Must have skills for Data Scientists in 2023
From clinical trials to driverless cars, from Path Optimization to Performance Evaluation techniques, and From Recommendation systems to Fraud Detection Systems, the unlimited and endless reach of Data Science is leading to its adoption in every facet and dimension of every business domain.
Further advancement in today’s digital world is leading to new path-breaking discoveries in Data Science and subsequently, a better way of living. Companies gather tremendous amounts of data, which require non-traditional data processing methods and software. As the world is progressing deeper into the consumption and manipulation of data, the skills required to extract useful information are growing day by day in all industry verticals alike.
Data Science is an umbrella term that includes but is not limited to Artificial Intelligence, Machine Learning, Deep Learning, Data Analytics, Data Mining, and other related disciplines. The power of these concepts is collectively leveraged by businesses worldwide for gaining competitive advantage in terms of income potential and career opportunities. Anything that has data is valuable to a business and organizations need to analyze and interpret this data to help them make better decisions and stay one step ahead of the competition. Data Administrators, Analysts, Architects, Engineers and Scientists are some of the roles one can take up to assist companies make better business decisions to cater to the growing market and capitalize on their profits.
Data Science involves skills that are primarily driven by data and its analysis. Data scientists collect, store, and analyze tremendous amounts of unstructured data to find trends or patterns benefiting the business. Product Development, Process Improvement, and Customer Retention require Data scientists to interpret large datasets to provide valuable business insights. The palpable demand for data scientists and their extensive skillsets is exponentially increasing, to say the least. Employment of data scientists is projected to grow 36 percent from 2021 to 2031, much faster than the average for all occupations. Students and working professionals are more curious than ever about this gradual change over the decade, that has the world running on it and has left them asking about what data science skills are needed to pursue careers in data science.
Technical Skills Required for Data Scientists in 2023
Data Scientists need strong interpersonal and communication abilities and proficiency in several computer languages and statistical calculations. Apart from having a strong background in mathematics, statistics, and computer science, they need to master many technical and non-technical skills. Let’s dive into the must-have technical skills for data science in 2023:
1. Statistics and Mathematics
Mathematics is predominant in data science. Clear understanding of foundational Mathematical concepts like linear algebra and calculus is required to solve real world problems on a wide scale. Fields like probability and statistics enable a data scientist to identify trends, patterns and dependencies in data, and subsequently forecast future trends.
2. Coding
Python is one of the key data science skills that provides a variety of useful libraries like Pandas and NumPy to manipulate data and build data models. R is the most popular programming tool used by statisticians and data scientists to analyze and gain insights from data. It is necessary to learn R and Python programming on big data processing frameworks like Hadoop. This enables data scientists to build use cases applicable to the real world using different techniques and algorithms.
3. Data Cleaning and Wrangling
Data cleaning and wrangling are processes that transform raw data into a format that can be used for specific analysis. This is a crucial skill to have as it involves formatting inconsistent, irrelevant, missing and duplicate data and making the data ready for analysis. By transforming the data into a comprehensible format, data scientists can ensure reliable outcomes.
4. Data Visualization
In 2023, being able to visualize data is crucial for a data scientist. Data scientists should be able to uncover hidden patterns and trends in the data. They should be capable of describing the findings in a manner that can be interpreted by both technical and non-technical audiences. Thus, in-depth knowledge of various data visualization tools and techniques helps data scientists provide clear insight into their data-driven insights.
5. Machine Learning
Machine learning is a vast discipline and Data Science relies on this discipline for most of its data-driven outcome. Machine learning algorithms can be used for Search Engines, Pattern and Speech Recognition, Image classification, and so on. By leveraging Machine Learning techniques, data scientists can uncover patterns and relationships within data.
Furthermore, other advanced ML approaches are useful for data scientists to know about. It includes Transfer Learning and Hyperparameter Tuning that improves performance leading to optimal outcomes. Automated Machine Learning can also be employed to automate the process of selecting, training, and deploying machine learning models.
6. Data Warehousing and Data Mining
Data Scientists should be skilled in Data Warehousing that enables storing current and historical data for multiple systems to drive a project. They use an ETL (Extract, Transform and Load) tool that extracts data from multiple data sources, transforms it and loads it into a data warehouse. It helps in answering data-related questions and analyzing business performance.
Data mining is an essential skill for data scientists to extract useful information from data through techniques like clustering, classification, and association rules. As a data scientist in 2023, you need to be well-versed in data storage and manipulation techniques to sift through numerous data sources to find meaningful trends or patterns and make necessary decisions.
7. Database Management
In Data science, a significant focus lies on analyzing unstructured data and thus expert knowledge in various NoSQL databases is required to create and execute complex queries on unstructured data.
Structured query language is critical for establishing data pipelines, altering data, and extracting data from databases. It enables data scientists to efficiently extract meaningful insights and draw conclusions from the analyzed data.
8. Big Data Processing
Big data processing is the ability to process, store, and analyze large amounts of data using technologies like Hadoop, and Spark. In 2023, the ability to process big data is of primary importance for data scientists. The voluminous amount of data being generated necessitates the need to handle and analyze this data effectively to make well-informed decisions.
Hadoop is another primary skill for data scientists that facilitates large-scale data analysis and accelerates data-driven innovation. A data scientist must be familiar with various Hadoop components like Distributed File System, MapReduce, Pig, Hive, Sqoop, and Flume. Spark is a primary component that utilizes efficient query execution and in-memory caching for quick analytic queries against any quantity of data.
9. Artificial Neural Networks and Deep Learning
In 2023, It’s not enough to know the basics of Artificial Intelligence and Machine Learning. One should be familiar with Artificial neural networks (ANN) and Deep Learning techniques too. ANN is a type of machine learning algorithm modeled to be similar to the structure and function of the human brain.
Deep learning encompasses multiple layers of artificial neural networks that focus on creating algorithms that can learn patterns in data. Certain deep learning techniques like Generative Adversarial Networks and Explainable AI help in creating advanced algorithms and models and are good additions to the data science skillset.
Further, there are many cutting-edge technologies like Natural Language Processing AI that handle processing and understanding of human language. Time Series Analysis & Forecasting for sales or revenue analysis and Experimental Design & A/B Testing to test hypotheses and make decisions based on data.
10. Cloud Computing
Cloud computing encompasses cloud-based technologies and platforms like AWS, Azure, or Google Cloud to store and process data. Data scientists need to access vast amounts of computing resources and data storage through the Internet, keeping the time and economic constraints in check to be able to provide valuable insights.
Non-technical skills for data scientists in 2023
Anyone pursuing data science as a career must have the technical skills mentioned above but, to be a cut above the rest, the following non-technical skills, mostly inherent in a data scientist will be an added advantage when landing a job in data science. Let’s shift our focus to a few of them:
1. Business Acumen/ Expertise
It is significant for a data scientist to identify roadblocks that are making or breaking the business and cater to fostering the organization’s growth by solving them. Making well-informed decisions and exploring new opportunities requires the expertise of a skilled professional.
2. Communication Skills
Data scientists are responsible to convey their findings through various formats and visualization tools to all stakeholders effectively to draw meaningful insights based on the requirements.
3. Data Intuition
To develop data that can be analyzed and interpreted, sound data intuition is necessary. Data scientists need to think from different perspectives, analyze from all dimensions and draw actionable conclusions that will enable businesses to grow.
4. Critical Thinking
Data scientists need to dig deep, ask questions, seek answers, share ideas and choose the optimal solution after a thorough understanding of the problem at hand. This can be achieved through critical thinking skills essential for gaining valuable insights and keeping up with industry trends.
5. Decision Making
Data Scientists empower organizations based on data-driven facts to make outcome-based decisions. By taking a thoughtful approach to decision-making, data scientists can assist organizations to make choices that align with their goals, leading to better outcomes.
6. Team player
Teamwork and Collaboration are necessary to overcome the obstacles of data science in the real world. To analyze complex data sets and build models, data scientists need to work with other skilled professionals so that they can design and execute projects in a reasonable time frame.
7. Intellectual curiosity
To excel as a data scientist, one must possess a sense of curiosity and a willingness to ask questions and challenge assumptions. By adopting these principles, they will be able to uncover insights and make knowledgeable decisions.
8. Data Storytelling
By mastering the ability to communicate analytic results and discoveries to different stakeholders, meaningful conclusions can be drawn. A data scientist must have storytelling skills so that he/she can use the data to tell a story effectively that is easy for everyone to understand.
Conclusion
The scope of the role of a data scientist is vast and the skillset is not limited to the ones mentioned above. One can become a data scientist by gaining expertise in data manipulation, analysis, and visualization. Mastering machine learning techniques and algorithms. Building a portfolio of projects showcasing one’s skills. Continuous learning and staying updated with industry trends are also essential for success in this field.
Data Science has seeped deep into our lives. It is predominantly changing the way we travel, eat, read and even watch shows on OTT. Media service providers measure user engagement and retention based on what, when why and where we are watching a particular show from. These companies use advanced data science metrics to present better movies and show recommendations to their users and create better content for them. This is just one out of a million ways data science is taking over our lives for the better.
The Data Scientists of the modern world have a major role to play in businesses across the globe. They can extract useful insights from vast amounts of raw data using sophisticated techniques. Upskilling and Upgrading in the latest trends and technologies is the need of the hour to carve a niche in today’s digital world.
