How to become a Data Scientist? A complete roadmap

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These days, data science, AI, and machine learning are all over the news. 

And if it’s not news, you might have come across AI tools like Chat GPT on social media channels. Whichever fascinated you most, you can’t deny that you must have thought of starting a career in these fields.

One of the most popular and high-paying related careers is data science. But it requires a lot of patience, hard work, and consistent efforts to master.

However, newbies face another big issue – figuring out where to start and the best path to become a data scientist. This article covers the entire roadmap you need to follow to go from being a junior data scientist to an expert. 

Let’s get started!

Qualifications required to become a data scientist

A report by the U.S. Bureau of Labor Statistics says to pursue a career in data science or a similar profession, a candidate must have a bachelor’s or master’s degree in mathematics, statistics, computer engineering, or information technology. However, a bachelor’s degree is one of many ways to start a career in data science. 

The first and foremost step to becoming a data scientist is to have a bachelor’s degree in a related field, next is to become competent in all the other required fields, such as programming, data analysis, and wrangling. 

Let’s find out each of these skills below! 

Skills you need as a data scientist

Data science is a multidisciplinary field in which you have to master programming, statistics, and mathematics. Irrespective of the degree, you need to have a good hold on all these areas. So, here’s a quick rundown of what skills you have to acquire to become a data scientist. 

Programming

Data science relies on various tools and programs developed specifically for data analysis. For instance, Python, R, SQL, Sckitlearn, and Seaborn are used in various data processes such as data cleaning, wrangling, and visualization.

Statistics 

To master data science you should have a strong hold on statistics formulas that are used to dig deep into data.

For instance, business owners might complain that customers only buy products when they are on sale. So, data science will analyze all customer data that business owners collect, categorize customers based on their buying behavior, and examine how changes affect certain groups. 

To do so, data scientists need to use statistical methods, including dimensionality, clustering, or latent variable analysis. 

Mathematics

Concepts like linear algebra, calculus, and probability theory are used in data analysis. Let’s understand the role of math in data analysis through an example! 

Linear Algebra is used to construct linear equations, the central component of making graphs in data visualization. In a sane way, calculus is one of the critical concepts of mathematics that is used in data visualization, that is, to create extensive data dashboards. 

To master data science you should have a strong hold on statistics formulas that are used to interpret and draw conclusions from big data. 

Applying for Data Science jobs

After learning all the skills, you can’t directly jump into the field of data science. Instead, you have to start with entry-level jobs or internships. 

To become a data scientist, begin by applying for a junior data scientist or data analyst job. Then, with time and experience, you will have more knowledge and experience that you can leverage to grab a role as a senior data scientist or another higher executive role.

Even if you are unable to crack the junior data scientist interview, then we would recommend you find jobs such as a Data Engineer, Data Analyst, Business Analyst, Data Architect, and Database Administrator

These jobs will help you gain significant experience that will add relevant knowledge and help you advance in this field.

If you’ve gotten to this point, that means that you’ve built a foundation and you’re ready to build a portfolio.  

Below is a guide that will help you build an attention-grabbing resume. 

How to build a data science portfolio?  

A well-crafted resume helps you stand out from other candidates. Before working on your CV, you must know what skills and qualifications you have to mention. 

Here’s a job description example with tips on how to craft your resume.

The ideal resume/CV should grab the recruiter’s attention while showcasing all your skills and qualifications. Therefore, before heading to the interview make sure resume your includes the following sections:

  • X & Y years of experience working in industries A, B, and C
  • Bachelor’s degree in computer science, mathematics, statistics, physics, chemistry, and electrical engineering
  • Z years experience in using Python, R, or any preferred programming language 
  • N years in using platforms like Hadoop, Hive, MySQL, etc.
  • Proficient in communication and technical writing 
  • Strong problem-solving skills 
  • Understanding and application of statistical techniques, data mining, and machine learning.

 

A good resume’s anatomy comprises skills, qualifications, abilities, experiences, and certificates. Keep in mind that your resume is easy to understand and showcases that you are the right fit for the job.

Furthermore, you can add relevant certificates and courses to boost your resume. 

If you have all the required qualifications, skill set, and experience, your chances of getting a job will be high. Remember to prepare for the interview, and practice all the common questions. 

What are the roles and responsibilities of a data scientist? 

Once you are selected as a data scientist, it’s time to start working. Well, there are so many intricacies once you are hired as a data scientist. Sometimes this becomes overwhelming for freshers. But it’s important to stay grounded and understand the roles and responsibilities.

As a data scientist, your responsibilities will be:

  • Establish communication within the organization to improve outcomes. 
  • Find the relevant internal and external data resources and mine them.
  • Data cleaning, data mining, cleaning, validation, and analysis. 
  • Develop and apply data models and algorithms to data sets.
  • Test models to ensure data accuracy
  • Make detailed reports of your findings, recommendations, and solutions.

Wrapping Up 

In summary, to become a data scientist, you need to meet two criteria. First, have a degree in mathematics, data science, or any other related field. Second, learn all the required skills that are used in data analysis. 

Additionally, you can opt for data science certification courses to validate your skills and qualifications. If your resume covers all these criteria, you can start applying for internships or data analyst roles.

Do you want to know more about data science? – Visit Skolar’s blog!

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