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What do you need to know to become a Data Scientist in 2022?
Data science is a hot field right now and data scientists are highly sought after. Data scientists are highly sought-after because they do everything, from automatically captioning images to creating self-driving cars. Given all the exciting applications, it makes sense that data science has a high demand career.
The paper doesn't cover everything you will need to become a data scientist in 2021. It focuses on the essential skills that every data scientist should have, new and old.
1. Python 3
While R is still used in some cases by data scientists, Python is the best programming language for applied data science.
Since Python 2 support was discontinued by most libraries on January 1, 2020, Python 3 (the most recent version) is now the default language version for most applications. It is crucial to choose a course that supports this version if you're learning Python for data sciences.
A good knowledge of the basic syntax of the language and how loops, functions, and modules are written is essential. You should be familiar with Python object-oriented and functional programming to be able develop, run, and debug programs.
Pandas, the most popular Python library, is still the best for data manipulation, processing, and analysis. In 2021, this is one of the most important skills for data scientists.
Pandas Data Frames are the core of any project in data sciences. They will enable you to extract, clean and process data and draw insights from it. Pandas Data Frames can be used as standard input by many machine learning libraries.
3. NoSQL and SQL
SQL has been around since the 1970s but it is still one of the most important skills for data scientists. Most companies use relational databases for their analytical data stores. SQL will give you this information as a data scientist.
NoSQL, which stands for "not just SQL", is a database that doesn't store data in relational tables but instead stores it as key value pairs or wide-columns or graphs. NoSQL databases include Amazon Dynamo DB and Google Cloud Bigtable.
Businesses are increasingly collecting more data and using unstructured data in machine learning models more often, so organisations start to use NoSQL databases as an alternative or complement to traditional data warehouses. This trend will continue through 2021. It is essential to have a basic understanding of the data science and how it interacts with.
According to a January report by O'reilly entitled 'Cloud adoption 2020', 88 percent of people use some type of cloud infrastructure. The impact of Covid-19 is likely to have accelerated this adoption.
Cloud-based data storage, analytics, and machine learning solutions are often used in other areas of a company's business. Google Cloud Platform, Amazon Web Services, and Microsoft Azure are all rapidly developing machine learning training, deployment, and service tools.
As a data scientist in 2021 or beyond, it is likely that you will be working with cloud-based data such as Google Big Query. This area will be highly in demand as we move into 2021.
Apache Airflow is an open-source workflow management tool that many companies are quickly adopting. It allows for management of ETL processes as well as machine learning pipelines. It is used by many large tech companies like Google and Slack. Google also built their cloud composer tool.
Airflow is becoming a more desirable skill for data scientist job advertisements. As I mentioned in the beginning, data scientists will need to be able build and manage their own data pipelines to support analytics and machine learning. Airflow is a popular tool that is sure to grow in popularity. It is an open-source tool that every budding data scientist should use.
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