Tips to Learn How to Become a Data Scientist

While data scientists can (and do) perform data analysis, they do so within the realm of building and deploying predictive models which often incorporate machine learning and deep learning protocols. Data scientists must also have a meta-level understanding of which models are the best fit for the data being analyzed. Since all models are approximations of current and future environments, they require fine-tuning which, in turn, relies on the data scientists’ mathematical expertise. Although data scientists are not data engineers, they should (ideally) have some knowledge of how databases are constructed, and how to pull data from an organization’s preferred database management system (DBMS). Due to the extensive knowledge requirements, including academic and professional training and/or experience, companies, research organizations, and governmental agencies are scrambling to find qualified data scientists.

This guide provides a basic overview of some of the opportunities in this emerging field and lists the steps required to become a data scientist. For informational purposes, a detailed job description, salary information, and the data science job outlook are included.


Step 1: Preparation

Future data scientists can begin preparations before they even step foot on a university campus or launch themselves into an online degree program. Becoming proficient with the most widely used programming languages in data science such as Python, Java, and R  — and refreshing their knowledge in applied math and statistics — will help aspiring data scientists get a head start. In fact, entering college with an already established skillset frequently improves a student’s learning rate. But, also, early exposure to data science knowledge requirements is helpful for determining whether a data science career is the right fit.

Step 2: Complete undergraduate studies

The most sought-after majors for data science are statistics, computer science, information technologies, mathematics, or data science(if available). Minoring in one of the aforementioned fields is also recommended. Continue to learn programming languages, database architecture, and add SQL/MySQL to the “data science to-do list.” Now is the time to start building professional networks by looking for connections within college communities, look for internship opportunities, and ask professors and advisors for guidance.

Step 3: Obtain an entry-level job

Companies are often eager to fill entry-level data science jobs. Search for positions such as Junior Data Analyst or Junior Data Scientist.  System-specific training or certifications in data-related fields (e.g., business intelligence applications, relational database management systems, data visualization software, etc.) might help when looking for entry-level data science jobs.

Step 4: Earn a Master’s Degree or a Ph.D.

Data science is a field where career opportunities tend to be higher for those with advanced degrees. The in-demand graduate degrees for data science include the exact same specifications for an undergraduate degree: data science (if available), computer science, information technology, math, and statistics. However, many companies also accept STEM degrees such as biotechnology, engineering, and physics (among others). Also keep in mind that data scientists need to understand how to use enterprise-grade data management programs and how distributed storage and computation operate (e.g., Hadoop, MapReduce, and Spark) in relation to model building and predictive analytics.

Step 5: Get promoted 

Additional education and experience are key factors that lead to being promoted or becoming a data scientist in high demand. Businesses value results. Coupling strong technical skills with project management and leadership experience will generally chart a course towards more significant opportunities and higher compensation.

Step 6: Never Stop Learning

Staying relevant is crucial to the ever-evolving field of data science. In this age of constant technological innovation, continuing education is a hedge against shifts in the career market. This is also the case for data science since the field isn’t as established as other statistically and technologically focused careers. 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 boot camps and conferences.


As described above, data scientists must have expertise in several different disciplines. Summarily, data scientists must possess the statistical knowledge and computer skills that are needed for solving complex problems. Using descriptive, predictive, inferential, and causal models, they can explore and anticipate problems then work to model a solution based on a multitude of factors.

Data scientists are part mathematician and part computer scientist. Their skill set encompasses both the business and information technology sectors, which is why they are highly sought after.

Data science is deep knowledge discovery through data exploration and inference. This discipline focuses on using mathematical and algorithmic techniques to solve some of the most analytically complex business problems. In doing so, they leverage troves of raw data to figure out the hidden insight that lies beneath the surface. The core of the field centers around evidence-based analytical accuracy and building strong decision capabilities. However, data scientists must also verbally and visually communicate their findings to stakeholders who may or may not understand the statistical jargon. Thus, data scientists must be excellent communicators.


Essentially, a data scientist extracts meaning from the varying types of data (e.g., structured, unstructured, semi-structured) that flow into the enterprise. On any given day, a data scientist may be extracting data from a database, preparing the data for various analyses, building and testing a statistical model or creating reports that include easily understandable data visualizations. There is a data science cycle which isn’t a set of rules as much as it is a heuristic:

  • Data collection
  • Data preparation
  • Exploratory data analysis (EDA)
  • Evaluating and interpreting EDA results
  • Model building
  • Model testing
  • Model deployment
  • Model optimization

The above is iterative, meaning a data scientist will be in “evaluation mode” throughout the entire process. Or, perhaps, after the EDA phase, they find that the data doesn’t fit the problem they are trying to solve (or the question they are attempting to answer). They may need to start over or carefully choose which portions of the data that does apply, then go back and collect additional data. Such is the reason they need a higher level of combined skills including research design.


While data science projects and tasks may vary depending on the enterprise, there are primary job functions that tend to be common among all data science positions such as:

  • Collecting massive amounts of data and converting it to an analysis-friendly
  • Problem-solving business-related challenges while using data-driven techniques and tools.
  • Using a variety of programming languages, as well as programs, for data collection and analysis.
  • 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 analytical tools.
  • Recommend cost-effective changes to existing procedures and strategies.


  1. Experience and Fluency in many of these computer/coding programs: SAS, SPSS, MATLAB R, Python, Java, C/C++, Hadoop Platform, SQL/NoSQL Databases.
  2. Business Savviness: Data scientists need to understand the business sector they are working in and create solutions to complex problems that align with business logic/objectives.
  3. Communication skills: A data scientist can clearly and fluently translate their technical and analytical findings to a non-technical department. 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. A data scientist must empower the business to make decisions by presenting robust and verifiable information.
  4. Expert Technical skillsin the following:
    • Math (g., linear algebra, calculus, and probability)
    • Statistics
    • Machine learning tools and techniques
    • Data mining
    • Data cleaning and munging
    • Data visualization and reporting techniques
    • Unstructured data technique



I’m Joseph, and I started this blog as a way to share ideas with others. I wanted to create a space where people could share their thoughts and feelings, and where we could all have a good laugh. Since then, the blog has grown into something much larger than I ever imagined. We have posts on everything from humorous essays to comics to interviews. And our weekly columns cover sports, video games, college life, and software.
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