What Data Scientists Do & How To Become One

Due to their work on anything from developing self-driving cars to automatically annotating photographs, data scientists are in high demand.

A data scientist uses the information to comprehend, explain, and aid businesses’ decision-making.

Being a data scientist may be intellectually demanding and analytically fulfilling, but it can also put you at the cutting edge of new technological developments.

As the use of big data in organizational decision-making continues to grow, data scientists are becoming more prevalent and in demand.

Below is a closer look at what they are, what they do, and how to become one.

What Data Scientists Do

Data scientists spend a lot of their time obtaining and preparing data since they frequently work with enormously massive data sets, or “big data,” as it is commonly referred to.

A data scientist may next evaluate this data to derive useful business insights, feed it to an AI or machine learning project, create new tools to simplify the data wrangling process or store and arrange it in a database, depending on the particulars of the function.

Read: What is Data Science and Why is it Important?

Popular Roles Within Data Science

Here is a list of some of the most well-known jobs and positions in the data science industry.

Data Scientist

An all-encompassing position whose duties might include gathering and organizing massive amounts of data, creating tools and algorithms to automate the data wrangling process, conducting exploratory data analysis on the data, and visualizing the data so that different people can easily understand the resulting insights.

Data Analyst

This position can be found in various sectors, including technology, healthcare, entertainment, and finance.

Among the responsibilities are:

• Receiving inquiries and information requests from company decision-makers and stakeholders.
  
  
• Gathering pertinent data and organizing it in relational databases so that it is ready for searching and analysis.
  
  
• Taking the data and extracting useful and profitable ideas, then showing them for easy understanding.
  
  
• Presenting the findings and outcomes to the appropriate company staff.

Data Engineer

A position that concentrates on gathering and cleansing data and has duties like:

• Constructing pipelines and systems for effective data collection.
  
  
• Large-scale data structure and organization for analysis.
  
  
• Making a company’s data available and prepared for usage by other data science roles.

It should be noted that this position is most frequently seen in major corporations, where it is feasible and effective to have separate positions for data wrangling and data analysis.

A data scientist or data analyst at smaller businesses might take both of these jobs.

Data Architect

Many sectors focusing on the development and use of databases also have a role.

Among the responsibilities are:

• Creating for a business large-scale data storage and organizing solutions.
  
  
• Deciding which databases should be created and for what uses.
  
  
• Creating data collection and production systems and pipelines.

Database Administrator

This position is present in almost every industry that generates or uses data.

Typical obligations include transforming unstructured data into relational databases to enable effective storage, maintaining the multiple databases’ new additions, enhancing consumer and employee accessibility and designing and implementing security measures to keep data safe and stored legally.

Read: 8 Beginner-Friendly Data Visualization Tools

How to Become a Data Scientist

1. Education

Although there are notable exceptions, a very strong educational background is typically needed to obtain the depth of knowledge required to be a data scientist.

Data scientists are highly educated; 88% have at least a Master’s degree, and 46% have PhDs.

To work as a data scientist, you could obtain a bachelor’s degree in computer science, social sciences, physical sciences, or statistics.

Computer science (19%), engineering (16%), mathematics and statistics (32% each) are the most popular disciplines of study.

If you earn a degree in one of these programs, you will acquire the abilities necessary to process and evaluate large data. But you’re not finished yet after completing your degree program.

The majority of data scientists hold a Master’s or PhD in addition to taking online courses to hone specialized skills like using Hadoop or Big Data searching.

As a result, you can apply to master’s degree programs in various related fields, including astronomy, mathematics, and data science.

You can easily transfer to data science because of the abilities you have acquired throughout your degree program.

In addition to classroom instruction, you can put what you learn in the classroom into practice by creating an app, launching a blog, or dabbling in data analysis.

2. Develop the Right Data Skills

You can still become a data scientist if you lack relevant work experience, but you will need to build the necessary foundation to pursue a career in data science.

Data Science is a high-level career.

Thus, before you specialize to that extent, you should have a solid foundation of expertise in a related area.

This could be in mathematics, engineering, statistics, data analysis, programming, or information technology.

Some data scientists have even come from backgrounds in business and baseball scouting.

Mathematics, engineering, programming, statistics, data analysis, and information technology are related fields for data scientists.

But no matter what area you start in, you should know Python, SQL, and Excel.

These abilities will be necessary for handling and arranging raw data. Additionally, since you’ll use Tableau frequently to build visuals, it doesn’t hurt to be familiar with it.

The more your experience allows you to deal with data, the more it will aid you in the following phase.

So, keep an eye out for possibilities to help you begin thinking like a data scientist.

3. Familiarize Yourself With the Essential Data Science Tools

The data science industry uses a few well-known tools.

An analytics platform called Apache Spark is used for massively scalable data engineering, processing, and machine learning.

With the help of Tableau’s data visualisation tool, users may produce robust and interesting visual representations of their data connected to databases.

Users might utilise the statistical software SAS to organize, analyze, forecast, and visualize data.

75% of data scientists usually use the readability-focused programming language Python.

47% of data scientists utilize R as their statistical programming language of choice.

BigML is a well-known machine learning platform.

4. Get Your First Entry-Level Job as a Data Scientist

Even though there are many ways to become a data scientist, working in a relevant entry-level position is an excellent place to start.

Once you’ve obtained the necessary abilities and/or specialism, you should be prepared for your first data science position!

Making an online portfolio to promote a few of your work and your achievements to prospective employers may be helpful.

Since your first data science job might not include the title “data scientist,” but rather more of an analytical role, you might also want to think about a company with a possibility for progression.

Consider careers as data analysts, business intelligence analysts, statisticians, data engineers, or similar jobs.

You’ll pick up teamwork skills and best practices rapidly, preparing you for more senior positions.

5. Build Your Portfolio

If you lack official schooling or experience, building a sizable, impressive, and high-effort portfolio is a terrific method to demonstrate your talent and dedication.

Since individuality and creativity are highly regarded, no two portfolios must be identical, and you are free to select any data science projects that catch your attention.

The most crucial factor in your job search may be your portfolio.

Think about using GitHub instead of (or in addition to) your own website to showcase your work when applying for a Data Scientist post.

GitHub makes displaying your work, progress, and final products simple while also raising your profile in a public network.

Don’t stop there, though.

Your portfolio is an opportunity for you to showcase your communication abilities and show that you can do more than just add and subtract.

To help the employer see your merit, add an engaging narrative to your facts and highlight the issues you’re trying to tackle.

Don’t submit your entire body of work when looking for a specific position.

Only a handful of your strongest points about the position you’re applying for should be highlighted.

That will most effectively demonstrate your breadth of abilities throughout the entire data science process, from starting with a fundamental data set to defining an issue, performing a cleanup, developing a model, and finally discovering a solution.

Read: The Role of Data Analytics in Decision-Making and Business Strategy

Conclusion

In conclusion, becoming a data scientist is a journey that combines education, skill development, and practical experience.

Start by building a strong foundation in relevant fields like mathematics, programming, or engineering.

Familiarize yourself with essential data science tools, and seek entry-level positions to gain experience.

As you progress, create a portfolio highlighting your skills and showcasing your unique problem-solving approach.

With determination and the right mindset, you can navigate the dynamic field of data science, positioning yourself at the forefront of technological innovation.

Start today, and let your data-driven journey begin.

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