What are skills required for Data Scientist ?
Data science is a fast-growing industry that’s constantly evolving, which makes it both rewarding and demanding for its practitioners. Newcomers and senior data scientists alike must be willing to keep learning and improving in order to stay valuable and progress in their careers.
However, this is easier said than done. Which skills are the most important to develop, and how should you go about developing them? To help you find the answer to this question, we’ve put together this guide covering all the major skills you need to either start or grow your career in data science.
Are There Any Must-Have Data Science Skills?
Yes. To be a data scientist, you’ll need to be able to gather and analyze data, then present your findings. This includes technical skills such as programming, manipulating databases, advanced mathematics, and data visualization, along with soft skills like collaboration and public speaking.
Technical Skills for a Data Science Career
Data science is a fast-growing industry that’s constantly evolving, which makes it both rewarding and demanding for its practitioners. Newcomers and senior data scientists alike must be willing to keep learning and improving in order to stay valuable and progress in their careers.
However, this is easier said than done. Which skills are the most important to develop, and how should you go about developing them? To help you find the answer to this question, we’ve put together this guide covering all the major skills you need to either start or grow your career in data science.
Programming Languages
Programming skills are essential for data scientists because it’s how we communicate with computers and give them instructions. While hundreds of programming languages exist, some of them are more suited to data science than others.
Here are some of the most popular and well-used programming languages for data science.
Python Programming
Python is a general-purpose programming language that’s popular across a range of different sectors, including data science, web development, and game development.
Thanks to the large community of Python users, there are thousands of libraries available that can cover just about any data science task you can think of. Here are some popular examples:
Pandas: a library for manipulating databases
NumPy: a library for basic and advanced array operations
Matplotlib: a library for generating data visualizations
There are many beginner’s courses for learning Python, both for general-purpose and for specific data science tasks.
R Programming
R is an open-source language specifically designed for data science. It can be used for statistical computing and machine learning, plus data manipulation and visualization.
Besides Python, it’s the most popular language for doing data science, and also benefits from a large community of contributing users. Some of the most commonly-used R libraries belong to the Tidyverse group.
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SQL
Structured Query Language (SQL) is a domain-specific language specially designed for interacting with databases. Rather than competing with Python and R, this language is used alongside them to edit and extract data from different relational databases.
SQL has a simple and straightforward syntax which is much easier to learn, as compared to a lot of other languages. Introductory SQL courses are available from all sorts of providers, such as IBM, Google, and various universities.
Mathematics, Statistical Analysis, and Probability
While mathematical skills are often not necessary for general-purpose coding, data science is another story. Calculus, algebra, probability, and statistics are the four mathematical areas that matter the most in data science.
If you already have high school mathematics under your belt, all you need to do is build on that strong foundation. Data science mathematics courses can be found on sites like Coursera, and these will help guide your study and develop a deep understanding.
Data Mining
Data mining refers to the gathering, sorting, and analyzing of large datasets. Within large sets, there’s plenty of not-so-useful data mixed in with the gold nuggets that are going to provide valuable insights.
Through various mining techniques like linear regression analysis, clustering analysis, and anomaly detection, data scientists can sort and analyze data from different perspectives to get the insights they need.
Data mining is an indeterminate data scientist skill that is often taught within a comprehensive career-focused course.
Machine Learning and AI
While any data scientist should be familiar with the basic concepts of machine learning, deep learning, and AI, these areas actually count as separate specializations. These areas do overlap. Machine learning requires data delivered by data science to train its algorithms, as data science uses a range of deep learning and machine learning models, such as decision trees and predictive models, to mine data.
Familiarity With Hadoop
Hadoop is an open-source framework that allows you to process large datasets more efficiently by using a network of many computers, rather than just one. Data scientists that often work with particularly large data sets will use this tool regularly, so it’s good to be familiar with it.
Data Visualization
Visualizing data is an important part of communicating the insights you’ve uncovered as a data scientist. Essentially, it’s the process of turning data into tables, pie charts, bar charts, scatter plots, heat maps, and other visualizations that help us comprehend information.
Data visualization can be done using various visualization tools, from creating visualizations directly in Python, or using software like Tableau. Data storytelling and presenting insights is as much the job of a data scientist as uncovering the insights, so visualizations and presentation skills are covered in many data science bootcamps.
Business Strategy
In order to unearth insights that will be genuinely useful for stakeholders and decision-makers, data scientists need to have a good understanding of business strategy themselves. These skills are taught as part of any good data science bootcamp, and you’ll also learn a lot through direct experience on the job.
Cloud ComputingThe data that data scientists use isn’t stored directly on the computer in front of them. Instead, big data is typically stored through cloud computing, so knowing how to interact with the cloud, and understanding the basic principles of how it works, can be a useful skills for data scientists.
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