How to Become a Data Scientist

What is a Data Scientist?

Before learning how to become a data scientist, you must know what exactly is a data scientist. A data scientist is a professional who specializes in analyzing and interpreting data. They use their data science skills to help organizations make better decisions and improve their operations. Data scientists typically have a strong background in mathematics, statistics, and computer science. They use this knowledge to analyze large data sets and find trends or patterns. Additionally, data scientists may develop new ways to collect and store data.

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Data Scientist vs Data Analyst

Both data scientists and data analysts work with large amounts of data; however, their roles are separate. Their roles differ in complexity and scope. An analyst primarily interprets existing data, identifies trends, and creates reports to inform business decisions. They use data analysis tools like Excel, SQL, and visualization software to make data accessible. 

On the other hand, a data scientist builds predictive models, using machine learning algorithms and working with big data to extract insights and drive strategy. They require a strong statistics, programming, and advanced analytics background to create data-driven solutions for complex business problems.

Related Read: Detailed Differences Between a Data Analyst and a Data Scientist

Steps to Become a Data Scientist

Data science is the area of study that involves extracting knowledge from all of the data gathered. There is a great demand for professionals who can turn data analysis into a competitive advantage for their organizations. In a career as a data scientist, you’ll create data-driven business solutions and analytics.

Step 1: Earn a Bachelor’s Degree or Data Science Degree

A great way to get started in Data Science is to get a bachelor’s degree in a relevant field such as data science, statistics, or computer science. It is one of the most common criteria companies look at for hiring data scientists. 

Step 2: Improve Your Data Science Skills and Competencies

While a Bachelor’s degree might give you a theoretical understanding of the subject, it is essential to brush up on relevant programming languages such as Python, R, SQL, and SAS. These are essential languages when it comes to working with large datasets.

Step 3: Master Essential Data Science Tools

In addition to different languages, a Data Scientist should also have knowledge of working with a few tools for Data Visualization, Machine Learning, and Big Data. When working with big datasets, it is crucial to know how to handle large datasets and clean, sort, and analyze them.

Step 4: Earn Your Data Science Certification

Tool and skill-specific certifications are a great way to show your knowledge and expertise about your skills.  Here are a few great certifications to help you pave the path:

• Tableau Certification Training Course
• Power BI Certification Course

These two are the most popular tools used by Data Scientist experts and would be a perfect addition to start your career journey.

Step 5: Data Science Internships

Internships are a great way to get your foot in the door to companies hiring data scientists. Seek jobs that include keywords such as  data analyst, business intelligence analyst, statistician, or data engineer. Internships are also a great way to learn hands-on what exactly the job with entail.

Step 6: Kickstart Your Data Science Journey

Once your internship period is over, you can either join in the same company (if they are hiring), or you can start looking for entry-level positions for data scientists, data analysts, data engineers. From there you can gain experience and work up the ladder as you expand your knowledge and skills.

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Data Analyst vs Data Scientist: What’s the Difference?

Now that you know how to become a data scientist, let’s break down the key differences between a data analyst and a data scientist first:

• Primary Focus

A data analyst's job is to analyze data and produce insightful reports that support business decision-making. Their main goal is to comprehend historical performance and patterns. A data scientist goes above and above, employing cutting-edge methods to tackle increasingly challenging issues, frequently including forecasts and future results.

• Skills Required

Data analysts require tools like Excel, SQL, and visualization software in addition to abilities like statistical analysis and data cleaning. They ought to be adept in conveying findings and detail-oriented. However, data scientists also need to be proficient in cloud computing, big data technologies, and machine learning. They also put more of an emphasis on creating experiments and utilizing sophisticated statistical models.

• Typical Tasks

In order to identify trends and aid teams in understanding the data, data analysts often clean up data, produce reports, and design dashboards. On the other hand, data scientists create algorithms, conduct A/B testing, and create predictive models in order to identify trends or forecast future outcomes.

• Example Scenario

A data analyst might review sales data to help the marketing team improve their strategies. A data scientist, however, could develop a recommendation system that suggests products to customers based on their past shopping behavior.

• Educational Background

A bachelor's degree in business analytics, statistics, or arithmetic is typically held by data analysts. A master's or doctorate degree in computer science or data science, with an emphasis on advanced modeling techniques, is typically held by data scientists.

• Decision Making

Data analysts examine current data and offer insights into past events to assist firms in making wise decisions. On the other hand, data scientists utilize data to address more complicated problems and frequently make predictions about what might happen next in addition to offering insights.

• Tools Used

Data analysts rely on tools like Excel, SQL, and Tableau for data analysis and visualization. Data scientists use more advanced tools such as Python, R, and big data technologies like Hadoop, alongside cloud platforms like AWS for data storage and processing. 

What Does a Data Scientist Do? 

Once you are clear on how to become a data scientist, you should also learn about the job role, qualifications, career prospects and more. Data Scientists collect and clean data from various sources, perform exploratory data analysis to identify patterns, and create predictive models using ML and statistical techniques.

Data Scientists also play a crucial role in feature engineering, model evaluation, and deploying models into production. Their work spans industries, aiding businesses in optimizing operations, improving products, and driving data-driven strategies for success. They are instrumental in transforming data into actionable knowledge that drives innovation and competitive advantage.

Role of a Data Scientist

1. Data Cleaning and Preparation: Scrubbing data to ensure its quality and readiness for analysis. This involves handling missing values, detecting outliers, and ensuring data consistency.
2. Data Exploration and Analysis: Using statistical methods to explore the relationships between different variables in datasets, identify patterns, and detect anomalies.
3. Predictive Modeling: Developing models that predict future outcomes based on historical data. This involves selecting the appropriate model, training it with data, and validating its accuracy.
4. Machine Learning and Advanced Analytics: Applying machine learning algorithms to build models that can automate decision-making processes or enhance predictions with increasing accuracy over time.
5. Data Visualization and Reporting: Creating visual representations of data findings and analysis results to make them accessible and understandable to non-technical stakeholders.
6. Cross-functional Collaboration: Working with other departments, such as engineering, product, and business teams, to understand their data needs and deliver insights to drive strategic decisions.
7. Innovative Solution Development: Identifying opportunities to apply data science techniques to new organizational areas, leading to innovative products, services, or operational improvements.
8. Big Data Technologies: Utilizing big data technologies and tools to handle, process, and analyze large datasets that traditional data processing applications cannot manage.
9. Continual Learning: Staying current with the latest technologies, algorithms, and methodologies in data science to continually improve processes and outcomes.
10. Ethical Oversight: Ensuring the ethical collection, handling, and use of data, including considerations for privacy, consent, and bias mitigation.

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Data Science Qualifications and Eligibility Required

The next step in understanding how to become a data scientist is learning about the qualifications.

Data scientists should have a bachelor's degree in computer science, data science or related fields with many employers preferring professional candidates to possess at least a master's degree in data science or similar disciplines.

You will need to have strong analytical and mathematical skills. You should be able to understand and work with complex data sets. Additionally, you should be able to use statistical software packages and be familiar with programming languages such as Python or R. Data scientists also typically have a certification from an accredited program.

Skills to Become A Data Scientist

If you are figuring out how to become a data scientist, the tech and non tech data science skills is something you need to focus on. Becoming a data scientist requires diverse skills that span technical, analytical, and soft skills. Here's a detailed look at the key skills necessary to become a data scientist:

1. Data Visualization

The ability to transform data and findings into understandable and visually appealing formats. Tools like Tableau, Power BI, and libraries in Python (e.g., Matplotlib, Seaborn) are crucial.

2. Machine Learning

Understanding and applying machine learning algorithms, including supervised and unsupervised learning, to predict outcomes and uncover patterns in data.

3. Communication

Translating complex data findings into clear, concise, and actionable insights for technical and non-technical stakeholders.

4. Programming

Proficiency in programming languages, especially Python and R, is essential for data manipulation, statistical analysis, and machine learning.

5. Statistics and Probability

A strong foundation in statistics and probability to analyze data sets, understand distributions and apply statistical tests and models.

6. Business Acumen

Understanding business processes, goals, and strategies to align data projects with organizational objectives.

7. Computing

Familiarity with cloud computing services (like AWS, Google Cloud, Azure) and big data technologies (like Hadoop and Spark) for processing large data sets.

8. Mathematics

Knowledge of linear algebra, calculus, and optimization techniques is foundational to algorithms and machine learning models.

9. Curiosity

A natural curiosity to ask questions, explore data for hidden patterns, and a continuous desire to learn and discover new techniques and methodologies.

10. Data Wrangling

The ability to clean, structure, and enrich raw data into a desired format for analysis. This involves handling missing values, outliers, and merging datasets.

11. Deep Learning

Understanding neural networks and deep learning frameworks (like TensorFlow and PyTorch) for tasks requiring image recognition, natural language processing, etc.

12. Python

Python is a must-have for data science due to its extensive libraries (Pandas, NumPy, Scikit-learn) and versatility in handling data tasks.

13. Critical Thinking

The ability to approach problems logically, question assumptions, and evaluate the strength of arguments or methodologies.

14. Data Science Theory

Understanding the principles and theories underlying data science practices, including machine learning models and statistical methods.

15. Database Management

Knowledge of SQL and NoSQL databases for storing, querying, and managing data efficiently.

16. Model Deployment

Skills in deploying models into production environments, ensuring they are scalable, maintainable, and can provide real-time insights.

17. Business Intelligence

Using BI tools and techniques to analyze data, produce reports, and support decision-making processes.

18. Unstructured Data

Handling unstructured data (text, images, audio) using techniques like natural language processing (NLP) and computer vision.

19. Analytical Mindset

The ability to approach data analytically, identify trends and anomalies, and make data-driven decisions.

20. Data Intuition

Developing an intuition for data involves understanding what looks right or wrong and where to dig deeper.

21. Big Data

Managing and analyzing large volumes of data with big data technologies, understanding the complexities and challenges of big data environments.

22. Analytics and Modeling

Applying various analytical and modeling techniques to understand data, make predictions, and test hypotheses.

23. Data Transformation

Transforming data into a more useful and interpretable form using normalization, scaling, and feature engineering techniques.

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Data Scientist Salary and Job Growth

Data scientists' salary and job growth are highly favorable, reflecting the increasing demand for their skills across various industries. On average, a Data Scientist in the United States earns a salary ranging from $95,000 to $150,000 annually, with top professionals in the field earning even more. Factors influencing salary include experience, location, industry, and education.

Job growth for Data Scientists is robust, with the Bureau of Labor Statistics projecting a 36 percent increase in employment from 2021 to 2031, much faster than the average for all occupations. This growth is driven by the ever-expanding need for data-driven decision-making in businesses, the rise of big data, and advancements in AI and machine learning, making data science one of the most lucrative and secure career paths today.

Careers in the Data Science Field

Once you’ve mastered these skills, you’ll have a range of career opportunities available in data science. 

1. Data Engineer

Average salary: $105K

Data engineers assemble large, complex data sets. They identify, design, and implement internal process improvements and then build the infrastructure for optimal data extraction, transformation, and loading. They also build analytics tools that utilize the data pipeline.

2. Data Architect

Average salary: $138K

Data architects analyze the structural requirements for new software and applications and develop database solutions. They install and configure information systems and migrate data from legacy systems to new ones.

3. Data Analyst

Average salary: $84K

Data analysts acquires data from primary or secondary sources and maintain databases. They interpret that data, analyze results using statistical techniques, and develop data collections systems and other solutions that help management prioritize business and information needs.

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4. Business Analyst

Average salary: $92K

Business analysts assist a company with planning and monitoring by eliciting and organizing requirements. They validate resource requirements and develop cost-estimate models by creating informative, actionable and repeatable reporting.

5. Data Administrator

Average salary: $74K

Data administrators assist in database design and update existing databases. They are responsible for setting up and testing new database and data handling systems, sustaining the security and integrity of databases and creating complex query definitions that allow data to be extracted.

Future of Data Science

The future of data science is promising and expected to be integral to the evolution of technology, business, healthcare, and many other sectors. As data generation grows exponentially, sophisticated data analysis and interpretation demand will only increase.

1. Increased Demand Across Industries: Data science will continue to expand beyond tech and finance into healthcare, agriculture, education, and public services, driving innovation and efficiency across all sectors.
2. Advancements in AI and ML: Artificial intelligence (AI) and machine learning (ML) will become even more sophisticated, enabling more accurate predictions, automation of complex tasks, and the creation of intelligent systems that can learn and adapt over time.
3. Ethics, Privacy, and Data Governance: As data becomes more integral, ethical considerations, privacy concerns, and robust data governance frameworks will become increasingly important. Ensuring the ethical use of data, combating bias in AI, and protecting individual privacy will be central to the field's evolution.
4. Integration of IoT and Big Data: The Internet of Things (IoT) will generate vast amounts of data from connected devices, necessitating advanced data science techniques to analyze and derive value from this data. This integration will drive advancements in real-time data analysis and edge computing.
5. Automation in Data Science: Automation tools will streamline data processing, model development, and analysis, allowing data scientists to focus on more complex and innovative tasks. AutoML (Automated Machine Learning) will become more prevalent, making data science more accessible.
6. Augmented Analytics: Augmented analytics will use AI and ML to enhance data analytics, sharing, and business intelligence. This will make advanced analytics accessible to non-experts, democratizing data insights across organizations.
7. Quantum Computing: Though still in its infancy, quantum computing has the potential to revolutionize data science by processing complex datasets at unprecedented speeds. This could lead to cryptography, drug discovery, and complex system simulation breakthroughs.
8. Focus on Soft Skills: As technical skills become more common and tools become more sophisticated, soft skills like storytelling, critical thinking, and communication will become crucial for data scientists. The ability to translate complex findings into actionable business insights will be invaluable.
9. Data Science as a Service (DSaaS): The growth of cloud computing will lead to more organizations outsourcing data science tasks. DSaaS will become more common, providing businesses of all sizes access to advanced data analysis without needing in-house expertise.
10. Continuous and Interdisciplinary Learning: The field will require continuous learning in new technologies and algorithms and in understanding domain-specific knowledge. Data scientists increasingly need interdisciplinary knowledge to solve complex real-world problems.

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Conclusion

Now you know how to become a data scientist, which involves continuous learning, curiosity, and skill development. As we've explored, the path to entering the field of data science involves acquiring a blend of technical skills like programming, machine learning, and data visualization alongside soft skills like communication and critical thinking. The future of data science promises exciting opportunities and challenges, making it a rewarding career choice for those willing to navigate its complexities.

For those ready to embark on this journey, the Post Graduate Program in Data Science with Purdue University offers a detailed curriculum to equip you with the necessary knowledge and hands-on experience to thrive in the field. Whether you're starting from scratch or looking to deepen your expertise, this program covers everything from the fundamentals of data science to the most in-demand generative AI skills and tools. Enrol today!

FAQs

1. Why is data science considered an important career?

Data science is pivotal in turning vast data into actionable insights, driving industry decision-making and innovation. Its ability to predict trends, enhance efficiency, and unveil new opportunities makes it a crucial career in the digital age.

2. Can anyone become a data scientist, or do I need a specific background?

While a background in mathematics, statistics, or computer science is beneficial, anyone committed to learning and developing the necessary skills can become a data scientist. Diverse backgrounds bring unique perspectives to data analysis.

3. Are there any online courses available for aspiring data scientists?

Yes, there are numerous online courses designed for aspiring data scientists, ranging from introductory to advanced levels. These courses cover statistics, programming, machine learning, and more, often providing hands-on projects and certification.

4. What are the key tools and software I should learn for data science?

Key tools and software include programming languages (Python, R), data visualization tools (Tableau, Power BI), machine learning libraries (scikit-learn, TensorFlow), and data manipulation libraries (Pandas, NumPy).

5. What industries hire data scientists?

Data scientists are in demand across many industries, including technology, finance, healthcare, retail, e-commerce, and government. Any sector that generates data seeks data scientists to analyze and derive insights from it.

6. How to become a data scientist with no experience?

To become a Data Scientist with no experience, start by learning the basics of programming (like Python or R) and statistics. Take online courses in data science, practice with real-world datasets, and build projects to showcase your skills. Gaining proficiency in data analysis tools like SQL and visualization software is also key. Finally, consider internships, joining data science communities, and participating in competitions like Kaggle to gain practical experience and build a strong portfolio.

7. How long does it take to become a data scientist?

Becoming a Data Scientist typically 6 months to 2 years, depending on your starting point and dedication. If you're starting from scratch, you'll need time to learn programming, statistics, and machine learning. Building a strong portfolio and continuously learning new skills are key factors influencing how quickly you can enter the field.