How do I get a Data science job?
Everyone wishes they had the foresight to choose and prepare for their perfect profession, but life isn’t always a straight line and that’s part of what makes it intriguing. Furthermore, as a result of the quick pace of technological innovation, new industries and occupations are continually being created.
In a high-growth, in-demand professional industry with attractive job prospects, now is a good moment to examine whether data science is the best next step for you in your career.
The good news is that being a data scientist does not necessitate any previous work or educational background. You can self-learn data science abilities using a variety of approaches that are available to you.
Please allow me to describe what a data scientist works before we discuss the abilities you’ll need to become a data scientist without any prior experience.
Who is a data scientist?
Large volumes of data are collected and cleaned by data scientists, who then maintain user-friendly dashboards and databases, assess data to solve problems and conduct experiments, develop algorithms and present data to stakeholders in visually appealing visualizations.
In today’s world, there are multiple advantages to pursuing a career in data science, including high salary, a relatively stable and growing job market, even in the midst of a worldwide recession, and interesting problems to solve in a variety of industries.
Is data science hard?
The degree to which data science is tough or not is largely driven by your previous experience and preference for working with numbers and data in general. While data scientists do not require the same level of expertise in software engineering or machine learning as data engineers, they will need to learn to code in order to construct predictive models, which will necessitate learning to code.
There is a high learning curve in data science because it involves tough problems and a large amount of data, as well as technical expertise and subject knowledge. However, there are numerous free online tools available to get you started as an entry-level data scientist. Because data scientists are always up skilling and learning new technology, you should be open to the idea of continuing your education.
Do you need a degree to become a data scientist?
Not required. Even without a master’s degree or even a bachelor’s degree, it is possible to learn data science. Due to high demand for data scientists, employers are prepared to accept non-traditional candidates despite the fact that most job postings call for a master’s or a Ph.D. in an engineering-related field such as computer science, mathematics, or statistics. It is no longer necessary to have a college diploma to be considered for several large companies, including Google, Apple, and IBM.
It is possible to self-teach utilizing videos and modules if you do not have a degree in data science and wish to enter into the field. Online courses and certification programmers are also available.
The next six steps will guide you through the process of breaking into data science without any prior experience.
1: Polish up on your math skills
In the event that you come from a quantitative background, data science should be an obvious choice for you. The fundamentals of data analysis must be mastered before applying high-tech tools to it. These include plotting data points on graphs along the X and Y coordinates, as well as finding correlations and patterns between different variables.
Here are some recommended arithmetic principles to know in order to build efficient code and draw reliable conclusions:
● Probability theory and statistical methods
● Distributions of probabilities
● Calculus with multiple variables
● Linear algebra is a branch of mathematics that deals with the
● Testing hypotheses
● Modeling and fitting statistics
● Descriptive statistics and data summaries
● Analysis of Regression
● Bayesian modelling and thinking
● Markov chains are a type of algorithm that is used to
2: Learn a programming language or two
In comparison to other fields, data science is less about the status of your alma institution and more about what you know and how well you can demonstrate your relevant talents. The skill-based interviewing process has a tendency to level the playing field for people from various backgrounds.
Once you’ve mastered arithmetic, you may start learning SQL, R, Python, and SAS, which are all essential programming languages for aspiring data scientists.
This article will give you an overview of the abilities you’ll need as a data scientist, as well as which languages to focus on.
● Python is a scripting language with libraries for manipulating, filtering, and transforming large amounts of unstructured data. Web development, software development, deep learning, and machine learning are all possible with Python. It is the tool that data scientists utilise the most.
● R is a programming language that may be used to do complex mathematical and statistical calculations. It also provides data visualisation capabilities and a big support group to assist you in getting started.
● SQL is a relational database management system that allows you to query and join data from many tables and databases.
● SAS is an expensive statistical analysis, business intelligence, and predictive analytics tool utilised by major organisations, however it is not recommended for individuals due to the expense. You may quickly pick up SAS on the job if you know the other languages.
3: Take on side projects or internships
When putting together your resume, employers will look for evidence of professional practical experience. You will be able to use your skill set in real-world situations and receive real-time feedback as you continue to build your knowledge foundation.
It’s possible to find part-time jobs or internships on freelance platforms such as Upwork or Fiverr, as well as on social media and career websites. On Kaggle, there are also competitions with monetary prizes up for grabs.
Make sure you practise solving coding problems on LeetCode before your interview and that you explore possible data science interview questions before you go in for your interview.
Show examples of past work samples on Github, LinkedIn, or a personal website in order to develop a strong portfolio and online presence.
It may be tough to gain competence without prior experience, but by leveraging online networks and starting small, you can demonstrate that you have what it takes to turn data science knowledge into quantifiable business benefits.
4: Start as a data analyst
Data scientists and data analysts are not the same thing, despite the fact that they are both growing in popularity.
● Data analysts are in charge of data collection and identifying trends in datasets.
● Data scientists don’t just interpret data; they also use coding and mathematical modelling skills.
When it comes to entry-level employment, data analyst roles can be more difficult to come by and can serve as a good springboard for a career in data science.
Participants in Springboard’s mentor-driven data analytics bootcamp will learn about constructing structured thinking, evaluating business challenges, integrating data using SQL, visualising data with Python, and communicating analyses to anybody who wants to get their feet wet in data analytics.
5: Work hard and network harder
Learning more about various career opportunities and potentially meeting possible team members by getting to know other data scientists is the best strategy. Also available is information on what kinds of organisations (size, industry, and culture) you’d like to work for, as well as information on what projects you’re interested in and how to prepare for the job application process.
Even though it may be easier to get into smaller organisations without prior experience, larger technology companies with entry-level programmes may have a more robust infrastructure for training and mentorship built into their operations.
Another fantastic option is to make the shift from another position inside your organisation into data science. In most cases, if you’re in good standing, you can begin networking inside your organisation and exploring the potential of interviewing with a data science team, provided you meet the technical requirements.
As you set up virtual coffee sessions and phone chats, you may discover that the interactions pique your interest in specific job postings. This enables you to request tailored referrals from people in your network who are already familiar with you. According to CareerBuilder, 82 percent of businesses believe that recommendations provide the best return on investment, and many companies offer financial incentives to employers that are actively seeking new employees.
6: Explain your career transition to potential employers
Given the diversity of data science as a discipline, it is doubtful that all of the prior information will be lost entirely. Data scientists must be able to connect their models to specific business results in order for them to be effective. However, while your CV and cover letter should emphasise your data science experience, you should also include information about previous employment in which you used Microsoft Excel or developed business skills such as communication and cooperation, as well as other transferrable skills.
When applying for data science jobs without prior experience, include a brief summary section on your resume explaining your shift, using keywords, and listing courses you’ve taken, technical languages you’ve learned, and any project work you’ve completed to frame your expanding data science skillset in the best possible light.
Conclusion
Because data science is a constantly growing subject, it is critical to keep up with the latest developments in order to remain relevant. It is not necessary to enrol in a course in order to learn here. It entails devoting time to learning about the most recent innovations as well as more efficient ways of doing things. These teachings will have a multiplier effect on your professional progress as a result of your participation.
