What are the Data Science Projects that you should include in your Curriculum Vitae?

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What are the Data Science Projects that you
should include in your Curriculum Vitae?
In todays fast-changing world, data has changed
the way companies view the market. How data has
allowed analysts to understand and predict the
fluctuations that may happen in the market due
to several circumstances both predictable and
uncertain has brought about a rapid change in the
way firms do business nowadays. The explosion of
big data has left firms ranging from
multinational giants to small businesses to grab
the biggest piece of data which might help them
establish their trade in the market and at the
same time globalize and reach the farthest
corners of the globe. The boost in demand for
Data Science professionals has resulted in a
massive shift in trend. Companies are heavily
investing in skilled professionals who have
prior experience in some inter-related data
science projects. As a novice in the field of
data science, the firms require the individual
not only to have hands-on experience in using
the tools but also to understand and evolve the
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mastery of the implication of these tools in
real-life worst-case scenarios. Data Science
professionals should be able to handle big data
sets and a fully integrated data science project
with ease.

• To make it easier to capture the concept here are
  a few data science projects every beginner can
  add to their portfolio to make their CV stand
  apart from the crowd
• Computer Vision Projects
• These data science projects employ the most basic
  software or applications to achieve a highly
  programmable or artificially integrated project
  to yield astonishing results with the least
  upgraded software and hardware.
• Computer Vision projects may include the use of
  applications like Python, MS Excel, etc., to
  achieve a different style of data science
  projects for resume. E.g., data science projects
  which include computer vision functionality have
  employed the use of Python to utilize or enact
  facial recognition post- analysis of pictures or
  portraits of family and members.
• Object Recognition
• Artificial Intelligence and machine learning have
  surpassed their previous versions and advancing
  at an enormous rate of improvement. From facial
  recognition to object detection. The implication
  of such a data science portfolio will help any
  data science professional ace any interview they
  land into. Object detection is a branch of
  computer vision that deals with realizing the
  kind of object in the camera or the picture.
  Object detection is being employed in
  technologies like driverless cars which will be a
  huge leap in the future for mankind. One such
  software that allows the employment of an Object
  detection algorithm with close and approximate
  feedback and results is the YOLO v4.
• Image Classification

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• Image classification comes easy to us humans
  because we are taught about it from the time we
  are born through everyday routine. The same
  cannot be said about machines/computers because
  they do not have a conscience as we humans do.
• Thus, the art of image classification also a
  branch of computer vision helps computers
  reimagine the world around us and identify
  objects and other things in the vicinity. Such
  Data science projects would only aid in enhancing
  the data scientists portfolio. Recently
  Microsoft launched its image classification and
  machine learning application also known as LOBE,
  currently, the application can only classify
  images based on the pre-fed content in the
  application memory bank. The application can also
  be fed new information.
• Image Coloring
• Image coloring is another kind of computer vision
  that helps the user fill colors in images or
  other forms of media by just mapping the size,
  shape, and structure of the object in the image.
  It amalgamates the power of generative
  adversarial networks with semantic class
  distribution learning. As a result, the
  application can imaginatively fill colors through
  a semantic understanding of the captured image.
• One such application is ChromaGAN which employs
  generative networks to employ a color-coding
  sequence into any captured image without any
  human intervention through the semantic class
  distribution learning aspect of artificial
  intelligence or machine learning.
• Natural Language Processing
• With applications like ChatBots, topic modeling,
  and many more Natural Language Processing (NLP)
  at present is the most famous and hottest topic
  in artificial intelligence, machine learning, and
  computer vision. Thus, several multinational
  giants are investing in NLP and looking for
  bright individuals who are well versed with such
  data science projects or have at least worked on
  them as part of their beginners projects.

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6. Electra

• The word stands for (Efficiently Learning
  an Encoder that Classifies Token Replacements
  Accurately) which is a pre-training approach
  aimed at matching or superseding the lowkey
  performance of a masked Language module
  pre-configured by the model employed by BERT
  whilst utilizing the bare minimum computing
  resources for the pre- configuration stage.
• The pre-configuration task in ELECTRA relies on
  detecting replaced keys in the fed sequence.
  This setup employs 2 transformer modules, a
  generator, and a discriminator similar to
  generative adversarial networks.
• Here is a link to the data science portfolio for
  GitHub, GitHub, and Electra paper to give the
  user definitive computing prowess of the two
  applications for comparison.
• Topic Modelling
• This feature may be utilized with an application
  also known as Top2Vec. Top2Vec employs an
  algorithm for discovering semantic assembly or
  subjects in a given set of data. This
  application uses doc2vec to generate semantic
  space.
• This prototype does not necessitate stop-word
  lists, stemming, or lemmatization and it
  automatically discovers the number of subjects.
  The resulting topic vectors are amalgamated with
  the document and word vectors with the distance
  between them representing semantic resemblance.
• ALBERT
• It is the self-supervised learning of language
  depictions. Generally, it is found that
  augmented model magnitude in language
  representation glitches results in enhanced
  performance and a comparative rise in training
  time. To resolve this matter there are proposed
  two methods to diminish the memory consumption
  and training time of the traditional BERT model

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• Piercing the embedding matrix into two smaller
  matrices
• Using repetitive layers split among groups
• According to the researchers, this prototype
  outclassed the GLUE, RACE, and SQUAD scale
  examinations for natural language understanding.
• Time Series Analysis
• It is an influential modeling method that deals
  with annotations having different values at
  different time stamps. It is a highly useful
  technique for companies for forecasting sales,
  traffic on the website, predicting stock prices,
  and much more.
• Rocket
• This is an application that unlike the Time
  series classification is an interesting
  alternative as the time series classification
  feature possesses an order/sequence which is
  unavoidable.
• But the state-of-the-art procedures used for time
  series classification include rich complexity
  and a higher learning curve even on smaller
  datasets. Also, they are not efficient
  against large datasets. Rocket (RandOm
  Convolutional KErnel Transform) can accomplish
  the identical level of precision in just a
  portion of time with the employment of distinct
  algorithms, including convolutional neural
  networks.
• To achieve accuracy and scalability Rocket
  algorithm first utilizes randomized
  convolutional kernels to transform the time
  series features. Later, permits these altered
  structures into a classifier.
• Prophet
• This is an open-source tool employed or utilized
  by Facebook to aid the firm in predicting time
  series data. It crumbles down the time series
  into trends, seasonality, and holidays. Besides,
  Prophet has intuitive parameters that are easy
  to tune.

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It is fully automated, accurate, and fast. Thus,
making the application easy to use for someone
who lacks a deep proficiency in time series
forecast. It employs the best time series that
have robust seasonal effects and several seasons
of historical data. Also, Prophet is vigorous in
missing data and shifts in the trend and
typically handles discrepancies well. There are
multiple other data science projects which can be
employed and utilized which can help a data
scientist with data science projects for resume
building and acing any interview. Data
Scientists personal website may also be another
mode of employing these projects showcasing the
different fields of expertise of the data
scientist.
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