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How to make a Career in Data Science - Career Path for Aspiring Data Scientists

  • Data Science
  • Career Path
  • Certifications
  • Published by: André Hammer on Nov 14, 2022
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Almost everything we do in our everyday lives is a result of data, in one form or the other. Until 2003, there were only five billion gigabytes of data on the internet. But something changed in 2011, the amount of data that had ever existed, was created in just two days. As of 2013, this volume was produced once every 10 minutes. As a result, it should not come as a surprise that the creation of 90% of all the data occurred during the previous decade.

Data is useful, we know that, but before the advent of the idea of ‘big data’, it had been mostly ignored. As per a definition from Gartner, “Big Data are high volume, high velocity, or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization.”

To put it simply, big data is a collection of very large data sets that can't be processed by normal computer methods. This term refers not only to the data, but also to the different frameworks, tools, and methods used to work with them. Industry players have to find other ways to handle the data because of technological progress, the rise of new ways to communicate, like social networking, and the release of new, more powerful devices.

Application of Data Science

McKinsey said that big data initiatives in the US healthcare system "could save $300 billion to $450 billion in healthcare costs, or 12 to 17 percent of the $2.6 trillion baselines in US healthcare costs." On the other hand, it is thought that bad data costs the US about $3.1 trillion a year.

Big Data is useless without the skills of professionals who know how to turn cutting-edge technology into insights that can be used. Today, more and more organizations are letting big data in and using its power. This makes a data scientist who knows how to get actionable insights out of gigabytes of data, extremely valuable. So if Data Science is the path you have chosen, you are likely on a lucrative path.

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So what does a data scientist do?

Data science teams are usually small and work on multiple business problems. Data scientists are often expected to be independent right from day one.

As per the book Doing Data Science: In a broader sense, a data scientist is someone who understands how to draw conclusions and draw inferences from large amounts of data, using a combination of human intuition and the methodologies and tools of statistics and machine learning. They put in a lot of hours gathering, cleaning, and munching data because, well, data is never clean. This process requires patience, statistics, and software engineering skills. These are the same skills that are needed to understand biases in the data and to debug code that logs output. After the data is organized, exploratory data analysis (which combines visualization and data sense) plays a critical role. They will discover patterns, construct models, and develop algorithms, some of which will be used to get insight into how the product is being utilized and its general health, while others will be used as prototypes before being integrated into the final product. A data scientist plays a key role in data-driven decision-making as well as experiment designing. Even if their coworkers aren't data experts, they will make sure to communicate the implications of data, through visuals and clear language.

Specific tasks of a data scientist include:

  • Identifying data-analytics issues that can deliver the highest opportunities to the organization
  • Collecting large sets of structured and unstructured data from disparate sources
  • Determining accurate and correct data sets and variables
  • Cleaning and validating the data to ensure completeness, accuracy, and uniformity
  • Analyzing data to recognize patterns and trends
  • Devising and applying algorithms to data mining
  • Interpreting the data to discover solutions and opportunities
  • Communicating findings to stakeholders

Here’s a fun fact: Netflix relies heavily on data science to build shows that their audiences are bound to like! The following are some of the ways that the firm measures user engagement and retention:

  • When do viewers pause the recording, rewind it, or speed it up
  • When do viewers typically watch material, both throughout the week and on weekends, and at what time of day?
  • When and why do they stop consuming material?
  • The way in which viewers browse and navigate the page

Netflix is currently available on more than 120 million different devices across the world. Netflix employs cutting-edge data science metrics to facilitate the processing of all of this information. This enables it to provide its consumers with better suggestions for movies and shows, as well as to produce better content specifically for those people. Data science and big data were utilized during the production of the critically acclaimed series House of Cards. Netflix gathered information on its users from the television show The West Wing, which is another drama series. They took into account both the points at which viewers stopped viewing the show as well as the points at which they ceased fast-forwarding. Netflix was able to develop a show that they believed to be perfectly engaging by analyzing this data.

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What does it take to be a good data scientist?

To understand whether you have what it takes to be a good data scientist, ask yourself the following questions:

  • Do I hold a degree in statistics, computer science, math, management information systems, or marketing? If not, do I plan on getting one?
  • Do I have work experience in any of the above-mentioned disciplines?
  • Am I interested in data collection and analysis?
  • Do I appreciate individualized work and problem-solving?
  • Do I have good verbal and visual communication skills?
  • Do I want to broaden my skills and take on new challenges?

If you said ‘yes’ to any of these questions, you may be a good contender for a data scientist.

To be a data scientist, you need to know math or statistics. It's also important to be naturally curious and to be able to think creatively and critically. To get the most out of the data, you need to be good at putting things together and want to find answers to questions that haven't been asked yet.

According to KDnuggets, 88% of data scientists have at least a master’s degree, and 46% have PhDs.

You also need to know how to program so you can come up with the models and algorithms you need to mine big data stores. Python and R are two of the best programming environments for data science.

In addition to this, it would be beneficial if you have a good eye for business strategy. If you are unable to devise your own methods and build your own infrastructures to slice and dice the data that will lead you to new discoveries and new visions for the future, it is possible that you will not be successful in your endeavors. Even if you collaborate with other data specialists or even with an interdisciplinary team of professionals, this is an important skill. You must also be able to communicate complex ideas to your nontechnical stakeholders in a way they can easily understand. Data science software tools can help you visualize your findings, but you will also need verbal communication skills to tell the story clearly.

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A step-by-step guide to becoming a data scientist, for beginners

Here’s a simple guide that you can follow step by step to begin your journey as a data scientist:

Step 1: Get familiar with programming languages

It is vital to brush up on relevant programming languages such as Python, R, SQL, and SAS, even if you have a Bachelor's degree in the field, as this degree may just provide you with a theoretical grasp of the topic. When it comes to dealing with enormous datasets, these are the languages that are absolutely necessary.

Step 2: Get certified

Skill and tool-specific certifications are a great way to show your knowledge and expertise about your skills.Certifications make your CV more valuable, teach you important skills in short periods of time and also give you the confidence to take on more. Power BI Certification is a great one, to begin with. Learn about the various methods and best practices that are in line with business and technical requirements for modeling, visualizing, and analyzing data with Power BI. At Readynez, we believe that certification is the way to close the knowledge gap. Hence we have a wide collection of courses ready for you to take on!

Step 3: Be an intern

The best method to get your foot in the door at organizations that are recruiting data scientists is to obtain some experience in the field through an internship. Look for employment opportunities with titles like "data analyst," "business intelligence analyst," "statistician," or "data engineer," for example. Internships are an excellent opportunity to acquire hands-on experience in the field of one's choice and to have a better understanding of the duties and responsibilities associated with the position for which one is applying.

Step 4: Get an entry-level Job

After you have completed your internship, you have the option of staying with the same firm (if they are looking to fill open jobs) or beginning your search for entry-level work as a data scientist, data analyst, or data engineer. From that vantage point, you may advance your career by gaining experience and moving up the ladder while simultaneously expanding your knowledge and abilities.

Step 5 is to upgrade your skills. Below is a list of skills that you should work on to advance your career as a data scientist.

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What skills should you work on to be a good data scientist?

In order to pursue a career as a data scientist, it would benefit you to acquire expertise in the following fields:

1) Gaining expertise in databases is the first step in storing and analyzing data with programs like Oracle, Database, MySQL, Microsoft SQL Server, and Teradata. This expertise is necessary.

2) Mastering statistics, probability, and quantitative analysis should be your second objective. The field of study known as statistics focuses on the creation and investigation of techniques for the collection, examination, interpretation, and presentation of empirical data. Probability may be seen as a measurement of how likely it is that a certain occurrence will take place.

3) Analysis in mathematics refers to the study of limits and the ideas that are associated with them, including differentiation, integration, measure, infinite series, and analytic functions.

4) Mastery of at least one programming language is required. When doing analytics on data, programming tools such as R, Python, and SAS are of utmost importance.

5) Data wrangling: Learning how to clean, manipulate, and organize data is part of a required skill, which is called "data wrangling." R, Python, Flume, and Scoop are all well-known and widely used tools for data wrangling.

6) Understanding Big Data tools such as Apache Spark, Hadoop, Talend, and Tableau, which are used to deal with large and complex data that cannot be dealt with using traditional data processing software.

7) Data Visualization being able to envision results. Integrating various data sets and producing a graphical representation of the findings through the use of various types of diagrams, charts, and graphs are referred to as "data visualization."

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In Conclusion

Any company that is able to make effective use of the data they collect may benefit from the use of data scientists. Data science is beneficial to every organization, regardless of the sector, it operates in because it can provide statistics and insights across processes, assist in the recruiting of new employees, and help senior staff members make better-informed decisions.

After you have completed most of the steps mentioned in this article, you will have a range of career opportunities available. Get certified in a range of data science areas with Readynez to begin your journey!

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