5 answers
Anna
Student
2
Questions
Asked
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How can i become a data scientist in 3 months?
I started learning on youtube
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5 answers
Vera Vetter
Product Manager
17
Answers
Mountain View, California
Updated
Vera’s Answer
Hi Anna, frankly speaking, I think the timeline largely depends on your background and prior knowledge. In other words, transitioning to Data Science after completing a PhD in Math, for example, is not the same as starting completely from scratch. As the first step, you might want to identify the gaps and goals both in terms of actual knowledge as well as credentials you might need to land a job (assuming that's the goal). After that, you might want to select a program/curriculum that will be best suited for your goals.
Elliot Jordan
Web Developer
75
Answers
Indianapolis, Indiana
Updated
Elliot’s Answer
What's your hurry Anna? I'd say take your time and enjoy the journey! Don't rush it and impose arbitrary timelines - work smart and hard and learn well. It will take as long as it takes. 3/6/9/12 months. A year. Two years. It really takes a while to get up to speed with all the things you'll need to learn. I don't believe in Boot Camp mentality. Yes, you can learn quickly but it still takes time for it all to sink in. Be reasonable, get rest, take breaks and don't be too stressed about jumping out of the Data Science gate so fast. Start where you are. Do what you can. Enjoy learning Data Science!
Kimberly Vides
Junior Cybersecurity Analyst
11
Answers
Gainesville, Florida
Updated
Kimberly’s Answer
Hello Anna!
It's fantastic that you're eager to dive into data science! While becoming a fully-fledged data scientist in three months is an ambitious goal, you can certainly make significant progress with a focused plan. Here are some steps and resources to help you on your journey:
Set Clear Learning Goals
- Understand the Basics: Familiarize yourself with the fundamental concepts of data science, including statistics, machine learning, data visualization, and programming.
- Define Your Focus: Determine what aspect of data science interests you the most (e.g., predictive modeling, data analysis, machine learning).
Foundational Knowledge and Tools
Before diving into data science, it's crucial to have a solid understanding of some foundational topics. These will help you grasp more advanced concepts more easily.
- Statistical Foundations Having a solid understanding of statistics is crucial. If you haven't already, consider taking a dedicated course on statistics. Resources: Khan Academy (Statistics and Probability - Free), Coursera (Statistics with R Specialization - Paid).
- Excel and SQL: Knowledge of Excel and SQL is beneficial for data manipulation and analysis. Excel: Learn to use Excel for data analysis, including functions, pivot tables, and data visualization. Resources: LinkedIn Learning (Excel Essential Training - Free with LinkedIn Premium), Udemy (Excel for Data Analysis - Paid), Codecademy (Learn SQL - Free and Paid options), Khan Academy (Intro to SQL - Free), DataCamp (Introduction to SQL - Paid).
- Power BI: Learning a data visualization tool like Power BI can also be advantageous. Resources: Microsoft Learn (Power BI Guided Learning - Free), Coursera (Getting Started with Power BI - Paid).
Create a Learning Plan
Once you have the foundational knowledge, you can focus on a structured learning plan for data science. Here are some recommended courses and how to incorporate them into your study schedule:
- BM Data Science Professional Certificate on Coursera: This comprehensive program covers data science basics, Python, data analysis, machine learning, and more. It’s a PAID course with a certificate upon completion.
- Cisco Networking Academy's Introduction to Data Science: This FREE course offers a good starting point, focusing on data science basics and Python.
Practical Application
- Practice What You Learn: Apply what you’ve learned through hands-on projects and labs. Kaggle and DrivenData are excellent platforms for this.
- Kaggle: Participate in competitions and work on datasets to build your portfolio.
- DrivenData: Engage in data science competitions focused on social impact.
While becoming a data scientist in three months is a challenging goal, especially without prior experience, these steps will set a solid foundation for your learning journey. Remember that continuous learning and practice are key in the field of data science.
Good luck!
It's fantastic that you're eager to dive into data science! While becoming a fully-fledged data scientist in three months is an ambitious goal, you can certainly make significant progress with a focused plan. Here are some steps and resources to help you on your journey:
Set Clear Learning Goals
- Understand the Basics: Familiarize yourself with the fundamental concepts of data science, including statistics, machine learning, data visualization, and programming.
- Define Your Focus: Determine what aspect of data science interests you the most (e.g., predictive modeling, data analysis, machine learning).
Foundational Knowledge and Tools
Before diving into data science, it's crucial to have a solid understanding of some foundational topics. These will help you grasp more advanced concepts more easily.
- Statistical Foundations Having a solid understanding of statistics is crucial. If you haven't already, consider taking a dedicated course on statistics. Resources: Khan Academy (Statistics and Probability - Free), Coursera (Statistics with R Specialization - Paid).
- Excel and SQL: Knowledge of Excel and SQL is beneficial for data manipulation and analysis. Excel: Learn to use Excel for data analysis, including functions, pivot tables, and data visualization. Resources: LinkedIn Learning (Excel Essential Training - Free with LinkedIn Premium), Udemy (Excel for Data Analysis - Paid), Codecademy (Learn SQL - Free and Paid options), Khan Academy (Intro to SQL - Free), DataCamp (Introduction to SQL - Paid).
- Power BI: Learning a data visualization tool like Power BI can also be advantageous. Resources: Microsoft Learn (Power BI Guided Learning - Free), Coursera (Getting Started with Power BI - Paid).
Create a Learning Plan
Once you have the foundational knowledge, you can focus on a structured learning plan for data science. Here are some recommended courses and how to incorporate them into your study schedule:
- BM Data Science Professional Certificate on Coursera: This comprehensive program covers data science basics, Python, data analysis, machine learning, and more. It’s a PAID course with a certificate upon completion.
- Cisco Networking Academy's Introduction to Data Science: This FREE course offers a good starting point, focusing on data science basics and Python.
Practical Application
- Practice What You Learn: Apply what you’ve learned through hands-on projects and labs. Kaggle and DrivenData are excellent platforms for this.
- Kaggle: Participate in competitions and work on datasets to build your portfolio.
- DrivenData: Engage in data science competitions focused on social impact.
While becoming a data scientist in three months is a challenging goal, especially without prior experience, these steps will set a solid foundation for your learning journey. Remember that continuous learning and practice are key in the field of data science.
Good luck!
Sahida Khatun
Software Developer
68
Answers
Kolkata, West Bengal, India
Updated
Sahida’s Answer
Becoming a data scientist in 3 months is an ambitious goal, but it's possible to make significant progress with a focused and structured approach. Here are some steps you can take,
Steps to Becoming a Data Scientist in 3 Months
Set Clear Goals:
Define what you want to achieve in 3 months (e.g., mastering basic data science concepts, building a portfolio of projects, learning specific tools).
Break down your goals into weekly and daily tasks.
Structured Learning Path:
Weeks 1-2: Focus on Python programming.
Learn basic syntax, data structures (lists, dictionaries, sets), and libraries like NumPy and Pandas.
Weeks 3-4: Study statistics and probability.
Understand descriptive statistics, probability distributions, hypothesis testing, and basic statistical methods.
Weeks 5-6: Dive into data visualization and exploratory data analysis (EDA).
Learn to use Matplotlib, Seaborn, and other visualization tools to analyze and present data.
Weeks 7-8: Get comfortable with machine learning algorithms.
Study supervised and unsupervised learning, including linear regression, logistic regression, decision trees, and clustering algorithms.
Weeks 9-10: Work on real-world projects.
Apply your knowledge to solve practical problems, participate in Kaggle competitions, and build a portfolio.
Weeks 11-12: Learn about data science tools and frameworks.
Get familiar with Jupyter notebooks, Scikit-Learn, TensorFlow, or PyTorch.
Resources:
Courses: Consider enrolling in structured courses on platforms like Coursera, edX, Udacity, or DataCamp for a more comprehensive curriculum.
Books: Supplement your learning with books like "Python for Data Analysis" by Wes McKinney and "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron.
Online Communities: Join data science forums and communities (e.g., Reddit, Stack Overflow, LinkedIn groups) to ask questions, share knowledge, and network with others.
Practice and Projects:
Datasets: Use open datasets from sources like Kaggle, UCI Machine Learning Repository, or government data portals to practice.
Projects: Build a few projects that demonstrate your skills. Examples include predictive modeling, data visualization dashboards, and natural language processing applications.
Mentorship and Feedback:
Mentor: Find a mentor who can guide you, provide feedback on your projects, and help you navigate the field.
Peer Reviews: Participate in study groups or online forums where you can review others' work and get feedback on yours.
Constructive Criticism of Starting with YouTube
Lack of Structure: While YouTube offers a wealth of information, it can be overwhelming and lacks the structure of a formal course. You may miss out on critical foundational knowledge or advanced topics.
Quality Variance: The quality of content varies significantly. Some videos may be outdated or contain inaccuracies.
Depth of Knowledge: YouTube tutorials often provide a surface-level understanding. To become proficient, you need a deeper understanding that comes from more rigorous and comprehensive resources.
Practical Application: Watching videos alone won't make you a data scientist. You need hands-on practice, real-world projects, and continuous learning.
Recommendations for Improvement
Complement YouTube with Other Resources: Use YouTube as a supplementary resource rather than your primary source of learning. Combine it with structured courses, books, and hands-on practice.
Follow a Curriculum: Choose a well-defined curriculum from a reputable online course provider. Many courses offer a progression of topics that build on each other.
Focus on Projects: Apply what you learn immediately through projects. This will help solidify your understanding and give you practical experience.
Seek Feedback: Join online communities and seek feedback on your projects. Learning from others' experiences and mistakes can accelerate your growth.
Sample Weekly Schedule
Here's a sample weekly schedule to give you an idea of how to structure your learning:
Monday-Friday:
2 hours of structured coursework (e.g., online course or book)
1 hour of hands-on practice (coding exercises, small projects)
30 minutes of video tutorials on specific topics (YouTube)
Saturday-Sunday:
3-4 hours working on a larger project
1-2 hours of review and reinforcement (e.g., revisiting challenging topics, taking quizzes)
By following a structured plan, supplementing your YouTube learning with other resources, and focusing on practical application, you can make significant progress toward becoming a data scientist in 3 months.
Steps to Becoming a Data Scientist in 3 Months
Set Clear Goals:
Define what you want to achieve in 3 months (e.g., mastering basic data science concepts, building a portfolio of projects, learning specific tools).
Break down your goals into weekly and daily tasks.
Structured Learning Path:
Weeks 1-2: Focus on Python programming.
Learn basic syntax, data structures (lists, dictionaries, sets), and libraries like NumPy and Pandas.
Weeks 3-4: Study statistics and probability.
Understand descriptive statistics, probability distributions, hypothesis testing, and basic statistical methods.
Weeks 5-6: Dive into data visualization and exploratory data analysis (EDA).
Learn to use Matplotlib, Seaborn, and other visualization tools to analyze and present data.
Weeks 7-8: Get comfortable with machine learning algorithms.
Study supervised and unsupervised learning, including linear regression, logistic regression, decision trees, and clustering algorithms.
Weeks 9-10: Work on real-world projects.
Apply your knowledge to solve practical problems, participate in Kaggle competitions, and build a portfolio.
Weeks 11-12: Learn about data science tools and frameworks.
Get familiar with Jupyter notebooks, Scikit-Learn, TensorFlow, or PyTorch.
Resources:
Courses: Consider enrolling in structured courses on platforms like Coursera, edX, Udacity, or DataCamp for a more comprehensive curriculum.
Books: Supplement your learning with books like "Python for Data Analysis" by Wes McKinney and "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron.
Online Communities: Join data science forums and communities (e.g., Reddit, Stack Overflow, LinkedIn groups) to ask questions, share knowledge, and network with others.
Practice and Projects:
Datasets: Use open datasets from sources like Kaggle, UCI Machine Learning Repository, or government data portals to practice.
Projects: Build a few projects that demonstrate your skills. Examples include predictive modeling, data visualization dashboards, and natural language processing applications.
Mentorship and Feedback:
Mentor: Find a mentor who can guide you, provide feedback on your projects, and help you navigate the field.
Peer Reviews: Participate in study groups or online forums where you can review others' work and get feedback on yours.
Constructive Criticism of Starting with YouTube
Lack of Structure: While YouTube offers a wealth of information, it can be overwhelming and lacks the structure of a formal course. You may miss out on critical foundational knowledge or advanced topics.
Quality Variance: The quality of content varies significantly. Some videos may be outdated or contain inaccuracies.
Depth of Knowledge: YouTube tutorials often provide a surface-level understanding. To become proficient, you need a deeper understanding that comes from more rigorous and comprehensive resources.
Practical Application: Watching videos alone won't make you a data scientist. You need hands-on practice, real-world projects, and continuous learning.
Recommendations for Improvement
Complement YouTube with Other Resources: Use YouTube as a supplementary resource rather than your primary source of learning. Combine it with structured courses, books, and hands-on practice.
Follow a Curriculum: Choose a well-defined curriculum from a reputable online course provider. Many courses offer a progression of topics that build on each other.
Focus on Projects: Apply what you learn immediately through projects. This will help solidify your understanding and give you practical experience.
Seek Feedback: Join online communities and seek feedback on your projects. Learning from others' experiences and mistakes can accelerate your growth.
Sample Weekly Schedule
Here's a sample weekly schedule to give you an idea of how to structure your learning:
Monday-Friday:
2 hours of structured coursework (e.g., online course or book)
1 hour of hands-on practice (coding exercises, small projects)
30 minutes of video tutorials on specific topics (YouTube)
Saturday-Sunday:
3-4 hours working on a larger project
1-2 hours of review and reinforcement (e.g., revisiting challenging topics, taking quizzes)
By following a structured plan, supplementing your YouTube learning with other resources, and focusing on practical application, you can make significant progress toward becoming a data scientist in 3 months.
Rajeev Kumar
Technical Project Manager
12
Answers
Austin, Texas
Updated
Rajeev’s Answer
Kudos to you for showing interest in becoming a data scientist! This field is rapidly growing and in high demand, making it an excellent choice. While you may grasp the basics in just three months, remember that the job market is competitive, and it will require more than just the basics to truly stand out.
Here are some key skills that a data scientist should master:
- Python programming
- Data analysis
- Data modeling
- Machine learning
- Handling large data sets
- Data-driven storytelling
Comparing these skills to those required for application programming, it's clear that data science can be a bit more challenging. However, don't let that discourage you! Participating in an online course for 3 to 6 months could provide a solid foundation and help you assess if this career path suits you.
Remember, to truly excel in this field and potentially earn a high salary, investing in further education is key. A degree or 2-3 years of specialized education can set you on the path to success.
Here's to your future in data science – best of luck!
Here are some key skills that a data scientist should master:
- Python programming
- Data analysis
- Data modeling
- Machine learning
- Handling large data sets
- Data-driven storytelling
Comparing these skills to those required for application programming, it's clear that data science can be a bit more challenging. However, don't let that discourage you! Participating in an online course for 3 to 6 months could provide a solid foundation and help you assess if this career path suits you.
Remember, to truly excel in this field and potentially earn a high salary, investing in further education is key. A degree or 2-3 years of specialized education can set you on the path to success.
Here's to your future in data science – best of luck!