How to Become a Data Scientist Ultimate Guide

What does it mean to be a data scientist?

Data scientists are big data wranglers who gather and analyze large amounts of unstructured and structured data. Data scientists combine computer science, mathematics, and statistics. They process, analyze, and model data, then interpret the results to develop actionable plans for companies or other organizations.

Data scientists are experts in technology and social sciences who use their knowledge to identify trends and manage data. To solve business problems, they use their industry knowledge, context understanding, and skepticism about existing assumptions.

Data scientists are responsible for making sense of unstructured, messy data from various sources, including smart devices, social media feeds and emails that don’t fit neatly into a database.

However, technical skills aren’t the only thing that matters. Data scientists are often found in business environments and are responsible for communicating complex ideas and making data-driven decisions. It is important that data scientists are effective communicators, leaders, and team members, as well as analytical thinkers.

Data scientists and managers with experience are responsible for developing best practices in data management. This includes cleaning, processing, and storing data. They collaborate cross-functionally with other departments within their company, including marketing, customer success and operations. Their salaries and job growth reflect this.

Steps to become a data scientist

These are six steps you should consider if your goal is to pursue a career as a data scientist.

  1. Study for a bachelor’s degree in data science, or another closely related field.
  2. To become a data scientist, you will need to learn the skills required.
  3. Consider a specialization
  4. Get your first entry-level data scientist job
  5. Additional data scientist certifications are available.
  6. A master’s degree is available in data science.

How to Become Data Scientist in 2021

1. Study for a bachelor’s degree in data science, or another closely related field.

To get a job as an entry-level data scientist, you will need a bachelor’s degree in either data science/computer-related fields. However, most data science jobs will require a master’s degree. You can also include structure, internships and networking, as well as academic qualifications to your resume. If you have a bachelor’s in another field, you might need to concentrate on acquiring the skills required for the job via online courses or bootcamps.

2. To become a data scientist, you will need to learn the skills required.

  • Programming
  • Machine Learning techniques
  • Data Visualization and Reporting
  • Risk Analysis
  • Math and statistical analysis
  • Effective communication
  • Software Engineering Skills
  • Data Mining, Cleaning, and Munging
  • Research
  • Big Data Platforms
  • Cloud Tools
  • Data warehouse and data structures

3. Consider a specialization

Data scientists can choose to specialize in one industry or have strong skills in other areas, such as machine learning, artificial intelligence, research, and database management. You can increase your earning potential by specializing and doing meaningful work.

4. As a data scientist, this is your first entry-level job

After you have the skills and/or specialization you need, you can start your data science career. You may want to make an online portfolio that showcases your achievements and highlights to potential employers. Consider a company that has room to grow since your first job in data science may not be a data scientist but more an analytical one. It will take you a while to learn how to work in a team, and the best practices that will help you prepare for higher-ranking positions.

5. Examine additional data scientist certifications, and post-graduate learning

These certifications are focused on practical skills.

Certified Analytics Professionals (CAP)

The Institute for Operations Research and the Management Sciences created CAP and was designed for data scientists. Candidates must demonstrate their proficiency in the entire process of end-to-end analysis during the certification exam. This includes the analysis of business and analytical problems, data and methodology, modeling, deployment, and life cycle management.

SAS Certified Predictive Modeler using SAS Enterprise Miner 14

This certification is for SAS Enterprise Miner users performing predictive analytics. Candidates should have a solid, practical understanding about the predictive modeling capabilities in SAS Enterprise Miner 14.

6. A master’s degree is available in data science.

You might not realize how important academic qualifications can be. Is a master’s necessary for most data science jobs? It all depends on the job. Some data scientists are graduates of a bootcamp or have a bachelor’s degree. Burtch Works data shows that over 90% of data scientists have a graduate degree.

Data Scientist Responsibilities

Data scientists may have the following responsibilities on any given day:

  • Undirected research is a way to solve business problems and open-ended questions can be used to frame industry issues.
  • Massive amounts of unstructured and structured data can be extracted. They use programming languages like SQL to query structured data in relational databases. They also use APIs and surveys to gather unstructured data.
  • To prepare data for predictive and prescriptive modeling, use sophisticated analytical methods, machine-learning and statistical methods
  • Cleanse data thoroughly to eliminate irrelevant information.
  • To find trends and/or opportunities, perform exploratory data analysis (EDA).
  • Finding new algorithms to solve problems, and building programs that automate repetitive tasks
  • Effective data visualizations and reports enable you to communicate your findings and predictions to IT and management.
  • Recommend cost-effective modifications to existing strategies and procedures

Each company has a different approach to data science jobs. Some companies treat their data scientists as data analysts while others combine their tasks with . Others need high-level analytics specialists who are skilled in intensive machine learning and data visualizations.

Data scientists’ responsibilities change as they gain more experience or move to new jobs. A person working in a small company might spend most of their day data cleaning and munging. High-ranking employees in businesses that offer data-based services might be asked to create new products or structure big data projects.

What Characteristics Make a Data Scientist Professional Successful?

Data scientists need to understand programming languages and how to manage databases. They also need to be able to visualize data. Data scientists might have personality traits similar to quality assurance departments. They may be meticulous when reviewing large quantities of data and looking for patterns and answers. Data scientists are creative and can create new algorithms to crawl data, or organize databases warehouses.

Data science professionals must be able to communicate with their clients, team members, and stakeholders in multiple ways. Data scientists must have the ability to persevere and be resilient despite all the bumpy roads and dead ends.

“Data scientists who are successful have strong technical backgrounds, but they also have great intuition about data. Do the features have meaning? Are they consistent with what you believe they should be? Which model should you use, given the distribution of your data? What is it and how should it be used? Data scientists who are the best at communicating with non-technical people and other data scientists are among the most successful. Our analyses must be technically sound and communicate clearly and easily to other employees of Airbnb to make them effective.

-Lisa Qian, Data Scientist at Airbnb

Skills required for a Data Scientist

Programming Python SQL, Scala Java, R, MATLAB

Machine Learning: Natural Language Processing, Classification, Clustering,

Ensemble methods, Deep Learning

Data Visualization: Tableau, SAS, D3.js, Python, Java, R libraries

Big data platforms: MongoDB, Oracle, Microsoft Azure, Cloudera

Data Science Job Outlook

According to the Bureau of Labor and Statistics, growth in computer information and research scientist from 2020 to 2030 was 22%. There is a lot of demand for data scientists, but you must start somewhere. Data scientists can start their career as entry-level analysts by extracting structured data from CRM systems or MySQL databases. They also develop basic visualizations in Tableau and analyze A/B test results. You can think of what you can do in data science if you want to go beyond your current analytical role.

  • Data/Big Data Engineer
  • Data/Big Data Architect

There are many companies of all sizes and industries looking for data scientists to help them deal with big data. Certain companies may have “new-look” data scientists who are responsible for financial planning and ROI assessments, budgets, and other tasks related to managing an organization.

Data Scientist Salary

The salary of a data scientist depends on years of experience, skill set, education, and geographic location. Employers place more value on data scientists who have specialized skills such as Artificial Intelligence or Natural Language Processing, according to The Burtchworks Study. According to the BLS, computer researchers and information scientists (which includes data scientists) have excellent job prospects due to high demand. Below is the 2019 salary data taken from the Bureau of Labor Statistics.

Data Scientist

Average Salary for Data Scientists: $126 830 per Year

Minimum 10%: $72,210

Highest 10%: $194,430

Senior Data Scientist

Median Sr. Data Scientist Salary: $194,430

Total Pay Range: $190,000.- $200,000

Take a look at our bootcamp guides:

  • Data Science Bootcamp Guide
  • Data Analytics Bootcamp guide
  • Coding Bootcamp guide

Data Scientist Career FAQ:

How can I become a data scientist

A bachelor’s degree is the most common way to become a data scientist. However, there are many other methods to learn data science skills like a bootcamp or through military service. Before you get your first job as a data scientist, you might consider getting a master’s or specialization in data science.

What are the skills required to become a data scientist

There are many skills data scientists need depending on their industry and job responsibilities. Data scientists are proficient in programming languages like R and Python. They also have a good understanding of data cleaning techniques, statistical analysis, machine learning techniques and data warehouses.

What is the average time it takes to become a data scientist

It depends on what your career goals are and how much money you want to invest in your education. There are four-year bachelor’s degrees in data science, as well three-month bootcamps. A master’s degree can be earned by those who have already completed a bachelor’s or bootcamp. It takes as little as one to complete. According to the Burtch Works study, almost all data scientists have an advanced degree.

Data Science Bootcamps are a great way to learn more and get a job in data science.

Tech bootcamps offer a fast way to get experience in data science and learn programming languages like Python, R, SQL. Bootcamps in data science are usually short programs that can be done online, part-time, or on campus. Some bootcamps can take only a few weeks, while others could take several months. Bootcamps can help you build your network and offer career services that will help with job placements after graduating.

The bootcamp will allow you to work on projects and build a portfolio that can be used to show potential employers your skills. Most data science bootcamps cover topics like machine learning, natural language processing , different kinds of data analytics and data visualization.

It is important to think about your career goals when researching bootcamps. Also, consider what you want to achieve from the program. Some bootcamps are designed for beginners while others are more suited to those who have some programming or computer science knowledge. It is also worth considering the backgrounds of the instructors and the cost. Is it possible to commit to a full-time immersion experience and take time off? Are there any discounts or scholarships offered by the bootcamp? Ask about your financing options.


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