There are still some cases where data scientists may use R but generally speaking if you are doing applied data science

You will need a good understanding of the basic syntax of the language and how to write functions, loops and modules. Be familiar with both object-oriented and functional programming in Python, and be able to develop, execute and debug programs.

Apache Airflow, an open source workflow management tool, is rapidly being adopted by many businesses for the management of ETL processes and machine learning pipelines. Many large tech companies such as Google and Slack are using it and Google even built their cloud composer tool on top of this project.

I am noticing Airflow being mentioned more and more often as a desirable skill for data scientists on job adverts. As mentioned at the beginning of this article I believe it will become more important for data scientists to be able to build and manage their own data pipelines for analytics and machine learning. The growing popularity of Airflow is likely to continue at least in the short term, and as an open source tool, is definitely something that every budding data scientist should at learn.

The use of cloud in other areas of a business usually goes hand in hand with cloud-based solutions for data storage, analytics and machine learning. The major cloud providers such as Google Cloud Platform, Amazon Web Services and Microsoft Azure are developing out tooling for training, deploying and serving machine learning models at a rapid pace.

“At first glance, cloud usage seems overwhelming. More than 88% percent of respondents use cloud in one form or another. Most respondent organizations also expect to grow their usage over the next 12 months.”, Cloud Adoption 2020, By Roger Magoulas and Steve Swoyer.

SQL has been around since the 1970’s but it still remains one of the most vital and saught after skills for data scientists. The vast majority of businesses use relational databases as their analytical data stores and as a data scientist SQL is the tool that will deliver you this data.

Data science code is traditionally messy, not always well tested and lacking in adherence to styling conventions. This is fine for initial data exploration and quick analysis but when it comes to putting machine learning models into production then a data scientist will need to have a good understanding of software engineering principles.

Pandas is still the number one Python library for data manipulation, processing and analysis. In 2021 this is still one of the most vital skills to have as a data scientist.

Python 3 (the latest version) has now firmly become the default version of the language for most applications as support for Python 2 was dropped by the majority of libraries on 1st January 2020. If you are learning Python for data science now it is important to choose a course that works with this version.

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scussed the 3 levels of data science. Level 1 competency can be achieved within 6 to 12 months. Level 2 competencies can be achieved within 7 to 18 months. Level 3 competencies can be achieved within 18 to 48 months. It all depends on the amount of effort invested and the background of each individual.

As a data scientist working in 2021 and beyond it is very likely that you will be working with data housed in a cloud-based database such as Google BigQuery and developing cloud based machine learning models. Experience and skills in this area are likely to be in high demand as we move into 2021.

NoSQL (“not only SQL”) are databases that don’t store data as relational tables, instead data is stored as key value pairs, wide-columns or graphs. Example NoSQL databases include Google Cloud Bigtable and Amazon DynamoDB.

Data is at the very heart of any data science project and Pandas is the tool that will enable you to extract, clean, process and derive insights from it. Most machine learning libraries also generally take Pandas DataFrames as a standard input these days.

According to a report from O’reilly in January this year, titled ‘Cloud adoption in 2020’, 88% of organisations were at this time using some form of cloud infrastructure. The impact of Covid-19 is likely to have further accelerated this adoption.

As the volumes of data collected by companies increases and unstructured data becomes more regularly used in machine learning models organisations are turning to NoSQL databases, either as a complement or as an alternative to, the traditional data warehouse. This trend is likely to continue into 2021 and as a data scientist it is important to gain at least a basic understanding of how to interact with data in this form.