Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconData Sciencebreadcumb forward arrow iconMyth Busted: Data Science doesn’t need Coding

Myth Busted: Data Science doesn’t need Coding

Last updated:
4th Nov, 2021
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Myth Busted: Data Science doesn’t need Coding

The global market for data science careers is increasing rapidly and is expected to grow at a CAGR of 30% from 2019 to 2024. Data Science is slowly becoming one of the most important domains in the computer science industry. This is because more businesses are adopting advanced data science technologies for data collection, performance analysis, trend prediction, and revenue maximization.

A common misconception around the data science career path is that it requires you to be proficient in coding and computer algorithms. However, data science consists of many more subjects like statistics, mathematics, data visualization, regression, error-solving, etc. It is based on data and has a lot to do with what you do with it, not necessarily how.

What does Data Science consist of?

In a career in data science, professionals work on massive amounts of data or information to find patterns like consumer preferences and marketing trends to help a company strategize. Such data-driven decision-making capabilities are required for marketing, product design, revenue generation, brand awareness, etc.

The main three skill sets that you will need to master as a data scientist are:

  1. Mathematical reasoning for solving real-world problems as quickly as possible.
  2. Communication skills to explain your observations and conclusions.
  3. Analytical tools and software to work with big data and its structures and shape business policies.

Skills required in Data Science

Although it is good to know Coding through programming languages like Python, R, and Java, not being an expert in Coding won’t close any doors to a successful career in data science. There are a few essential technical and soft skills you can learn.

1. Statistics

While working with data, you need to know how to extract vital information from raw data as required by the organization. Then, you need to deduce useful patterns from the consolidated data using statistical analysis, graphical representations, and regression techniques.

The basic concepts you need to master for a career in data science are probability, sampling, data distribution, hypothesis testing, correlation, variance, and regression techniques. You will also need to learn different statistical methods for data modeling and error reduction processes to refine the data for further use.

2. Data ELT

The processes of data extraction, data loading, and data transformation (Data ELT) are crucial skills in data science and analytics. A data scientist manages the functionalities involved in these departments.

The first step, data extraction, includes gathering data from various sources like files, database management systems, NoSQL databases, user-tracking websites, etc., using data extraction tools. This collected data is then transformed as per business logic to amount to a value-providing exercise. Once the data is cleansed, redundancy eliminated, and manipulated, data integration is done, and it is sent for data warehousing. Finally, the data scientist loads it into a data warehouse for reporting and analytics.

3. Exploratory Data Analytics

Data wrangling and exploration together are known as exploratory data analytics. They form an essential skill for data scientists. It involves cleaning the data to rid it of all errors, validating it for business use, structuring it for further processing, and standardizing it.

If you aren’t confident with Coding, you can try the following exploratory data analysis tools:

  • Microsoft Excel
  • Rapid Miner
  • Trifacta
  • Weka
  • Tableau Public
  • Data Science Studio
  • Tanagra Project

These tools will help you work with advanced machine learning models for data visualization, clustering, regression, deploying, etc.

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4. Machine Learning

Predictive modeling using machine learning techniques, tools, and algorithms is crucial for a career in data science. The concepts you should have a good grip over are tree models, regression algorithms, clustering, classification techniques, and anomaly detection. There is numerous software on the Internet to assist you in working on datasets without having to write any Python code.

Machine learning is a great way to visualize data and its patterns to make business decisions. You can take the help of Graphics User Interface (GUI) tools to design charts, graphs, histograms, and other graphics useful in client-end meetings.

Top Data Science Skills You Should Learn

5. Big Data Processing Frameworks

A big data processing framework takes care of data pre-processing, modeling, transformation, and computational efficiencies. The top frameworks a data scientist must know today are:

  • Hadoop
  • Spark
  • Apache Flink
  • Apache Storm
  • Apache Samza

The skill that a data scientist must give maximum attention to is the ability to make high-value inferences from a given dataset. These business insights will then help improve the marketing and sales section of the company. The above-mentioned big data processing frameworks will help you in just that.

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Data Scientist Career Path

To get started with your career in data science, you can begin gaining theoretical knowledge and hands-on experience in the skills listed above. You can turn to online courses like the Executive Programme in Data Science offered by IIIT Bangalore in association with upGrad.

This is a 12-month long online certification program teaching you all the required data science topics through 400+ hours of video content, 60+ industrial projects, and 40+ live sessions under professional mentors. It is designed for working professionals and covers the following topics:

  • Introduction to Python programming (You’ll know the basics)
  • Inferential statistics
  • Hypothesis testing
  • Linear regression
  • Tree models
  • Clustering
  • Tableau visualization
  • Storytelling case study
  • Natural language processing 
  • Introduction to neural networks

With industry projects like the Uber supply-demand study, Telecom churn case study, and IMDb movie rating study, this course aims at equipping the student with advanced data science skills. Moreover, it offers placement assistance and profile-building workshops to help you land a job in this domain easily.

Once you learn your concepts well, you need to focus on soft skills to survive in the data scientist career path. For non-programmers, the best support to take is that of GUI tools for smoothing the operation of machine learning methods for data analytics. Furthermore, become a captive storyteller. Even though the machine algorithms take care of the data, you should be able to convey the inferences so that the stakeholders grasp the idea almost immediately.

Read our popular Data Science Articles


Once you begin your career in data science, develop strong business acumen in your industry, and become a skilled expert in any one domain (finance, technology, healthcare, retail, etc.). There is high scope in this career line in the upcoming decade.


Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.

Frequently Asked Questions (FAQs)

1How much does a data scientist earn on average?

On average, a data scientist in India earns about INR 7 lakhs. However, this goes up with skills and experience, and senior-level data scientists can even earn up to INR 1.13 crore per annum.

2hat are some industry projects at the beginners’ level?

You can do Exploratory Data Analytics (EDA) projects, sentiment analysis, chatbot development, and recommendation system designing at the beginners’ level.

3What are the top data science trends in 2021?

The top trends that the data science domain will see in 2021 are: a). Scalable and more secure AI for businesses, b). Data fabrication for the cohesion of hardware and software, c). Cloud-backed data analytics, d). Augmented reality (Internet of Things), e). Customized AI automation facilities, f). Intelligent feature generation, g). The rising dependence on blockchain technology

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