Kazeem Razaq @K.Razaq / 4:00 PM EDT. June 07, 2022.
Nowadays, data science is taking over the whole world. From small companies to big corporations, every company uses different approaches in order to get the best outcome. However, there is one thing that every company can use and that is a simple classification of their tools: tools that improve their work conditions in the long run.
From Silicon Valley to Wall Street, high-paying companies are fighting over data scientists. The median salary for a data scientist is $120,000.
Are you interested in taking up Data Science as a profession? Or are you just curious about what it involves? Having a conversation about “the biggest job in data” with your peers and colleagues is not uncommon. But, do you feel that the common discourse doesn't go much beyond its buzz factor?
There's never been a better time to pursue a career as a data scientist. The job market is hotter than ever, with countless companies desperate to hire someone who has the right skillset.
If you're reading this, it's likely that you've sensed the recent escalation of the importance of data within not only business but media, entertainment and society – as a whole. The volumes of data being produced have increased massively in the past five years alone and it looks like that trend is set to continue for some time yet.
What is data science?
Data science is the practice of working with data to solve problems. It includes activities like collecting and cleaning data, analyzing it and presenting your results to others. Data scientists often work in teams with other analysts, developers and project managers on projects that involve analyzing large amounts of data from many sources.
The skills required for a job as a data scientist vary by employer and industry but typically include statistics, computer programming and machine learning.
Who is a Data Scientist?
A data scientist is a problem-solver: a person who uses data to solve problems. Data scientists combine statistics, computer science, and other disciplines to analyze data, identify patterns, and extract meaning from information.
Data scientists typically have expertise in statistics and machine learning, but their work is also influenced by knowledge of many other areas such as communication, business intelligence, software engineering, and mathematics.
The job market is changing, and there's a new name in town. Data scientist is the hottest job title in the business world, and it's not just because of its high salary.
Data scientists have the ability to predict trends and make smarter decisions by analyzing data. They're also able to communicate their findings effectively, which makes them more valuable than ever before.
But what does it take to become a data scientist? Is it possible for someone without a technical background to make this career jump? (Yes it is - keep reading to discover how you, yes -- you can quickly and easily break into the Data Science job market).
Data science is the sexiest job of the 21st century. According to McKinsey & Company, the United States alone will face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.
Data scientists are in demand for many reasons
Data science is about more than crunching numbers — it's about understanding people and businesses. Data scientists use their technical expertise to understand human behaviour through data collection, storage, and analysis. It's not just about making accurate predictions using big data; it's also about understanding why those predictions are accurate or not. And it's not just about helping businesses make better decisions; it's also about using technology to improve people's lives by making them healthier or more productive in their jobs.
A data scientist uses statistics, machine learning algorithms, artificial intelligence (AI), computer vision and pattern recognition techniques to find patterns within large amounts of raw data that can't be seen otherwise. Data scientists use these patterns to create models that can be used for predictive analytics purposes. These models are built based on historical data and tested across different scenarios so they can be used in real-time situations when needed.
What do data scientists do?
Data scientists work with massive amounts of information to find patterns and gain insights that will improve business performance. They use various tools and techniques such as statistical models, machine learning algorithms, natural language processing (NLP), predictive modelling, graph database management systems (GDMS), and big data processing frameworks like Apache Spark or Hadoop.
In addition to analyzing large datasets, data scientists often work closely with software developers on building new applications based on their findings or writing.
They must understand the business goals of their company or client and then use that knowledge to choose the right tools for their analysis. They also need to be able to communicate their findings clearly and effectively so that their audience understands why certain actions should be taken based on the results of their work.
How To Become A Data Scientist: The Skills You Need.
Data science is an interdisciplinary field that combines skills from computer science, statistics, mathematics, and business. The core of data science is based on coding and analyzing large datasets using statistical methods like machine learning and artificial intelligence (AI).
Here's how to become a data scientist:
1. Obtain A Solid Foundation In Computer Science
Data scientists need a strong foundation in computer science, which includes understanding algorithms and software engineering. They also need to understand databases and have an understanding of distributed computing systems like Hadoop or Spark.
2. Develop Your Statistical and Probability Skills.
The most important skills for a data scientist are statistics and probability. These are two very different things that go hand-in-hand — both deal with uncertainty and randomness, but they use different methods to handle this uncertainty.
While not every company needs someone with statistical training, most major corporations do require this skill set for their data scientists. This means that if you want to pursue a career in this field, you need to be a pro at statistics first! Learn about probability distributions and learn how they work together with machine learning algorithms to give you actionable results from your analysis workflows.
3. Learn Python or R (or both)
Python and R are the two most popular programming languages used by data scientists (although R is more popular outside of Silicon Valley). They are also extremely easy for beginners to learn. The best way to get started is with free introduction courses for both languages, which will teach you how to write code and execute basic tests on datasets from scratch. If you want a more advanced course that'll teach you how to handle larger datasets, check out our data analytics course here.
4. Learn SQL.
SQL is a programming language designed specifically for working with databases (hence its name). It's beneficial for manipulating large amounts of data and pulling out interesting insights from it — something that's essential for any aspiring data scientist! You can get started learning SQL at Udemy or EdX, which offer free courses.
Data science is a relatively new field, so there isn't a lot of formal training available yet. But that doesn't mean you can't learn everything you need to know on your own. In fact, learning everything on your own is one of the best ways to become a skilled data scientist.
If you want to become a data scientist, you'll need an undergraduate degree in computer science or a related field and advanced knowledge of statistics, mathematics and machine learning. But I've seen people with a variety of backgrounds become data science practitioners: people with engineering, health and life science, and arts backgrounds can easily transition into data science roles. It helps if you have technical skills and experience with statistical software such as R or Python and knowledge of SQL databases like MySQL or PostgreSQL.
5. Get professionally certified
While there aren't official certifications for data scientists yet, many companies are starting to recognize certifications from prominent institutions like BusyQA, Coursera and edX when hiring candidates for this position. Graduates from our company, BusyQA, have been hired by ALL of Canada's top 100 companies (yes - each one of them!). That's because every single one of our courses comes with a co-op internship designed to give you on-the-job skills and experience. To find out which of our many tech certifications might be relevant for your career goals, check out our Data Analytics and Business Analysis certification specialization course here.
Data Scientist Salary scale and position.
Data science is a field that is growing at a rapid pace. Data scientists have the ability to use data to solve complex problems, but they also have the ability to craft solutions that are used by millions of people every single day. If you want to be a data scientist, it’s important that you know the job market for these professionals and how much they earn on average.
The first thing that you should know about data scientists is that their salaries vary greatly depending on where they live. According to Glassdoor, the average salary for data scientists in San Francisco is $143,000 per year. This figure can go as high as $183,000 if you work at top companies like Uber or Facebook.
If you want to become a data scientist, it’s important that you understand how much these professionals make throughout their careers so that you can plan accordingly when making decisions about your future career path.
Let's compare salaries across the globe. The average salary for a Data Scientist is between $100,000 USD to $175,000 USD per year in the USA. The average salary for a Data Scientist in Canada is around $85,000 CAD per year. The average salary for a Data Scientist in the UK is roughly £50,000 per year. In India, the average salary for a Data Scientist is Rs12 lakhs per annum (or $20k).
If you're looking to make more money as a data scientist, then consider moving into management or consulting roles where you can increase your earning potential significantly.
Data scientists are in high demand, and the salaries for data scientists are increasing rapidly.
Data Scientist Salary by Industry.
Data science skills are in high demand in many industries including finance, healthcare and insurance, manufacturing, retail and e-commerce, and technology services. Data scientists who have specialized knowledge in one of these fields can expect to earn higher salaries than those with general knowledge across multiple industries. However, even those who possess generalized skills can earn significant pay increases by focusing on one industry over time.
Data scientist salary trends
Data scientists are in hot demand. According to Indeed, there were over 7,000 job openings for data scientists in the U.S. alone in March 2021 — a 67% increase from 2016. The average salary for these positions was $113,000 per year (which is up from $110,000 in 2021).
Data scientists pay varies widely based on several factors:
What industry do you work in (e.g., e-commerce vs financial services)
Your level of experience (in industry and as a data scientist)
The company where you work (size of company and seniority/title)
Geographic location (cost of living and labour market).
Career Options in Data Science.
Data science is a relatively new field and so the career options are limited right now. But these are some of the most common data science job titles:
Data scientist.
Data analyst
Analytics manager
Statistician
Machine learning engineer
Programmer analyst
...and many others!
There are many roles in the Big Data field and data science is just one of them. One of the best ways you can become a data scientist is to gain enough experience and knowledge in different sectors around Big Data. Being well-versed in all the main aspects of Big Data such as Hadoop, Spark, Python/R, Tableau, and MongoDB -- just to name a few, will make you not only a top candidate -- but one of the best data scientists in the field.
At the end of the day, becoming a data scientist is certainly not easy. But it is attainable, as long as you put in the hard work and don't let yourself get discouraged along the way. And if you prove yourself to be an exceptional candidate for data science jobs, I'm sure you'll find that your hire-ability will increase exponentially.
Do you dream of becoming a data scientist or analyst? At BusyQA, you have access to our premium IT courses that will help you get there. Best of all, we offer paid co-op internships with all of our courses so that you build valuable working experience while you study with us -- and you'll have the opportunity to get hired by Canada's top 100 companies (all of which have hired OUR graduates). Get in touch with us to discover our cutting edge curriculum - with BusyQA, you could be on your way to the top in just a few short months, click here to find out more.
Komentáře