A career in data science is lucrative, no doubts about that. But the amount of precise knowledge present on starting this career—is rather mediocre. Because there are so many questions which start running in our head. And there seems to exist no proper guide on starting a career in data science.
Like how important mathematics is? Should you focus on learning R or python? Is it easy to peruse a career in data science? Do you need a degree to get going and so on? Perhaps there is no single and straightforward guide to that.
But the breaking news is, we are going to patch together every piece of information into the heading, sub-heading, and then fill it with small chunks of digestible paragraphs. So that you can both enjoy the read, as well as—get your career rolling. Let’s start with the basics.
1. Basic Questions about Career in Data Science
a) Who is a Data Scientist?
These professionals compile and clean vast volumes of data and they try to come up with a convenient dashboard to analyze data—so as to execute tests and algorithms which they show to different stakeholders.
Data scientists use expertise in technology, social science, statistics, mathematics, market experiment, and some intuition. To find patterns that can help them in discovering answers to complicated problems of a company.
In very easy English—they use all that to make sense of big, messy, and chunky data. This data is taken from smartphones, social media, emails, and other places which is hard to fit in any structure.
b) Do you Need a Degree?
So most of the job descriptions ask for a bachelor’s or master’s degree or even a Ph. D in disciplines like computer science, engineering, math, and statistics.
But hear us out—it’s not always the case. Because there is so much demand for a data scientist that many companies hire people who have sharp skills rather than shiny degrees. Self-taught people!
Therefore, it’s feasible to start a career in data science without having a traditional education. Companies like Apple, Google, IBM, Tesla, ad so on, don’t ask for degrees—and well, these companies are doing pretty good.
c) Is it Hard Though?
Whether data science is hard or easy—depends upon your background. Background in math, stats, IT, and data analysis. And If you enjoy these subjects then you are gold.
If you are already overwhelmed, then consider it not easy, but If you had this bit of idea in your mind then consider it somewhat easy for you. Moreover, data science is a type of career that demands constant adjustment with new skills and learning.
There is one other important aspect also: communication. Yes, communication is crucial for a career in data science. As you’d need to explain your solutions and ideas to stakeholders. To make sense, if you have good knowledge of the domain and you can communicate it as well—chances are, you are going to go BIG!
2. What You Need to Learn for Building a Career in Data Science?
a) Type of Math you will Need
Googling about the math requirements in data science will show you mostly three types of topics: calculus, statistics, and linear algebra. However, in most cases, you’d only need statistics to launch your career in data science.
But just for the sake of knowing, it’s only necessary to understand the fundamentals of calculus, and how these fundamentals could influence your models. The same goes for linear algebra: understanding the fundamentals is necessary—you don’t have to become a math genius.
However, when it comes to probability and statistics—things can get messy—you need to learn! But, every concept in these two domains is comparatively easier to learn, so that’s a win-win. Other than these three types of math you will need discrete math, graph theory, and information theory.
b) Pick a Programming Language
As we asked above: whether to learn Python or R? Because both of these are great options as programming languages. But if you compare, Python is widely used in corporations, and R is widely used in academia. And both are even likable when it comes to supporting data science workflow.
However, choosing one language should be enough—focus on mastering a single language and the data science and its ecosystem. By mastering, we don’t mean to learn everything. Rather pick some topics from them and start learning them.
Other than Python and R languages, some tools are used alongside. These tools are called SQL and SaaS.
c) Machine Learning and AI
A significant percentage of data scientists are not trained in machine learning techniques because it is rather complex. Therefore, if you want to build an effective career in data science, machine learning is a must.
Some of the machine learning approaches include supervised machine learning, decision trees, logistic regression, and so on.
Once you are capable of these approaches, you can answer several problems and tackle challenges faced with big data outcomes.
d) Work with Unstructured Data
Unstructured data as it sounds—is unspecified content that does not match the SQL server or database. Few examples of unspecified content include blogs, videos, customer ratings, social media feed, email, and so on.
This type of content is—a heavy text which is wrapped around. Having to sort this form of data is complicated because it is not organized. Yet, it is equally important to understand how to work with unspecified data.
Because when you can work with unspecified data, you can sort out some of the complex problems faced during decision making. Therefore to start a career in data science, you should be able to work and manage this type of data from various platforms.
3. Make a Career in Data Science
a) Work on Projects or Join an Internship
So whatever you’ve learned so far, it’s time for you to start implementing it in the real world. After all, you have to showcase your projects and practical knowledge on your resume. Therefore, participating in projects and real-time problems can offer you real-time feedback that looks good on your job interviews.
To get real-time projects, you can opt for freelancing websites like freelancer.com or Upwork where you can work with clients and get exposure. Similarly, you can also look for internships on different job boards. Both options are equally good, but internships are going to be more flexible and freelancing is going to be more challenging.
To increase your chances of getting these opportunities, you still need some proof that you have practical problem-solving knowledge. For that, you can show your work samples from Github, LinkedIn, or better build a personal website that can act as a portfolio.
b) Guidance and Networking
Data science is a relatively new field, hence it can get hard to follow a path that guarantees success. Therefore, getting connected with similar people—alumni of data science can be the best way to learn and grow. You can get to learn from professionals—what they did right or wrong.
They can help you to get different career prospects and help you decide what companies to choose, what projects to work for, and how to prepare yourself to get a nice job. Getting a nice job in a big corporation is not easy.
Therefore once you start networking—you can leverage it and get inside a big corporation. You can ask your mentors or seniors to get you refereed which increases your chances of getting hired. This works because most of the companies look for emerging talent as well.
Launching your career in data science is not going to be easy—but most of the great things are not easy to achieve. The key should be to stay focused and learn every day. You should be able to enjoy the process!
A data scientist is one of the most lucrative jobs out there and for good reasons. Companies are investing an enormous amount of money in hiring these professionals. Therefore, taking the right actions can make your career the most sort-after.
We tried to provide as much value and insight as there can be. These steps are practical and the step-by-step guide is simply a roadmap to your dream—a career in data science!