This article highlights critical trends evolving in terms of the skills essential for machine learning experts and data scientists. It summarizes information obtained from authority journals, websites, job ads, and expert opinions. Moreover, this article is not intended as a comprehensive list. It undoubtedly takes many more skills and experience to become a good data scientist. In this article, however, we want to address some of the most critical skills likely needed in 2021.

Data Science and Machine Learning - What to Learn in 2021

Python

Python is the most popular and most widely used language in data science and machine learning. The syntax is easy to learn. You will exploit Python’s wide variety of libraries and packages. Python programming is a vital component for data manipulation, machine learning models, writing DAG files, etc. In some instances, data scientists can still use R, but Python is ideal for applied data science. The latest Python 3 has now become the default version of the language for most applications. You would require an in-depth knowledge of the language’s basic syntax and know how to write functions, loops, and custom modules. Familiarity with object-oriented and functional programming is also desirable.

Pandas

In 2021, pandas’ expertise is one of the most desirable skills for a data scientist. Pandas is the most popular Python library for data manipulation, processing, and analysis.  Data is essential to every data science project. Pandas is a platform for collecting, cleaning, processing data, and collecting insights as the DataFrames have evolved as the default machine learning framework. Dataframes are the standard input for most machine learning models.

Airflow

Apache Airflow, an open-source workflow management tool, allows you to automate workflows. Many businesses are adopting it for the management of ETL processes and data and machine learning pipelines. Many big tech corporations such as Google and Slack are using it. Google has also developed its cloud writing tool on top of this initiative. Airflow is now a desirable capability for career advertising data scientists. It would become more critical for data scientists to develop and maintain their own data pipelines for research and machine learning. The increasing popularity of Airflow is expected to continue. Airflow is powerful since it helps you to deploy models of machine learning.

SQL

SQL is the most sought-after skill for data scientists. Data scientists and machine learning experts need SQL knowledge because it is a fundamental database manipulation language. It assists in pre-analysis & pre-modeling stages in the data lifecycle. Sufficient SQL skills enable advanced data extraction and manipulation. Besides, writing effective and scalable queries is critical for companies that work with petabytes of data. The overwhelming majority of organizations utilize relational databases as their analytical data stores. As a data scientist, SQL is the platform that gives you access to data. Alternatively, NoSQL databases do not contain data as relational tables. Instead, data is processed as key-value pairs, wide columns, or graphs. The amount of data generated by businesses grows, and unstructured data is used more frequently in machine learning models. Enterprises shift to NoSQL databases either as a supplement or as an alternative to the conventional data warehouse.

Docker

Docker is a containerization framework that lets you install and operate applications such as deep learning models. It has been important that data scientists know how to develop and execute models. Many work opportunities now need some model implementation expertise. It is necessary to understand how to deploy models since the model does not provide market value unless incorporated with the process/product to which it relates.

GIT

Git is the primary version control system. The Tech community uses it to distribute its codebase. The data repositories are stored locally and in a central git hub server. If you mess up along the course of a data science project, it allows you to revert to older code versions. It enables you to work in collaboration with several other data scientists and programmers. It allows you to use the same codebase as others even if you work with different projects. Git allows you to do this as it tracks the history of your work. Another vital feature of git is that it makes you more productive and systematic in a project. You can quickly refer to the previous code and observe the modifications you have just created.

Cloud

Experience and skills of working on the cloud are likely to be in high demand in 2021. Cloud adoption was at an all-time high in 2020. Organizations preferred migrating their assets to the cloud. Most companies now use at least some form of cloud infrastructure.  Cloud-based solutions have been essential for data collection, visualization, and deep learning. Major cloud vendors such as Google Cloud Network, Amazon Web Services, and Microsoft Azure offer training, creation, and implementation services for machine learning models. As a data scientist employed in 2021, you are expected to create cloud-based machine learning models and deal with data collected in a cloud platform. BigQuery.

Software Engineering Principles and Data Visualization

Data scientists have to work with a messy code that requires a lot of testing and debugging. The code is often disorganized and does not follow proper conventions. This is fine for initial data exploration and quick analysis. To put machine learning models into production, a data scientist will need to understand software engineering principles. Therefore, it is essential to know about code conventions such as the PEP 8 Python style guide, dependencies and virtual environments, and unit testing.

Similarly, data scientists must present data visually in graphs, charts, infographics, videos, etc. Data storytelling refers to the visual representation backed by an engaging story that connects the visuals. Developing your data visualization and storytelling skills is essential for pitching your ideas.

Conclusion

This article deals with basic data science and machine learning skills. Companies are now recruiting data scientists based on their capacity to do advanced data analysis and not just research. Applied data science that brings value to an enterprise as quickly as possible requires practical skills. Suppose more businesses transfer their data and machine learning solutions to the cloud. In that case, it is becoming indispensable for data scientists to consider the latest tools and techniques involved. The Data Science Generalist is the ideal job for most organizations in 2021.