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7 essential skills of a data scientist in 2021

Data Science is one of the fastest growing fields along with great job opportunities. By 2024, the global machine learning market has been predicted to reach $20.83 Billion. While we compare a computer programmer’s average salary per year with a data scientist, there is a massive growth for data scientist’s average salary per annum. Ever wondered how Data science sees such growth and why people wish to become a data scientist? Their infinite applications create an increasing demand with new evolving field that includes Artificial Intelligence (AI), Machine Learning (ML), Big Data, along with other new technologies such as Blockchain, Digital Twins, Edge Computing, Serverless Computing, and others that implement various techniques within the Data Science industry. Though it’s a booming field, it’s hard to find the right data scientist’s skills. The following blog discusses on 7 essential skills that every aspiring data scientist should require to master in 2021 and beyond.

7 Essential skills:

  1. Fundamentals of Data Science and Statistics

  2. Programming knowledg

  3. Data Wrangling

  4. Machine Learning

  5. Deep Learning

  6. Software Engineering

  7. Business Acumen

Fundamentals of Data Science and Statistics

The inception points to become a data scientist is to understand the fundamentals of Data Science, their common terminologies and the difference between supervised and unsupervised learning, classification and regression, machine learning and deep learning, business analytics and data engineering.

Statistics is an essential concept we need to be aware of before we produce high-quality models. Machine learning domain knowledge starts as the base of statistics and advances thereafter. The descriptive statistics such as the mean, mode, median, the standard deviation and variance is required in statistics. They help in learning and understanding data science.

Programming knowledge

The computing power enhanced the concept of machine learning skills with their impressive programming. The programming helps us to communicate with machines. We can choose the programming language that we are comfortable with. Every programming language comes with their own pros and cons that is easy to deploy in applications or websites. Their active data science community makes it easier for the top tech companies to choose their preferred programming language. Few examples are


The fundamentals of Python let us to learn the Python libraries that consists of reusable codes that we can use in the place of rewriting simple commands. The top Python libraries for data science consists of Pandas, TensorFlow, Matplotlib, SciPy, NumPy, Seaborn, and Scikit-learn. Pandas is the most significant library to know in Python. It is a integration of Data manipulation and analysis. Their functionality and DataFrames became a common data structure for machine learning models.

Data Wrangling

Data Wrangling is an essential step in a machine learning project where we clean the data and transform them in a format which is analysed better in the next phase. It is a time-consuming process but holistically it helps in taking efficient data driven decisions. The data manipulation and wrangling are generally applied to find the missing value, imputation, outlier treatment, correcting data types, scaling, transformation, string formatting, and date formatting. Data Analysis helps us in understanding all about the data such as the average sales per week, products on demand, date time stamps, etc.

Machine Learning

Machine learning is a core skill for a data scientist that helps them to build predictive models. Machine learning algorithms assists in predicting the upcoming month’s number of customers by analysing the previous month’s data. Machine learning has a wide range of concepts such as linear regression model, logistic regression model, Random Forest, XGBoost, CatBoost, and so on. All data science roles do not require deep learning, Natural Language Processing, or data engineering skills; however, we must be aware of terms such as k-nearest Neighbors, ensemble methods, and random forests, especially if we are interested in working with big data.

Deep Learning

How cool is to have self-driving cars. Though there’s no flying cars, we are still astonished by self-driven cars. Thanks to Deep Learning skills and algorithms. It is a high growth vertical in Artificial Intelligence which is advanced in data storage capabilities and computational advancement. We must be aware of the advanced models such as CNN, RCNN, RNN, etc.

Software Engineering

Writing efficient and clean code with the basic concepts of software engineering such as lifecycle of software development projects, time-space complexity, compilers, data types, etc helps us in becoming a skilled data scientist. A good code helps us in the production stage of the project. The core software engineering skills that are required for aspiring data scientists are Object-Oriented Programming, Full-stack development, Databases & Big Data, Cloud Computing, etc. As new technologies emerge every day, data scientists must adopt to these new technologies for better performance.

Business Acumen

The quality of a great data scientist is to have good communication skills to brief how insightful the results can be and to formulate the problem statement. Their storytelling skills helps the clients to understand the projects through data. A data scientist’s structured thinking helps to look at problems from various perspectives. A larger goal can be segregated into multiple smaller goals to solve efficiently. The curiosity of a data scientist creates numerous questions within them. How did this happen? Why did this happen? What would be the result if worked in this way? These types of questions help them to evolve as a skilled data scientist.

Data Science is a vast field where learning never ends. Advanced concepts and technologies keep a data scientist to be curious and always learning.

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