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As the world of data grows, so does the world of data science. To keep up with the data science world is a full-time job in itself. The market is ever-growing and tools are developing and dropping into the market causing chaos. And then you have the problem of learning these new tools, understanding their full potential, what can it replace or if its just another add-on.

Keeping up with all of this can be draining. This is why it is important to have the right tools in your data scientist toolbox to excel at what you do.

A good tool improves the way you work. A great tool improves the way you think.

If youre going to choose a programming language for data science - it will most probably be Python. Its a gold standard, with the largest data science user base. A lot of data science tools are written using Python and the community is the largest, fastest growing and most active. Youll be silly not to have this in your toolbox!

Courses to learn Python:

Maths and statistics. The elements of data science that make sure data science makes sense! They are the building blocks of machine learning algorithms. They help you understand a problem and allow you to use them to find a solution. From identifying patterns to outputting desired results from large complex data sets, data scientists can extract insights and reliably interpret results using maths and statistics.

Courses to learn Maths and Statistics:

As a data scientist, you should take pride in your findings and make them look pretty! But also remember that other stakeholders may not be highly technically inclined therefore visualisations are important to them. Its how they understand data science. Being able to visualise your insights in various ways will help you better communicate them without having to do much talking.

There are different libraries you can use such as Matplotlib or there are visualisation tools available such as Tableau - you just need to find which one works for you and your organisation.

Courses to learn data visualisation:

Structured Query Language, SQL for short is a programming language designed for managing data in a relational database. As a data scientist, you will be managing a lot of databases and SQL is your key to combing through the data. With SQL, you will be able to work with structured data stored in databases in which you can easily extract, manipulate, and analyse data. You may want to learn primarily Python or SQL, or you may want to be untouchable and learn both!

Courses to learn SQL:

As the data science, machine learning and artificial intelligence world becomes prominent in our day-to-day lives. It is also important that there are tools and software that developers can use to ensure the pipeline is accurate and effective from start to finish. Frameworks provide a flexible range of software components that help developers accelerate software development to production deployment.

When it comes to frameworks, there are a range of frameworks that are popular in the data science world, for example, TensorFlow, PyTorch, Pandas, Keras and more. As a data scientist, you must learn all of these frameworks as they could be beneficial to you at different times.

Courses to learn different Frameworks:

A data scientist's learning journey is endless. There will always be new tools and software entering the market. However, if you have the right tools in your toolbox, learning new skills will be a breeze.

Nisha Arya is a data scientist, freelance technical writer, and an editor and community manager for KDnuggets. She is particularly interested in providing data science career advice or tutorials and theory-based knowledge around data science. Nisha covers a wide range of topics and wishes to explore the different ways artificial intelligence can benefit the longevity of human life. A keen learner, Nisha seeks to broaden her tech knowledge and writing skills, while helping guide others.

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5 Tools Every Data Scientist Needs in Their Toolbox in 2024 - KDnuggets