The popularity of data science has been growing for the last couple of years. As various countries’ economies go digital, the amount of data available for analysis is increasing.
Data Science is the field today that allows multiple organizations to make sense of the world around them using data. Therefore, it’s no surprise that the demand for data scientists and their related job roles is rapidly increasing.
India ranks second globally in data science opportunities with over 50,000 positions, just behind the United States.
Given the interest of various aspirants, knowing data scientist skills has become a hot topic. In this article, this very topic is explored as there are numerous requirements for data science, and one needs to have a deep understanding of the skills of data scientists before venturing into this field.
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Important skills to become a Data Scientist
Back in the day, business organizations used to be mainly familyrun. Some rudimentary data backed the decisions, often based on ‘experience’ or ‘gut feeling.’ Sometimes, these decisions were taken by consulting other stakeholders, typically other family members.
In today’s business landscape, datadriven decisionmaking is the norm, offering a more scientific and democratic approach to organizational management. With abundant data and accessible processing tools, leveraging data has become easier.
Accountable to shareholders and stakeholders, CEOs find databacked decisions advantageous for justifying actions. Consequently, many organizations are exploring Data Science, seeking skilled Data Scientists to handle data and derive actionable insights, though this pursuit is challenging in practice.
Data Science is a new field that, in a way, is still evolving. Also, like any other new field, it is an amalgamation of multiple preexisting fields that makes the skills required to master data science very peculiar.
 Also read: Top Data Science Tools to Learn
While there are numerous data science skills, they all can be broadly categorized into technical and nontechnical, and both are considered critical requirements for data science.
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Let’s first start with the technical skills required for a data scientist. These skills typically need some form of formal education and certifications.
Technical Skills
The field of data science is mainly about mastering the technical requirements. A data scientist requires diverse technical skills to analyze and interpret complex data effectively. Here are some key technical skills for a data scientist:

Programming Language
Among the preliminary data science requirements is programming knowledge. Knowing how to program is important as one needs to use some programming language to implement all the theoretical knowledge.
While many programming languages exist, some are closely associated with data science. Of these languages, particular programming languages solve specific data science problems. All these languages form the fundamental skills for data scientists. The need for coding and knowledge of programming language as essential data scientist skills will be explored in detail later in the article.

Exploratory Data Analysis
Knowledge of EDA (Exploratory Data Analysis) and related tools is essential for the various data science skills required. The idea behind EDA is that the data scientist gets an overall idea of the data they are dealing with to form the strategy accordingly.
EDA procedures include basic information about data structure, number of rows, columns, outliers, missing values, column names, data shape, data types, etc. Many tools are required to perform EDA, the most basic being MS Excel and similar spreadsheets.

Data Wrangling
Once the data set is understood, data wrangling is the next important requirement for data science. Data wrangling refers to the process where the raw data is transformed, manipulated, cleaned, etc., to be ready for the next processes.
Important data science skills include grasping the structure of various datasets in your organization, knowing how they are related, combining them with other datasets that can provide deep insights, etc.
Having the skills for performing effective data wrangling is among the essential skills of data scientists as it reduces processing time and provides deeper insights, enabling later processes to focus better on data modeling and ensuring that the datadriven decisions are correct and not based on unclean and flawed data. Knowledge of tools like SQL and VBA is essential for data science as these tools enable easy data wrangling.

Databases
Regarding the skills requirements to be considered as a fullstack data scientist, one needs to be a master of all trades. As discussed ahead, data scientists require numerous skills, from data preparation to modeling to model evaluation, validation, and deployment.
Given the complexity of data today regarding volume and variety, one must also have database management skills. This means you should know the tools to index, edit, and manipulate databases containing vast data. Important database management tools include MySQL, SQL Server, Oracle, PostgreSQL, and NoSQL databases (HBase, Cassandra, Redis, MongoDB, DynamoDB, and CouchDB).

Mathematics & Statistics
Among the skills required to master data science, mathematical and statistical skills form the theoretical basis for creating numerous data models that enable databacked decisionmaking.
Mathematical and statistical skills for data scientists are a topic of discussion as there are so many topics that one needs to master. In this article, a few of these topics are discussed.
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Apart from basic arithmetic, the essential mathematical data science requirements are the various topics of linear algebra and matrices. Knowing the rank, determinant, trace of an array, and eigenvalue of matrices allows for performing multiple feature reduction and other data science operations.
Understanding matrix & vector products, vector operations, and linear and tensor equations is crucial for understanding various modeling algorithms such as linear regression, logistic regression, etc.

Geometry
The data structure in data science is understood in terms of dimensions, and when dimensions are involved, knowledge of geometry comes in handy. Geometry is used in data science to perform varied kinds of data analysis, principal component analysis, cluster analysis, etc.

Calculus
Calculus has become highly important in advanced data science operations, including deep learning algorithms and artificial intelligence. Deep knowledge of partial derivatives, differentials, chain rules, etc., forms one of the most advanced data scientist skills.

Probability Distribution
Often, data science models try to predict future events, and the closest science has come to predicting a future event is by using probabilities. Therefore, the knowledge of probability distribution is very important as it allows you to use statistics to understand how randomlooking data can provide information regarding future events. Data scientists are expected to know common probability distributions such as Gaussian distribution, binomial distribution, Bernoulli trials, etc.

Descriptive Statistics
The next use of statistics is through descriptive statistics, which helps explain the data’s key features. You can describe a cube by measuring its length, breadth, and height. Similarly, one can describe a dataset using three fundamental measures – a measure of central tendency, a measure of variability, and a measure of shape. These three measures form the basis of descriptive statistics.

Inferential Statistics
The next type of statistics is inferential, which is heavily based on probability distribution. Concepts like hypothesis testing are found under inferential statistics that allow the data scientist to check if there has been any statistically significant change in the data. Knowledge gained from such testing helps them to gain conclusive evidence to form an opinion about an event or phenomenon.
Also read: Descriptive vs. Inferential Statistics

Regression
Data model algorithms can be categorized into interpretive and noninterpretive. While Machine Learning and Deep Learning algorithms work like a black box, the statistical models are highly interpretive and used to form strategies by organizations. The most critical statistical models are those backed by regressionbased algorithms such as linear regression, logistic regression, etc.
More blogs in the Regression Series : Learning Logistic Regression – Types, Algorithms, and more [Part 1 – Theory]
 Learning Logistic Regression – Applications using Python [Part 2 Practical]
 Random Forest Regression
 What is Linear Regression In ML?
 Logistic Regression in R

Dimensionality Reduction
GOGA (Garbage In – Garbage Out) is a prevalent concept in computer science. The idea is that if you feed a program with badquality data, the output generated by the program will be of suboptimal quality, too. Data quality can be defined in various ways in data science, including the number of columns and their use.
If a dataset has a lot of columns that don’t add to the overall information level, it can cause many problems when developing models. These include the curse of dimensionality and the problem of multicollinearity.
Therefore, knowledge of the various dimensionality reduction techniques, such as principal component analysis, factor analysis, variance inflation factor, Kbest, etc., is a crucial data scientist skill.
Also read: How Data Reduction Can Increase the Efficiency in Data Mining?

Visualization
Nobody likes to see a bunch of numbers in a table or long paragraphs of information. Visualization, i.e., charts and graphs, is vital in data science. Visualization allows data scientists to communicate with their technical and nontechnical stakeholders regarding their findings, identified problems, and plausible solutions.
Critical data scientist skills include the knowledge of various graphs when to use what graph, and how to combine multiple varied kinds of charts to explain a complex analytical finding effectively.
While programming languages like Python and R can be used for visualization, many relatively simple and dedicated visualization tools exist. These include Tableau and PowerBI, which are used by most organizations today. Other tools include Qlikview and D3.js.

Web Scrapping
Data is considered to be 21stcentury oil. Unsurprisingly, companies go to extreme lengths to acquire quality data. However, because of the low cost and vast internet availability, much data is available online. This is why today, an essential data science skill is web scraping. It is the process of extracting data from the web that includes text, images, videos, and other information.
Extracting information from the web is particularly handy when trying to understand customer behavior or product discrepancies by analyzing customer reviews, polls, etc. Global trends can also be understood by analyzing data on various social media platforms.
Standard tools that one needs to know to perform web scrapping include Beautiful Soup, Scrapy, and Pandas.

Machine Learning
One of the most crucial skills for data scientists today is their understanding of machine learning. As the volume and veracity of data have increased, so has the possibility of creating models that can solve highly complex problems.
Statistical models, as discussed before, are great because they are interpretive but require a lot of data preparation, the fulfillment of assumptions, and time to reach high levels of accuracy.
Machine Learning models have a blackbox architecture and can solve highly complex problems. They can work in supervised, semisupervised, and even unsupervised learning setups, making them valuable. Machine learning skills include knowledge of essential algorithms such as Random Forest, Naïve Bayes, Support Vector Machines, K Nearest Neighbor, Kmeans, DBSCAN, etc.
Apart from knowing about algorithms, a data scientist must also have the skillset of model evaluation and validation so that their model doesn’t fail when working in the real world.
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Deep Learning
The logical extension of machine learning has been deep learning. While considered a subset of machine learning, deep learning uses artificial neural networks inspired by the neuron cells found in the human brain to power their algorithms.
Deep learning algorithms have revolutionized the world of artificial intelligence (AI). The skills that make a data scientist proficient in this field include having a deep theoretical understanding of the various deep learning algorithms such as ANN, RNN, CNN, LSTM, etc.
One must also know libraries like Keras and TensorFlow to implement these deep learning models that can help create anything from chatbots to autonomous cars.
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Natural Language Processing
For a long time, data science focused on structured data, i.e., data in tabular format. This was mainly due to
 Lack of techniques to deal with other forms of data format
 Limited hardware capability
 Early adoption of data science by financial institutions (that had financial records in databases)
Today, however, we can also work with text due to more robust hardware, new deep learning algorithms, better data extraction techniques, and linguistics advancements. Natural Language Processing (NLP) is an important skill as it allows you to deal with data and create applications that effectively use text. Essential aspects of NLP include
 Techniques such as tfidf, use of stop words, stemming, lemmatization, etc
 Algorithms such as fasttext, word2vec, bert
 Packages like NLTK, spacy, gensim

Big Data
Due to the rapid increase in the volume, veracity, and velocity of data, big data has come into existence. Big Data refers to various processes, techniques, and tools that allow data scientists and others to deal with an otherwise unmanageable amount of data.
Skills related to big data are becoming increasingly crucial as many ETL and database management processes deal with large datasets that need to be analyzed in realtime.
Many tools cover various aspects of big data, including capturing, storing, extracting, processing, and analyzing large amounts of data in multiple locations.
Common tools include
 NoSQL: Database management system that includes tools like MongoDB, HBase, Cassandra, Redis
 KNIME: Data preparation tool
 RapidMiner: Helps in automation through visual workflow
 Integrate.io: A tool to prepare, process, integrate, and analyze data on the cloud
 Hadoop: among the most crucial big data tools that allows for storing and processing data
 Spark is another vital tool for fast analytical queries through a distributed processing system.
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Model Deployment
Once the model is developed, it’s useless if it doesn’t go into production. Model deployment, therefore, is essential for organizations employing data scientists. This is where skills related to DevOps come in handy as it is the amalgamation of various methods that combine IT operations and software development, helping in
 Reducing the development life cycle
 Configure, manage, and scale data clusters
 Managing information through perpetual data integration, deployment, and monitoring
Crucial DevOps tools include Docker, GitHub, BitBucket, MLFlow, Kubeflow, Apache Airflow, AWS Sagemaker, etc.

Cloud Computing
As data science started depending more on machine learning and deep learning and using big data, the need for sophisticated and highly powerful hardware increased rapidly. After a point, it became difficult for companies to have this hardware inhouse, and that’s where cloud computing became crucial.
Today, cloud services provide specially designed tools to manage, visualize, and analyze data stored on remote servers, i.e., the cloud. Cloud computing tools have made it easy to develop and validate various models such as predictive, forecasting, recommender systems, etc.
The requirement for cloud computing skills is increasing, and the important cloud computingbased tools include
As you can see, a long list of skills is required to become a data scientist. However, all the skills so far are technical. The issue with data science is that nontechnical skills are also crucial, and no matter how good you are with technical skills, you must also excel in nontechnical skills.
Also read: A Quick Guide to Cloud Deployment
NonTechnical Skills
Leadership in organizations dealing with data scientists have often emphasized that they have considered the nontechnical skills of a data scientist to be equally important, if not more.
This is mainly because data scientists need to work in teams and provide their findings to stakeholders who are often unfamiliar with the complex world of data science. Data scientists work in various organizations in different domains, making the need for nontechnical skills even more crucial.
The ten most critical nontechnical data science skills are discussed ahead.

Business Acumen
A business employs data scientists to solve business problems. Therefore, data scientists need a deep knowledge of the various technical aspects of data science and a good understanding of business.
If a data scientist’s business acumen is weak, their solutions will not be pragmatic and, worse of all, may not solve the problems of the business. This is why data science is considered more than just number crunching, as it applies considerable skills to solve peculiar business problems.

Communications Skills
Data literacy is a massive issue in organizations, and the data scientist is therefore considered the torch bearer. An important skill here is communication, as the insights found by the data scientists need to be effectively communicated with the various technical and nontechnical teams.
Also, communication skills become crucial as data scientists must communicate with multiple departments to acquire data and provide feedback regarding their analytical insights.

Data Intuition
A less talked about but essential skill to possess is data Intuition. When trying to find actionable insights, data scientists deal with datasets that are often complex and cluttered with an overload of information. The information you, as a data scientist, are looking for is not on the surface, and you need to dig deep.
Here, having a good intuition of data in terms of how the missing pieces can be completed by adding new information, transforming data, and deriving new variables becomes crucial.

Data Ethics
Data is being fetched from various sources, and while the technology can be neutral, the impact of their outcome may be biased. The responsible use of data science is a whole other topic in itself. Companies today face legal trouble when they fail to manage data correctly.
Therefore, today’s data scientists must have the skills to conduct themselves ethically when using data. This includes ensuring that the applications they build do not adversely impact individuals, communities, and society. Concepts like data privacy, model bias, and feedback loops must be considered when learning to become a data scientist.

Analytical Mindset
When dealing with data, data scientists have numerous options regarding the tools and techniques available. An excellent analytical mindset is essential here as that helps identify the best route in solving the business problem.

Story Telling
A data scientist deals with data on numerous levels. It starts with fetching data and cleaning, transforming and manipulating it, and eventually creating data models. When the insights are finally presented, the various steps undertaken and the complex insights must be presented as a story rather than a table with numbers or a Word file with long paragraphs. This storytelling is a crucial skill as it allows others to understand your thought process and how you derive a conclusion.

Decision Making
Certain data science roles don’t stop with you providing the insights and calling it a day. It’s sometimes required for you to take some actions on your insights. A data scientist must not only determine the stage of the model outcome but also make decisions at every step of the modelbuilding process. This includes selecting the correct methodology, choosing the appropriate datacleaning techniques, selecting the suitable algorithm, etc. This is why decisionmaking skills are considered necessary for a data scientist.

Collaboration
As mentioned earlier, a data scientist has to communicate with various teams. Also, data scientists work with other data scientists, software developers, business stakeholders, etc., which requires collaborative skills. Therefore, you must have the skill set to collaborate with different teams, stakeholders, and peers.

Time Management
Time is considered precious in every field, and it is no different in data science. A data science project often has to follow stringent timelines regarding the delivery of the models, and an organization needs to develop hundreds, if not thousands, of models. This is why a data scientist must complete their project on time and move it to production so that the benefits from the models can reach the target audience.

Curiosity to Learn
Lastly, the skill one must have, whether one wants to become a data scientist or not, is the skill to learn and upgrade oneself constantly. The curiosity to learn is a crucial skill in data science because this field is continuously evolving. New tools and techniques come in every month, and organizations intend to adopt them as soon as possible to stay ahead of the competition.
In total, there are 23 skills that you need to have, with 13 being technical and 10 being nontechnical. All these skills are crucial in your journey to becoming a fullstack data scientist.
Does Data Science Require Coding?
As people from all backgrounds enter the field of data science, the coding skill requirement is considered a contested topic.
The four main components of data science are mathematics, statistics, business, and technology. As mathematics and statistics more or less remain the same and data science is a businessagnostic field (i.e., any business can utilize data science), the technology aspect needs to be discussed. The programming languages can convert the understanding of the other three fields into actionable items in the form of data models.
To understand how crucial programming is, all the following actions in data science are made possible due to its use.
 Sourcing of data
 Cleaning and transformation of data
 Exploratory data analysis
 Hypothesis Testing
 Statistical, Machine Learning, and Deep Learning model development
 Visualization
 Model Deployment
 Dashboards and Application building
All the above actions are performed using some important data science tools and languages such as
 Python
 R
 MySQL
 Scala
 Java
 Julia
 MATLAB
 TensorFlow
 Tableau
 Amazon Web Service (AWS)
 Google Cloud Platform (GCP)
 Microsoft Azure
 Hadoop
 Apache Spark
As you can understand, programming is essential and the backbone of data science, without which the theoretical understanding cannot be implemented. Different job roles demand different levels of coding skills.
Machine learning engineers deal with Python, R, and SQL; Business analysts are more into visualization tools, while Data Engineers are involved with AWS, GCP, or Microsoft Azure. A fullstack data scientist needs to master all of the programming skills.
Conclusion
As mentioned at the beginning of this article, data science is an amalgamation of various fields used by organizations involved in different domains. The very nature of data science makes the required skills highly unique.
While all the skills discussed in this article will help you to become a full stack data scientist, you may not have to have all the skills immediately; you can first start with having the technical skills such as statistics and data analytics, intermediate level of programming and have decent communication and problemsolving skills.
You can then work your way up from here and learn more skills such as machine learning, deep learning, big data, and DevOps. You can also improve business acumen, presentations, communication skills, etc. To learn and become better at the skills mentioned in the article, you can try
 Data Science Blogs
 Online Courses
 YouTube Videos
 ebooks
 Bootcamp
 Community Participation
 Networking with other data scientists
 Joining and participating in online forums and communities
 Attending data science conferences
To become an efficient data scientist, you must possess the discussed skill sets and avoid going overboard; instead, learn one or two skills at a time.
FAQs:
 Is data science a hard skill?
Data Science is challenging as it requires you to master multiple skills that range from technical skills such as mathematics, statistics, programming, and visualization to soft skills like communication, teamwork, analytical aptitude, etc.
However, all these concepts complement each other. They are very logical in their use, making it easy to master data science, given that enough time is allocated to learning and practicing various aspects.
 What are the four pillars of data science?
The skills required to master data science are domain knowledge, statistics, computer science, and communication, which form the four pillars of data science.
 Domain Knowledge: Understanding of the business you, as a data scientist, are involved in.
 Mathematical and Statistical Skills: Linear Algebra, Multivariate Calculus, Hypothesis Testing, Optimization Techniques, etc.
 Computer Science: Python, R, Relational Database, NonRelational Database, ML, Distributive Computing, etc.
 Communication and Visualization: Good language skills and understanding of visualization tools like Tableau, Power BI, etc.
 What data science skills are in demand?
Of the numerous skills discussed in this article, the most demanding data science skills are machine learning, deep learning, big data, and DevOps.
We hope this article gave you an indepth understanding of the skills you need to master to become a fullstack data scientist. If you have any doubts or queries, please write back to us.