Trending February 2024 # 40 Questions To Test A Data Scientist On Deep Learning # Suggested March 2024 # Top 2 Popular

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Deep Learning has made many practical applications of machine learning possible. Deep Learning breaks down tasks in a way that makes all kinds of applications possible. This skilltest was conducted to test your knowledge of deep learning concepts.

A total of 853 people registered for this skill test. The test was designed to test the conceptual knowledge of deep learning. If you are one of those who missed out on this skill test, here are the questions and solutions. You missed on the real time test, but can read this article to find out how you could have answered correctly.

Here are the leaderboard ranking for all the participants.

Overall Scores

Below are the distribution scores, they will help you evaluate your performance.

You can access the final scores here. More than 270 people participated in the skill test and the highest score obtained was 38. Here are a few statistics about the distribution.

Mean Score: 15.05

Median Score: 18

Mode Score: 0

Useful Resources

A Complete Guide on Getting Started with Deep Learning in Python

The Evolution and Core Concepts of Deep Learning & Neural Networks

Practical Guide to implementing Neural Networks in Python (using Theano)

Fundamentals of Deep Learning – Starting with Artificial Neural Network

An Introduction to Implementing Neural Networks using TensorFlow

Fine-tuning a Keras model using Theano trained Neural Network & Introduction to Transfer Learning

6 Deep Learning Applications a beginner can build in minutes (using Python)

Questions & Answers

1) The difference between deep learning and machine learning algorithms is that there is no need of feature engineering in machine learning algorithms, whereas, it is recommended to do feature engineering first and then apply deep learning.



Solution: (B)

Deep learning itself does feature engineering whereas machine learning requires manual feature engineering.

2) Which of the following is a representation learning algorithm?

A) Neural network

B) Random Forest

C) k-Nearest neighbor

D) None of the above

Solution: (A)

Neural network converts data in such a form that it would be better to solve the desired problem. This is called representation learning.

3) Which of the following option is correct for the below-mentioned techniques?

AdaGrad uses first order differentiation

L-BFGS uses second order differentiation

AdaGrad uses second order differentiation

L-BFGS uses first order differentiation

A) 1 and 2

B) 3 and 4

C) 1 and 4

D) 2 and 3

Solution: (A)

Option A is correct.


4) Increase in size of a convolutional kernel would necessarily increase the performance of a convolutional neural network. 



Solution: (B)

Kernel size is a hyperparameter and therefore by changing it we can increase or decrease performance.


Question Context 

Now you want to use this model on different dataset which has images of only Ford Mustangs (aka car) and the task is to locate the car in an image.

5) Which of the following categories would be suitable for this type of problem?

A) Fine tune only the last couple of layers and change the last layer (classification layer) to regression layer

B) Freeze all the layers except the last, re-train the last layer

C) Re-train the model for the new dataset

D) None of these

Solution: (A)


6) Suppose you have 5 convolutional kernel of size 7 x 7 with zero padding and stride 1 in the first layer of a convolutional neural network. You pass an input of dimension 224 x 224 x 3 through this layer. What are the dimensions of the data which the next layer will receive? 

A) 217 x 217 x 3

B) 217 x 217 x 8

C) 218 x 218 x 5

D) 220 x 220 x 7

Solution: (C)


7) Suppose we have a neural network with ReLU activation function. Let’s say, we replace ReLu activations by linear activations.

Would this new neural network be able to approximate an XNOR function? 

Note: The neural network was able to approximate XNOR function with activation function ReLu.

A) Yes

B) No

Solution: (B)

If ReLU activation is replaced by linear activation, the neural network loses its power to approximate non-linear function.


8) Suppose we have a 5-layer neural network which takes 3 hours to train on a GPU with 4GB VRAM. At test time, it takes 2 seconds for single data point. 

Now we change the architecture such that we add dropout after 2nd and 4th layer with rates 0.2 and 0.3 respectively.

What would be the testing time for this new architecture?

A) Less than 2 secs

B) Exactly 2 secs

C) Greater than 2 secs

D) Can’t Say

Solution: (B)

The changes is architecture when we add dropout only changes in the training, and not at test time.


9) Which of the following options can be used to reduce overfitting in deep learning models?

Add more data

Use data augmentation 

Use architecture that generalizes well

Add regularization

Reduce architectural complexity

A) 1, 2, 3

B) 1, 4, 5

C) 1, 3, 4, 5

D) All of these

Solution: (D)

All of the above techniques can be used to reduce overfitting.


10) Perplexity is a commonly used evaluation technique when applying deep learning for NLP tasks. Which of the following statement is correct?

A) Higher the perplexity the better

B) Lower the perplexity the better

Solution: (B)


11) Suppose an input to Max-Pooling layer is given above. The pooling size of neurons in the layer is (3, 3).

What would be the output of this Pooling layer?

A) 3

B) 5

C) 5.5

D) 7

Solution: (D)

Max pooling works as follows, it first takes the input using the pooling size we defined, and gives out the highest activated input.


12) Suppose there is a neural network with the below configuration. 

If we remove the ReLU layers, we can still use this neural network to model non-linear functions.



Solution: (B)


13) Deep learning can be applied to which of the following NLP tasks?

A) Machine translation

B) Sentiment analysis

C) Question Answering system

D) All of the above

Solution: (D)

Deep learning can be applied to all of the above-mentioned NLP tasks.


14) Scenario 1: You are given data of the map of Arcadia city, with aerial photographs of the city and its outskirts. The task is to segment the areas into industrial land, farmland and natural landmarks like river, mountains, etc.

Deep learning can be applied to Scenario 1 but not Scenario 2.



Solution: (B)

Scenario 1 is on Euclidean data and scenario 2 is on Graphical data. Deep learning can be applied to both types of data.


15) Which of the following is a data augmentation technique used in image recognition tasks?

Horizontal flipping

Random cropping

Random scaling

Color jittering

Random translation

Random shearing

A) 1, 2, 4

B) 2, 3, 4, 5, 6

C) 1, 3, 5, 6

D) All of these

Solution: (D)


16) Given an n-character word, we want to predict which character would be the n+1th character in the sequence. For example, our input is “predictio” (which is a 9 character word) and we have to predict what would be the 10th character.

Which neural network architecture would be suitable to complete this task?

A) Fully-Connected Neural Network

B) Convolutional Neural Network

C) Recurrent Neural Network

D) Restricted Boltzmann Machine

Solution: (C)

Recurrent neural network works best for sequential data. Therefore, it would be best for the task.


17) What is generally the sequence followed when building a neural network architecture for semantic segmentation for image?

A) Convolutional network on input and deconvolutional network on output

B) Deconvolutional network on input and convolutional network on output

Solution: (A)


18) Sigmoid was the most commonly used activation function in neural network, until an issue was identified. The issue is that when the gradients are too large in positive or negative direction, the resulting gradients coming out of the activation function get squashed. This is called saturation of the neuron.

That is why ReLU function was proposed, which kept the gradients same as before in the positive direction.

A ReLU unit in neural network never gets saturated.



Solution: (B)

ReLU can get saturated too. This can be on the negative side of x-axis.


19) What is the relationship between dropout rate and regularization?

Note: we have defined dropout rate as the probability of keeping a neuron active?

A) Higher the dropout rate, higher is the regularization

B) Higher the dropout rate, lower is the regularization

Solution: (B)

Higher dropout rate says that more neurons are active. So there would be less regularization.


20) What is the technical difference between vanilla backpropagation algorithm and backpropagation through time (BPTT) algorithm?

A) Unlike backprop, in BPTT we sum up gradients for corresponding weight for each time step

B) Unlike backprop, in BPTT we subtract gradients for corresponding weight for each time step

Solution: (A)

BPTT is used in context of recurrent neural networks. It works by summing up gradients for each time step


21) Exploding gradient problem is an issue in training deep networks where the gradient getS so large that the loss goes to an infinitely high value and then explodes.

What is the probable approach when dealing with “Exploding Gradient” problem in RNNs?

A) Use modified architectures like LSTM and GRUs

B) Gradient clipping

C) Dropout

D) None of these

Solution: (B)

To deal with exploding gradient problem, it’s best to threshold the gradient values at a specific point. This is called gradient clipping.


22) There are many types of gradient descent algorithms. Two of the most notable ones are l-BFGS and SGD. l-BFGS is a second order gradient descent technique whereas SGD is a first order gradient descent technique.

In which of the following scenarios would you prefer l-BFGS over SGD?

Data is sparse

Number of parameters of neural network are small

A) Both 1 and 2

B) Only 1

C) Only 2

D) None of these

Solution: (A)

l-BFGS works best for both of the scenarios.


23) Which of the following is not a direct prediction technique for NLP tasks?

A) Recurrent Neural Network

B) Skip-gram model


D) Convolutional neural network

Solution: (C)


24) Which of the following would be the best for a non-continuous objective during optimization in deep neural net?



C) AdaGrad

D) Subgradient method

Solution: (D)

Other optimization algorithms might fail on non-continuous objectives, but sub-gradient method would not.


25) Which of the following is correct?

Dropout randomly masks the input weights to a neuron

Dropconnect randomly masks both input and output weights to a neuron

A) 1 is True and 2 is False

B) 1 is False and 2 is True

C) Both 1 and 2 are True

D) Both 1 and 2 are False

Solution: (D)

In dropout, neurons are dropped; whereas in dropconnect; connections are dropped. So both input and output weights will be rendered in useless, i.e. both will be dropped for a neuron. Whereas in dropconnect, only one of them should be dropped


26) While training a neural network for image recognition task, we plot the graph of training error and validation error for debugging.

What is the best place in the graph for early stopping?

A) A

B) B

C) C

D) D

Solution: (C)

You would “early stop” where the model is most generalized. Therefore option C is correct.


27) Research is going on to solve image inpainting problems using computer vision with deep learning. For this, which loss function would be appropriate for computing the pixel-wise region to be inpainted?

Image inpainting is one of those problems which requires human expertise for solving it. It is particularly useful to repair damaged photos or videos. Below is an example of input and output of an image inpainting example.

A) Euclidean loss

B) Negative-log Likelihood loss

C) Any of the above

Solution: (C)

Both A and B can be used as a loss function for image inpainting problem.

A) Sum of squared error with respect to inputs

B) Sum of squared error with respect to weights

C) Sum of squared error with respect to outputs

D) None of the above

Solution: (C)

29) Mini-Batch sizes when defining a neural network are preferred to be multiple of 2’s such as 256 or 512. What is the reason behind it?

A) Gradient descent optimizes best when you use an even number

B) Parallelization of neural network is best when the memory is used optimally

C) Losses are erratic when you don’t use an even number

D) None of these

Solution: (B)


30) Xavier initialization is most commonly used to initialize the weights of a neural network. Below is given the formula for initialization.

If weights at the start are small, then signals reaching the end will be too tiny.

If weights at the start are too large, signals reaching the end will be too large.

Weights from Xavier’s init are drawn from the Gaussian distribution.

Xavier’s init helps reduce vanishing gradient problem.

Xavier’s init is used to help the input signals reach deep into the network. Which of the following statements are true?

A) 1, 2, 4

B) 2, 3, 4

C) 1, 3, 4

D) 1, 2, 3

E) 1, 2, 3, 4

Solution: (D)

All of the above statements are true.


31) As the length of sentence increases, it becomes harder for a neural translation machine to perform as sentence meaning is represented by a fixed dimensional vector. To solve this, which of the following could we do?

A) Use recursive units instead of recurrent

B)Use attention mechanism

C) Use character level translation

D) None of these

Solution: (B)

32) A recurrent neural network can be unfolded into a full-connected neural network with infinite length.



Solution: (A)

Recurrent neuron can be thought of as a neuron sequence of infinite length of time steps.


33) Which of the following is a bottleneck for deep learning algorithm?

A) Data related to the problem

B) CPU to GPU communication

C) GPU memory

D) All of the above

Solution: (D)

Along with having the knowledge of how to apply deep learning algorithms, you should also know the implementation details. Therefore you should know that all the above mentioned problems are a bottleneck for deep learning algorithm.


34) Dropout is a regularization technique used especially in the context of deep learning. It works as following, in one iteration we first randomly choose neurons in the layers and masks them. Then this network is trained and optimized in the same iteration. In the next iteration, another set of randomly chosen neurons are selected and masked and the training continues.

A) Affine layer

B) Convolutional layer

C) RNN layer

D) None of these

Solution: (C)

Dropout does not work well with recurrent layer. You would have to modify dropout technique a bit to get good results.


35) Suppose your task is to predict the next few notes of song when you are given the preceding segment of the song.

For example:

The input given to you is an image depicting the music symbols as given below,

Your required output is an image of succeeding symbols.

Which architecture of neural network would be better suited to solve the problem?

A) End-to-End fully connected neural network

B) Convolutional neural network followed by recurrent units

C) Neural Turing Machine

D) None of these

Solution: (B)

CNN work best on image recognition problems, whereas RNN works best on sequence prediction. Here you would have to use best of both worlds!


36) When deriving a memory cell in memory networks, we choose to read values as vector values instead of scalars. Which type of addressing would this entail?

A) Content-based addressing

B) Location-based addressing

Solution: (A)

A) Affine layer

B) Strided convolutional layer

C) Fractional strided convolutional layer

D) ReLU layer

Solution: (C)

Option C is correct. Go through this link.


Question Context 38-40

GRU is a special type of Recurrent Neural Networks proposed to overcome the difficulties of classical RNNs. This is the paper in which they were proposed: “On the Properties of Neural Machine Translation: Encoder–Decoder Approaches. Read the full paper here. 

38) Which of the following statements is true with respect to GRU?

Units with short-term dependencies have reset gate very active.

Units with long-term dependencies have update gate very active

A) Only 1

B) Only 2

C) None of them

D) Both 1 and 2

Solution: (D)


39) If calculation of reset gate in GRU unit is close to 0, which of the following would occur?

A) Previous hidden state would be ignored

B) Previous hidden state would be not be ignored

Solution: (A)


40) If calculation of update gate in GRU unit is close to 1, which of the following would occur? 

A) Forgets the information for future time steps

B) Copies the information through many time steps

Solution: (B)


End Notes

If you missed out on this competition, make sure you complete in the ones coming up shortly. We are giving cash prizes worth $10,000+ during the month of April 2023.

If you have any questions or doubts feel free to post them below.

Check out all the upcoming skilltests here.


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The Journey Of A Senior Data Scientist And Machine Learning Engineer In Fintech Domain


Meet Tajinder, a seasoned Senior Data Scientist and ML Engineer who has excelled in the rapidly evolving field of data science. Tajinder’s passion for unraveling hidden patterns in complex datasets has driven impactful outcomes, transforming raw data into actionable intelligence. In this article, we explore Tajinder’s inspiring success story. From humble beginnings to influential figure, showcasing unwavering dedication, technical prowess, and a genuine passion for leveraging data to drive real-world results.

At a leading fintech company, Tajinder has revolutionized various aspects of the business using his data science expertise. His contributions have optimized internal processes, enhanced customer experiences, generated revenue, and fueled overall business growth. Tajinder’s journey stands as a testament to the immense potential of data science and machine learning when coupled with the right mindset and determination.

Let’s Get On with the Senior Data Scientist Interview! AV: Please introduce yourself. Provide us with an overview of your educational journey. How has it led you to your current role?

Tajinder: Certainly! Hello, my name is Tajinder, and I am a Senior Data Scientist and Machine Learning Engineer. My educational journey began with a bachelor’s degree in Computer Science, where I developed a strong foundation in programming, algorithms, and software development.

I started my professional career as a DB developer, working on various Software Engineering and Data Engineering projects. In this role, I gained extensive experience in database management, query optimization, and creating reports and Management Information Systems (MIS). While working on these projects, I discovered my keen interest in the field of Data Science.

Driven by my passion for data analysis and exploration, I decided to dive deeper into the Data Science domain. I embarked on a self-learning journey, studying and acquiring knowledge in areas such as statistical analysis, machine learning algorithms, and data visualization techniques. To further enhance my skills, I pursued additional courses and certifications in Data Science and Machine Learning.

As I continued to expand my expertise, I started applying my knowledge and skills to real-world problems. Through hands-on experience, I honed my skills in data preprocessing, feature engineering, and model development. Also gaining proficiency in tools and frameworks such as Python, R, TensorFlow, and scikit-learn.

Over time, continuous learning led me to assume increasingly challenging roles within the field of Data Science. I worked on diverse projects, ranging from predictive modeling and customer segmentation to Deep Learning systems and anomaly detection. Through these experiences, I developed a deep understanding of the end-to-end data science pipeline, from data acquisition and preprocessing to model deployment and monitoring.

Current Role

As a Senior Data Scientist and ML Engineer, I bring together my extensive knowledge in computer science, software engineering, and data science to design and implement cutting-edge solutions. I thrive on the opportunity to tackle complex problems, uncover valuable insights from data, and develop scalable machine learning systems that drive meaningful impact for businesses.

AV: What inspired you to pursue a career in Data Science? How did you get started in this field?

Tajinder: I was initially drawn to the field of Data Science due to my experience as a DB developer and my involvement in creating reports and Management Information Systems (MIS). Working with data sparked my curiosity and made me realize the tremendous potential in extracting valuable insights and knowledge from large datasets. I became fascinated by the idea of using data-driven approaches to solve complex problems and make informed decisions.

To get started in the field of Data Science, I took a proactive approach. I engaged in self-learning, exploring various online resources, tutorials, and textbooks that covered topics such as statistics, machine learning, and data visualization. I also participated in online courses and pursued certifications from reputable institutions to formalize my knowledge and acquire a solid foundation in this field.

In parallel, I sought practical experience by working on personal projects and taking part in Kaggle competitions. These platforms provided opportunities to apply my skills in real-world scenarios. And then, collaborate with other data enthusiasts, and learn from the community’s collective knowledge and expertise. I gained valuable hands-on experience in data preprocessing, feature engineering, model development, and evaluation by working on diverse projects.

AV: What challenges did you face while getting into the field of Data Science? How did you overcome those challenges?

Tajinder: When venturing into the field, I encountered several challenges, some of which align with the ones you’ve mentioned. Let’s dive deep into my challenges and how I overcame them.

Framing a problem into a Data Science problem: Initially, I struggled with translating real-world problems into well-defined Data Science problems. Understanding which aspects could be addressed using data analysis and machine learning required a deep understanding of the problem domain and collaboration with domain experts.

To overcome this challenge, I adopted a proactive approach. I engaged in discussions with subject matter experts, stakeholders, and colleagues with expertise in the problem domain. By actively listening and learning from their insights, I better understood the problem context and identified opportunities for data-driven solutions. I also sought mentorship from experienced Data Scientists who guided me in framing problems effectively. This collaborative approach helped bridge the gap between technical expertise and domain knowledge, enabling me to identify and solve Data Science problems more effectively.

One major challenge was acquiring a solid foundation in probability and statistics concepts. To overcome this, I dedicated significant time to self-study and enrolled in Udemy courses to deepen my understanding of statistical analysis and probability theory.

Another obstacle was gaining practical experience in implementing machine learning solutions. To address this, I participated in Machine Learning Hackathons, mostly on Kaggle and MachineHack.

AV: How did your skills working as a Software Engineer and Database Developer helped you become successful as a senior Data Scientist?

Tajinder: My skills as a Software Engineer and Database Developer have greatly contributed to my success as a senior Data Scientist. My expertise in SQL for data wrangling allows me to efficiently extract, transform, and load data. My knowledge of database design and optimization enables me to handle large-scale data processing. Software engineering practices help you write clean and reusable code while problem-solving and analytical thinking skills aid in solving complex data-driven problems. Additionally, my collaboration and communication abilities facilitate effective teamwork and stakeholder engagement. These skills have been instrumental in my achievements as a Data Scientist.

AV: What are some of the most important skills you think are essential for success?

Tajinder: I believe several skills and qualities are crucial for success in the field of Data Science. These include:

Problem Framing and Data Science Mindset: Identifying and framing problems as data science problems are essential. A data-driven mindset helps understand how data can be leveraged to extract insights and drive decision-making.

Business and Domain Understanding: A deep understanding of the business or domain you are working in is crucial. It allows you to align data science solutions with the goals and needs of the organization, ensuring that your work has a meaningful impact.

Solution-Oriented Approach: Considering solutions from an end-user perspective is essential to develop practical and actionable insights. Considering how stakeholders can effectively implement and utilize your work is key to delivering valuable results.

Technical Skills: Proficiency in technical tools and programming languages like SQL and Python is vital. These skills enable you to acquire, manipulate, and analyze data effectively. You could build machine learning models to derive insights and predictions.

AV: Can you share an example of your most proud achievement? What were some of the factors that contributed to its success and some challenges you faced? How did you overcome them?

Tajinder: One achievement I am proud of is successfully deploying machine learning models in a production environment to assist the business team in making impactful decisions. Factors contributing to this success include understanding the business domain, collaborating with stakeholders, and taking a data-driven approach. Challenges faced involved defining the problem and overcoming data limitations. By engaging with stakeholders, refining the problem statement, and applying innovative techniques, I overcame these challenges and delivered valuable insights for decision-making.

AV: Can you discuss a time when you successfully mentored or coached a junior data scientist or machine learning engineer, and what were the outcomes of this effort?

Tajinder: Certainly! I had the opportunity to mentor junior data scientists who were new to the field, and the outcomes of this effort were highly positive. To tailor the mentoring approach, I did the following:

Assessed the individual’s learning needs

Provided diverse learning resources

Regular feedback

Review sessions helped track progress and address any difficulties

Collaboration and Networking

Enhanced their exposure to industry experts and trends

AV: How can you remain up to speed with the most recent breakthroughs and trends in machine learning when you work in a continuously changing field?

Tajinder: To stay up to speed with the latest breakthroughs and trends in machine learning, I employ the following strategies:

Attending Conferences and Webinars: I actively participate in machine learning conferences, workshops, and webinars to gain insights from industry experts and researchers. These events provide opportunities to learn about recent breakthroughs, novel applications, and industry trends through presentations and networking. DataHour sessions on Analytics Vidhya, Random Webinars from Linkedin, or any other source according to my interest.

Develop a Personalized Learning Plan: The plan outlines specific areas of interest and goals. This plan includes milestones, deadlines, and resources, helping me stay organized and focused on continuous growth.

AV: Please mention an instance of a recent development that you find especially intriguing or promising.

Tajinder: One recent development that I find promising in the data science industry is the emergence of Language Models for Machine Learning (LLM). Language models, such as OpenAI’s Chat GPT etc, have showcased impressive capabilities in NLP, text generation, and understanding context.

Large Language models can enhance human-computer interaction by enabling more natural and conversational machine interactions. Voice assistants, customer service chatbots, and smart devices are becoming more sophisticated and user-friendly, enhancing productivity and convenience for individuals and businesses.

Language models can be leveraged in educational settings to enhance learning experiences. They can provide personalized tutoring, generate interactive educational content, and facilitate natural language interfaces for educational platforms. Students can benefit from adaptive learning, instant feedback, and access to knowledge.

AV: How do you see the field of machine learning evolving over the next few years? What steps are you taking to ensure your team is well-positioned to capitalize on these changes?

Prioritize continuous learning and skill development through participation in workshops, conferences, and online courses.

Research and exploration are encouraged to stay updated with cutting-edge techniques.

Collaboration and knowledge sharing foster collective expertise and idea exchange.

Hands-on experimentation and proofs-of-concept help assess emerging approaches.

The team invests in a robust infrastructure and actively seeks collaborations and partnerships with experts and organizations.

We uphold ethical considerations, fairness, and transparency in our projects.

By focusing on these strategies, my team remains prepared to adapt and deliver innovative solutions to meet evolving needs in machine learning.


We hope you enjoyed Tajinder’s fascinating journey as a senior data scientist and ML engineer. We hope you got fantastic insights about the data science industry from his perspective. If you want to read more success stories, then, head to our blog now! If you want to become a Data Scientist, enroll in the blackbelt plus program.


Ai Vs. Machine Learning Vs. Deep Learning

Since before the dawn of the computer age, scientists have been captivated by the idea of creating machines that could behave like humans. But only in the last decade has technology enabled some forms of artificial intelligence (AI) to become a reality.

Interest in putting AI to work has skyrocketed, with burgeoning array of AI use cases. Many surveys have found upwards of 90 percent of enterprises are either already using AI in their operations today or plan to in the near future.

Eager to capitalize on this trend, software vendors – both established AI companies and AI startups – have rushed to bring AI capabilities to market. Among vendors selling big data analytics and data science tools, two types of artificial intelligence have become particularly popular: machine learning and deep learning.

While many solutions carry the “AI,” “machine learning,” and/or “deep learning” labels, confusion about what these terms really mean persists in the market place. The diagram below provides a visual representation of the relationships among these different technologies:

As the graphic makes clear, machine learning is a subset of artificial intelligence. In other words, all machine learning is AI, but not all AI is machine learning.

Similarly, deep learning is a subset of machine learning. And again, all deep learning is machine learning, but not all machine learning is deep learning.

Also see: Top Machine Learning Companies

AI, machine learning and deep learning are each interrelated, with deep learning nested within ML, which in turn is part of the larger discipline of AI.

Computers excel at mathematics and logical reasoning, but they struggle to master other tasks that humans can perform quite naturally.

For example, human babies learn to recognize and name objects when they are only a few months old, but until recently, machines have found it very difficult to identify items in pictures. While any toddler can easily tell a cat from a dog from a goat, computers find that task much more difficult. In fact, captcha services sometimes use exactly that type of question to make sure that a particular user is a human and not a bot.

In the 1950s, scientists began discussing ways to give machines the ability to “think” like humans. The phrase “artificial intelligence” entered the lexicon in 1956, when John McCarthy organized a conference on the topic. Those who attended called for more study of “the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

Critics rightly point out that there is a big difference between an AI system that can tell the difference between cats and dogs and a computer that is truly intelligent in the same way as a human being. Most researchers believe that we are years or even decades away from creating an artificial general intelligence (also called strong AI) that seems to be conscious in the same way that humans beings are — if it will ever be possible to create such a system at all.

If artificial general intelligence does one day become a reality, it seems certain that machine learning will play a major role in the system’s capabilities.

Machine learning is the particular branch of AI concerned with teaching computers to “improve themselves,” as the attendees at that first artificial intelligence conference put it. Another 1950s computer scientist named Arthur Samuel defined machine learning as “the ability to learn without being explicitly programmed.”

In traditional computer programming, a developer tells a computer exactly what to do. Given a set of inputs, the system will return a set of outputs — just as its human programmers told it to.

Machine learning is different because no one tells the machine exactly what to do. Instead, they feed the machine data and allow it to learn on its own.

In general, machine learning takes three different forms: 

Reinforcement learning is one of the oldest types of machine learning, and it is very useful in teaching a computer how to play a game.

For example, Arthur Samuel created one of the first programs that used reinforcement learning. It played checkers against human opponents and learned from its successes and mistakes. Over time, the software became much better at playing checkers.

Reinforcement learning is also useful for applications like autonomous vehicles, where the system can receive feedback about whether it has performed well or poorly and use that data to improve over time.

Supervised learning is particularly useful in classification applications such as teaching a system to tell the difference between pictures of dogs and pictures of cats.

In this case, you would feed the application a whole lot of images that had been previously tagged as either dogs or cats. From that training data, the computer would draw its own conclusions about what distinguishes the two types of animals, and it would be able to apply what it learned to new pictures.

By contrast, unsupervised learning does not rely on human beings to label training data for the system. Instead, the computer uses clustering algorithms or other mathematical techniques to find similarities among groups of data.

Unsupervised machine learning is particularly useful for the type of big data analytics that interests many enterprise leaders. For example, you could use unsupervised learning to spot similarities among groups of customers and better target your marketing or tailor your pricing.

Some recommendation engines rely on unsupervised learning to tell people who like one movie or book what other movies or books they might enjoy. Unsupervised learning can also help identify characteristics that might indicate a person’s credit worthiness or likelihood of filing an insurance claim.

Various AI applications, such as computer vision, natural language processing, facial recognition, text-to-speech, speech-to-text, knowledge engines, emotion recognition, and other types of systems, often make use of machine learning capabilities. Some combine two or more of the main types of machine learning, and in some cases, are said to be “semi-supervised” because they incorporate some of the techniques of supervised learning and some of the techniques of unsupervised learning. And some machine learning techniques — such as deep learning — can be supervised, unsupervised, or both.

The phrase “deep learning” first came into use in the 1980s, making it a much newer idea than either machine learning or artificial intelligence.

Deep learning describes a particular type of architecture that both supervised and unsupervised machine learning systems sometimes use. Specifically, it is a layered architecture where one layer takes an input and generates an output. It then passes that output on to the next layer in the architecture, which uses it to create another output. That output can then become the input for the next layer in the system, and so on. The architecture is said to be “deep” because it has many layers.

To create these layered systems, many researchers have designed computing systems modeled after the human brain. In broad terms, they call these deep learning systems artificial neural networks (ANNs). ANNs come in several different varieties, including deep neural networks, convolutional neural networks, recurrent neural networks and others. These neural networks use nodes that are similar to the neurons in a human brain.

However, those GPUs also excel at the type of calculations necessary for deep learning. As GPU performance has improved and costs have decreased, people have been able to create high-performance systems that can complete deep learning tasks in much less time and for much less cost than would have been the case in the past.

Today, anyone can easily access deep learning capabilities through cloud services like Amazon Web Services, Microsoft Azure, Google Cloud and IBM Cloud.

If you are interested in learning more about AI vs machine learning vs deep learning, Datamation has several resources that can help, including the following:

Deep Learning For Image Super

This article was published as a part of the Data Science Blogathon


(SR) is the process of recovering high-resolution (HR) images from low-resolution (LR) images. It is an important class of image processing techniques in computer vision and image processing and enjoys a wide range of real-world applications, such as medical imaging, satellite imaging, surveillance and security, astronomical imaging, amongst others.


Image sup -resolution (SR) problem, particularly single image super-resolution (SISR), has gained a lot of attention in the research community. SISR aims to reconstruct a high-resolution image ISR from a single low-resolution image ILR. Generally, the relationship between ILR and the original high-resolution image IHR can vary depending on the situation. Many studies assume that ILR is a bicubic downsampled version of IHR, but other degrading factors such as blur, decimation, or noise can also be considered for practical applications.

In this article, we would be focusing on supervised learning methods for super-resolution tasks. By using HR images as target and LR images as input, we can treat this problem as a supervised learning problem.

Exhaustive table of topics in Supervised Image Super-Resolution

Upsampling Methods

Before understanding the rest of the theory behind the super-resolution, we need to understand upsampling (Increasing the spatial resolution of images or simply increasing the number of pixel rows/columns or both in the image) and its various methods.

1. Interpolation-based methods – Image interpolation (image scaling), refers to resizing digital images and is widely used by image-related applications. The traditional methods include nearest-neighbor interpolation, linear, bilinear, bicubic interpolation, etc.

Nearest-neighbor interpolation with the scale of 2

Nearest-neighbor Interpolation – The nearest-neighbor interpolation is a simple and intuitive algorithm. It selects the value of the nearest pixel for each position to be interpolated regardless of any other pixels.

Bilinear Interpolation – The bilinear interpolation (BLI) first performs linear interpolation on one axis of the image and then performs on the other axis. Since it results in a quadratic interpolation with a receptive field-sized 2 × 2, it shows much better performance than nearest-neighbor interpolation while keeping a relatively fast speed.

Bicubic Interpolation – Similarly, the bicubic interpolation (BCI) performs cubic interpolation on each of the two axes Compared to BLI, the BCI takes 4 × 4 pixels into account, and results in smoother results with fewer artifacts but much lower speed. Refer to this for a detailed discussion.

Shortcomings – Interpolation-based methods often introduce some side effects such as computational complexity, noise amplification, blurring results, etc.

2. Learning-based upsampling – To overcome the shortcomings of interpolation-based methods and learn upsampling in an end-to-end manner, transposed convolution layer and sub-pixel layer are introduced into the SR field.

and the green boxes indicate the kernel and the convolution output.

Transposed convolution: layer, a.k.a. deconvolution layer, tries to perform transformation opposite a normal convolution, i.e., predicting the possible input based on feature maps sized like convolution output. Specifically, it increases the image resolution by expanding the image by inserting zeros and performing convolution.

Sub-pixel layer – The blue boxes denote the input and the boxes with other colors indicate different convolution operations and different output feature maps.

s2 times channels, where s is the scaling factor. Assuming the input size is h × w × c, the output size will be h×w×s2c. After that, the reshaping operation is performed to produce outputs with size sh × sw × c

Super-resolution Frameworks

Since image super-resolution is an ill-posed problem, how to perform upsampling (i.e., generating HR output from LR input) is the key problem. There are mainly four model frameworks based on the employed upsampling operations and their locations in the model (refer to the table above).

1. Pre-upsampling Super-resolution –

We don’t do a direct mapping of LR images to HR images since it is considered to be a difficult task. We utilize traditional upsampling algorithms to obtain higher resolution images and then refining them using deep neural networks is a straightforward solution. For example – LR images are upsampled to coarse HR images with the desired size using bicubic interpolation. Then deep CNNs are applied to these images for reconstructing high-quality images.

2. Post-upsampling Super-resolution –

To improve the computational efficiency and make full use of deep learning technology to increase resolution automatically, researchers propose to perform most computation in low-dimensional space by replacing the predefined upsampling with end-to-end learnable layers integrated at the end of the models. In the pioneer works of this framework, namely post-upsampling SR, the LR input images are fed into deep CNNs without increasing resolution, and end-to-end learnable upsampling layers are applied at the end of the network.

Learning Strategies

error and producing more realistic and higher-quality results.

Pixelwise L1 loss – Absolute difference between pixels of ground truth HR image and the generated one.

Pixelwise L2 loss – Mean squared difference between pixels of ground truth HR image and the generated one.

Content loss – the content loss is indicated as the Euclidean distance between high-level representations of the output image and the target image. High-level features are obtained by passing through pre-trained CNNs like VGG and ResNet.

Adversarial loss – Based on GAN where we treat the SR model as a generator, and define an extra discriminator to judge whether the input image is generated or not.

PSNR – Peak Signal-to-Noise Ratio (PSNR) is a commonly used objective metric to measure the reconstruction quality of a lossy transformation. PSNR is inversely proportional to the logarithm of the Mean Squared Error (MSE) between the ground truth image and the generated image.

In MSE, I is a noise-free m×n monochrome image (ground truth)  and K is the generated image (noisy approximation). In PSNR, MAXI represents the maximum possible pixel value of the image.

Network Design

Various network designs in super-resolution architecture

Enough of the basics! Let’s discuss some of the state-of-art super-resolution methods –

Super-Resolution methods

Super-Resolution Generative Adversarial Network (SRGAN) – Uses the idea of GAN for super-resolution task i.e. generator will try to produce an image from noise which will be judged by the discriminator. Both will keep training so that generator can generate images that can match the true training data.

Architecture of Generative Adversarial Network

There are various ways for super-resolution but there is a problem – how can we recover finer texture details from a low-resolution image so that the image is not distorted?

The results have high PSNR means have high-quality results but they are often lacking high-frequency details.

Check the original papers for detailed information.

Steps –

1. We process the HR (high-resolution images) to get downsampled LR images. Now we have HR and LR images for the training dataset.

2. We pass LR images through a generator that upsamples and gives SR images.

3. We use the discriminator to distinguish HR image and backpropagate GAN loss to train discriminator and generator.

Network architecture of SRGAN


Key features of the method – 

Post upsampling type of framework

Subpixel layer for upsampling

Contains residual blocks

Uses Perceptual loss

Original code of SRGAN

conventional residual networks.

Check the original papers for detailed information.

Some of the key features of the methods – 

Residual blocks – SRGAN successfully applied the ResNet architecture to the super-resolution problem with SRResNet, they further improved the performance by employing a better ResNet structure. In the proposed architecture –

Comparison of the residual blocks

They removed the batch normalization layers from the network as in SRResNets. Since batch normalization layers normalize the features, they get rid of range flexibility from networks by normalizing the features, it is better to remove them.

The architecture of EDSR, MDSR

In MDSR, they proposed a multiscale architecture that shares most of the parameters on different scales. The proposed multiscale model uses significantly fewer parameters than multiple single-scale models but shows comparable performance.

Original code of the methods

So now we have come to the end of the blog! To learn about super-resolution, refer to these survey papers.

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Fake News Classification Using Deep Learning

This article was published as a part of the Data Science Blogathon.


Here’s a quick puzzle for you. I’ll give you two titles, and you’ll have to tell me which is fake. Ready? Let’s get started:

“Wipro is planning to buy an EV-based startup.”

Well, it turns out that both of those headlines were fake news. In this article, you will learn the fake news classification using deep learning.

Image – 1

The grim reality is that there is a lot of misinformation and disinformation on the internet. Ninety per cent of Canadians have fallen for false news, according to a 2023 research done by Ipsos Public Affairs for Canada’s Centre for International Governance Innovation.

It got me thinking: is it feasible to build an algorithm that can tell whether an article’s title is fake news? Well, it appears to be the case!

In this post, we go through the exploration of the classification model with BERT and LSTMs to identify the fake new classification.

Go through this Github link to view the complete code.

Dataset for Fake News Classification

We use the dataset from Kaggle. It consists of 2095 article details that include author, title, and other information. Go through the link to get the dataset.


Let us start analyzing our data to get better insights from it. The dataset looks clean, and now we map the values to our class Real and Fake such as 0 and 1.

data = pd.read_csv('/content/news_articles.csv') data = data[['title', 'label']] data['label'] = data['label'].map({'Real': 0, 'Fake':1}) data.head()

Image by Author

Since we have 1294 samples of real news and 801 samples of fake news, there is an approximately 62:38 news ratio. It means that our dataset is relatively biased. For our project, we consider the title and class columns.

Now, we can analyze the trends present in our dataset. To get an idea of dataset size, we get the mean, min, and max character lengths of titles. We use a histogram to visualize the data.

# Character Length of Titles - Min, Mean, Max print('Mean Length', data['title'].apply(len).mean()) print('Min Length', data['title'].apply(len).min()) print('Max Length', data['title'].apply(len).max()) x = data['title'].apply(len).plot.hist()

Image by Author

We can observe that characters in each title range from 2-443. We can also see that more per cent of samples with a length of 0-100. The mean length of the dataset is around 61.

Preprocessing Data

Now we will use the NLTK library to preprocess our dataset, which includes:


It is the process of dividing a text into smaller units (each word will be an index in an array)


It removes the endings of the word to the root word. It reduces the word children to a child.

Stop words Removal:

Words like the and for will be eliminated from our dataset because they take too much room.

#Import nltk preprocessing library to convert text into a readable format import nltk from nltk.tokenize import sent_tokenize from chúng tôi import WordNetLemmatizer from nltk.corpus import stopwords'punkt')'wordnet')'stopwords') data['title'] = data.apply(lambda row: nltk.word_tokenize(row['title']), axis=1) #Define text lemmatization model (eg: walks will be changed to walk) lemmatizer = WordNetLemmatizer() #Loop through title dataframe and lemmatize each word def lemma(data): return [lemmatizer.lemmatize(w) for w in data] #Apply to dataframe data['title'] = data['title'].apply(lemma) #Define all stopwords in the English language (it, was, for, etc.) stop = stopwords.words('english') #Remove them from our dataframe data['title'] = data['title'].apply(lambda x: [i for i in x if i not in stop]) data.head()

Image by Author

We create two models using this data for text classification:

An LSTM model (Tensorflow’s wiki-words-250 embeddings)

A BERT model.

LSTM Model for Fake News Classification

We split our data into a 70:30 ratio of train and test.

#Split data into training and testing dataset title_train, title_test, y_train, y_test = train_test_split(titles, labels, test_size=0.3, random_state=1000)

To get predictions based on the text from our model, we need to encode it in vector format then it is processed by the machine.

Word2Vec Skip-Gram architecture had used by TensorFlow’s wiki-words-250. Based on the input, Skip-gram had trained by predicting the context.

Consider this sentence as an example:

I am going on a voyage in my car.

The word voyage passed as input and one as the window size. The window size means before and after the target word to predict. In our case, the words are gone and car (excluding stopwords, and go is the lemmatized form of going).

We one-hot-encode our word, resulting in an input vector of size 1 x V, where V is the vocabulary size. A weight matrix of V rows (one for each word in our vocabulary) and E columns, where E is a hyperparameter indicating the size of each embedding, will be multiplied by the representation. Except for one, all values in the input vector are zero because it is one-hot encoded (representing the word we are inputting). Finally, when the weight matrix had multiplied by the output, a 1xE vector denotes the embedding for that word.

The output layer, which consists of a softmax regression classifier, will receive the 1xE vector. It had built of V neurons (which correspond to the vocabulary’s one-hot encoding) that produce a value between 0 and 1 for each word, indicating the likelihood of that word being in the window size.

Word embeddings with a size E of 250 are present in Tensorflow’s wiki-words-250. Embeddings applied to the model by looping through all of the words and computing the embedding for each one. We’ll need to utilize the pad sequences function to adjust for samples of variable lengths.

#Convert each series of words to a word2vec embedding indiv = [] for i in title_train: temp = np.array(embed(i)) indiv.append(temp) #Accounts for different length of words indiv = tf.keras.preprocessing.sequence.pad_sequences(indiv,dtype=’float’) indiv.shape

Therefore, there are 1466 samples in the training data, the highest length is 46 words, and each word has 250 features.

Now, we build our model. It consists of:

1 LSTM layer with 50 units

2 Dense layers (first 20 neurons, the second 5) with an activation function ReLU.

1 Dense output layer with activation function sigmoid.

We will use the Adam optimizer, a binary cross-entropy loss, and a performance metric of accuracy. The model will be trained over 10 epochs. Feel free to further adjust these hyperparameters.

#Sequential model has a 50 cell LSTM layer before Dense layers model = tf.keras.models.Sequential() model.add(tf.keras.layers.LSTM(50)) model.add(tf.keras.layers.Dense(20,activation='relu')) model.add(tf.keras.layers.Dense(5,activation='relu')) model.add(tf.keras.layers.Dense(1,activation='sigmoid')) #Compile model with binary_crossentropy loss, Adam optimizer, and accuracy metrics loss="binary_crossentropy", metrics=['accuracy']) #Train model on 10 epochs, y_train,validation_data=[test,y_test], epochs=20)

We get an accuracy of 59.4% on test data.

Using BERT for Fake News Classification

What would you reply if I asked you to name the English term with the most definitions?

That word is “set,” according to the Oxford English Dictionary’s Second Edition.

If you think about it, we could make a lot of different statements using that term in various settings. Consider the following scenario:

I set the table for lunch

The problem with Word2Vec is that no matter how the word had used, it generates the same embedding. We use BERT, which can build contextualized embeddings, to combat this.

BERT is known as “Bidirectional Encoder Representations from Transformers.” It employs a transformer model to generate contextualized embeddings by utilizing attention mechanisms.

An encoder-decoder design had used in a transformer model. The encoder layer creates a continuous representation based on the data it has learned from the input. The preceding input is delivered into the model by the decoder layer, which generates an output. Because BERT’s purpose is to build a vector representation from the text, it only employs an encoder.

Pre-Training & Fine-Tuning

BERT had trained using two ways. The first method is known to be veiled language modelling. Before transmitting sequences, a [MASK] token had used to replace 15% of the words. Using the context supplied by the unmasked words, the model will predict the masked words.

It is accomplished by

Using embedding matrix to apply a classification layer to the encoder output. As a result, it will be the same size as the vocabulary.

Using the softmax function to calculate the likelihood of the word.

The second strategy is to guess the upcoming sentence. The model will be given two sentences as input and predict whether the second sentence will come after the first. While training, half of the inputs are pairs, while the other half consists of random sentences from the corpus. To distinguish between the two statements,

Here, it adds a [CLS] token at the start of the first sentence and a [SEP] token at the end of each.

Each token (word) contains a positional embedding that allows information extracted from the text’s location. Because there is no repetition in a transformer model, there is no inherent comprehension of the word’s place.

Each token is given a sentence embedding (further differentiating between the sentences).

For Next Sentence Prediction, the output of the [CLS] embedding, which stands for “aggregate sequence representation for sentence classification,” is passed through a classification layer with softmax to return the probability of the two sentences being sequential.

Image by Author

Implementation of BERT

The BERT preprocessor and encoder from Tensorflow-hub had used. Do not run the content via the earlier-mentioned framework (which removes capitalization, applies lemmatization, etc.) The BERT preprocessor had used to abstract this.

We split our data for training and testing in the ratio of 80:20.

from sklearn.model_selection import train_test_split #Split data into training and testing dataset title_train, title_test, y_train, y_test = train_test_split(titles, labels, test_size=0.2, random_state=1000)

Now, load Bert preprocessor and encoder

# Use the bert preprocesser and bert encoder from tensorflow_hub

We can now work on our neural network. It must be a functional model, with each layer’s output serving as an argument to the next.

1 Input layer: Used to pass sentences into the model.

The bert_preprocess layer: Preprocess the input text.

The bert_encoder layer: Pass the preprocessed tokens into the BERT encoder.

1 Dropout layer with 0.2. The BERT encoder pooled_output is passed into it.

2 Dense layers with 10 and 1 neurons. The first uses a ReLU activation function, and the second is sigmoid.

import tensorflow as tf # Input Layers input_layer = tf.keras.layers.Input(shape=(), dtype=tf.string, name='news') # BERT layers processed = bert_preprocess(input_layer) output = bert_encoder(processed) # Fully Connected Layers layer = tf.keras.layers.Dropout(0.2, name='dropout')(output['pooled_output']) layer = tf.keras.layers.Dense(10,activation='relu', name='hidden')(layer) layer = tf.keras.layers.Dense(1,activation='sigmoid', name='output')(layer) model = tf.keras.Model(inputs=[input_layer],outputs=[layer])

The “pooled output” will be transmitted into the dropout layer, as you can see. This value represents the text’s overall sequence representation. It is, as previously said, the representation of the [CLS] token outputs.

The Adam optimizer, a binary cross-entropy loss, and an accuracy performance metric had used. For five epochs, the model had trained. Feel free to tweak these hyperparameters even more.

#Compile model on adam optimizer, binary_crossentropy loss, and accuracy metrics #Train model on 5 epochs, y_train, epochs= 5) #Evaluate model on test data model.evaluate(title_test,y_test)

Image by Author

Above, you can see that our model achieved an accuracy of 61.33%.


To improve the model performance:

Train the models on a large dataset.

Tweak hyperparameters of the model.

I hope you had found this post insightful and a better understanding of NLP techniques for fake news classification.


Image – 1: Photo by Roman Kraft on Unsplash

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Data Scientist Vs Data Analyst: Key Differences Explained

In the world of data-driven decisions, two prominent roles have emerged: data analysts and data scientists. These professionals play a crucial role in helping organizations harness the power of data, but their responsibilities and skill sets are quite different.

Data analysts focus on using data visualization and statistical analysis to understand data and identify patterns. They are usually required to have at least a bachelor’s degree in a relevant field like mathematics, statistics, computer science, or finance.

Broadly speaking, both professions involve extracting valuable insights from data; however, their approaches and skill sets do vary.

In this article, we will explore the differences between data scientists and data analysts and highlight the unique skills and responsibilities required for each role.

Let’s dive in.

While data scientists and data analysts both work with data, they have distinct roles and responsibilities.

Understanding the differences between these two roles is important for organizations seeking to build an effective data team. Also, it is crucial for those that would like a career in data to understand.

In this section, we will explore the key differences between data scientists and data analysts, including their educational backgrounds, technical skills, and the types of problems they are typically tasked with solving.

The table below gives a quick overview of the differences between the two roles:

Education/BackgroundData ScientistData AnalystDegreeBachelor’s degree in business, economics, statistics, or a related fieldBachelor’s degree in business, economics, statistics, or related fieldProgramming skillsProficient in languages such as Python, R, and SQLProficient in Excel, SQL, and basic scripting languagesMathematics skillsStrong mathematical skills, including linear algebra, calculus, and statisticsStrong statistical skills, including regression analysis and hypothesis testingWork experienceExperience with big data technologies, machine learning, and data visualizationExperience with statistical analysis, data modeling, and reporting

Joining a boot camp, using tutorials, or completing online courses or certificate programs may not cut it.

Data scientists should have a strong foundation in mathematics, statistics, and computer science, as well as hands-on experience with programming languages such as Python, R, and SQL.

Other data analyst skills include working with databases and having basic scripting language skills.

The job involves working on large data sets, developing predictive models, and extracting insights from data. Like data analysts, it also requires soft skills like communication and collaboration since you often need to work with different teams.

Data analysts: Very simply, a data analyst’s job involves analyzing and interpreting data to provide insights and recommendations to stakeholders.

You may be tasked with working with different data sources to identify trends and patterns that can inform business decisions.

Some specific responsibilities of data analysts can include:

Collecting, cleaning, and organizing data from various sources

Conducting statistical analysis to identify trends and patterns in data using software like Tableau

Creating reports and dashboards to visualize data and communicate insights to stakeholders

Identifying areas for process improvement and making data-driven recommendations to stakeholders

Developing and maintaining databases and data systems to support data analysis

Keeping up-to-date with the latest trends and developments in data analysis and visualization.

Now, things get a little more complex.

Data scientists: Being a data scientist involves analyzing complex data sets, developing predictive models, and extracting insights from data.

They work closely with stakeholders across different departments to provide insights and recommendations based on their data analysis.

Some specific responsibilities of data scientists include:

Conducting exploratory data analysis to identify patterns and trends in data

Developing predictive models using statistical and machine learning techniques

Building and testing machine learning models to improve predictive accuracy

Using problem-solving skills and business intelligence to come up with data-driven solutions to business problems

Communicating complex findings and recommendations to non-technical stakeholders

Collaborating with data engineers and software developers to build and deploy data-driven solutions

In the next two sections, we’ll take a look at the future job prospects and salary expectations for the two professions.

The job outlook for data scientists in 2023 is very promising as organizations across industries continue to collect and analyze increasing amounts of data.

According to the U.S. Bureau of Labor Statistics (BLS), employment of data scientists is projected to grow by 36% from 2023 to 2031, which is much faster than the average when compared to other occupations. Job opportunities in the field are driven by the increasing use of data and analytics to drive decision-making in organizations of all sizes.

According to Glassdoor, the national average salary for data scientists in the United States is around $103,000 per year. Many organizations also offer various additional forms of compensation for data scientists, such as bonuses, equity, and other benefits like medical insurance and paid time off.

Please note that compensation can vary widely depending on location, industry, and years of experience.

According to the BLS, employment of management analysts (which includes data analyst careers) is projected to grow by 11% from 2023 to 2030. Like data scientists, the job outlook for data analysts is very positive for the foreseeable future.

Compensation for data analysts may vary based on factors such as experience, industry, and location. Entry-level data analysts typically earn lower salaries, they can expect their pay to increase as their skills and expertise develop over time.

In terms of salary, the national average for data analyst positions in the United States is around $65,850 per year, according to Glassdoor.

The job prospects and compensation for both data scientists and data analysts are very promising, but how can you decide which career is right for you? We’re going to take a look at factors to consider in the next section.

Deciding which career path is right for you can feel daunting, but think of it as an exciting opportunity to explore this wonderful world of data!

The two fields may seem similar at first glance, and in a way, they are, but they require different skill sets and offer unique career paths.

With the right information and guidance, you can choose the path that is best suited for your skills, interests, and career goals.

In this section, we’ll provide some tips and insights to help you navigate this decision and choose the right path for you.

When considering a career in data science or data analysis, it’s important to think about your skills, interests, and career goals.

Here are some specific factors to consider:

Roles and responsibilities: Data scientists are often responsible for more strategic and complex initiatives, such as developing predictive models or creating machine learning algorithms. Data analyst roles focus more on day-to-day operations and providing insights to stakeholders.

Job outlook and salary: Both data scientists and data analysts have strong job prospects and competitive salaries, but the specific job outlook and salary can vary depending on the industry, location, and years of experience.

Ultimately, the right path for you will come down to your individual goals and aspirations.

Now one great thing about data skills is that they can be applied in most industries, let check them out.

The field of data science and data analytics is in high demand across a wide range of industries and company types.

Here are some examples of industries that both commonly employ data scientists and data analysts:

Finance and Banking: The finance and banking industry relies heavily on data analytics to identify trends, assess risk, and make informed business decisions. Business analysts are in high demand.

Healthcare: Healthcare organizations use data science and data analytics to improve patient outcomes, manage resources, and drive innovation in medical research.

E-commerce: E-commerce companies use data analytics to better understand their customer’s behavior, preferences, and purchasing habits in order to improve marketing and sales strategies.

Technology: Technology companies use data science and data analytics to develop new products and services, improve user experiences, come up with real-world solutions, and identify areas for innovation and growth.

There are employment opportunities across different company types, including startups, large corporations, consulting firms, and government agencies.

Understanding the diverse range of industries and company types that rely on data professionals is crucial for individuals looking to build successful careers in these fields.

It’s also important to note that both fields are evolving, and there are emerging trends that are worth considering.

In addition to industry types, consider emerging trends in data science and data analytics that are changing the landscape of the two fields.

Here are some current trends that are shaping the future of data science and data analytics:

Artificial intelligence and machine learning: AI and machine learning are increasingly being used in data science and data analytics to automate data processing, identify patterns, and make predictions. These technologies have the potential to revolutionize industries from healthcare to finance to marketing.

Cloud computing: Cloud computing has made it easier and more cost-effective to store, manage, and analyze large amounts of data. As cloud infrastructure and technology continue to improve, it’s expected that cloud-based data analytics and machine learning will become more widespread.

Data ethics and privacy: As more and more data is collected and analyzed, concerns about data ethics and privacy have come to the forefront. Data scientists and analysts are being called upon to ensure that data is being used ethically and responsibly and to implement measures to protect sensitive data.

Internet of things (IoT): The IoT refers to the network of interconnected devices and sensors that collect and share data. With the increasing adoption of IoT technology, there is a growing need for data scientists and analysts who can manage and analyze the vast amounts of data generated by these devices.

In the world of data, both data scientists and data analysts play important full-time roles in a business. While there are similarities between the two, they possess distinct differences in terms of responsibilities and required skills.

Data analysts primarily focus on working with structured data to solve tangible business problems using SQL, R, or Python programming languages, data visualization tools, and statistical analysis. They help organizations identify trends and derive insights from data.

On the other hand, data scientists are more involved in programming machines, optimizing systems, and creating frameworks and algorithms for collecting usable data. Their primary duties lie in collecting data and designing robust data-driven solutions.

While both job descriptions work within the realm of big data, identifying the right path depends on your interests, skills, and career goals. Whichever path you choose, both data scientists and data analysts are in-demand careers, making them an exciting and rewarding choices for those interested in working with data.

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