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  • How to become a data scientist? - Books, Videos, Tutorials, Resources?

    Ankita Katdare

    Administrator

    Updated: Feb 25, 2025
    Views: 1.3K
    Inspired from a query that the CrazyEngineers Fans Facebook page received this week, I thought that a much important topic that hasn't been yet discussed on CE is the job and role of a DATA SCIENTIST. How can one become a data scientist? What are the expectations from a data scientist? What are the resources or courses one should go through to become a good data scientist? Hence, this thread.

    Intrigued by the subject of 'data science', I began reading about it and so far, here are my findings -

    What is Data Science? What is the job of a Data Scientist?

    Data science is a broad term that encompasses any technology/technique/theory involving extraction of knowledge from data. Not limited to computer science specialists, data science is a field that brings IT, mathematics, machine learning, pattern recognition, statistics, programming etc. all under one roof. The job of a data scientist if to investigate any given complex problem on the basis of his expertise in any one of the above fields. That said, we can easily say that a Data Scientist can not work alone. He/She has to work in a team of engineers, mathematicians, programmers etc.

    In a very crude form we could say that - the role of a Data Scientist involves making sense of a large amount of data, without caring for the limitations of either hardware or software, and build visualizations/models and communicate his/her finding in such a way that it is understandable to others.

    Online Courses to learn concepts in Data Science -
    [In no particular order of importance]

    1. John Hopkins University - 'Data Science' on Coursera
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    2. Data Science Certificate from Harvard Extension School
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    3. Introduction to Data Science - Udacity
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    4. Online Data Science course at Berkeley
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    ^ All these are broad certification courses available over the internet.

    Now to move on to the topics that one has to learn (read: to master) in order to build a career as a Data Scientist.
    • Python
    • Hadoop
    • Machine learning
    • Data analysis
    • Data mining
    • SQL
    • Statistics
    <Post Developing.. I will keep adding videos, tutorials & other resources>
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  • Ankita Katdare

    AdministratorFeb 11, 2015

    Series of videos you can watch on YouTube. A collection of must-watch videos for anyone starting to look into a career as a data scientist -

    What is a Data Scientist? The question answered by


    The following video answers various questions such as -
    • How is Hadoop being used today?
    • What are the top lessons learned from those that are deep with Hadoop?
    • What are they accomplishing?
    • What are their plans for the future?

    DJ Patil, VP of Product at RelateIQ in Crunchbase, in one of the most well known Data Scientists in the world. He gives us a masterclass in which explains how to read dashboards, how to take decisions after figuring out what they data says, the 'must-haves' of a Data Scientist...


    The following Data Science course is designed to provide knowledge and skills to become a successful Data Scientist. The course covers a range of Hadoop, R and Machine Learning Techniques encompassing the complete Data Science study.


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  • Ankita Katdare

    AdministratorFeb 20, 2015

    Here are some fantastic tutorials about data mining worth checking out -

    Learn how to explore, analyze, and leverage data sets of any scale in this 60-minute webinar with Google's Search Scientist and Stanford Instructor Rajan Patel.


    An introductory course in machine learning (ML) from Caltech, that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications.

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  • shakyapreeti

    MemberFeb 25, 2025

    Becoming a data scientist requires a combination of technical skills, problem-solving abilities, and domain expertise. Here’s a structured approach to get started:

    1. Learn the Fundamentals Start with core subjects like statistics, probability, and linear algebra. Books like The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman provide a solid foundation.

    2. Master Programming

    Python and R are essential for data science. Books like Python for Data Analysis by Wes McKinney and online platforms like Kaggle offer hands-on coding experience.

    1. Explore Machine Learning & AI

    Understand supervised and unsupervised learning, neural networks, and deep learning. Courses from Andrew Ng on Coursera and books like Hands-On Machine Learning by Aurélien Géron are valuable resources.

    1. Work on Real-World Projects

    Practical experience is key. Participate in hackathons, contribute to open-source projects, or enroll in data science programs. Platforms like Uncodemy provide structured courses and real-world case studies to strengthen your skills.

    1. Follow Video Tutorials & Online Courses

    YouTube channels like Data School and freeCodeCamp offer excellent tutorials. Websites like Udacity, EdX, and Uncodemy provide in-depth, project-based learning.

    1. Practice with Datasets

    Use datasets from Kaggle, UCI Machine Learning Repository, and Google Dataset Search to develop practical skills.

    1. Stay Updated

    Follow industry trends through blogs, podcasts, and research papers. Websites like Towards Data Science and Analytics Vidhya are great for staying informed.

    By combining theoretical knowledge with hands-on practice, and leveraging structured training from platforms like Uncodemy, you can successfully transition into a career as a data scientist.

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