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Humans are social animals and language is our primary tool to communicate with the society. But, what if machines could understand our language and then act accordingly? Natural Language Processing (NLP) is the science of teaching machines how to understand the language we humans speak and write.

We recently launched an NLP skill test on which a total of 817 people registered. This skill test was designed to test your knowledge of Natural Language Processing. If you are one of those who missed out on this skill test, here are the questions and solutions. We encourage you t go through them irrespective of whether you have gone through any NLP Program or not.

Here are the leaderboard ranking for all the participants.

Overall Distribution

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

You can access the scores here. More than 250 people participated in the skill test and the highest score obtained was 24.

Helpful Resources

Here are some resources to get in-depth knowledge of the subject.

And if you are just getting started with Natural Language Processing, check out most comprehensive programs on NLP-

Skill Test Questions and Answers





F) 1, 2, 3 and 4

Solution: (C)

Lemmatization and stemming are the techniques of keyword normalization, while Levenshtein and Soundex are techniques of string matching.

2) N-grams are defined as the combination of N keywords together. How many bi-grams can be generated from a given sentence:

“Analytics Vidhya is a great source to learn data science”

E) 11

Solution: (C)

Bigrams: Analytics Vidhya, Vidhya is, is a, a great, great source, source to, To learn, learn data, data science

3) How many trigrams phrases can be generated from the following sentence, after performing following text cleaning steps:

Stopword Removal

Replacing punctuations by a single space

“#Analytics-vidhya is a great source to learn @data_science.”

E) 7

Solution: (C)

After performing stopword removal and punctuation replacement the text becomes: “Analytics vidhya great source learn data science”

Trigrams – Analytics vidhya great, vidhya great source, great source learn, source learn data, learn data science

4) Which of the following regular expression can be used to identify date(s) present in the text object:

D) None of the above

Solution: (D)

None if these expressions would be able to identify the dates in this text object.

Question Context 5-6:

You have collected a data of about 10,000 rows of tweet text and no other information. You want to create a tweet classification model that categorizes each of the tweets in three buckets – positive, negative and neutral.

5) Which of the following models can perform tweet classification with regards to context mentioned above?

C) None of the above

Solution: (C)

Since, you are given only the data of tweets and no other information, which means there is no target variable present. One cannot train a supervised learning model, both svm and naive bayes are supervised learning techniques.

A) Naive BayesB) SVMC) None of the above

6) You have created a document term matrix of the data, treating every tweet as one document. Which of the following is correct, in regards to document term matrix?

Removal of stopwords from the data will affect the dimensionality of data

Normalization of words in the data will reduce the dimensionality of data

Converting all the words in lowercase will not affect the dimensionality of the data

F) 1, 2 and 3

Solution: (D)

Choices A and B are correct because stopword removal will decrease the number of features in the matrix, normalization of words will also reduce redundant features, and, converting all words to lowercase will also decrease the dimensionality.

7) Which of the following features can be used for accuracy improvement of a classification model?

E) All of theseSolution: (E)

A) Frequency count of termsB) Vector Notation of sentenceC) Part of Speech TagD) Dependency GrammarE) All of these

All of the techniques can be used for the purpose of engineering features in a model.

8) What percentage of the total statements are correct with regards to Topic Modeling?

It is a supervised learning technique

LDA (Linear Discriminant Analysis) can be used to perform topic modeling

Selection of number of topics in a model does not depend on the size of data

Number of topic terms are directly proportional to size of the data

E) 100

Solution: (A)

LDA is unsupervised learning model, LDA is latent Dirichlet allocation, not Linear discriminant analysis. Selection of the number of topics is directly proportional to the size of the data, while number of topic terms is not directly proportional to the size of the data. Hence none of the statements are correct.

9) In Latent Dirichlet Allocation model for text classification purposes, what does alpha and beta hyperparameter represent-

D) Alpha: density of topics generated within documents, beta: density of terms generated within topics TrueSolution: (D)

A) Alpha: number of topics within documents, beta: number of terms within topics FalseB) Alpha: density of terms generated within topics, beta: density of topics generated within terms FalseC) Alpha: number of topics within documents, beta: number of terms within topics FalseD) Alpha: density of topics generated within documents, beta: density of terms generated within topics True

Option D is correct

10) Solve the equation according to the sentence “I am planning to visit New Delhi to attend Analytics Vidhya Delhi Hackathon”.

C = (# of words with frequency count greater than one)What are the correct values of A, B, and C? E) 6, 4, 3

Solution: (D)

Nouns: I, New, Delhi, Analytics, Vidhya, Delhi, Hackathon (7)

Verbs: am, planning, visit, attend (4)

Hence option D is correct.

11) In a corpus of N documents, one document is randomly picked. The document contains a total of T terms and the term “data” appears K times.

What is the correct value for the product of TF (term frequency) and IDF (inverse-document-frequency), if the term “data” appears in approximately one-third of the total documents?

D) Log(3) / KTSolution: (B)

A) KT * Log(3)B) K * Log(3) / TC) T * Log(3) / KD) Log(3) / KT

formula for TF is K/T

formula for IDF is log(total docs / no of docs containing “data”)

= log(1 / (⅓))

= log (3)

Hence correct choice is Klog(3)/T

Question Context 12 to 14:

12) Which of the following documents contains the same number of terms and the number of terms in the one of the document is not equal to least number of terms in any document in the entire corpus.

D) d5 and d6Solution: (C)

A) d1 and d4B) d6 and d7C) d2 and d4D) d5 and d6

Both of the documents d2 and d4 contains 4 terms and does not contain the least number of terms which is 3.

13) Which are the most common and the rarest term of the corpus?

D) t5, t6Solution: (A)

A) t4, t6B) t3, t5C) t5, t1D) t5, t6

T5 is most common terms across 5 out of 7 documents, T6 is rare term only appears in d3 and d4

14) What is the term frequency of a term which is used a maximum number of times in that document?

D) t1 – 2/6Solution: (B)

A) t6 – 2/5B) t3 – 3/6C) t4 – 2/6D) t1 – 2/6

t3 is used max times in entire corpus = 3, tf for t3 is 3/6

15) Which of the following technique is not a part of flexible text matching?

D) Keyword HashingSolution: (D)

A) SoundexB) MetaphoneC) Edit DistanceD) Keyword Hashing

Except Keyword Hashing all other are the techniques used in flexible string matching

16) True or False: Word2Vec model is a machine learning model used to create vector notations of text objects. Word2vec contains multiple deep neural networks

B) FALSESolution: (B)


Word2vec also contains preprocessing model which is not a deep neural network

17) Which of the following statement is(are) true for Word2Vec model?

D) All of the aboveSolution: (C)

A) The architecture of word2vec consists of only two layers – continuous bag of words and skip-gram modelB) Continuous bag of word (CBOW) is a Recurrent Neural Network modelC) Both CBOW and Skip-gram are shallow neural network modelsD) All of the above

Word2vec contains the Continuous bag of words and skip-gram models, which are deep neural nets.

D) 6Solution: (D)

A) 3B) 4C) 5D) 6

Subtrees in the dependency graph can be viewed as nodes having an outward link, for example:

Media, networking, play, role, billions, and lives are the roots of subtrees

Text cleaning

Text annotation

Gradient descent

Model tuning

Text to predictors

D) 13452

Solution: (C)

A right text classification model contains – cleaning of text to remove noise, annotation to create more features, converting text-based features into predictors, learning a model using gradient descent and finally tuning a model.

20) Polysemy is defined as the coexistence of multiple meanings for a word or phrase in a text object. Which of the following models is likely the best choice to correct this problem?

D) All of theseSolution: (B)

A) Random Forest ClassifierB) Convolutional Neural NetworksC) Gradient BoostingD) All of these

CNNs are popular choice for text classification problems because they take into consideration left and right contexts of the words as features which can solve the problem of polysemy

21) Which of the following models can be used for the purpose of document similarity?

D) All of the aboveSolution: (D)

A) Training a word 2 vector model on the corpus that learns context present in the documentB) Training a bag of words model that learns occurrence of words in the documentC) Creating a document-term matrix and using cosine similarity for each documentD) All of the above

word2vec model can be used for measuring document similarity based on context. Bag Of Words and document term matrix can be used for measuring similarity based on terms.

22) What are the possible features of a text corpus

Count of word in a document

Boolean feature – presence of word in a document

Vector notation of word

Part of Speech Tag

Basic Dependency Grammar

Entire document as a feature

F) 123456

Solution: (E)

Except for entire document as the feature, rest all can be used as features of text classification learning model.

23) While creating a machine learning model on text data, you created a document term matrix of the input data of 100K documents. Which of the following remedies can be used to reduce the dimensions of data –

Latent Dirichlet Allocation

Latent Semantic Indexing

Keyword Normalization

D) 1, 2, 3

Solution: (D)

All of the techniques can be used to reduce the dimensions of the data.

24) Google Search’s feature – “Did you mean”, is a mixture of different techniques. Which of the following techniques are likely to be ingredients?

Collaborative Filtering model to detect similar user behaviors (queries)

Model that checks for Levenshtein distance among the dictionary terms

Translation of sentences into multiple languages

D) 1, 2, 3

Solution: (C)

Collaborative filtering can be used to check what are the patterns used by people, Levenshtein is used to measure the distance among dictionary terms.

25) While working with text data obtained from news sentences, which are structured in nature, which of the grammar-based text parsing techniques can be used for noun phrase detection, verb phrase detection, subject detection and object detection.

D) Continuous Bag of WordsSolution: (B)

A) Part of speech taggingB) Dependency Parsing and Constituency ParsingC) Skip Gram and N-Gram extractionD) Continuous Bag of Words

Dependency and constituent parsing extract these relations from the text

26) Social Media platforms are the most intuitive form of text data. You are given a corpus of complete social media data of tweets. How can you create a model that suggests the hashtags?

D) All of theseSolution: (D)

A) Perform Topic Models to obtain most significant words of the corpusB) Train a Bag of Ngrams model to capture top n-grams – words and their combinationsC) Train a word2vector model to learn repeating contexts in the sentencesD) All of these

All of the techniques can be used to extract most significant terms of a corpus.

27) While working with context extraction from a text data, you encountered two different sentences: The tank is full of soldiers. The tank is full of nitrogen. Which of the following measures can be used to remove the problem of word sense disambiguation in the sentences?

C) Use dependency parsing of sentence to understand the meaningsSolution: (A)

A) Compare the dictionary definition of an ambiguous word with the terms contained in its neighborhoodB) Co-reference resolution in which one resolute the meaning of ambiguous word with the proper noun present in the previous sentenceC) Use dependency parsing of sentence to understand the meanings

Option 1 is called Lesk algorithm, used for word sense disambiguation, rest others cannot be used.

28) Collaborative Filtering and Content Based Models are the two popular recommendation engines, what role does NLP play in building such algorithms.

D) All of theseSolution: (D)

A) Feature Extraction from textB) Measuring Feature SimilarityC) Engineering Features for vector space learning modelD) All of these

NLP can be used anywhere where text data is involved – feature extraction, measuring feature similarity, create vector features of the text.

29) Retrieval based models and Generative models are the two popular techniques used for building chatbots. Which of the following is an example of retrieval model and generative model respectively.

D) Recurrent neural network and convolutional neural networkSolution: (B)

A) Dictionary based learning and Word 2 vector modelB) Rule-based learning and Sequence to Sequence modelC) Word 2 vector and Sentence to Vector modelD) Recurrent neural network and convolutional neural network

choice 2 best explains examples of retrieval based models and generative models

30) What is the major difference between CRF (Conditional Random Field) and HMM (Hidden Markov Model)?

D) Both CRF and HMM are Discriminative modelSolution: (B)

A) CRF is Generative whereas HMM is Discriminative modelB) CRF is Discriminative whereas HMM is Generative modelC) Both CRF and HMM are Generative modelD) Both CRF and HMM are Discriminative model

Option B is correct

End Notes

If you want to learn more about Natural Language Processing and how it is implemented in Python, then check out our video course on NLP using Python.

Happy Learning!


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The Difference Between Data Scientist Vs Machine Learning Scientist

Detail analysis of the career differences among data scientists and machine-learning scientists. You can learn AI skills now, whether you are just starting out or recently laid off.

Today we’ll learn more about the differences in career between Data Scientists & Machine Learning Scientists

What is Data Science?

Data science is the in-depth analysis of huge amounts of data stored in an organization’s or company’s archive. This includes analyzing the data’s origin and quality, as well as determining if it can be used for future corporate development.

Data scientists specialize in the transformation of unstructured data into business information. These experts are familiar with algorithms, data processing, artificial intelligence, statistics, and other forms of programming.

Also read: Top 7 Work Operating Systems of 2023

What’s Machine Learning?

Machine learning is a branch of computer science that allows computers to learn by themselves without needing to be programmed.

Machine learning is the use of algorithms to analyze data and make predictions, without the involvement of humans. Machine Learning relies on a series of instructions, information, or observations as inputs. Machine learning is used extensively by companies like Facebook, Google, etc.

The Difference between Data Scientists & Machine Learning Scientists

These jobs may seem similar to recruiters. However, if you’re a specialist in one of these areas, you will know that there’s a difference. Both professions depend on machine learning algorithms but their day-to-day tasks may be quite different.

Machine learning scientists specialize in use cases such as signal processing, object identification, automobile/self-driving, and robots, whereas data scientists work on use factors like fraud detection, product categorization, or customer segmentation.

Data Scientists

Data scientists may have more standard job descriptions. They might also be required to learn the skills and education that they need.

A data scientist is expected to identify a problem and create a dataset. Then, they will evaluate machine learning algorithms, produce results, analyze those results, and then communicate the results with stakeholders. Data scientists are focused on business and stakeholder collaboration.

Also read: Best 10 Email Marketing Tools in 2023

Data scientists can expect to get the following education and skills.


BS or MS degree oriented

Data Science


Business Analytics


Python or R

Data Analysis


Jupyter Notebook



Model Building

Data scientists are often able to use code in Python or R to automate projections using machine-learning tools.

There may be a different path to becoming either a data scientist or a machine-learning scientist. For example, a data scientist may have worked as a statistician, business analyst, data analyst, or business intelligence analyst before becoming one.

Also read: The Five Best Free Cattle Record Keeping Apps & Software For Farmers/Ranchers/Cattle Owners

Machine Learning Scientists

Machine learning scientists are, however, more focused on algorithms and the software engineering involved in implementing them. Machine learning scientists often use the term “research” in their titles.

This means that you need to spend more time learning algorithms before creating a simpler method. These positions might be identical at different companies, so it is up to you to spot the differences when you read job descriptions.

Also read: How to Calculate Your Body Temperature with an iPhone Using Smart Thermometer

The following are some of the variations in education and abilities required:


degree oriented

Machine Learning

Computer Science






Signals & Distributed Systems


C++ or C

Quality Assurance


Model Deployment


Artificial Intelligence


Data science is an interdisciplinary field that draws insights from large amounts of data and high computing power. Machine learning is one of the most exciting developments in modern data science.

Machine learning allows machines to learn from large amounts of data and operate independently. Although these technologies have many applications, they do not come without their limitations. Data science is powerful but can only be used to its full extent if there are skilled workers and high-quality data.

Should I Become A Data Scientist (Or A Business Analyst)?


One of the common queries I come across repeatedly on several forums is “Should I become a data scientist (or an analyst)?” The query takes various forms and factors, but here is a common real-life anecdote:

“I have been doing Sales for multiple BFSI giants for last 3 years, but I have stopped enjoying my role. After reading about Business Analytics and machine learning, my interest in this area has grown. Should I make a switch and learn data science? If so, How do I do this?

When I reflect back on how I took the decision, I realized – I happened to be lucky! The decision was relatively easier for me. Why? I knew the industries/roles, I would not enjoy – these included roles in Sales, roles in Physical engineering, and a few others. I was open to roles in data science in retail banks and investment banks and luckily ended up with Capital One.

Today, after spending ~8 years in the industry, it is far easier for me to guide and mentor people on whether Analytics is the right role for them or not. So, I thought, I’ll try and put my thoughts in a framework and share it with the audience of this blog. The aim of this post is to help those people who are sitting on the fence and thinking about which job/role is right for them. So, if you are someone deliberating a move in data science or are wondering whether you are a right fit with this industry, here is a neat framework that might help.

The role of a mentor in building a career is priceless. Being from the industry, the mentor can help you navigate your learning path so that you don’t fall into traps. Certified AI & ML BlackBelt Plus Program comes with 100+ hours of live-course, 100+hours of self-paced video, 18+ real-life projects, and the most important – 1:1 mentorship so that you can focus on becoming an industry-ready professional with the relevant guidance. 🙂


I have put a framework in the form of a very simple test. This test is based on the attributes every analyst should possess. You should score yourself against each of the questions (out of the score mentioned after the question) and then add your scores. A good analyst should score more than 70 and anyone scoring below 50 should seriously re-consider a decision to be a data scientist.

Test Questions:

Do you love number crunching and logical problem solving – i.e. puzzles, probabilities, and statistics? (score out of 20)

By love I don’t mean like, I don’t mean you don’t mind numbers – I mean, do you have an obsession with numbers! Do you love doing guess-estimates at any time of the day – I have done those estimates while I am taking a shower, while I am driving, while I am watching a movie, or even when I am swimming (and lost my count of laps)! I know my friend Tavish does these calculations in his mind too – while he is driving or while he is playing badminton. If you want me to space out of a discussion, just ask me a really hard logical problem!


5 – dread mathematics & statistics, but can face to some extent

10 – Comfortable with mathematics and statistics, but need calculators and excel to work on problems. Don’t mind attempting puzzles

15 – Love crunching numbers and solving logical puzzles anywhere

20 – Can’t live without number crunching and logical puzzles – an obsession!

Do you enjoy working/handling unstructured problems? (score out of 20)

An analyst will inevitably be tested against unstructured and amorphous business problems. And it is how you solve these unstructured problems, that decides how good or bad an analyst you are. My first project in my first role stated: “In last few months, we have seen a high increase in high-risk customers of type X. You need to come up with a data-based strategy to measure, control, and improve this situation.“

Even the business did not have a clear definition of these customers. Can you handle this kind of ambiguity and provide a direction on your own? Do you enjoy these situations or you would rather be comfortable in a more defined role?


5 – Have tried these problems in past – but not my cup of tea!

10 – A score of 10 would mean, you like solving these problems once in a while (say 3 – 6 months)

15+ – You prefer unstructured problems over-structured. You don’t enjoy someone else structuring problems for you.

Do you enjoy deep research and can spend hours slicing and dicing data? (score out of 20)

Going back to the first project I faced, it took me 3 months to understand the business, have multiple discussions with stakeholders, brings them on the same page, and then mine the data to bring out solutions. You need an outlook of a researcher to be a good business analyst. When was the last time you spent hours and hours immersed in solving a problem? Can you do that again and again?


5 – You want a change every few hours. You can’t work on a single problem for the entire day

10 – You can work on a research problem – but need some side work to help you out of boredom

15 – You feel the side work is distracting you from making progress on the key problem you are working on. Would be happy if they are taken away

20 – Can’t stand distractions

Do you enjoy building and presenting evidence-based stories? (score out of 20)

A data scientist needs to be a fluid presenter. What is the use of all the hard work, if he is not able to influence his stakeholders? Communicating with data and presenting stories backed by data is one of the most important elements in the life of a data scientist. Imagine being part of companies like Google and Amazon – you have all the data you need (probably more than that) for the domain you are working on, but you need to convert it into a meaningful story, present it to the stakeholders and influence them to take the right decision!


5 – You struggle to communicate my mathematical thoughts to the audience

10 – You can manage telling stories with a lot of practice. Can’t think of doing this on the fly!

15+ – Any time, anywhere!

Do you always find yourself questioning people’s assumptions and are always curious to know ‘Why”? (score out of 10)

This is probably the best part and the most fun part! Here is a quote a read somewhere on Linkedin: Arguing with an Engineer is a lot like wrestling in the mud with a pig: After a few hours, you realize the pig likes it. Similarly, asking why comes naturally to a good data scientist. Some of the best data scientists would stop anyone and ask for a rationale if they are not clear – Why did you ask this question? What was your thought process? Why do you assume so? are just a few examples of these questions!


5 – You only ask questions when they are critical to be asked

8+ – You can’t stand the anxiety of not understanding something! Jumping to ask questions!

Do you enjoy problem-solving and thrive on intellectual challenges? (score out of 10)

Analysts require a knack for problem-solving. Most of the problems businesses would face would be unique to them and it would take a smart solver to solve them. Solutions, which work for one organization may not work for another – you need to be someone who quickly develops a deep understanding of a problem and then come out with innovative ways to solve these problems


3 – You don’t mind thinking about solving problems – but you struggle.

6 – You can solve problems at times

9 / 10 – You just love the process of intellectual thinking

End Notes:

What is my score? I would score somewhere between 80 – 85 on this test. It is your turn now. Do take the test and let me know, how much do you score? Also, do let me know if you think the test was helpful or otherwise.

Did you like this framework? We at Analytics Vidhya follow an analytical approach to problem-solving. If you want to become a data scientist with this analytical mindset, check out the Certified AI & ML BlackBelt Plus Program which offers 100+ hours of live-course, 100+hours of self-paced video, 18+ real-life projects, and the most important – 1:1 mentorship. The course is carefully crafted by experts so that you can become an industry-ready professional! 

Now that you know that you can / can not become a data scientist, you might be asking “How do I become a data scientist?”. Here’s the Roadmap – 


Data Scientist Vs Machine Learning

Differences Between Data Scientist vs Machine Learning

Hadoop, Data Science, Statistics & others

Data Scientist

Standard tasks:

Allocate, aggregate, and synthesize data from various structured and unstructured sources.

Explore, develop, and apply intelligent learning to real-world data and provide essential findings and successful actions based on them.

Analyze and provide data collected in the organization.

Design and build new processes for modeling, data mining, and implementation.

Develop prototypes, algorithms, predictive models, and prototypes.

Carry out requests for data analysis and communicate their findings and decisions.

In addition, there are more specific tasks depending on the domain in which the employer works, or the project is being implemented.

Machine Learning

The Machine Learning Engineer position is more “technical.” ML Engineer has more in common with classical Software Engineering than Data Scientists. It helps you learn the objective function, which plots the inputs to the target variable and independent variables to the dependent variables.

The standard tasks of ML Engineers are generally like Data scientists. You also need to be able to work with data, experiment with various Machine Learning algorithms that will solve the task, and create prototypes and ready-made solutions.

Strong programming skills in one or more popular languages (usually Python and Java) and databases.

Less emphasis on the ability to work in data analysis environments but more emphasis on Machine Learning algorithms.

R and Python for modeling are preferable to Matlab, SPSS, and SAS.

Ability to use ready-made libraries for various stacks in the application, for example, Mahout, Lucene for Java, and NumPy / SciPy for Python.

Ability to create distributed applications using Hadoop and other solutions.

As you can see, the position of ML Engineer (or narrower) requires more knowledge in Software Engineering and, accordingly, is well suited for experienced developers. The case often works when the usual developer must solve the ML task for his duty, and he starts to understand the necessary algorithms and libraries.

Head-to-Head Comparison Between Data Scientist and Machine Learning (Infographics)

Below are the top 5 differences between Data scientists and Machine Learning:

Key Difference Between Data Scientist and Machine Learning

Below are the lists of points that describe the key Differences Between Data Scientist and Machine Learning:

Machine learning and statistics are part of data science. The word learning in machine learning means that the algorithms depend on data used as a training set to fine-tune some model or algorithm parameters. This encompasses many techniques, such as regression, naive Bayes, or supervised clustering. But not all styles fit in this category. For instance, unsupervised clustering – a statistical and data science technique – aims at detecting clusters and cluster structures without any a-prior knowledge or training set to help the classification algorithm. A human being is needed to label the clusters found. Some techniques are hybrid, such as semi-supervised classification. Some pattern detection or density estimation techniques fit into this category.

Data science is much more than machine learning, though. Data in data science may or may not come from a machine or mechanical process (survey data could be manually collected, and clinical trials involve a specific type of small data), and it might have nothing to do with learning, as I have just discussed. But the main difference is that data science covers the whole spectrum of data processing, not just the algorithmic or statistical aspects. Data science also covers data integration, distributed architecture, automated machine learning, data visualization, dashboards, and Big data engineering.

Data Scientist and Machine Learning Comparison Table

Feature Data Scientist Machine Learning

Data It mainly focuses on extracting details of data in tabular or images. It mainly focuses on algorithms, polynomial structures, and word adding.

Complexity It handles unstructured data, and it works with a scheduler. It uses Algorithms and mathematical concepts, statistics, and spatial analysis.

Hardware Requirement Systems are Horizontally scalable and have High Disk and RAM storage. It requires Graphic processors and Tensor Processors, that is very high-level hardware.

Skills Data Profiling, ETL, NoSQL, Reporting. Python, R, Maths, Stats, SQL Model.

Focus Focuses on abilities to handle the data. Algorithms are used to gain knowledge from huge amounts of data.


Machine learning helps you learn the objective function, which plots the inputs to the target variable and independent variables to the dependent variables.

A Data scientist does a lot of data exploration and arrives at a broad strategy for tackling it. He is responsible for asking questions about the data and finding what answers one can reasonably draw from the data. Feature engineering belongs to the realm of Data scientists. Creativity also plays a role here, and An Machine Learning engineer knows more tools and can build models given a set of features and data – as per directions from the Data Scientist. The realm of Data preprocessing and feature extraction belongs to ML engineers.

Data science and examination utilize machine learning for this archetypal validation and creation. It is vital to note that all the algorithms in this model creation may not come from machine learning. They can arrive from numerous other fields. The model desires to be kept relevant always. If the situations change, the model we created earlier may become immaterial. The model must be checked for certainty at different times and adapted if its confidence reduces.

Data science is a whole extensive domain. If we try to put it in a pipeline, it would have data acquisition, data storage, data preprocessing or cleaning, learning patterns in data (via machine learning), and using knowledge for predictions. This is one way to understand how machine learning fits into data science.

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This is a guide to Data Scientist vs Machine Learning. Here we have discussed Data Scientist vs Machine Learning head-to-head comparison, key differences, infographics, and comparison table. You may also look at the following articles to learn more –

How To Run A Memory Test On Windows 10

How to Run a Memory Test on Windows 10 [Quick steps] Check out the different ways to run the Memory Diagnostic Tool on your PC




The Memory Diagnostic Tool can help you fix some memory-related issues and errors.

You can access the tool on your PC in different ways.

You can open the Diagnostic Tool via the Command Prompt or the Run dialogue.



Easily get rid of Windows errors

Fortect is a system repair tool that can scan your complete system for damaged or missing OS files and replace them with working versions from its repository automatically. Boost your PC performance in three easy steps:

Download and Install Fortect on your PC.

Launch the tool and Start scanning


readers have already downloaded Fortect so far this month

While this tool is a saver, users can run into trouble opening this tool. So, in this guide, we will share many quick methods to run a memory test on Windows 10 using the Memory Diagnostic tool. So, let us get right into it.

How can I run a memory test on Windows 10 using the Diagnostic Tool? 1. Use the Start menu

Press the Win key to open the Start menu.

Type Windows Memory Diagnostic and open it.

You can select from the two below options:

Restart now and check for problems (recommended)

Check for problems the next time I start my computer

The tool will find any issues and fix the problem.

This is the easiest way to access the Windows Memory Diagnostic tool on your Windows 10 PC. But, of course, you can also follow the same steps for Windows 11.

2. Use Windows Search

Press the Win + S keys to open the Windows Search.

Type Windows Memory Diagnostic and open it.

Select from the two below options:

Restart now and check for problems (recommended)

Check for problems the next time I start my computer

The tool will find any issues and fix the problem.

To run the memory test on Windows 10, you can also run the Windows Memory Diagnostic tool from the Windows Search.

3. Use the Command Prompt

Press the Win key to open the Start menu.

Open Command Prompt as an admin.

Type the below command to run the Windows Memory Diagnostic tool and hit Enter. MdSched

Choose any of the two below options:

Restart now and check for problems (recommended)

Check for problems the next time I start my computer

The tool will find any issues and fix the issue.

Command Prompt is another way to help you run a memory test on Windows 10. This method could come in handy when your PC isn’t booting.

In that case, you can access the recovery mode, open the command prompt, and run the tool to fix memory issues.

4. Use the system settings

Expert tip:

5. Use the Control Panel

Control Panel is another option for running the memory test on Windows 10 using the Diagnostic Tool.

6. Use File Explorer

Open the File Explorer.

In the address bar, type MdSched and hit Enter.

Choose any of the two below options:

Restart now and check for problems (recommended)

Check for problems the next time I start my computer

The tool will find any problems and fix the issue.

7. Use the Task Manager

It is a bit complicated to run the memory test on Windows 10; however, it can come in handy when all you have access to is the Task Manager.

8. Use the Run dialogue

Press the Win + R key to open the Run dialogue.

Type MdSched and press Enter.

You can choose from any of the two below options:

Restart now and check for problems (recommended)

Check for problems the next time I start my computer

The tool will find any problems and fix the issue.

That is it from us in this guide. Our guide explains what you can do if the Memory Diagnostic Tool gets stuck on your PC.

If you are getting a hardware problem with the Memory Diagnostic Tool, then you can refer to the solutions in our guide to fixing the issue.

For users facing the Memory Refresh Timer error on their Windows PCs, we have a guide that explains a bunch of solutions to resolve the problem.

Still experiencing issues?

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Trees In Data Structure Every Data Scientist Should Know About

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


Data structures refer to the pattern of data arrangement on a disK that allows for convenient storage and display in the computing domain. They are related to the field of data science, which is expected to be a lucrative career choice in 2023. Large-scale deep learning models and next-generation smart devices, according to predictions for the next few years, will pave the way for this sector’s future.

We use Data structures to design the pathways for allocation, management, and retrieval of information. Data structures are especially important for drafting and improving the overall efficiency of processed data. They manage data by grouping and organising it to make information exchange more efficient.

Trees in Data Structures

ADTs (Abstract Data Types) which follow a hierarchical pattern for data allocation is known as ‘trees.’ A tree is essentially a collection of multiple nodes connected by edges. These ‘trees’ form a tree-like data structure, with the ‘root’ node leading to ‘parent’ nodes, which eventually lead to ‘children’ nodes. The connections which are formed by lines known as ‘edges.’

Endpoints that have no children nodes are referred to as ‘leaf’ nodes. Trees in data structures play an important role due to the non-linear nature of their structure. This allows for a faster response time during a search as well as greater convenience during the design process.

Types of Trees in Data Structure

1. General Tree

2. Binary Tree

3. Binary Search Tree

4. AVL Tree

5. Red Black Tree

6. Splay Tree

7. Treap

8. B-Tree

Let’s discuss each in detail below:

1. General Tree

A general tree is characterised by the lack of any configuration or limitations on the number of children a node can have. Any tree with a hierarchical structure can be described as a general tree. A node can have any number of children, and the tree’s orientation can be any combination of these. The degree of the nodes can range from 0 to n.

The data structure below is a classic example of a general tree, with ‘2′ at the top as the root node.


2. Binary Tree

A binary tree is made up of nodes that can have two children, as described by the word “binary,” which means “two numbers.” In a binary tree, any node can have a maximum of 0, 1, or 2 nodes. Data structures’ binary trees are highly functional ADTs that can be further subdivided into a variety of types.

They are most commonly used in data structures for two reasons:

1) For obtaining nodes and categorising them, as observed in Binary Search Trees.

2) For representing data through a bifurcating structure.

A basic diagram of a binary tree is a data structure is shown below:


3. Binary Search Tree

A Binary Search Tree (BST) is a subtype of binary tree that is organised in such a way that it allows for faster searching, lookup, and addition/removal of data. The representation of nodes in a BST is defined by three fields: the data, its left child, and its right child.

BST is governed by the following factors:

· Every node on the left side (left child) must have a value less than the value of its parent node.

· Every node on the right side (right child) must have a higher value than its parent node.

An arrangement like this reduces the search times to half of a linear search, as found in an array. In comparison to other ADTs, binary search trees in data structures are widely applicable for searching and sorting.


Despite the fact that both BTs and BSTs are primarily trees in data structures, but don’t be confused by their names.

4. AVL Tree

The Adelson-Velsky-Landis (AVL) tree is named after its creators, Adelson-Velsky and Landis. The self-balancing characteristic of the AVL tree is unique. The heights of two subtrees from its root nodes are limited to two. The child nodes are rebalanced when the height difference exceeds one.

They (AVL trees) are height-balanced and are rebalanced through single or double rotations. The balancing factor is the difference in heights between the left and right subtrees, and its values are -1, 0, and 1.

AVL tree with balance factors (green).  SOURCE 

5. Red Black Tree

This type is similar to the AVL trees because red-black trees are also height-balanced. What differentiates them is that they can be balanced in less than three rotations. They have an extra bit that defines whether a node is red or black, these colours are mainly used to make sure that the tree remains balanced during insertions and deletions. During changes, the red-black colour coding is also repainted but at almost no extra cost of memory.

6. Splay Tree

The splay tree, a subtype of the binary search tree, has the unique feature of performing rotational operations to adjust the most recent node. By performing a rotation, the most recently accessed node is arranged as the root node. Although it is a balanced tree, it is not of the height-balanced variety.

As tree rotations are performed in a specific manner, the act of ‘splaying’ occurs after the initial binary tree search. The tree is rotated to balance itself after each operation, and the searched element is arranged at the top as a root node.


7. Treap

Treaps are a combination of trees and heaps in data structures. In BSTs, the value of the left child must be less than the value of the root node, while the value of the right child must be greater. The root node in a heap data structure has the lowest value, and its child nodes (both left and right) have higher values.

As a result, a treap has a value in the form of a key (similar to BSTs) and a priority (like heaps). The nodes with the highest priority are inserted first into a binary search tree so that the priority numbers are independent random numbers. They keep a dynamic set of ordered keys and support binary searches within them.

A treap with alphabetic key and numeric max heap order. SOURCE

8. B-Tree

B-Tree is a self-balancing type of tree in data structures that sorts data in logarithmic time to allow for search, sequential access, deletions, and insertions. A B-tree, unlike a binary tree, allows its nodes to have more than two children. They can read and write larger blocks of data in databases and file systems.

In data structures, a B-tree is used for larger storage systems, such as discs. All of the leaves contain no information and appear on the same level. A B-tree’s internal nodes can have a range of child nodes with varying sizes.



These are the trees in data structures that programmers use to design the flow of data. Understanding their distinct characteristics and applications is important to your journey to becoming a data scientist. Another way to improve your skills is to work on projects that require knowledge of trees in data structures and other types of ADTs.

About The Author Prashant Sharma

Currently, I Am pursuing my Bachelors of Technology( B.Tech) from Vellore Institute of Technology. I am very enthusiastic about programming and its real applications including software development, machine learning and data science.

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