How to become a data scientist with online degree courses in 2025
Silki Joshi30 May, 2025
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Becoming a data scientist through online degree courses involves selecting a reputable program that covers essential skills such as programming, statistics, and data analysis. Focus on practical experience by working on real-world projects and building a portfolio to showcase your abilities. Engage with online communities and seek mentorship to expand your network and gain industry insights. Stay current with evolving technologies and methodologies in data science to enhance your expertise and increase your employability in this rapidly growing field.
What is a Data Scientist?
A data scientist is a professional who utilises statistical analysis, programming, and machine learning techniques to extract meaningful insights from complex data sets. To become a data scientist, one typically needs a strong educational background in mathematics, statistics, or computer science, along with proficiency in data manipulation tools and programming languages like Python or R. Data scientists analyse data to identify trends, develop predictive models, and inform strategic decision-making, ultimately driving innovation and efficiency within organisations across various industries.PW Data Science Course Overview
The Data Science with Generative AI course by PW (Physics Wallah) is structured to deliver practical and theoretical learning in key areas of data science. It covers essential concepts such as Python programming, statistics, machine learning, deep learning, NLP (Natural Language Processing), and generative AI. The curriculum also includes optional tools like Power BI for data visualization and special modules on soft skills and aptitude for those enrolled in the Pro Plan.
The course follows a blended learning format with live sessions conducted on weekends and recorded content accessible at any time. Learners are expected to complete assignments, projects, and a capstone project, which collectively contribute to building a professional portfolio. Upon successful completion, a certification is provided.
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PW Data Science Course Overview |
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Parameter |
Details |
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Course Name |
Data Science with Generative AI |
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Provider |
PW (Physics Wallah) |
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Commencement Date |
30th May 2025 |
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Duration |
6 Months |
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Mode of Delivery |
Live (weekend sessions) + Recorded |
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Language |
English |
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Fee |
₹4,999 (starting) |
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Certification |
Yes, upon course completion |
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Support Offered |
Email support, peer networking, SME sessions |
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Career Services |
Resume building, interview prep, career guidance |
Data Science Course Fees at PW
The Data Science with Generative AI course by PW is priced with accessibility in mind, starting at ₹4,999. This fee includes access to both live weekend sessions and recorded content, along with assignments, capstone projects, peer networking opportunities, and certification upon completion. While the base plan provides essential features, additional services such as soft skills and aptitude training are available under the Pro Plan, offering more comprehensive support for learners seeking career advancement.
How to Enroll in the PW Data Science Course?
Enrolling in the PW Data Science with Generative AI course is a straightforward process designed to get you started quickly. Follow these simple steps to register, gain access to course materials, and begin your learning journey in data science with practical, industry-relevant skills.
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Open any browser of your choice on a device with an active internet connection.
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Then, in the search bar of the browser, look for the official website of PW.
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Or students can also enter the ‘Data Science Course by PW’ in the search bar.
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From the provided SERPs, click on the official result.
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Go through all the details related to the course structure and fee.
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Hit the Buy Now button to get yourself successfully enrolled by completing the process.
How to Become a Data Scientist?
The following are the major steps that one needs to follow to become a data scientist.- Begin your journey by obtaining a bachelor’s degree in a relevant field such as mathematics, statistics, computer science, or data science. A formal education provides foundational knowledge in analytical thinking, programming, and statistical methods. Many universities now offer specialised data science programs that integrate these disciplines, equipping you with the theoretical and practical skills needed for the field.
- Enhance your data science skills by focusing on key areas such as statistics, data analysis, machine learning, and programming. Engage in online courses, workshops, and coding bootcamps to deepen your understanding. Additionally, work on soft skills like problem-solving, critical thinking, and communication, as these are essential for effectively conveying insights and collaborating with cross-functional teams in a professional environment.
- Familiarise yourself with essential data science tools and technologies, including programming languages like Python and R, data manipulation libraries (Pandas, NumPy), and visualisation tools (Matplotlib, Seaborn). Learn to use SQL for database management and querying. Understanding machine learning frameworks such as TensorFlow and scikit-learn is also crucial, as they enable you to build and deploy predictive models effectively.
- Consider obtaining a data science certification to validate your skills and knowledge. Certifications from reputable organisations, such as Coursera, edX, or DataCamp, can enhance your resume and demonstrate your commitment to the field. Look for programs that cover essential topics, including machine learning, data analysis, and big data technologies, providing you with practical experience and recognised credentials.
- Gain practical experience through internships in data science or related fields. Internships provide hands-on opportunities to apply your skills in real-world settings, allowing you to work on actual data projects and collaborate with experienced professionals. This experience not only enhances your resume but also helps you build a network within the industry, which can be invaluable for future job opportunities.
- Start your career by applying for entry-level data science positions or related roles such as data analyst or business analyst. Showcase your skills through a strong portfolio of projects, including internships and personal projects. Networking through industry events and online platforms like LinkedIn can also help you connect with potential employers. Continuous learning and adapting to new technologies will further enhance your career prospects in this dynamic field.
Data Analyst vs Data Scientist: What's the Difference?
Candidates can get an idea of the major differences between data analyst and data analyst from the section below.| Data Analyst vs Data Scientist | ||
| Feature | Data Analyst | Data Scientist |
| Core Focus | Uncovering insights from existing data to inform decision-making. | Developing advanced models and algorithms to predict future trends and uncover hidden patterns. |
| Data Handling | Primarily works with structured data. | Often deals with both structured and unstructured data, including text, images, and audio. |
| Tools and Techniques | Uses tools like SQL, Excel, and BI tools for data cleaning, analysis, and visualisation. | Employs programming languages (Python, R), statistical modeling, and machine learning techniques for complex analysis. |
| Skillset | Strong in statistical analysis, data visualisation, and business acumen. | Requires a solid foundation in statistics, programming, machine learning, and domain expertise. |
| Career Progression | Can advance to roles like Senior Data Analyst, Data Team Lead, or Business Analyst. | May progress to roles like Machine Learning Engineer, Data Architect, or Chief Data Officer. |
What Does a Data Scientist Do?
A data scientist analyses complex data sets to extract meaningful insights and inform decision-making processes. They employ statistical methods, machine learning algorithms, and data visualisation techniques to interpret data and solve business problems. By collaborating with cross-functional teams, data scientists drive innovation and strategy.1. Data Collection and Preprocessing
Data scientists are responsible for gathering data from various sources, including databases, APIs, and web scraping. They clean and preprocess this data to ensure accuracy and consistency, handling missing values and outliers. This foundational step is crucial, as high-quality data directly impacts the reliability of analyses and models, ultimately influencing business decisions and strategies.2. Data Analysis and Interpretation
Data scientists analyse complex datasets to uncover trends, patterns, and insights. They apply statistical methods and exploratory data analysis techniques to interpret data effectively. By transforming raw data into actionable insights, they help organisations understand customer behaviour, market dynamics, and operational efficiencies. This analytical process is essential for informed decision-making and strategic planning across various business domains.3. Model Development and Machine Learning
A key role of data scientists involves developing predictive models using machine learning algorithms. They select appropriate models based on the problem, train them on historical data, and evaluate their performance using metrics like accuracy and precision. By leveraging these models, organisations can forecast outcomes, optimise processes, and automate decision-making, driving innovation and efficiency in operations.4. Data Visualisation and Communication
Data scientists create visualisations to present complex findings clearly. They use tools like Tableau, Matplotlib, or Power BI to design dashboards and reports that highlight key insights. Effective communication of data-driven results is crucial, as it enables stakeholders to grasp findings quickly and make informed decisions, bridging the gap between technical and non-technical audiences.5. Collaboration with Cross-Functional Teams
Data scientists often work closely with cross-functional teams, including business analysts, product managers, and engineers. This collaboration ensures that analytical solutions align with organisational goals and user needs. By understanding different perspectives, data scientists can tailor their analyses and models to address specific challenges, fostering a data-driven culture and enhancing the overall effectiveness of projects within the organisation.How to become a data scientist with online degree courses FAQs
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