Become a Data Scientist in 6 Months with No Coding Experience

Data science is a rapidly expanding field, and establishing a career as a data scientist is difficult for those coming from a completely different background. A data scientist is a specialist in data analysis and interpretation. They apply their data science expertise to assist organizations in making better decisions and improving their operations. They are typically well-versed in mathematics, statistics, and computer science.

Coding skills are essential for a data scientist, but students with no experience in coding can also become data scientists. Professionals who can turn data analysis into a competitive advantage for their organizations are in high demand. To do so, data scientists investigate various marketing channels, ad types, and creatives used to promote products.Here are some steps that can be followed to learn data science in six months without any coding experience and become a data scientist. 

1. Familiarize With The Fundamentals Of Data Science

The first step towards becoming a data scientist is to familiarize oneself with the fundamentals of data science. This includes understanding statistics, machine learning, and data visualization. There are numerous online resources available to help them learn, such as Coursera, edX, and Udemy. They can also find introductory books and videos on the subject. The goal is to gain a solid understanding of the field, its applications, and how data analysis and visualization work. 

2. Improve Mathematical Skills

The foundation of data analysis begins with arranging data points on graphs along the X and Y axis and identifying correlations and patterns between various variables before analyzing data with high-tech instruments. Some of the mathematical concepts that a data scientist should be familiar with include - Statistical methods and probability theory, Probability distributions, Multivariable Calculus, Linear algebra, Hypothesis testing, Statistical modeling and fitting, Data summaries and descriptive statistics, Regression analysis, Bayesian thinking and modeling, and Markov chains.

3. Start Learning A Programming Language

After having a solid understanding of data science fundamentals and mathematical skills, one should start learning a programming language. The most commonly used programming language in data science is Python. If learners do not have any prior experience with coding, it's recommended that they start with Python as it is a relatively simple language to learn, and it's widely used in data science. There are numerous online resources available to help students learn Python, such as Codecademy, edX, and Udemy. It is recommended that students spend at least 4 to 6 hours a week learning the language, with the goal of becoming proficient within six months.

R, SQL, and SAS tools are also very highly recommended programs that aspiring data scientists should learn. 

4. Learn About Data Analysis Tools

After having a good understanding of programming languages, data analysis tools can be used. Data analysis tools are software that collects and analyzes different types of data. Data analysis tools include learning about libraries and frameworks such as Pandas, Numpy, and Matplotlib. These tools will allow the learners to clean, manipulate, and visualize data. There are numerous online resources available to help students learn these tools, such as edX, Coursera, and Udemy. Microsoft Power BI, Tableau, Sisense, Qlik, etc are some of the most commonly used data analysis tools. 

5. Get Familiar With Machine Learning

Machine learning is a critical component of data science and It involves using algorithms to find patterns in data and make predictions. To become data scientists, they will need to learn the basics of machine learning, including regression analysis, decision trees, and neural networks. There are numerous online resources available to help the data scientist with this, such as Coursera, edX, and Udemy.

6. Start Looking For Internships And Side-Projects

Students can put their skill set to use in real-world situations as they begin to expand their knowledge base and receive immediate feedback as employers want to see professional practical experience on their resumes.

Students can look for part-time work or internships through social media and job sites, as well as freelance marketplaces like Upwork and Fiverr. Kaggle also hosts contests with cash rewards. LeetCode can be used to practice coding and to research possible data science interview questions. 

7. Practice Data Science Tools and Coding

The key to becoming a successful data scientist is practice. The more students work with data and apply the concepts that they have learned, the more efficient they will become. There are numerous resources available to help aspiring data scientists practice, such as Kaggle and DataCamp. Kaggle is a platform that allows them to work on real-world data science problems and compete against other data scientists. DataCamp is a platform that provides interactive courses and projects that help them in developing their skills.

8. Start As Data Analyst And Ascend Up To A Data Scientist

Data analyst positions are equally popular and in demand as data scientists and are easier to break into. With proper preparation and a strong background in data science basics and fundamentals, it is possible to get easily hired as a data analyst which can act as an excellent starting point for a career in data science. 

While data scientists analyze data and use coding and mathematical modeling skills, data analysts handle data collection and spot trends in datasets. 

9. Networking and Finding Opportunities

It is very important to network and finds opportunities to apply their skills and greatest method to learn more about various employment options and perhaps even meet members of their teams. Additionally, they can learn about the industry size, and culture of the organization, the types of projects that interest them, and how to get ready for the application process.

Smaller businesses may be simpler to break into when starting out without expertise, but larger tech organizations with entry-level programs might have more infrastructure in place for training and mentorship.

10. Explaining The Transition To A Potential Employer

The ability to link models to immediate business impact is a requirement for data scientists. Although they should emphasize their data science experience in their resume and cover letter. They should also point out any prior employment that involved the usage of data science tools like Microsoft Excel or the development of other transferable abilities like business, communication, and teamwork.

Conclusion

Becoming a data scientist in six months with no coding experience is achievable. By familiarizing yourself with the fundamentals of data science, learning programming languages, learning data analysis tools, and practising with real-world projects. Students can develop the skills and knowledge necessary to become successful data scientists. 

However, it requires dedication, discipline, and a willingness to continuously learn and grow. With the right approach and resources, anyone can become a data scientist in six months, regardless of prior coding experience.