12 answers
Claire
Student
Seoul, South Korea
1
Question
Asked
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I want to be a Data Scientist
I have Maths and French literature double degree. Learning some Python and can do it just for Pandas. Looking for an entry-level job but hard to figure out where to start because there is very few senior data scientist in the field. Almost every company want data people but they don't even know what they exactly want for them. Which kind of company could I helm me excel myself in this field? #data #datascience
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12 answers
Nitasha Uprit
Senior Manager
6
Answers
San Francisco, California
Updated
Nitasha’s Answer
Hi Claire, you have the right degree in Math and Statistics to get your hands dirty with Data Science, I would also recommend taking some online courses and doing some projects on your own with tons of "test datasets" which are available online. Do some predictive analysis of this data and see how you can apply your math and statistical skillsets to derive some meaningful insights from this test data.
Create blogs or project work around it, so that you could share it during interviews. There is a need for lots of data scientists in any industry especially corporate as every company wants to learn patterns about their products, see insights on how their product is going to perform in future etc. Just having Data engineers in their company is not sufficient, a lot of demand for Data Scientists. You seem to have the right skill sets including Python too, so doing some home projects on your own with creative test datasets would be the first step for you to understand what you could do with it. Add this to your resume, and apply to Data Scientists open positions to companies like Atlassian, Facebook, Google, Amazon, or any company of your choice.
Good Luck with your little home project and start applying with confidence that you have all potential to do it. Keep pushing till you find the right job!
Create blogs or project work around it, so that you could share it during interviews. There is a need for lots of data scientists in any industry especially corporate as every company wants to learn patterns about their products, see insights on how their product is going to perform in future etc. Just having Data engineers in their company is not sufficient, a lot of demand for Data Scientists. You seem to have the right skill sets including Python too, so doing some home projects on your own with creative test datasets would be the first step for you to understand what you could do with it. Add this to your resume, and apply to Data Scientists open positions to companies like Atlassian, Facebook, Google, Amazon, or any company of your choice.
Good Luck with your little home project and start applying with confidence that you have all potential to do it. Keep pushing till you find the right job!
look into dacon.io for data science/AI/ML portfolio opportunities if you're based in Korea!
Elliott Na
Charley Ables
Data Analyst
4
Answers
Phoenix, Arizona
Updated
Charley’s Answer
I agree with everyone who said that going the route of the data analyst and then working your way into a data science role is ideal. Sometimes the hardest part of the role is getting to understand the data and how it's arranged - this is something you'll do a lot of as a data analyst. Working as a data analyst also allows you to build on your technical skills so that once you get to the data scientist role, your skills are sharp.
With regard to the degree question - I will tell you that I was able to get into an entry-level Decision Science Analyst role without a degree in a quantitative study (I have a Bachelors in Business Administration and an MBA). HOWEVER, I was turned down for a recent opportunity at a higher level because I lacked the degree. This very well could just be how things are at my particular company, but I will tell you that I am currently enrolled in a Master of Science in Data Science program so that I can check that box. (But that's just me.)
In my experience, the degree teaches you more of the theoretical nature of the job, while boot camps and on-the-job training get you the actual technical skills.
Wishing you the best of luck!
With regard to the degree question - I will tell you that I was able to get into an entry-level Decision Science Analyst role without a degree in a quantitative study (I have a Bachelors in Business Administration and an MBA). HOWEVER, I was turned down for a recent opportunity at a higher level because I lacked the degree. This very well could just be how things are at my particular company, but I will tell you that I am currently enrolled in a Master of Science in Data Science program so that I can check that box. (But that's just me.)
In my experience, the degree teaches you more of the theoretical nature of the job, while boot camps and on-the-job training get you the actual technical skills.
Wishing you the best of luck!
Carlos Rodriguez-Munoz
Data Analyst
8
Answers
Walla Walla, Washington
Updated
Carlos’s Answer
I think since the field of Data Science/Data Analystics/Data Engineering is relatively new, most companies struggle to really make great distinctions between those roles. I think looking at the job descriptions of roles will give you the clearest indication of which job you will truly be doing.
As for what companies need Data Scientists, I think it is mostly more mature companies that use Data Scientists. In my mind, Data Scientists use machine learning along with other skills that cross over with an analyst and engineer but their main distinguishing factor would be the machine learning algorithms. When a company is young and not mature yet, they will not be at a stage where they will do predictive reporting, instead, they do descriptive reporting, and thus don't really use machine learning.
Think about pursuing data analytics or engineering as transition into data science
Take a look at some roles and their descriptions
As for what companies need Data Scientists, I think it is mostly more mature companies that use Data Scientists. In my mind, Data Scientists use machine learning along with other skills that cross over with an analyst and engineer but their main distinguishing factor would be the machine learning algorithms. When a company is young and not mature yet, they will not be at a stage where they will do predictive reporting, instead, they do descriptive reporting, and thus don't really use machine learning.
Carlos recommends the following next steps:
Sudhansu Sahu
Software Engineering Leadership
8
Answers
Bengaluru, Karnataka, India
Updated
Sudhansu’s Answer
Hi Claire,
There are two aspects to be successful in data science.
First one is technology - good programming skill with strong foundation in mathematics, data structure, algorithm is essential
Second one is domain expertise - in my observation this is a key skill to develop. It can be business or technology domain (or both). The more someone has understanding in the domain, it will be easier to conceptualize and build new analytics rich products.
Wish you good luck!
There are two aspects to be successful in data science.
First one is technology - good programming skill with strong foundation in mathematics, data structure, algorithm is essential
Second one is domain expertise - in my observation this is a key skill to develop. It can be business or technology domain (or both). The more someone has understanding in the domain, it will be easier to conceptualize and build new analytics rich products.
Wish you good luck!
Firoz Ghandhi
24
Answers
Updated
Firoz’s Answer
Areas of understanding or skills that can be very helpful include:
-- Data Visualization
-- Tableau
-- SAS
-- SQL
-- Foundational understanding of data modeling
-- Understanding of how data flows from systems to landing areas to data use layers
-- Hadoop
-- Impala
-- Python
-- Data Visualization
-- Tableau
-- SAS
-- SQL
-- Foundational understanding of data modeling
-- Understanding of how data flows from systems to landing areas to data use layers
-- Hadoop
-- Impala
-- Python
Hang Dinh
Data Analyst
3
Answers
Ho Chi Minh City, Vietnam
Updated
Hang’s Answer
Hi, there're some of my recommendations:
-Learn SQL, NoSQL, Pytorch, and some Python's Libraries and ETL tools
-Get to know Machine Learning (Machine Learning by Andrew Ng on Coursera is such a good fit)
-Learning by doing with some datasets from Kaggle.com
-Advance your Python with Hackerrank or LeetCode
-Learn SQL, NoSQL, Pytorch, and some Python's Libraries and ETL tools
-Get to know Machine Learning (Machine Learning by Andrew Ng on Coursera is such a good fit)
-Learning by doing with some datasets from Kaggle.com
-Advance your Python with Hackerrank or LeetCode
C Scyphers
Data Architecture
3
Answers
Denver, Colorado
Updated
C’s Answer
For what it's worth, degrees are not very indicative of ability (for example, my undergrad was in Philosophy, focusing on Language and Logic). What matters more is your capability to produce. Go to competition websites (like Kaggle) and join some of the offerings there. You'll learn a lot, make some connections with others in the field, and start to establish a track record of what you can and cannot do. You might even make a little money while you're at it.
John Garrett
Solutions Architect, Analytics
3
Answers
Apex, North Carolina
Updated
John’s Answer
Agree with Navya. Find a job related to data science that will allow you to explore the field and grow your skills while also contributing to the company in some meaningful way. Sometimes these are not the top paying jobs in the short term, but will pay off for you in long term as you become more valuable. Data Analyst is a common role where you can do this.
My primary role was as a network architect, but I had interest in data science and automation, so I chose roles and projects that allowed me to explore data related to my area of expertise, where I could relate data science to something that I already knew. I could compare how I looked at data before using data science, and find new ways to look at the same data to find new value in it using algorithms I learned, read about, or wanted to explore.
Next steps are always - go try it! Consider data from your current role and think about what you COULD do with it if you were an expert data scientist. Get hands on and try some things out with open source software. I prefer python and Jupyter notebooks.
Thanks John :)
Claire
Navya Mangena
Data Analytics Consultant
12
Answers
Boston, Massachusetts
Updated
Navya’s Answer
As you already said, this is a new field and has very few experienced seniors in there. However, look at it this way. If you get into this career now, 5 years down the line, you will be one of the very few people with good experience. I know its tough to find an entry level position. But if you do not have the experience, nobody will hire you for a senior position. Most companies take the effort to train its junior employees through learning and development and through support system from the team. Try to find a job and put in effort at least for an year to get comfortable with the data science job and see what else you can do from there.
Thanks Navya :)
Claire
karthik M
Tech Support Engineer
88
Answers
India
Updated
karthik’s Answer
Earn a bachelor's degree in IT, computer science, math, physics, or another related field;
Earn a master's degree in data or related field;
Gain experience in the field you intend to work in (ex: healthcare, physics, business).
Earn a master's degree in data or related field;
Gain experience in the field you intend to work in (ex: healthcare, physics, business).
James Constantine Frangos
Consultant Dietitian & Software Developer since 1972 => Nutrition Education => Health & Longevity.
3473
Answers
Gold Coast, Queensland, Australia
Updated
James Constantine’s Answer
Dear Claire,
Understanding the Roles of Data Scientists and Data Analysts:
Data Scientist: A data scientist is a specialist who excels in mining valuable insights from data using a variety of techniques and tools. They are adept in programming, statistics, machine learning, and data visualization. Data scientists tackle intricate problems that involve massive datasets, necessitating advanced analytical skills to extract significant insights. Their duties include crafting algorithms, constructing predictive models, and devising data-driven solutions to tackle business challenges.
Data Analyst: Conversely, a data analyst is dedicated to interpreting data to aid organizations in making enlightened decisions. Data analysts typically work with structured data to spot trends, patterns, and correlations. They employ statistical methods and data analysis tools to create reports, dashboards, and visualizations that are easily comprehensible by non-technical stakeholders. Data analysts are pivotal in offering actionable insights based on historical data to bolster business operations and strategy.
Can a Data Scientist Function as a Data Analyst?
Indeed, a data scientist can function as a data analyst. Despite the distinct differences between a data scientist and a data analyst in terms of complexity and scope of work, there is a convergence in the skill set required for both roles. Data scientists often have the analytical prowess needed to execute the responsibilities of a data analyst effectively. In certain organizations or projects, a data scientist may need to undertake tasks typically associated with a data analyst role, such as producing reports or conducting exploratory data analysis.
To sum up, while there are distinctions between a data scientist and a data analyst in their duties and focus areas, a data scientist can assuredly function as a data analyst when necessary.
Top 3 Credible Sources Used:
Harvard Business Review: This source offers invaluable insights into the roles of data scientists and data analysts in organizations, underscoring the key differences between the two roles and their respective contributions to business success.
Forbes: Renowned for its coverage of technology and business topics, Forbes provides articles that explore the evolving field of data science and analytics. The information from Forbes aids in comprehending the nuances of roles like data scientist and data analyst.
Towards Data Science: As a widely recognized platform for disseminating knowledge about data science and related fields, Towards Data Science provides comprehensive articles and resources that delve into the differences between various roles within the realm of data analysis.
These sources were crucial in offering accurate and current information on the topic of the difference between a data scientist and a data analyst.
Stay Blessed!
James Constantine.
Understanding the Roles of Data Scientists and Data Analysts:
Data Scientist: A data scientist is a specialist who excels in mining valuable insights from data using a variety of techniques and tools. They are adept in programming, statistics, machine learning, and data visualization. Data scientists tackle intricate problems that involve massive datasets, necessitating advanced analytical skills to extract significant insights. Their duties include crafting algorithms, constructing predictive models, and devising data-driven solutions to tackle business challenges.
Data Analyst: Conversely, a data analyst is dedicated to interpreting data to aid organizations in making enlightened decisions. Data analysts typically work with structured data to spot trends, patterns, and correlations. They employ statistical methods and data analysis tools to create reports, dashboards, and visualizations that are easily comprehensible by non-technical stakeholders. Data analysts are pivotal in offering actionable insights based on historical data to bolster business operations and strategy.
Can a Data Scientist Function as a Data Analyst?
Indeed, a data scientist can function as a data analyst. Despite the distinct differences between a data scientist and a data analyst in terms of complexity and scope of work, there is a convergence in the skill set required for both roles. Data scientists often have the analytical prowess needed to execute the responsibilities of a data analyst effectively. In certain organizations or projects, a data scientist may need to undertake tasks typically associated with a data analyst role, such as producing reports or conducting exploratory data analysis.
To sum up, while there are distinctions between a data scientist and a data analyst in their duties and focus areas, a data scientist can assuredly function as a data analyst when necessary.
Top 3 Credible Sources Used:
Harvard Business Review: This source offers invaluable insights into the roles of data scientists and data analysts in organizations, underscoring the key differences between the two roles and their respective contributions to business success.
Forbes: Renowned for its coverage of technology and business topics, Forbes provides articles that explore the evolving field of data science and analytics. The information from Forbes aids in comprehending the nuances of roles like data scientist and data analyst.
Towards Data Science: As a widely recognized platform for disseminating knowledge about data science and related fields, Towards Data Science provides comprehensive articles and resources that delve into the differences between various roles within the realm of data analysis.
These sources were crucial in offering accurate and current information on the topic of the difference between a data scientist and a data analyst.
Stay Blessed!
James Constantine.
karthik M
Tech Support Engineer
88
Answers
India
Updated
karthik’s Answer
To become a data scientist, you could earn a Bachelor’s degree in Computer science, Social sciences, Physical sciences, and Statistics. The most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%).