, there are more career opportunities all over the United States. Be mindful that many companies classify a data scientist as a “data architect,” “data engineer” or “data analyst,” which may have the same meaning and job description.