Top Data Science Questions and Answers for Beginners
Top Data Science questions for beginners have been compiled here with answers by experts associated with Careers360. These top questions on Data Science are generally the queries that are asked by students or even professionals venturing into Data Science. The top Data Science questions ranging from Data Science for non-maths background people, how and when to start learning, difference between Ai and Data Science as well as career prospects have been listed here with detailed answers.
Q. Which is the most basic way to start learning Data Science? Should I begin with self-learning or can I jump straight onto a certification?
A. Data Science is an umbrella term which comprises multiple disciplines including data management and data analytics. Since the domain comprises many subtopics you are bound to get deflected with self-learning. This is where Data Science online training is seen as one of the most preferred modes of learning as it allows a lot of flexibility..
Let me give you an example, let us assume you are learning about a mathematical algorithm via Youtube and the instructor uses Python libraries to quickly solve some data problems. You have no clue about Python. The immediate thought you would have is to first learn python and then get back to the current video. This breaks the learning journey. Generally, any journey which is not continuous and sequential is often not pursued after a while. You need a learning structure that would support your objective. That is why going with a beginner certification first is a good idea.
Here is a list of reasons why this approach should help you in getting the necessary target skills.
The basic skills needed to understand a particular topic are always covered at the beginning.
The courses outline the prerequisites in a sequential manner for self-learning.
The sequence of lectures is carefully designed, so that each topic forms a stepping stone for the next one.
There is enough offline material readily available in the lectures itself for self-learning. You need not search further.
The Data Science courses are designed to suit beginners as well as intermediate learners.
The courses are interactive. They have Q/A features enabled for quick query resolution.
Q. I am very weak in maths. How difficult would it be for me to complete the Data Science certification?
A. To begin with I must say that none of us are “weak” in maths. Some have maths “anxiety” which creates a mental block while solving problems. Now let us assume you are someone who has to deal with such a situation. We still have some good news for you. You need not be good at maths to excel or learn Data Science. Check below some reasons on why you can still learn Data Science even when you feel you are not that good in maths.
It is true that Data Science models involve a lot of maths, but they are more conceptual in nature rather than being scary and abstract. The maths behind most models is highly conceptual in nature and you would be able to visualize their applied value. This makes Data Science fun and intriguing.
The realms of Data Science mostly deal with 4 types of mathematical resources -Linear Algebra, Statistics, Probability and Calculus. Now you need not be an expert at all. You can learn them parallelly as you understand the maths behind the models. As you progress on this journey you will understand that most Data Science algorithms have easy to use libraries which we can simply import and start using. So, the “maths” part is mostly taken care of by these libraries. Data Science has to do a lot with data management than only math. The applicability of numbers makes it simple for you to understand the logic behind models and then use them to find effective solutions.
Keeping things simple is the best way to retain knowledge and minimize your learning time. So, get started on your first course and do not worry about those numbers scaring you away.
Q. What would the role of a Data scientist be?
A. Well, a Data scientist should be able to guide the team on deciding which algorithm would suit the data type in question. Various models/algorithms are dependent on the type of data and the use case that is being solved. Now one needs to be very careful while making their choice of algorithms. There is a lot of trial and error involved and your expertise as a data scientist would come in handy if you could lower the time for that trial and error while forming a solution. Here is a handy list of responsibilities expected from a data scientist.
Develop and implement a plan for Data management activities such as product discovery, user personalization, forecasting & planning, entity modeling.
Work together with data engineering teams and setup a scalable architecture.
Partner with product managers to design, route, and analyze solutions and hypotheses.
Collaborate with different verticals to identify use cases and break them down into data-driven complications; translate business problems into mathematical mockups.
Conduct statistical analysis to provide actionable acumens, identify trends, and measure outcome; create systems that monitor inputs and performance.
Ability to be a go-to person and manage a team of associate Data Scientists and Analysts.
Stay informed about relevant developments and complementary domains to ensure that models and their outcomes are always appropriate.
Q. Does Data Science involve a lot of mathematical formulas and theoretical concepts?
A. Data Science is an effective way to solve and forecast real world business problems. There are multiple tools available to help us in visualizing the outcomes. There is an industry saying that Data Science is more of an art than science. Data Science is not all about theoretical mathematical formulas. To understand this, let us understand what the high-level structure of concepts taught in Data Science is and how much theory is involved in each of them.
Data Management and Analysis – Models would heavily involve formulas and algorithms.
Overview on python and SQL – You are expected to use python libraries which are math based.
Various ML (Machine learning) techniques – Largely depends on mathematical models but also includes other data management techniques.
NLP (Natural Language Processing) & DL (Deep Learning) solutions – Dependent on open source libraries. You would need core domain understanding to build your own models.
Data clustering – Needs a deep understanding of various clustering techniques.
Data engineering – Involves mathematical techniques and tools which help in data management.
Business Analytics & Business Intelligence – Math forms the core, but as a BA or Business Intelligence Analyst you would be required to have a good knowledge on libraries/tools & SAAS solutions rather than theoretical concepts.
Data cases studies and solutions – Actual industry implementations and examples.
This list is suggestive rather than being exhaustive, but we hope you get a gist of what you would be undergoing while learning Data Science. Let us now try to understand what we mean by Data Science being an art rather than a science.
Assume you are working on a project where you need to predict when the industrial machinery would fail. The project is in the oil & gas domain and you can clearly see from the data, that the performance of these machines fluctuates largely with changing temperatures. You would also have to analyze and derive insights such as the temperatures at oil rigs have high fluctuation levels. This knowledge cannot just be analyzed through data alone, you would need to understand the domain (oil & gas) as well.
Now after you are done gathering all such valuable information, you can successfully derive insights on when the machinery would fail. You see the domain expertise and data insights are of greater value than running or building models. In the age of SAAS and PAAS services the role of data scientist is less dependent on mathematical theory and more inclined towards data strategy and deriving insights.
At the end, we circle back and realize that Data Science is much more than theory and math. It is conceptual and requires continuous learning. So, get started and keep learning.
Q. I don’t have any coding or python background. Would an advanced Data Science certification still be relevant to me? Should I first learn the basics of python?
A. No. You do not need to learn python or be an expert in python to learn Data Science. Python is just an enabler and is one of the best languages we have today to quickly run and solve Data Science problems. Let us look at all the reasons that make python a favorite.
Simplicity – Python is a high level, free open source language. It has a huge community of developers supporting it.
Libraries – Python has hundreds of libraries supporting Data Science.
Automation- Python has multiple automation frameworks supporting it.
Scalable – Quick to scale and easy to use. Unlike R, python can easily scale to support your projects.
All this makes python great for Data Scientists, but you need not be an expert in python to learn Data Science. See Python courses before deciding.
The courses/certifications do not consider coding or python as a prerequisite if one needs to learn. They are good to have skills, but you can also learn them in parallel. Let us go through a quick check list that would help you in learning Data Science
Statistics and Math
Basic fundamentals of analytics
Machine Learning techniques
Managing Big Data and Data Management techniques
Problem solving using the right data
Now you need not be an expert in all as you embark upon your journey to learn and become an expert in this domain. The key is to learn in parallel and most courses help you in achieving that. The content is built to suit experts as well as beginners. Often you would find optional lectures if you would like to learn more on a particular topic while starting from scratch.
So, when it comes to learning Data Science there is no fixed sequence that you need to follow. What you need to do is touch upon various topics as you find the need to explore further.
Let me give you an example. Assuming you are learning the basics of a mathematical model and the instructor runs it on his/her system within seconds using python libraries. You understand what happened, but you are unable to figure out how python instantly solved such a complex topic that involves reading and analyzing thousands of data points. The trick here is to simply go and read on how the library actually worked. This part would be covered in the instructions as well. So, what happens here is that you focus on the science behind the implementation rather than the implementation itself.
Hope this demystifies the intersection between Data Science and Python. To know more look at some of the following courses
Q. I just entered college but I am passionate about learning AI & Data Science. How do I get started?
A. It’s great to hear that you are eager to learn and also the fact that you want to start early. Now let us start to untangle the entire AI & Data Science web so that it helps you to pick a career trajectory and a field of interest.
Let’s get started with AI. Here are the top 5 pillars that make up this space
Now you see that AI is just a broad umbrella term used to encapsulate all these technologies. What you need to do is to get an overview on these topics/domains and see what interests you.
To begin with we recommend doing a beginner certification on AI which would cover all these topics. Once you have the insights on what these domains talk about, you can deep dive further to gain more information. It is essential that you make an informed decision.
You might be interested in domains which currently have most projects and industry implementations. ML & Virtual agents are such spaces. On the contrary you might be interested in spaces which still have a lot of space for growth and discovery. Deep learning and speech analytics are such domains.
When we talk about Data Science, the domain captures all forms of analytics on data including data management strategies. Actual industry projects often leverage multiple skills across the AI & Data Science domain. They are an amalgamation of these two domains. Data forms the backbone to multiple AI solutions. Industry projects leverage all of these technologies to deliver an end to end solution.
Let me give you a few examples. One cannot form image analytics solutions unless they have trained their model on thousands of images. Now it becomes essential to manage so many images and get them in the right form. Similarly, to solve a machine learning problem you need to be ready with clean segmented data and with various algorithm accuracy levels to see what model best suits your requirement.
Here is a simplified trajectory to follow that will aid an early start:
Year 1 - Beginner course on Data Science
Year 2 - Self-study on analytical models and advanced analytics + Certification on Data Strategies
Year 3 - Course on basics of R & Python. Self-study on ML models
Year 4 – Certification on advanced Data Science or a certification in Machine Learning
Year 1 on the job (parallel learning) – Certification on AI (Intermediate level, which includes a focused study deep learning)
Year 2 on the job – Advanced certifications in AI & Data Science
This trajectory should be modified as per your interests and technological advancements. Today we see a wave of virtual agent and ML projects but as research progresses, we would start seeing large scale applications of Deep Learning as well. This might influence your interests and career prospects.
What we suggest is that you keep learning Data Science in parallel with your mainstream courses. You would soon discover your interests and domains that are a best fit.
Q. Should I do a Data Science course first or should I pick an AI certification?
A. Data Science is often seen as a backbone to AI solutions. Since the lines are thin, it is recommended to begin with Data Science first and then enter the realms of AI. A Data Science certification online may be a good place to start.
AI solutions have been so effective in recent times because of the availability of data. Not just any data but quality data that has been segregated, cleaned, structured and organized. To understand the role of Data Science, let us look at some questions that are answered using data, while developing any AI solution.
Is the solution comprehensive?
Depends on the data. For example, the phone number field including the county codes is an example of good quality data.
Is the solution consistent?
Depends on the data points that is fed to each solution running in parallel.
What are the accuracy levels?
Depends on how accurate the training data is, in comparison to real-world data.
Is the solution generic to solve problems across the domain?
Depends on the data availability across the domain and how data is managed.
So, you see how important it is to answer data questions first, before even talking about any AI engine. This is why Data Science would always be preferred to be learnt before AI. If you jump onto AI courses directly you would still be able to understand the domain but your knowledge will be superficial. You would know what’s happening but would not be exactly sure “how”.
What do Data Science courses include?
Let us get some insights on how these Data Science courses are structured to make them applicable to answer AI solutions. Here is all that a typical Data Science (advanced) course would include.
Data Management and Analysis
Overview on Python and SQL
Various ML (Machine learning) techniques
NLP (Natural Language Processing) & DL (Deep Learning) solutions
Business Analytics & Business Intelligence
Data cases studies and solutions
This list is suggestive and not exhaustive. It is hoped that an understanding would have formed that Data Science would form the stepping stones to AI. Now this is not a compulsion. Even if you have no Data Science background, AI would not be gibberish to you. However, it is good to have a data background for AI.
Data Science courses online and otherwise are designed to make learning easy, fun and interactive. So fret not! jump on the bandwagon and start learning today!
Q. Can I finish a 9-month Data Science certification in 6 months if I know the basics of analytics?
A. You would be surprised to know that Data Science is not all about analytics and running algorithms. The domain talks about acquiring and managing data, analytics and visualization. Your learning journey also will depend on the type of certification that you pick. Not all are the same. The instructor-led certifications need a certain amount of credits to be completed and completing such courses in less than the stipulated time would be a daunting task. We highlight both sides of the coin here - factors that would aid in faster completion of the courses and factors which might hinder the same.
Factors aiding in completing the courses/certifications faster are given here. If you
have a background in Python and understand the basic libraries that enable data analytics.
understand the popular methods of data management and data strategy.
are familiar with the mathematical concepts behind algorithms (few, not all).
are comfortable with tools and platforms which enable data management and analytics.
have a background in data visualization and representation.
Giving undivided attention to learning Data Science is one of the biggest factors to learning the course well ahead of time. Most often, working professionals work in parallel via Data Science online courses . It is quite difficult to focus on learning and working unless you already are a data scientist and connected to this domain.
Also read more about data management courses.
Factors that might hinder your fast pace Data Science learning trajectory are now listed. You might
be overwhelmed by the number of popular algorithms taught in this domain.
discover a whole world of data types and data management techniques.
be intrigued to learn more on a particular algorithm or technique.
spend a lot of time “managing” data rather than running the algorithms.
need to analyze the domain of the use-case first, even before you start to narrow down on the Data Science techniques.
have to spend a considerable time setting up the entire data pipeline.
This list is specially curated so that you appreciate and understand a lot of factors which are important but often overlooked. The basics of analytics will give you a solid platform to build upon, but it is not the only factor one must look at. The techniques and the reasoning behind the methods are what matter the most.
Q. What kind of project influx do we see at large MNCs in the Data Science domain?
A. We have spoken to many industry experts in this domain and the common answer is that projects on “Machine Learning” and “Data Strategy” are currently in hot demand.
This is however a very abstract answer, as the umbrella terms used here (ML & Data Strategy) can get quite ambiguous. There are no pin pointed answers to this as the domain of Data Science itself is an amalgamation of data management and analytics. We can still narrow down our target skills to a few larger points so that we are “Industry” ready.
Here is a handy list of skills/techniques that a typical large MNC would look for while hiring Data Science experts for their projects.
Advising on the best data strategy needed for the project.
Idea on the necessary tools needed to manage the data pipeline.
Selection of algorithms needed for a particular data type.
Which method to use (analytical, ensemble, specific) while managing projects.
The best data structure for your system (sql vs no-sql).
Understanding of the data types and deriving values from the outcomes (Algo-outcomes).
Specific domain knowledge where the data is coming from, examples: Oil & Gas, retail etc.
Data cleaning and data mining techniques to be used.
Managing dashboards and deriving meaningful insights from models.
Model building and maintenance.
Designing the feedback loop and making sure it stays relevant throughout the project.
Now you see that when someone hires a Data Scientist, they look for someone who manages data with algorithms and dashboards. So, it’s just not all about the analytical techniques that the hiring manager is looking for. Since data is the new oil there is a rush of projects in the Machine Learning domain, such that every company makes effective use of their data. Machine learning is a great technique to gain edge over your competition by analyzing the present to forecast the future. Not just that, ML has a handful of other wonderful, actual industry usage which makes it a popular choice.
Deep learning, Augmented reality, AI (some domains) are yet to catch up, such that they can bring actual value to industries. Industries will only adopt a technology when they see revenue or cost benefits. Other domains are yet to establish that. Hence, machine learning is at the pole position.
However, we recommend not to restrict your learning to a particular technique or domain. Keep it wide and flexible. One never knows where the next revolution may come from; until then stay hungry, stay foolish.
Q. Will the Data Science domain have jobs in the future, or would it all be offloaded to software and SAAS companies?
A. The recent past has seen a boom in SAAS based startups and a few have turned out to be unicorns as well. Looking at these trends, one is bound to ponder on the future of Data Science and the role of data scientists. The rise in automated solutions such as Auto-ML and data bricks have increasingly made the life of a data scientist easy. A lot of analysis which was once part of a data scientist’s profile, have now been offloaded to SAAS based solutions.
The trend is here to stay; however, we believe the world would still need the support of expert data scientists for multiple problem solving. Like AI, these solutions are built to augment the human counterpart, rather than replace them altogether. These are great tools which are meant to reduce the lead time of solution building and improvise on the accuracy of multiple solutions.
Data Science is often seen as an art, rather than a science. You need to understand the domain before you begin working on the data. Let us assume you are working on a data problem in the Oil & Gas domain. You must first understand the Oil & Gas domain. Study on how the temperatures fluctuate and the external factors which affect various use cases. Only then can you effectively solve the data problem. Let us try to map the high-level responsibilities of a data scientist:
Implement the best data strategy for the use case.
Work on algorithm selection. This would often depend on the data type and problem statement.
Analyze the domain to understand model building and model outcomes.
Advise on the overall data solution design and implementation.
These are the top 4 responsibilities that firms look for while hiring data scientists. All the above points stress upon Data Science being an art rather than a mechanical process. Multiple aspects of data solving can be automated and augmented by SAAS solutions; however, data scientists would still need to manage the data pipeline, set up proper experimentations, and provide the magical AI tool with the right attribute in order to deliver and interpret the output. It's impossible to construct any model correctly when you don't know how it works.
We have all seen the rise of industries, the rise of AI, the rise of automation and the rise of industrial machinery. We all feared that these would eventually replace humans. That did happen in parts. Eventually we learned and acquired skills which are more suited for “human” knowledge. The population grew exponentially but we still managed to learn on how to interpret the data models rather than just focusing on building them. It is the need of the hour for data scientists to evolve and manage the entire data pipeline, rather than just focusing on model building. The ones who embrace it would move forward and continue to create more tools to help them. These tools would eventually be capable of solving more entangled issues, with lower human effort.
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