Technical Skills for a Data Science Career<\/h2>\n\n\n\n
Data scientists are skilled professionals, meaning the average untrained person simply could not do the job. Before you can enter the industry, there\u2019s a variety of technical skills you need to develop first. <\/p>\n\n\n\n
Programming Languages<\/h3>\n\n\n\n
Programming skills are essential for data scientists because it\u2019s 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. <\/p>\n\n\n\n
Here are some of the most popular and well-used programming languages for data science. <\/p>\n\n\n\n
Python Programming<\/h4>\n\n\n\n![\"Python](\"https:\/\/www.springboard.com\/blog\/wp-content\/uploads\/2020\/07\/python-programming-data-science-skills.png\")
Source: ajaytech<\/a><\/figcaption><\/figure>\n\n\n\nPython<\/a> is a general-purpose programming language that\u2019s popular across a range of different sectors, including data science<\/a>, web development, and game development. <\/p>\n\n\n\nThanks 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:<\/p>\n\n\n\n
\n- Pandas<\/a>: a library for manipulating databases<\/li>\n\n\n\n
- NumPy<\/a>: a library for basic and advanced array operations<\/li>\n\n\n\n
- Matplotlib<\/a>: a library for generating data visualizations<\/li>\n<\/ul>\n\n\n\n
There are many beginner\u2019s courses for learning Python<\/a>, both for general-purpose and for specific data science tasks. <\/p>\n\n\n\nR Programming<\/h4>\n\n\n\n![\"R](\"https:\/\/www.springboard.com\/blog\/wp-content\/uploads\/2020\/07\/r-programming-data-science-skills.png\")
Source: hackernoon<\/a><\/figcaption><\/figure>\n\n\n\nR is an open-source language<\/a> specifically designed for data science. It can be used for statistical computing and machine learning, plus data manipulation and visualization. <\/p>\n\n\n\nBesides Python, it\u2019s 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<\/a> group.<\/p>\n\n\n\nSome data science courses<\/a> will choose to focus on Python exclusively, so if you want to study R, it can be helpful to search for R-specific courses<\/a>. <\/p>\n\n\n\nSQL<\/h4>\n\n\n\n![\"SQL,](\"https:\/\/www.springboard.com\/blog\/wp-content\/uploads\/2020\/07\/sql-data-science-skills.webp\")
<\/figure>\n\n\n\nStructured Query Language (SQL)<\/a> 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. <\/p>\n\n\n\nSQL has a simple and straightforward syntax which is much easier to learn, as compared to a lot of other languages. Introductory SQL courses<\/a> are available from all sorts of providers, such as IBM, Google, and various universities. <\/p>\n\n\n\nMathematics, Statistical Analysis, and Probability<\/h3>\n\n\n\n
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.<\/p>\n\n\n\n
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<\/a>, and these will help guide your study and develop a deep understanding.<\/p>\n\n\n\nData Mining<\/h3>\n\n\n\n
Data mining<\/a> refers to the gathering, sorting, and analyzing of large datasets. Within large sets, there\u2019s plenty of not-so-useful data mixed in with the gold nuggets that are going to provide valuable insights. <\/p>\n\n\n\nThrough various mining techniques like linear regression analysis<\/a>, clustering analysis<\/a>, and anomaly detection, data scientists can sort and analyze data from different perspectives to get the insights they need.<\/p>\n\n\n\nData mining is an indeterminate data scientist skill<\/a> that is often taught within a comprehensive career-focused course<\/a>.<\/p>\n\n\n\nMachine Learning and AI<\/h3>\n\n\n\n
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<\/a> learning<\/a> 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<\/a> and predictive models, to mine data.<\/p>\n\n\n\nFamiliarity With Hadoop<\/h3>\n\n\n\n
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\u2019s good to be familiar with it. <\/p>\n\n\n\n
Data Visualization<\/h3>\n\n\n\n
Visualizing data is an important part of communicating the insights you\u2019ve uncovered as a data scientist. Essentially, it\u2019s the process of turning data into tables, pie charts, bar charts, scatter plots, heat maps, and other visualizations that help us comprehend information.<\/p>\n\n\n\n
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<\/a>. <\/p>\n\n\n\nBusiness Strategy<\/h3>\n\n\n\n
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\u2019ll also learn a lot through direct experience on the job. <\/p>\n\n\n\n
Cloud Computing<\/h3>\n\n\n\n
The data that data scientists use isn\u2019t stored directly on the computer in front of them. Instead, big data is typically stored through cloud computing<\/a>, so knowing how to interact with the cloud, and understanding the basic principles of how it works, can be a useful skill for data scientists. <\/p>\n\n\n\nSoft Skills for a Data Science Career<\/h2>\n\n\n\n![\"Soft](\"https:\/\/www.springboard.com\/blog\/wp-content\/uploads\/2020\/07\/soft-skills-for-a-data-science-career.jpg\")
<\/figure>\n\n\n\nOf course, technical skills are not the only thing a data scientist needs to be successful. Collaborating with colleagues and other departments and presenting key insights to stakeholders is also part of the role, and these tasks require a range of non-technical skills (or soft skills). <\/p>\n\n\n\n
Communication<\/h3>\n\n\n\n
Communicating clearly and respectfully is the key to building good working relationships and getting the information you need as efficiently as possible. Whether you\u2019re an aspiring data scientist or already working, make sure to get involved with group projects to start polishing your communication skills. <\/p>\n\n\n\n
Analytical Mindset<\/h3>\n\n\n\n
Thinking objectively and analytically helps data scientists approach problems thoroughly and from every angle before forming opinions and assumptions. This keeps their work accurate and unbiased and helps them to know when they need to dig deeper to find the whole story.<\/p>\n\n\n\n
Developing an analytical mindset and critical thinking skills takes time, and the best way to do it is through hands-on practice with data science projects and work. <\/p>\n\n\n\n
\u201cOut-of-the-Box\u201d Thinking<\/h3>\n\n\n\n
An important part of data science is framing the right questions, as well as finding the right answers. Data is intricate and expansive, and there are many ways it can tell us what we want to know, so thinking outside of the box can often help a data scientist find a new lead and unearth powerful new insights. <\/p>\n\n\n\n
You\u2019ll develop this skill naturally with time and experience, and by listening to colleagues\u2019 and seniors\u2019 experiences. <\/p>\n\n\n\n
Decision Making<\/h3>\n\n\n\n
Working independently is a very valued skill for data scientists. Stakeholders and managers want their data science team to go away and come back with the desired insights without having to be micromanaged. <\/p>\n\n\n\n
Collaboration<\/h3>\n\n\n\n
Teamwork is an important part of data science, and learning to listen to others and utilize their input is an essential skill. Collaboration can increase working efficiency, minimize mistakes, and improve the overall quality of work. <\/p>\n\n\n\n
If you\u2019re currently learning data science, finding a team or partner to work on projects with is a great way to get a head start on building your interpersonal skills. <\/p>\n\n\n\n
Storytelling<\/h3>\n\n\n\n
Storytelling is a term used for presenting data and insights to non-technical colleagues in order to persuade them to see the value of your conclusions and act on your suggestions. It\u2019s a skill that blends public speaking, business acumen, and data visualization to create a relevant, clear, and persuasive story.<\/p>\n\n\n\n
You can practice this skill independently by putting together presentations for your personal projects as if you had a group of managers to report to. <\/p>\n\n\n\n
Attention to Detail<\/h3>\n\n\n\n
The conclusions data scientists come to completely depend on the data they\u2019re drawing upon and how they\u2019re looking at it. Having an eye for detail is a crucial skill that helps them approach a problem from all angles, and explore different possibilities in order to uncover the most accurate information. <\/p>\n\n\n\n
Continuous Learning<\/h3>\n\n\n\n
While you don\u2019t need to go off and earn an advanced degree halfway through your career, the willingness to keep learning is a mark of a valuable data scientist and employee. The fields of data science and machine learning are constantly evolving, and you need to stay up to date and keep learning to stay ahead of the game. <\/p>\n\n\n\n
How To Develop (or Improve Upon) Essential Data Science Skills<\/h2>\n\n\n\n![\"How](\"https:\/\/www.springboard.com\/blog\/wp-content\/uploads\/2020\/07\/how-to-develop-or-improve-upon-essential-data-science-skills.jpg\")
<\/figure>\n\n\n\nKnowing what you need to improve on and knowing how to do it are two different things, and developing your skills is never done overnight. However, by starting good habits, building a network, and becoming involved in the community, you can benefit your career in a number of ways.<\/p>\n\n\n\n
Take Advantage of Online Resources<\/h3>\n\n\n\n
Online resources contain in-depth knowledge on all sorts of both specialized and common fields of data science. You can improve your knowledge of programming, your foundation in mathematics, your exploratory data analysis skills, or your understanding of business analytics just by consuming online content. <\/p>\n\n\n\n
Blogs<\/h4>\n\n\n\n
There are so many blogs and resources on data science that it can be a little difficult to get started. But once you\u2019ve found the best data science blogs<\/a> for you, they\u2019ll become a weekly resource that will keep you up to date on the latest trends with little to no effort. <\/p>\n\n\n\nOnline Courses<\/h4>\n\n\n\n
Online courses have exploded in popularity over the last decade, and you can now find a university professor or a Google Analytics<\/a> expert to teach you just about any data science skill you can think of. <\/p>\n\n\n\nIf you\u2019re new to data science, you can also enroll in online career-based bootcamps<\/a> that will take you from beginner to job-ready in less than a year.<\/p>\n\n\nGet To Know Other\tData Science Students<\/h4>
Python<\/a> is a general-purpose programming language that\u2019s popular across a range of different sectors, including data science<\/a>, web development, and game development. <\/p>\n\n\n\n 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:<\/p>\n\n\n\n There are many beginner\u2019s courses for learning Python<\/a>, both for general-purpose and for specific data science tasks. <\/p>\n\n\n\n R is an open-source language<\/a> specifically designed for data science. It can be used for statistical computing and machine learning, plus data manipulation and visualization. <\/p>\n\n\n\n Besides Python, it\u2019s 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<\/a> group.<\/p>\n\n\n\n Some data science courses<\/a> will choose to focus on Python exclusively, so if you want to study R, it can be helpful to search for R-specific courses<\/a>. <\/p>\n\n\n\n Structured Query Language (SQL)<\/a> 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. <\/p>\n\n\n\n SQL has a simple and straightforward syntax which is much easier to learn, as compared to a lot of other languages. Introductory SQL courses<\/a> are available from all sorts of providers, such as IBM, Google, and various universities. <\/p>\n\n\n\n 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.<\/p>\n\n\n\n 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<\/a>, and these will help guide your study and develop a deep understanding.<\/p>\n\n\n\n Data mining<\/a> refers to the gathering, sorting, and analyzing of large datasets. Within large sets, there\u2019s plenty of not-so-useful data mixed in with the gold nuggets that are going to provide valuable insights. <\/p>\n\n\n\n Through various mining techniques like linear regression analysis<\/a>, clustering analysis<\/a>, and anomaly detection, data scientists can sort and analyze data from different perspectives to get the insights they need.<\/p>\n\n\n\n Data mining is an indeterminate data scientist skill<\/a> that is often taught within a comprehensive career-focused course<\/a>.<\/p>\n\n\n\n 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<\/a> learning<\/a> 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<\/a> and predictive models, to mine data.<\/p>\n\n\n\n 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\u2019s good to be familiar with it. <\/p>\n\n\n\n Visualizing data is an important part of communicating the insights you\u2019ve uncovered as a data scientist. Essentially, it\u2019s the process of turning data into tables, pie charts, bar charts, scatter plots, heat maps, and other visualizations that help us comprehend information.<\/p>\n\n\n\n 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<\/a>. <\/p>\n\n\n\n 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\u2019ll also learn a lot through direct experience on the job. <\/p>\n\n\n\n The data that data scientists use isn\u2019t stored directly on the computer in front of them. Instead, big data is typically stored through cloud computing<\/a>, so knowing how to interact with the cloud, and understanding the basic principles of how it works, can be a useful skill for data scientists. <\/p>\n\n\n\n Of course, technical skills are not the only thing a data scientist needs to be successful. Collaborating with colleagues and other departments and presenting key insights to stakeholders is also part of the role, and these tasks require a range of non-technical skills (or soft skills). <\/p>\n\n\n\n Communicating clearly and respectfully is the key to building good working relationships and getting the information you need as efficiently as possible. Whether you\u2019re an aspiring data scientist or already working, make sure to get involved with group projects to start polishing your communication skills. <\/p>\n\n\n\n Thinking objectively and analytically helps data scientists approach problems thoroughly and from every angle before forming opinions and assumptions. This keeps their work accurate and unbiased and helps them to know when they need to dig deeper to find the whole story.<\/p>\n\n\n\n Developing an analytical mindset and critical thinking skills takes time, and the best way to do it is through hands-on practice with data science projects and work. <\/p>\n\n\n\n An important part of data science is framing the right questions, as well as finding the right answers. Data is intricate and expansive, and there are many ways it can tell us what we want to know, so thinking outside of the box can often help a data scientist find a new lead and unearth powerful new insights. <\/p>\n\n\n\n You\u2019ll develop this skill naturally with time and experience, and by listening to colleagues\u2019 and seniors\u2019 experiences. <\/p>\n\n\n\n Working independently is a very valued skill for data scientists. Stakeholders and managers want their data science team to go away and come back with the desired insights without having to be micromanaged. <\/p>\n\n\n\n Teamwork is an important part of data science, and learning to listen to others and utilize their input is an essential skill. Collaboration can increase working efficiency, minimize mistakes, and improve the overall quality of work. <\/p>\n\n\n\n If you\u2019re currently learning data science, finding a team or partner to work on projects with is a great way to get a head start on building your interpersonal skills. <\/p>\n\n\n\n Storytelling is a term used for presenting data and insights to non-technical colleagues in order to persuade them to see the value of your conclusions and act on your suggestions. It\u2019s a skill that blends public speaking, business acumen, and data visualization to create a relevant, clear, and persuasive story.<\/p>\n\n\n\n You can practice this skill independently by putting together presentations for your personal projects as if you had a group of managers to report to. <\/p>\n\n\n\n The conclusions data scientists come to completely depend on the data they\u2019re drawing upon and how they\u2019re looking at it. Having an eye for detail is a crucial skill that helps them approach a problem from all angles, and explore different possibilities in order to uncover the most accurate information. <\/p>\n\n\n\n While you don\u2019t need to go off and earn an advanced degree halfway through your career, the willingness to keep learning is a mark of a valuable data scientist and employee. The fields of data science and machine learning are constantly evolving, and you need to stay up to date and keep learning to stay ahead of the game. <\/p>\n\n\n\n Knowing what you need to improve on and knowing how to do it are two different things, and developing your skills is never done overnight. However, by starting good habits, building a network, and becoming involved in the community, you can benefit your career in a number of ways.<\/p>\n\n\n\n Online resources contain in-depth knowledge on all sorts of both specialized and common fields of data science. You can improve your knowledge of programming, your foundation in mathematics, your exploratory data analysis skills, or your understanding of business analytics just by consuming online content. <\/p>\n\n\n\n There are so many blogs and resources on data science that it can be a little difficult to get started. But once you\u2019ve found the best data science blogs<\/a> for you, they\u2019ll become a weekly resource that will keep you up to date on the latest trends with little to no effort. <\/p>\n\n\n\n Online courses have exploded in popularity over the last decade, and you can now find a university professor or a Google Analytics<\/a> expert to teach you just about any data science skill you can think of. <\/p>\n\n\n\n If you\u2019re new to data science, you can also enroll in online career-based bootcamps<\/a> that will take you from beginner to job-ready in less than a year.<\/p>\n\n\n\n
R Programming<\/h4>\n\n\n\n
SQL<\/h4>\n\n\n\n
<\/figure>\n\n\n\nMathematics, Statistical Analysis, and Probability<\/h3>\n\n\n\n
Data Mining<\/h3>\n\n\n\n
Machine Learning and AI<\/h3>\n\n\n\n
Familiarity With Hadoop<\/h3>\n\n\n\n
Data Visualization<\/h3>\n\n\n\n
Business Strategy<\/h3>\n\n\n\n
Cloud Computing<\/h3>\n\n\n\n
Soft Skills for a Data Science Career<\/h2>\n\n\n\n
<\/figure>\n\n\n\nCommunication<\/h3>\n\n\n\n
Analytical Mindset<\/h3>\n\n\n\n
\u201cOut-of-the-Box\u201d Thinking<\/h3>\n\n\n\n
Decision Making<\/h3>\n\n\n\n
Collaboration<\/h3>\n\n\n\n
Storytelling<\/h3>\n\n\n\n
Attention to Detail<\/h3>\n\n\n\n
Continuous Learning<\/h3>\n\n\n\n
How To Develop (or Improve Upon) Essential Data Science Skills<\/h2>\n\n\n\n
<\/figure>\n\n\n\nTake Advantage of Online Resources<\/h3>\n\n\n\n
Blogs<\/h4>\n\n\n\n
Online Courses<\/h4>\n\n\n\n
Get To Know Other\tData Science Students<\/h4>
![\"Esme](\"https:\/\/res.cloudinary.com\/springboard-images\/image\/upload\/v1629203193\/Student%20Success\/Esme_Gaisford_125x125.png\")
<\/a>![\"Mikiko](\"https:\/\/res.cloudinary.com\/springboard-images\/image\/upload\/v1629203192\/Student%20Success\/Mikiko_Bazeley_125x125.png\")
<\/a>![\"Samuel](\"https:\/\/res.cloudinary.com\/springboard-images\/image\/upload\/v1635255723\/Student%20Success\/Samuel_Okoye_125x125.png\")
<\/a>![\"YouTube](\"https:\/\/img.youtube.com\/vi\/WUvTyaaNkzM\/sddefault.jpg\")