A Step-by-Step Guide to Becoming an In-Demand Data Scientist

February 02, 2023 | Data Science

According to the Global News Wire, the total valuation of the data science industry will reach USD 322.9 billion by 2026. That’s a whopping 27.7% compound growth from where it currently stands now. In the last two decades, there hasn’t been a single field that didn’t benefit from data science, thus raising genuine curiosity about the field. From designing radical business strategies to revolutionizing healthcare, every contemporary development has data at its roots. So, what is data science, and how do you become a data scientist? Let’s find out.

What is Data Science?

Data science concerns itself with studying data and extracting actionable insights useful for businesses. It is an intelligent mix of statistical analysis, mathematical modeling, and data mining that combines with organizational data to fetch contextual information relevant to the industry. Data science aims to technologically integrate human society with the staggering 2.5 quintillion data that is churned out every day. It is an ever-learning, ever-wakeful ecosystem that thrives on analyzing data in real-time.

What is a Data Scientist?

If data science is the discipline that analyzes data, a data scientist is a practitioner who facilitates the analytical processes involved. A large number of data experts together keep track of all the processes of the entire data science lifecycle, from data ingestion to interpretation. Usually, data scientists team up with software engineers and machine learners to optimize the final results of the analysis. The success of a data scientist depends on their ability to sew all the minute observations of different stages into one set of holistic insights that could prove beneficial to stakeholders.

What Does a Data Scientist Do?

Before asking how to become a data scientist, one must understand the scope of work and knowledge required in the domain. A data scientist undertakes extensive collaborative work on any given day. For instance, when data is in the pipeline, data engineers consult data scientists to regulate the kind of data that would prove useful. On the other hand, data scientists team up with machine learners to scale machine learning models, adding software engineering to the data scientist’s pool of expertise. A data scientist also needs to collaborate with business analysts to come up with a harmonious decision-making process. This calls for thorough and updated industry-specific knowledge that ensures the organization has the most stable and secure business strategies.

How to Become a Data Scientist

While considering data science as a career, one must know what it means to look at data analytically. Encountering the data counterpart of real-life problems will only accelerate the learning process. Not only will it generate curiosity around real-life situations, but the learning process will also mimic a typical day in the life of a data scientist. Reading this holistic guide will help you understand how to become a data scientist.

Step 1: Asking the Right Questions and Reflecting on the Data

Developing an analytical mindset is key to underlining the most relevant questions that a specific business should be asking. Amateurs should ideally try to find patterns in data of any domain that they find interesting, and document their observations. This hones one’s critical thinking ability, crucial to excelling in this field.

Step 2: Finding the Right Courses and Learning the Fundamentals

Mixing critical thinking alongside comprehensive data science, online courses are a good way to become a professional data scientist. As industry trends evolve, the data scientist’s toolkit now comprises a large number of specialized applications. Technical training ensures a complete package of updated and dynamic knowledge that can compete with the unique problems of the business world. The training includes:

Collection and storage of data
Modeling data
Making predictive data models
Visualizing and explaining data in the form of insights

Step 3: What are the Key Programming Software Required for Data Science?

Amateur programmers should ideally start in this field by learning Python and Excel. Python is a favorite among programmers for the following reasons:

Wealth of libraries
Consistent syntax
High scalability and easy integration with web apps
Superior compatibility with other programming languages and big data platforms

In addition to a strong base in Python programming and Excel, here are the most abundantly used data science tools:

R and Hive: Statistical programming languages like Python
SQL: Query language
Tensorflow: End-to-end machine learning platform
Apache Mahout: Designs scalable machine-learning algorithms
H2O.ai: An advanced Artificial Intelligence (AI) cloud platform

Step 4: Mastering Practical Data Skills

Once familiar with the various data science tools, it is time to implement them in real-life applications. Here are a few tips to help choose the right projects to get started with:

Choose projects that feature a large number of data types
Classify data types and stages of analysis. It typically involves data lifecycle stages such as industry research, collecting data, cleaning data, etc.
Develop analytical models to dissect the data
Combine data visualization with analysis

Prerequisites or Pre-Experience to Become a Data Scientist

Data science is a new discipline that involves a little of all aspects of Science, Technology, Engineering, and Mathematics (STEM). So, one of the prerequisites of how to become a data scientist is to have a Bachelor’s degree in any of the STEM subjects.

Important Data Science Skills

Here is a list of some essential skills a data scientist must acquire apart from mastering the technical knowhow of data analysis:

Considerable practical knowledge of the current business domain to identify fault lines
Strong business acumen to mix with statistical analysis
Combine all available sources of analysis including AI, natural language processing, and machine learning
articulation skills to explain analytical results to stakeholders and decision-makers
Strong communication skills to highlight the effectiveness of the results in solving business problems

How to Build a Portfolio to Showcase Your Data Science Skills

Employers show a preference for hiring candidates who have in-depth knowledge of data science and a sound understanding of data workflows. Hence, building a strong portfolio of data science skills is crucial. Platforms like GitHub and Deepnote can be beneficial for aspiring data scientists as they are interactive and help showcase their body of work. An active GitHub profile is an added benefit when it comes to accessing a data scientist’s profile. The following are some tips to make your profile stand out from the competition:

Create a complete profile page as recruiters always prefer a profile replete with all the essential details
Documentation is crucial. Note down links to all projects including project descriptions and images
Fork projects that are appealing and send pull requests to accommodate more interesting projects for the recruiter to check.
Remain active on the platforms.

Learning data science can give you access to a thriving career in a large number of fields. An BLS report predicts a growing demand for data scientists through this decade, making it the perfect time to think of major career shifts and land an average annual income of over USD 1 million. Anybody deeply passionate about data and finding insights should upskill their knowledge base. If you want a career in data science, explore courses from MIT xPRO.