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what should I learn to be a data scientist?
I want to be a data scientist, what online courses should I take ? #datascience
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Sejal’s Answer
- SQL: The very first topic I suggest is to learn SQL. It is an absolute must in any field of data, for you to pull the data, analyze it, do any kind if manipulation.
- Python: One of the points that makes a data scientist different from a data analyst is the ability to deep dive into machine learning models. Python is the most commonly used programming language for building these models. Also, a few specific topics like Pandas, NumPy under python are used extensively to analyze data.
- Extensive study of data analytics topics: Start from the basics like small concepts of data analysis, manipulation, data correction, and then moving into the data modeling topic. Recommended book: An Introduction to Statistical Learning: with Applications in R
- Python: One of the points that makes a data scientist different from a data analyst is the ability to deep dive into machine learning models. Python is the most commonly used programming language for building these models. Also, a few specific topics like Pandas, NumPy under python are used extensively to analyze data.
- Extensive study of data analytics topics: Start from the basics like small concepts of data analysis, manipulation, data correction, and then moving into the data modeling topic. Recommended book: An Introduction to Statistical Learning: with Applications in R
Updated
Aroquiamarie Kavitha’s Answer
The tasks and responsibilities of a data scientist vary between companies and sometimes within verticals. I assume the query is generic and the motivation is to get into an entry level job with data science functions. For starters, an aspirant needs to get familiar with the following.
1. Basic math for data science (Linear algebra, elementary calculus and statistics)
2. Writing code with basic programming constructs (either python or R)
3. Data wrangling skills (use of Database technologies like SQL to handle larger data that doesn't fit in spreadsheet)
4. Hands on mindset to play around with different data tools / softwares on linux based systems.
5. Understanding the nature of how the data is created and the business function of the data
6. Storytelling with data (talking different stakeholders of the business on the findings and observations about the data)
Good to have:
1. Basic knowledge of handling data in cloud systems like AWS, Google cloud, Azure.
Basic mini courses
kaggle courses (they have a curriculum from beginner to intermediate level)
https://www.kaggle.com/learn [kaggle.com]
If you have programming experience and looking for an experiential learning with more hands activities via programming try fastai
https://course18.fast.ai/ml.html [course18.fast.ai]
If you have good foundation in high school math and prefer the traditional learning methodology, Stanford CS229 Machine learning is a good place to start
https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU [youtube.com]
Once done you can start working on portfolio projects of interests and showcasing them in your resume as suggested in the recommended courses. Often try to solve real world problems by taking part in kaggle competitions.
1. Basic math for data science (Linear algebra, elementary calculus and statistics)
2. Writing code with basic programming constructs (either python or R)
3. Data wrangling skills (use of Database technologies like SQL to handle larger data that doesn't fit in spreadsheet)
4. Hands on mindset to play around with different data tools / softwares on linux based systems.
5. Understanding the nature of how the data is created and the business function of the data
6. Storytelling with data (talking different stakeholders of the business on the findings and observations about the data)
Good to have:
1. Basic knowledge of handling data in cloud systems like AWS, Google cloud, Azure.
Basic mini courses
kaggle courses (they have a curriculum from beginner to intermediate level)
https://www.kaggle.com/learn [kaggle.com]
If you have programming experience and looking for an experiential learning with more hands activities via programming try fastai
https://course18.fast.ai/ml.html [course18.fast.ai]
If you have good foundation in high school math and prefer the traditional learning methodology, Stanford CS229 Machine learning is a good place to start
https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU [youtube.com]
Once done you can start working on portfolio projects of interests and showcasing them in your resume as suggested in the recommended courses. Often try to solve real world problems by taking part in kaggle competitions.
Updated
Thomas’s Answer
There is no one-size-fits-all answer to this question, as the best skills to learn for data science vary depending on your specific interests and goals. However, some of the most important skills for data science include:
- Statistics and mathematics: Data science is heavily reliant on statistics and math, so it's important to have a strong foundation in these areas.
- Programming languages: It's essential to be able to write code in order to manipulate and analyze data. Python is a popular language for data science, but there are many others that can be used as well.
- Database management: A large part of data science involves working with and managing large datasets. Mastery of database management tools and
- Statistics and mathematics: Data science is heavily reliant on statistics and math, so it's important to have a strong foundation in these areas.
- Programming languages: It's essential to be able to write code in order to manipulate and analyze data. Python is a popular language for data science, but there are many others that can be used as well.
- Database management: A large part of data science involves working with and managing large datasets. Mastery of database management tools and
Updated
karthik’s Answer
To become a Data Analyst, you must have key data analysis skills and an ability to glean insights from large data sets.
Many job postings list advanced degrees as requirements for data-related positions. Sometimes, that’s non-negotiable, but as demand outstrips supply—and given the often specialized, highly technical nature of the work—the proof is increasingly in the pudding. That is, data skills often outweigh mere credentialism. What’s most important to hiring managers is an ability to demonstrate mastery of the subject in some way, and it’s increasingly understood that this demonstration doesn’t have to follow traditional channels.
In the end, there’s no single path toward becoming a Data Analyst, and that’s good news if you’re hoping to land a data analysis role. Because Data Analysts can work across many different industries, may be generalists or highly specialized, and often play an interdisciplinary role in a company, even job titles in data analysis can be quite varied
Many job postings list advanced degrees as requirements for data-related positions. Sometimes, that’s non-negotiable, but as demand outstrips supply—and given the often specialized, highly technical nature of the work—the proof is increasingly in the pudding. That is, data skills often outweigh mere credentialism. What’s most important to hiring managers is an ability to demonstrate mastery of the subject in some way, and it’s increasingly understood that this demonstration doesn’t have to follow traditional channels.
In the end, there’s no single path toward becoming a Data Analyst, and that’s good news if you’re hoping to land a data analysis role. Because Data Analysts can work across many different industries, may be generalists or highly specialized, and often play an interdisciplinary role in a company, even job titles in data analysis can be quite varied
Updated
Fabio’s Answer
Hello Chong G.
I am not a data scientist, but I think I can give you some advice on this. Nowadays, an increasing number of professions are requiring analytics capabilities.
There are some core things you should learn to handle great amount of data, like:
Relational Database concepts;
SQL - Computer language for creating and managing databases;
Excel;
Programing languages such as C, VBA, R...
You should also consider learning how to display the data in an organized way and Power BI / Think-Cell are great for that
There are several tutorials around the internet about those topics and also focused courses. I personally recommend the latter, because it is easier to progress through the topics.
Hope my advice was helpful to you!
James Constantine Frangos
Consultant Dietitian & Software Developer since 1972 => Nutrition Education => Health & Longevity => Self-Actualization.
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James Constantine’s Answer
Hello Chong,
Embarking on Your Data Scientist Journey
To carve your path as a data scientist, it's crucial to build a robust foundation in areas such as statistics, programming, machine learning, data visualization, and domain expertise. Here's an actionable roadmap to kickstart your data science journey:
1. Master Programming Languages:
Python: Python's simplicity and versatility make it a favorite in the data science field. Concentrate on becoming proficient in libraries like NumPy, Pandas, Matplotlib, and Scikit-learn.
R: R is another language that's popular in data science, particularly in research and academia. Acquiring R skills can be advantageous for statistical analysis and data visualization.
2. Grasp Statistics and Mathematics:
Statistics: A solid grasp of statistics is vital for data scientists, as it aids in interpreting data through hypothesis testing, regression analysis, and more.
Linear Algebra and Calculus: Understanding concepts from linear algebra and calculus is key to deciphering machine learning algorithms.
3. Dive into Machine Learning:
Machine Learning Algorithms: Get comfortable with various machine learning algorithms such as linear regression, logistic regression, decision trees, random forests, and support vector machines.
Deep Learning: Grasping deep learning concepts and frameworks like TensorFlow or PyTorch can be beneficial when dealing with intricate problems.
4. Hone Data Wrangling and Visualization Skills:
Data Cleaning: Learn to clean and preprocess raw data before diving into analysis.
Data Visualization: Become proficient in tools like Tableau or Matplotlib to create insightful visualizations that effectively communicate your findings.
5. Acquire Hands-On Experience:
Kaggle Competitions: Joining Kaggle competitions can give you practical experience in tackling real-world data science challenges.
Personal Projects: Undertake personal projects that demonstrate your ability to handle datasets, construct models, and extract insights.
6. Enroll in Online Data Science Courses: Numerous online platforms offer courses specifically designed for budding data scientists:
Coursera: Coursera partners with universities like Stanford and Johns Hopkins to provide courses on topics from machine learning to data analysis.
edX: edX hosts courses from esteemed institutions like Harvard and MIT, covering fundamental data science concepts.
Udacity: Udacity's nanodegree programs focus on the practical skills necessary for a data science career.
Top 3 Trusted Sources Used:
Coursera: Coursera is a renowned online learning platform that collaborates with universities to deliver top-notch courses on a variety of subjects, including data science.
edX: edX is another esteemed online platform that offers courses from leading institutions globally, making it an invaluable resource for data science learning.
Udacity: Udacity provides industry-relevant nanodegree programs designed to arm learners with the skills needed for roles such as data scientists.
These sources were leveraged to ensure the information provided regarding the steps to become a successful data scientist through online courses and self-study resources is accurate and reliable.
Blessings,
JC.
Embarking on Your Data Scientist Journey
To carve your path as a data scientist, it's crucial to build a robust foundation in areas such as statistics, programming, machine learning, data visualization, and domain expertise. Here's an actionable roadmap to kickstart your data science journey:
1. Master Programming Languages:
Python: Python's simplicity and versatility make it a favorite in the data science field. Concentrate on becoming proficient in libraries like NumPy, Pandas, Matplotlib, and Scikit-learn.
R: R is another language that's popular in data science, particularly in research and academia. Acquiring R skills can be advantageous for statistical analysis and data visualization.
2. Grasp Statistics and Mathematics:
Statistics: A solid grasp of statistics is vital for data scientists, as it aids in interpreting data through hypothesis testing, regression analysis, and more.
Linear Algebra and Calculus: Understanding concepts from linear algebra and calculus is key to deciphering machine learning algorithms.
3. Dive into Machine Learning:
Machine Learning Algorithms: Get comfortable with various machine learning algorithms such as linear regression, logistic regression, decision trees, random forests, and support vector machines.
Deep Learning: Grasping deep learning concepts and frameworks like TensorFlow or PyTorch can be beneficial when dealing with intricate problems.
4. Hone Data Wrangling and Visualization Skills:
Data Cleaning: Learn to clean and preprocess raw data before diving into analysis.
Data Visualization: Become proficient in tools like Tableau or Matplotlib to create insightful visualizations that effectively communicate your findings.
5. Acquire Hands-On Experience:
Kaggle Competitions: Joining Kaggle competitions can give you practical experience in tackling real-world data science challenges.
Personal Projects: Undertake personal projects that demonstrate your ability to handle datasets, construct models, and extract insights.
6. Enroll in Online Data Science Courses: Numerous online platforms offer courses specifically designed for budding data scientists:
Coursera: Coursera partners with universities like Stanford and Johns Hopkins to provide courses on topics from machine learning to data analysis.
edX: edX hosts courses from esteemed institutions like Harvard and MIT, covering fundamental data science concepts.
Udacity: Udacity's nanodegree programs focus on the practical skills necessary for a data science career.
Top 3 Trusted Sources Used:
Coursera: Coursera is a renowned online learning platform that collaborates with universities to deliver top-notch courses on a variety of subjects, including data science.
edX: edX is another esteemed online platform that offers courses from leading institutions globally, making it an invaluable resource for data science learning.
Udacity: Udacity provides industry-relevant nanodegree programs designed to arm learners with the skills needed for roles such as data scientists.
These sources were leveraged to ensure the information provided regarding the steps to become a successful data scientist through online courses and self-study resources is accurate and reliable.
Blessings,
JC.
Updated
Rafael’s Answer
You should search for Algorithm videos. Usually when studying data, you would need to know about databases structure, analytics skills, and some other logics. Another thing you could do would be start analyzing some small real cases like how long does it take to go from your house to the supermarket and what you could do to reduce the time? or how often do you drink water (time gap between each occurence). How could you track that? and how could you improve it? is it good?
these are a few examples on how you could analyze stuff.