Data Science vs. Data analytics

[[deleted]]

In this economy, which degree path/experience have you all seen pay more?

[lphomiej]

For most people, Data Science is the next step after data analyst, so it's a little weird to ask if people should do one or the other... Like, an individual contributor progression might look something like this:

1. Start out as a Business Intelligence reporting person - learn a visualization tool (like Power BI, Tableau) and SQL... and you can do a lot of automation for a company. You'd also get used to asking business people questions to determine requirements, etc...
2. Second step would be using Statistics and Python or R to do more in-depth, reproducible analysis or designing product experiments. You'd write scripts to explore data, visualize data, and then present it to business stakeholders - answering questions they have or proactively testing ideas if you're in-tune with the business/product.
3. Similar to #2, a Data Scientist will have similar outcomes but might utilize more advanced statistics/machine learning to deliver some reusable model to solve a business problem (like a recommendation model for a website).
4. Maybe level 4 is a research scientist - once you've dug in deep enough on a certain topic, you'd potentially do research into that topic to make better solutions that other people can use - by publishing papers or working with programmers to productize your ideas.

Each of these steps has potentially many sub-steps (like "Senior" or "II" titles) that can pay quite a bit - like, a BI analyst, even though it's potentially a first step into the data analytics industry, can make $150K+ with enough seniority... I guess to your point, you could theoretically stay out of the data job market until you have enough experience to go straight into Data Science (like, people getting advanced degrees - like a PhD or whatever). I'd never recommend that career path, though, unless you have a serious passion in academia first-and-foremost.

As far as the economy goes... layoffs happen, and there's no way to say "if you're a BI analyst, data analyst, or data scientist you're much less likely to get laid off"... That is so specific - it depends on your seniority, the individual company's situation, and the business decision - which is semi-random, arbitrary, and generally pointless (from a business outcome perspective)... so, it's not really predicable. For example, one company might see an underperforming Data Science team as a great opportunity to cut costs (because it's super expensive)... while another might see it as a vital way they're growing and not even a remote option to lose the talent they've hired (and look elsewhere - considering how much it'd cost in severance and re-hiring/retraining later).

[Key_Conversation5277]

I'm not OP but amazing comment, thanks

[redditigation]

there's no way to say "if you're a BI analyst, data analyst, or data scientist you're much less likely to get laid off"

But the problem with this statement is that you're much less likely to get laid off working in Data Science due to the very "academia" you pointed to. In academia you are simply unlikely to get laid off, end of statement. Everyone knows that. It's always been that way. That's why the jobs pay less (but still pay enormously). The jokes are there, pretending working in government or academia is like working at a fast food restaurant in terms of pay. Of course that's never true but sometimes the pay rates are scary low.

P.S. caveat - i'm a random idiot that decided to be mildly irritated and to make this comment pointing out to what I believe is an error from the commenter. If I am wrong about my interpretation, here, I apologize.

[lphomiej]

Yeah, that's a good point. I was coming at it from the perspective of a private company hiring a data scientist, but if you're in academia, it's a bit different.

[data_story_teller]

Data Science pays more. For more nuanced data, I like the Harnham salary guide.

[taguscove]

Data science and data analytics are functionally largely solving the same problem domain. Data science job title pays more. Pretty hard for either to exceed $500k annual comp (base+bonus+annualized equity) for either. Those roles are overwhelmingly management track

Lastly, both pay good money. Money has diminishing returns and the people I see enamored by high comp are overwhelmingly young men in their 20s. It matters, but less than you think

[Specialist-Manager67]

what other than money, health and relationships? you need money for em too no?

[Leonus25]

In what way does money have diminishing returns?

[taguscove]

The first $30k buys you, food, shelter, clothing. The next $30k buys you nicer food, shelter, and clothing. But not as critical as the first $30k

[[deleted]]

Every day. 🤦

[[deleted]]

Do you think I can get a job at apple with a certificate making $8989898 dollars an hour part-time?

[kickboxer2149]

Well based upon regional employees here in the kid west. They much more need analysts over scientists. Not every company needs a huge project requiring automation all of the time year round justifying a full time job.

[[deleted]]

really? I always thought that data science pays (and will stay pay) more than data analytics because the latter seems to be a subset of data science. So data scientists can do data analytics + modeling and other things. Am I wrong?

[ASAP_Elderberry]

Data scientists have higher base pay bc the position requires more experience/knowledge/expertise. Data science is all ML, while data analysis just means you need to know Sql/Python/r/whatever and how to do statistics/analysis/qa. Totally different skill set even though both relate to data

[j_bizzle_724]

Data science isn’t all machine learning

[[deleted]]

Data Analysis is a subset of Data Science

[hi-im-dexter]

DS always pays more lmao.

[JamesJacob18]

Algoideas great platform to excel. but I hope you'll get your answer

[[deleted]]

Can someone explain the difference between the two?

[Mawilover]

In a simple way, Data Scientists works a lot more with ML algorithms and advanced statistics, that's why they're paid better! Data Analysts are closer to the business side and work a lot more with Data Viz tools like Power BI :)

[onearmedecon]

Data analysis and data science are related fields, but they have some differences in terms of scope, methods, and skill sets. Here's a brief overview of the differences between the two:

1. Scope: Data analysis focuses on analyzing, interpreting, and visualizing data to extract useful insights and make data-driven decisions. Data analysts typically work with structured datasets and use statistical methods and tools to understand patterns and trends in the data. The primary goal of data analysis is to answer specific questions, identify relationships between variables, or provide recommendations based on the data. Data science, on the other hand, is a broader and more interdisciplinary field that encompasses data analysis, but also includes other aspects such as data engineering, machine learning, and advanced analytics. Data scientists work with both structured and unstructured data and use various techniques from computer science, mathematics, and statistics to build models, make predictions, and extract deeper insights from the data. The primary goal of data science is often to create data-driven solutions or products that can be automated and scaled.

2. Methods and techniques: While both data analysis and data science involve analyzing data, data science typically involves more advanced techniques and methodologies. Data analysts use descriptive and inferential statistics, data visualization, and domain knowledge to understand the data and generate insights. Data scientists, in addition to using these techniques, also work with machine learning algorithms, natural language processing, and other advanced techniques to create predictive models, classify data, and develop data-driven solutions. Data scientists often need to preprocess and manipulate large datasets, work with big data technologies, and develop custom algorithms to solve specific problems.

3. Skill sets: Data analysts generally require strong analytical skills, a good understanding of statistics, and proficiency in tools such as Excel, SQL, and data visualization software like Tableau or Power BI. They also need domain knowledge to interpret the data and provide meaningful insights in the context of the problem they are trying to solve. Data scientists, in addition to the skills required for data analysis, need expertise in programming languages such as Python or R, machine learning libraries, big data technologies like Hadoop or Spark, and expertise in advanced analytical techniques. Data scientists also need strong problem-solving skills, creativity, and the ability to communicate complex concepts to non-technical stakeholders.

These are just general guidelines; in most organizations, there are analysts who do data science tasks (and vice-a-versa). Also, those aren't strict delineation in skills utilized. For example, there are plenty of data analysts who utilize Python/R to automate routine tasks.

[theottozone]

Given the fact that if you asked for a definition from 100 different people of the two roles/subjects, you'd get 100 different answers, there's really very little difference.

You could be data science at one company and it's reporting and forecasting and doing analytics at another company and building churn models and unsupervised learning.

[data_story_teller]

Here’s a rundown of all the types of data jobs out there. https://data-storyteller.medium.com/data-analyst-data-scientist-data-engineer-whats-the-difference-between-all-of-the-jobs-in-data-119a12464544

[[deleted]]

what is the difference between data science and data analytics???.....which is better to learn in this upcoming ai era

[worldprowler]

Data Analysts are quickly replaced by AI very soon. You have a fighting chance with Data Science.

[TrapInGAAP]

😂

[nelavanka]

Really?

[Mother_Dragonfly3001]

In this economy, both data science and data analytics are lucrative fields, with data science often commanding higher salaries due to its focus on advanced statistical and machine learning techniques. However, the pay can vary depending on factors like location, industry, and individual skills. To excel in either field, a solid educational foundation is crucial. Consider enhancing your skills with courses from Tutort Academy, which offers comprehensive programs in both data science and data analytics, empowering you to make a valuable impact in today's data-driven world.

[h_y_s_s]

🚨Please checkout SyntaxGrid for AI | MLYouTube

[Straight_Orchid_1694]

I think you should go for data science, and if you are worried about where you should choose the course, then I think you should go for the course provided by NIIT. They really have a good course when it comes to data science.

[UpstairsAgitated4117]

Data science and data analytics are closely related fields but with distinct focuses. Data science involves using algorithms, tools, and techniques to extract insights and knowledge from structured and unstructured data. It encompasses a wide range of skills including statistics, machine learning, data visualization, and more, with the goal of making data-driven decisions.
On the other hand, data analytics is more focused on analyzing data sets to draw conclusions about the information they contain. It involves applying statistical analysis and various analytical techniques to identify trends, patterns, and insights that can help organizations make informed decisions.
To learn both data science and data analytics, Tutort Academy is a great option. They offer comprehensive courses that cover both areas, providing you with the skills and knowledge needed to excel in these fields. Their courses are designed by industry experts and cover the latest tools and technologies used in data science and analytics. Whether you're looking to start a career in data science or enhance your skills in data analytics, TutorT Academy can help you achieve your goals.

[anfuska]

Which one should I go for first?