Data science is everywhere right now. One after the other, companies worldwide are turning to data science to solve the most diverse problems out there. This situation has put data scientists in an advantageous position regarding employment and payment.<\/p>\n\n\n\n
Naturally, a lot of people are becoming interested in learning how to become a data scientist. If you are reading this article, you may be one of those people. Here, we will show everything you need to know about becoming a data scientist, including an effective and attractive way to learn what you need to get started in the data science world.<\/p>\n\n\n\n
But first…<\/p>\n\n\n\n
Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. <\/p>\n\n\n\n
This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results.<\/p>\n\n\n\n
Data science is important because it combines tools, methods, and technology to generate meaning from data. Modern organizations are inundated with data; there is a proliferation of devices that can automatically collect and store information. Online systems and payment portals capture more data in the fields of e-commerce, medicine, finance, and every other aspect of human life. <\/p>\n\n\n\n
Data scientists are analytical experts who extract meaning from and interpret data to solve complex problems. They use industry knowledge, contextual understanding, and skepticism of existing assumptions to uncover solutions to business challenges.<\/p>\n\n\n\n
A data scientist\u2019s role combines computer science, statistics, and mathematics to collect and organize data from many different data sources, translate results into actionable plans, and communicate their findings to their organizations. Successful data scientists must be effective communicators, leaders, team members, and high-level analytical thinkers.<\/p>\n\n\n\n
As a specialty, data science is young. It grew out of the fields of statistical analysis and data mining. The Data Science Journal<\/strong> debuted in 2002, published by the International Council for Science: Committee on Data for Science and Technology. And by 2008, the title of data scientist emerged, and the field quickly took off. <\/p>\n\n\n\n However, there remains a shortage of data scientists, even though more and more colleges and universities have started offering data science degrees.<\/p>\n\n\n\n A data scientist\u2019s duties can include developing strategies for analyzing data, preparing data for analysis, exploring, analyzing, and visualizing data, building models with data using programming languages, such as Python and R, and deploying models into applications.<\/p>\n\n\n\n Essentially, data scientists work to deeply understand and analyze data to provide actionable insights. <\/p>\n\n\n\n In a business environment, they work across multiple teams to lay the foundations for robust analytics. This means developing strategies to capture, gather, and clean data from a range of sources. After organizing and exploring this data, they can then build solutions and communicate their findings to the wider business. <\/p>\n\n\n\n Data scientists often work with other professionals, including data engineers, analysts, IT architects, application developers, and business stakeholders. In addition to a data scientist, this team might include a business analyst who defines the problem, and a data engineer who prepares the data and how it is accessed. <\/p>\n\n\n\n It might also include an IT architect who oversees the underlying processes and infrastructure and an application developer who deploys the models or outputs of the analysis into applications and products.<\/p>\n\n\n\n Every company will have a different take on a data scientist job description. Some treat their data scientists as data analysts or combine their duties with that of data engineers, others need top-level analytics experts skilled in intense machine learning and data visualizations.<\/p>\n\n\n\n As data scientists achieve new levels of experience or change jobs, their responsibilities invariably change. For example, a person working alone in a mid-size company may spend a good portion of the day in data cleaning and munging. A high-level employee in a business that offers data-based services may be asked to structure big data projects or create new products.<\/p>\n\n\n\n If you are ready to explore the capabilities of data science platforms, there are some key capabilities to consider:<\/p>\n\n\n\n Essentially, to succeed in data science, you\u2019ll need an assortment of hard and soft skills. <\/p>\n\n\n\n These are the technical skills you\u2019ll need to develop to become a fully-fledged data scientist: <\/p>\n\n\n\n These are now usually referred to as \u2018power skills\u2019 or \u2018human skills,\u2019 and they are cross-functional skills that aren\u2019t job-specific but are still necessary. For a data scientist, these include: <\/p>\n\n\n\n As you work to become a data scientist, you\u2019ll often encounter large and complex datasets. To make sense of this information (for yourself and others), you\u2019ll need to learn how to deal with it. This is where skills in data wrangling come in handy. This is where you\u2019ll clean, organize, and transform raw data into a format you can analyze and draw conclusions from. <\/p>\n\n\n\n There are various tools you can use with data wrangling, but libraries like pandas in Python are often the go-to choice. When it comes to presenting your data with impact, you\u2019ll need to master reporting and data visualization. <\/p>\n\n\n\n While you don\u2019t necessarily need a degree that covers these fields, you do need a functional knowledge of these areas. Generally, you\u2019ll want to cover areas such as calculus, linear algebra, and statistics. However, it\u2019s worth assessing your intent behind learning these things. <\/p>\n\n\n\n For example, learning calculus can help you learn to create optimization algorithms for machine learning, although there are functions in both Python and R that can take care of the calculus for you. As a specific example, understanding a topic like gradient descent can help you measure the change in a function\u2019s output when you tweak the inputs, which can help refine machine learning models. <\/p>\n\n\n\n Two of the most obvious choices for data scientists are Python and R, given their versatility and ubiquity. Of course, working with data also means working with databases, so SQL is another essential programming language. Thankfully, it\u2019s a relatively straightforward language once you\u2019ve learned Python and R. <\/p>\n\n\n\n Julia is a good choice once you\u2019ve learned Python, R, and SQL. With Julia, you get a language that\u2019s built from the ground up for data science, making it fast and intuitive. If you start working with very big data sets, you may need other languages, but until then, these four will be sufficient.<\/p>\n\n\n\n With a language like Java, you get an open-source language known for its efficiency and performance. For data science, Java Virtual Machines provide a solid and efficient framework for popular big data tools, such as Hadoop, Spark, and Scala. <\/p>\n\n\n\n Other coding languages worth learning for data science with very large data sets include Scala, C\/C++, JavaScript, Swift, Go, MATLAB, and SAS. <\/p>\n\n\n\n Relational databases allow data scientists to store structured data in a fast and efficient way. When collecting and organizing data, you\u2019ll often find that SQL is your preferred tool. <\/p>\n\n\n\n SQL allows you to handle structured data, query databases, wrangle, prepare, and experiment with data, and more. What\u2019s more, SQL is often deployed alongside Python, with libraries such as SQLite, PostgreSQL, and MySQL helping you connect different data sources. <\/p>\n\n\n\n In an era where everything and everyone is producing more data than ever before, these data sets are increasingly huge, making them harder to collect, maintain, and process. <\/p>\n\n\n\n Yet from these huge data sets, a skilled data scientist can extract new and detailed insights. So, learning to use cloud platforms such as AWS, Microsoft Azure, and Google Cloud can all benefit your career as a data scientist. Similarly, tools like Apache Spark can help with big data processing, analysis, and machine learning. <\/p>\n\n\n\n As with any career, to become a data scientist, you\u2019ll need as much experience and practice as possible. Luckily, there are many ways that you can get involved with communities, work on projects, and develop your data science skills. <\/p>\n\n\n\n For example, DataCamp Workspace<\/a> provides a collaborative cloud-based notebook that allows you to analyze data, collaborate with others, and share insights. Workspace is designed to take you from learning data science to doing data science. It also has built-in datasets, so you can start analyzing data within minutes.<\/p>\n\n\n\n You can also apply your knowledge to various data science projects, allowing you to solve real-world problems from your browser. <\/p>\n\n\n\n Once you\u2019ve developed all of the skills mentioned above, you\u2019ll want to start applying them in more professional settings. Once you\u2019re confident you have the data scientist skills needed to meet the expectations of a role, you can start applying for internships or jobs. <\/p>\n\n\n\n If you want to become a data scientist, you\u2019re going to need to keep up-to-date with a fast-paced industry. There is no better way to stay informed about developments in data science than by engaging with what can often be a generous and dedicated community. <\/p>\n\n\n\n As well as social media sites such as LinkedIn, Twitter, Discord, and Reddit, there are all kinds of niche sites, blogs, and data science leaders you can follow. Look for people who are interested in the same areas as you, reach out for advice, contribute to discussions, and get involved with what\u2019s going on. <\/p>\n\n\n\n Here are six common steps to consider if you\u2019re interested in pursuing a career in data science:<\/p>\n\n\n\n You will generally need at least a bachelor\u2019s degree in data science or a computer-related field to get your foot in the door as an entry-level data scientist. However, some data science careers require a master\u2019s or doctoral degree. Degrees add structure, internships, networking, and recognized academic qualifications to your r\u00e9sum\u00e9. <\/p>\n\n\n\n However, if you\u2019ve received a bachelor\u2019s degree in a different field, you may need to focus on developing skills required for the job through continued education, like online short courses or boot camps.<\/p>\n\n\n\n Data scientists may specialize in a particular industry or develop strong skills in areas such as artificial intelligence, machine learning, research, or database management. Specialization may be a good way to increase your earning potential and do work that is meaningful to you. <\/p>\n\n\n\n Once you have acquired the necessary skills and specialized knowledge, you can begin the search for your first data science role. Creating an online portfolio is a valuable way to display a few projects and showcase your accomplishments to potential employers. You may want to consider a company with room for growth since your first data science job may not have the title data scientist but could be more of an analytical role. <\/p>\n\n\n\n You will likely learn how to work within a team and master best practices that can prepare you for more senior positions.<\/p>\n\n\n\n Data science boot camps are short-term, immersive educational programs that teach critical data science skills and programming languages such as Python, R, and SQL. There are lots of boot camps online; some may take a couple of weeks to complete, while others may take up to a couple of months. <\/p>\n\n\n\n Boot camps can help you expand your network. As a student in a boot camp, you can also take advantage of dedicated career services offered through the program to help with job placement after completion. Data science boot camps typically cover various topics such as machine learning, natural language processing, data analytics, data visualization, and more.<\/p>\n\n\n\n Here are a few certifications that focus on and help improve valuable skills:<\/p>\n\n\n\n Academic qualifications could play a crucial role in the selection process for positions in the field. However, a master’s degree in data science depends on the job.<\/p>\n\n\n\n Some professionals have a bachelor\u2019s or graduated from a data science boot camp. However, because some employers prefer that candidates have a graduate degree, a master\u2019s could expand your career prospects.<\/p>\n\n\n\nWhat Does a Data Scientist Do? <\/strong><\/span><\/h2>\n\n\n\n
Other responsibilities of Data Scientists<\/strong><\/span><\/h2>\n\n\n\n
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What a data scientist needs in a platform<\/strong><\/h2>\n\n\n\n
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What Skills Do Data Scientists Need? <\/strong><\/span><\/h2>\n\n\n\n
Hard Skills<\/strong><\/h3>\n\n\n\n
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Soft Skills <\/strong><\/h3>\n\n\n\n
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How to Become a Data Scientist <\/strong><\/h2>\n\n\n\n
Learn data wrangling, data visualization, and reporting <\/strong><\/span><\/h3>\n\n\n\n
Work on your statistics, math, and machine-learning skills <\/strong><\/span><\/h3>\n\n\n\n
Learn to code <\/strong><\/span><\/h3>\n\n\n\n
Understand databases <\/strong><\/span><\/h3>\n\n\n\n
Learn to work with big data <\/strong><\/span><\/h3>\n\n\n\n
Get experience, practice, and meet fellow data scientists <\/strong><\/span><\/h3>\n\n\n\n
Take an internship or apply for a job <\/strong><\/span><\/h3>\n\n\n\n
Follow and engage with the community <\/strong><\/span><\/h3>\n\n\n\n
Professional Steps to Become a Data Scientist<\/strong><\/h2>\n\n\n\n
Step 1: Pursue an undergraduate degree in data science or a closely related field<\/strong><\/h3>\n\n\n\n
Step 2. Consider a specialization<\/strong><\/h3>\n\n\n\n
Step 3. Get your first entry-level job as a data scientist<\/strong><\/h3>\n\n\n\n
Step 4. Advance your skills with a data science boot camp (optional)<\/strong><\/h3>\n\n\n\n
Step 5. Review additional data scientist certifications and post-graduate learning (optional)<\/strong><\/h3>\n\n\n\n
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Step 6. Earn a master\u2019s degree in data science<\/strong><\/h3>\n\n\n\n
What is an Average Data Scientist’s Salary? <\/strong><\/span><\/h2>\n\n\n\n
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Data Analyst vs Data Scientist: What’s the Difference?<\/strong><\/span><\/h2>\n\n\n\n
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Recommended Articles<\/strong><\/span><\/h2>\n\n\n\n
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References<\/strong><\/span><\/h2>\n\n\n\n
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