Everything You Need to Know About Starting A Career In Data Science
In 2013, a groundbreaking study revealed that 90 percent of the entire world’s data had been created within the previous two years. Well, let this sink in! In just two years, we collected and processed 9 times more information than the previous 92,000 of mankind combined! It is projected that by 2025, the amount of data collected and processed will be an astounding 50 zettabytes.
So, how can this data be made useful? What are its real-world implications? The answers to these questions lie in Data Science. The field of Data Science is growing quite rapidly, and with it revolutionizing many industries. Thus, it is not possible to limit its capabilities with a single definition. That said, Data Science is generally devoted to the extraction and integration of information from raw data for purposes of formulating actionable insights.
Who is a data scientist?
Basically, a data scientist is a mix of a mathematician, a computer scientist, and a trend-spotter. The data scientist’s role is to extract large volumes of data and carry out analysis with the goal of gaining a deeper insight into what it means. A data scientist operates between the IT and business worlds and drives organizations by analyzing complex datasets to help companies make business decisions based on the insights gained from the gathered and processed data.
The pillars of Data Science
While data scientists come from different educational and work backgrounds, they are required to be experts in these four fundamental areas:
- Mathematics (including probability and statistics)
- Computer science ( including software, data architecture and engineering)
- Communication ( both verbal and written)
How Data Science Works
Data Science involves a range of expertise and disciplines to result in a holistic, thorough, and refined processing into raw data. A data scientist must be skilled in pretty much everything from math, data engineering, advanced computing, statistics, and visualizations in order to effectively process tons of information and make sense of it.
Data scientists rely heavily on Artificial Intelligence, especially sub-fields of deep learning and machine learning to create models and use algorithms and other techniques to make predictions.
Why Learn Data Science
The Harvard Business Review hailed Data Science as the “sexiest job of the 21st century.” Data scientists are some of the highest paid professionals in the job market. In fact, Glassdoor ranks Data Science as a topmost profession. Data is, without doubt, the fuel that powers businesses. Specifically, Big Data has revolutionized organizations giving them a competitive edge. Organizations are looking for individuals who are proficient in handling, managing, analyzing, and understanding trends in data.
Here are the reasons why you should consider making a career in Data Science:
A fuel of the 21st century
Data is the new driving force behind successful organizations. Businesses in all industries are utilizing data to outsmart competition. Businesses need data to boost their performance, grow and create better products for their customers.
A data scientist navigates through an organization’s data to make meaningful observations that helps organizations make sensible business decisions.
Question of demand and supply
As already mentioned, there is a huge abundance of data. However, there are not enough experts to convert this data into useful insights. Skilled Data Scientists are in short supply. Much of this can be attributed to the fact that Data Science is still at its infancy stage. To fill this vacuum in supply, you need to study Data Science and its underlying fields.
A lucrative field
Glassdoor reports that the average annual salary of a data scientist is $117,345. This is almost triple the national average of $44,500. Thus, a data scientist’s salary is 163% more than the national average wage. This definitely makes Data Science a lucrative career choice.
Data Science makes the world a better place
Data Science and Big Data are not just Business Intelligence tools. Several social and philanthropic organizations are actually using data to create solutions for social good. Also, healthcare providers use data to gain better insights into patients’ health.
Data Science: The career of tomorrow
With organizations becoming increasingly data-driven and new innovations emerging every day, there is no doubt that Data Science is the career of the future. The world of technology has become dynamic and with more and more people taking to the internet to make decisions, more data is being created. Organizations are constantly on the look for data scientists who can help them use data for purposes of making smarter data decisions and creating better products for their consumers.
Top Programming languages you need to master for Data Science
This is an extremely popular general-purpose, widely used, and dynamic language for data scientists. In fact, data scientists believe it is the easiest programming language to master. And since it combines quick improvement with the ability to generate high-performance algorithms written in C or FORTRAN, Python has become the major programming language for data scientists.
R is another popular programming language. It is an open-source language and software environment for graphics and statistical computing. These skill sets have an extremely high demand for Data Science and machine learning experts.
This general-purpose language runs on the Java Virtual Machine (JVM). Thanks to its high demand, Java skills are extremely popular especially for software engineers, DevOps engineers, data scientists, and software engineers.
Structured Query Language (SQL) is one of the most popular skills for Data Science. It is applied in querying and editing the information stored in relational databases. It is also applied for storing and retrieving data. Possessing SQL skills can be one of your biggest assets as a data scientist as this is one of the most preferred skills by organizations.
Julia is another popular dynamic programming language designed to address the needs of high-performance numerical analysis and scientific computing. While it is a newer language, it is capable of performing general programming tasks. Thanks to its faster execution, Julia has become a popular choice for handling projects with high volumes of data.
Scalable language (Scala) is one of the popular languages with one of the largest user bases. It is an ideal language of choice for working with high-volume data sets and has full support for functional programming. And since Scala is developed to run on JVM, it allows interoperability with Java, making it an excellent general-purpose language while also being an excellent skill for Data Science.
Developed by Math Works, MATLAB is fast and stable, ensuring solid algorithms for numerical computing language. Matlab’s wide use in day-to-day statistical analysis and applications in advanced functionalities make it a must-have skill for data scientists.
Yet another popular open-source application for numerical computation, TensorFlow is an excellent machine learning framework ideal for handling large-scale data. One of the most important features of TensorFlow is that it lets you break your project into multiple chunks so they can run in parallel over various CPUs or GPUs. This way, you can train complex neural networks on immense training sets over a very short time period.
Definitely one of the oldest programming languages, C has been a source to most modern-day languages and still continues to be a popular language for data scientists.
F-Sharp is a mature, open-source programming language that empowers users to tackle complex computing problems with simple, intuitive, and easy to maintain codes, making it a favorite for data scientists.
Career pathways in Data Science
Data Science is definitely a broad subject. In order to help you understand the nuances within this subject and tailor your resume, here is a breakdown of the career paths available in Data Science:
This is a professional who fine-tunes mathematical and statistical models that are applied on data to solve a problem. They can also build models to make predictions and find solution to business problems. A data scientist is the bridge between programming and implementation of Data Science, the theory of data, as well as the business implications of data.
Skill you need to be a data scientist:
You will need knowledge of statistics, algorithms, mathematics, as well as a broad knowledge of programming languages like Python and R. You also need to know how to structure data problems, from framing the questions to effective communication of the results.
Data scientist salary:
The average annual salary of a data scientist is $115,000
These professionals majorly rely on their software engineering background to work with large amounts of data. A data engineer utilizes their computer science background to process large datasets. As a data engineer, your work will mainly focus on cleaning up datasets, coding, and implementing data queries.
Skills needed to be a Data Engineer:
You will need a deep knowledge of data storage and warehousing solutions (SQL and NoSQL-based flavors), as well as programming frameworks like Spark and Hadoop to source and process data.
Data Engineer Salary
The average annual salary of a data engineer is $100,000.
Finally, there is a data analyst. This professional looks through data in order to generate reports and visualizations that explain the insights contained in the data. Simply put, a data analyst helps decision-makers understand specific queries with charts. A business analyst is a subset of a data analyst who helps businesses unravel the implications of the data and the resulting actions.
Skills you need to be a data analyst
To be a data analyst, you will need a solid grasp of data manipulation using tools like Excel and data communication.
Data Analyst Salary
The average salary of a data analyst is $65,000 per annum.
Choosing the right online Data Science course
Technology has greatly transformed the way people work and learn. It is, therefore, no surprise that more and more people are taking to online learning to advance their skills. Whether you are a busy professional or a stay at home parent, you can benefit from online learning platforms and gain insightful skills at your pace, and at a fraction of the cost of the traditional classroom.
However, before jumping into online learning, here are some questions that can help you find a good online Data Science course:
Is the course accredited?
While an individual’s learning goals may vary, accreditation is often an important consideration when choosing an online course. Sign up for online Data Science courses that have affiliations with reputable universities.
What are the qualifications of the course instructors?
Having an engaging tutor is quite important especially when face-to-face interaction is missing. Look for previous students’ reviews and the experience with the course tutors. Additionally, pay attention to the qualification of the instructor. You want a tutor who is both qualified and experienced in Data Science.
Will you get a certificate upon the successful completion of the online Data Science course?
Does the platform offer an associate degree in Data Science of professional certification? Will the certificate be recognized by potential employers? These are important questions when looking for an online learning platform for any program, including Data Science.
How tech-savvy is the program?
Technology, or lack of it, can make or break your online education experience. The quality of video tutorials, learner portal design and graphics, and audio quality are important factors when choosing a course. Additionally, it is important that you understand how to access the resources necessary for you to complete your online Data Science course successfully.
Does the program accommodate different learning styles and needs?
More and more platforms are focusing on incorporating Universal Design for Learning (UDL) into their courses. This way, every learner is given an opportunity to excel in the course. Find out if the program is flexible enough to accommodate your learning style and pace.
How big is the class and what is the course completion rate look like?
It is important to consider factors like course completion rate. This will help you gauge how strong the course is and how much time the instructors are able to allocate towards your learning and success. Also, find out if the course is attracting a lot of learners. Finally, find out if the program has an online support portal where you can seek clarification for concepts you do not understand.
Is there an opportunity for peer interaction?
Does the program have group projects? This is important for fostering a sense of community during the learning process. The opportunity to build relationships with fellow learners who will later become industry peers is priceless. Peer interaction is a great opportunity for you to collaborate with like-minded people. It can also be helpful when preparing for exams and class assignments.
What is the design of the course?
Online learning should be a continuous process. A good online program should find a way of keeping learners engaged with out-of-the-box thinking. Choose a course that has invested in online learning innovations and is utilizing multiple formats for learner interaction.
Important tips for starting a career in Data Science
Let’s face it! Learning Data Science online can be quite intimidating, more so when you are just getting started. Which programming language to learn – Python or R? What techniques to pay attention to? How much statistics to learn? Should you learn to code? These are some of the questions you need to ask and answer, as part of your Data Science career journey.
Here are a few helpful tips that can give you a head start.
Choose the right role
There are several roles in the Data Science field. A machine learning expert, a data visualization expert, a data scientist, a data engineer, etc are some of the roles you can opt for. Depending on your educational background and work experience, choose a role that would be easy to get into. For instance, if your background is software development, it would be quite easy to go into data engineering. Be clear about what you want to become and stay focused on the path to take and the skills you need to master.
Do not just hastily jump onto a role without first understanding what the field requires and how to prepare for it.
Find the right course and complete it
Having settled on a role, the next sensible thing to do is put in an effort to understand the role. This means finding the right course to suit your data learning process. And there are thousands of online Data Science courses out there to take you through, from beginner to intermediate, all the way to advanced levels.
Once you have settled on a course, actively go through it. Follow the coursework, complete your assignments and projects, and actively participate in class discussions. For instance, if you want to focus on Machine Learning, you can sign up for Machine Learning by Andrew Ng.
Choose a programming tool/ language and stick to it
As already mentioned, it is important that you get an end-to-end experience of whichever program you are interested in pursuing. An important question you need to ask is, which programming language should you opt for?
This is especially an important question if you are a beginner. And the most logical answer would be to choose one of the mainstream programming languages like Python or R and begin your Data Science journey. Remember, tools are just a means for implementation; understanding the Data Science concepts is more important.
Join a peer group
Now that you are clear about the role you are interested in and have signed up for a course, the next important thing is to find and join a peer group. The Peer group keeps you motivated. Pursuing a new interest may seem daunting when you are all alone, but when you have a team alongside you, the learning process will be much easier. So be sure to join the program’s peer platform and actively interact with other learners.
Focus on both the practical and the theory
While learning Data Science online, it is important that you pay attention to the practical applications of the concepts you are learning. This is important for understanding the concepts and gaining a deeper sense of how you will apply them in the real world.
Here are a few tips to help with this:
- Be sure to do all the assignments and exercises to master the applications
- Reinforce your learning by working on open data sets
- Look at the solutions by people who have worked in the field. This will direct you in the right approach.
Get the right resources
To make learning continuous, it is important that you adopt each and every source of knowledge you can find. Consider checking out blogs run by influential data scientists. Read about Data Science and ensure that you are up to date with the latest developments in the industry.
Work on your communication
Most people overlook the importance of communication skills in executing their Data Science roles. They assume that technical proficiency is all you need to ace an interview. This is not true. Communication skills are quite important in the Data Science field. To share your findings with your team, you should know how to be an effective communicator.
Network, but do not spend too much time on it!
Your initial focus should be learning Data Science. Once you have mastered the subject, you need to get out and participate in industry conferences and events, meetups, and participate in workshops in your area. These meetups can present an excellent opportunity for you to meet peers and make your mark in the Data Science industry. Networking contacts might:
- Help you gain important information on the developments in the world of Data Science
- Help you get mentorship support
- Help you with your job search
Common Mistakes to Avoid When Working Out Your Career in Data Science
If you wish to have a successful career in Data Science, you can save yourself weeks or even months of frustration by avoiding these nine costly beginner mistakes. These mistakes, if you are not watchful, can eat into your most precious resources: your motivation, energy, and time.
I. Mistakes to avoid while learning Data Science
These are probably the toughest mistakes to spot. They can slowly, but certainly, drain your time and energy without warning, and they are rooted in the misconceptions surrounding Data Science. Here are these mistakes:
Spending too much time on theory
Most beginners fall into the trap of spending most of their time on theory, be it math-related (statistics or linear algebra) or machine learning related (derivations or algorithms). Here are three main reasons why this approach is inefficient:
- First, it is quite slow and daunting. If you are overwhelmed by all there is to lean, chances are you have sunk into this trap
- Second, you will not have the chance to train on concepts. Keep in mind that Data Science is an applied field, and the most effective way to consolidate your skills is through regular practice.
- Finally, you risk becoming demotivated if you fail to connect what you are learning to the real world.
How to avoid this mistake
- Be sure to balance your studies with projects that promote the hands-on practice
- Be comfortable with partial knowledge. You will naturally fill in the gaps as you continue learning
- Understand how each piece fits into the bigger picture.
Coding too many algorithms from scratch
This is yet another mistake that causes most learners to miss the forest from the trees. You really do not need to code every algorithm from scratch when you are just getting started with Data Science. While it is sensible to know how to code, the truth of the matter is algorithms are becoming commodities. In fact, most Data Science practitioners never code from scratch. All you need to do is understand how to apply the right algorithms in the right settings.
How to avoid this mistake
- Pick up general-purpose machine learning libraries like Caret (R) or Scikit-Learn (Python).
- If you choose to code an algorithm from scratch, do so with the goal of learning rather than perfecting your implementation
- Understand the landscape of modern machine learning algorithms and their pros and cons
Jumping straight into the deep end
Some people get into the field of Data Science because they want to build the technology of tomorrow: Advanced Robotics, Self-Driving Cars, Computer Vision, and so on. These are powered by techniques like natural language processing and deep learning. However, it is important that you master the basics first.
How to avoid this mistake:
- Start by mastering algorithms and techniques of “classical” machine learning, which are the building blocks for advanced topics
- Understand that classical machine learning still has untapped potential and that while algorithms are mature, we are still at the primary stages of appreciating their potential
- Set up a systematic approach to structuring Data Science and machine learning projects
II. Mistakes to Avoid when Applying for Data Science jobs
This next set of mistakes can cost you your dream job! Learn how to avoid these mistakes when applying for a Data Science job.
Including too much technical jargon in your resume
Most applicants make the mistake of suffocating their resume with too much technical jargon. Your resume should be able to paint a picture while your bullet points tell a story. Your resume should clearly outline the impact you are bringing to the organization, especially if you are seeking an entry-level position.
How to avoid this mistake:
- Simply list the programming libraries or languages you are conversant with and provide a brief description of how you used them
- Remember that less is more. Identify the most important skills you need empathize and allow them to shine
- Create a master template of your resume that you can spin off different versions for different roles.
Overestimating the value of academic qualifications
Sometimes, people tend to overestimate the value of their education. While a strong qualification can definitely improve your chances, it is often neither sufficient nor the key factor.
How to avoid this mistake:
- Supplement your coursework with several projects using real-world data sets.
- Take up relevant internship programs, even if they are part-time
- Hook-up with data scientists in your area with coffee chats and LinkedIn
Narrowing your job search
Data Science is a relatively new field and businesses are still in the initial stages of figuring out the impact of data on their operations. As such, focusing your search on “Data Scientist” openings alone can greatly limit you.
How to avoid this mistake:
- Conduct your search by required skills (Data Visualization, Machine Learning, SQL, etc)
- Perform your job search by responsibilities ( Data Analytics, Predictive Modeling, A/B Training, etc)
- Search by technologies applicable to the role ( R, Python, Scikit-Learn, Keras, etc)
- Search by job title ( Quantitative Analyst, Data Analyst, Machine Learning Engineer, etc)
III. Mistakes to Avoid During a Job Interview
The last set of mistakes has to do with the interview process. You have come a long way. You need to finish strong. Here interview mistakes you need to be aware of:
Being unprepared to discuss the projects
Having relevant projects in your portfolio serves as a major safety net for “how to do” kind of interview questions. Quality projects will definitely speak for you. After all, most hiring managers are looking for self-sufficient candidates since most Data Science roles come with elements of project management. This means that you should understand the entire Data Science workflow and know-how every piece falls into place.
How to avoid this mistake:
- Complete end-to-end projects that enable you to practice every crucial step ( such as Model Training, Data Cleaning, etc)
- Come up with an organized methodology (Remember, Data Science ought to be deliberate, not spontaneous).
- Review and practice past projects from internships, jobs, and classes you have taken.
Underestimating the value of domain knowledge
Machine learning knowledge and technical skills are crucial prerequisites for landing your dream Data Science job. However, if you truly want to stand out from the competition, you need to acquire specific skills about the specific industry you will be applying your skills to. Remember, Data Science does not exist in a vacuum.
How to avoid this mistake:
- If you are prepping for a position at a bank, it works well if you brush up some crucial finance concepts
- If you are interviewing for a start-up, be sure to learn about its niche and try to explore how your input will help it gain a competitive advantage
- Point is, taking a little bit of initiative can set you apart from the competition
Overlooking the importance of communication skills
Currently, Data Science teams in most organizations are quite smaller compared to analyst and developer teams. Thus, while an entry-level software engineer position will often be handled by a senior engineer, data scientists tend to work in more cross-functional environments. As such, the hiring manager will be looking for your ability to communicate with a team of individuals from technical and mathematical backgrounds.
How to avoid this mistake:
- Practice articulating technical concepts to non-techie people. You can practice explaining your favorite algorithms to a friend
- Prepare bullet responses to common interview questions and practice answering them
- Practice analyzing various datasets, extracting crucial insights, and presenting your findings.
Data Science is an exciting field that is increasingly attracting fresh minds. This is because of its promising future and that of related fields like Business Intelligence. Data Science focuses on scientific methods, processes, algorithms, and systems, to make smart business decisions. If you are aspiring to be a data scientist, there are various online Data Science courses available that can help you acquire the right skills for this exciting career. Whether you want to learn Machine Learning, Python or R language, Business Intelligence, or Data Mining, there is an online course out there for you. We hope this article will help you make the right decisions and start a lucrative career in Data Science.
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