Data Science Vs Machine Learning Vs Data Analytics - PowerPoint PPT Presentation

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Data Science Vs Machine Learning Vs Data Analytics

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Terms like ‘Data Science’, ‘Machine Learning’, and ‘Data Analytics’ are so infused and embedded in almost every dimension of lifestyle that imagining a day without these smart technologies is next to impossible. However, quite often it is witnessed that beginners get confused over similar terms being used interchangeably, like ‘Data Science’ and ‘Data Analytics’. This PPT gives you a clear idea about why should you choose a particular Data field and what are career prospects in that domain. Read the detailed blog here: – PowerPoint PPT presentation

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Title: Data Science Vs Machine Learning Vs Data Analytics


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What is Data Science?
Data Science is a field of technology that
deals with exploring, modeling, and analyzing
the big data to get meaningful insights from
them that can solve a crucial business problem
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Data Scientist Major Responsibilities
  • Feature Engineering To prepare the proper input
    dataset that is compatible with the requirements
    of Machine Learning Algorithm.
  • Predictive Modeling To predict the outcomes with
    the help of data models. These models are used
    for predicting various activities, events,
    phenomenon, etc.
  • Machine Learning and Deep Learning Machine
    Learning seeks to educate the machines without
    human intervention. Deep Learning deals with
    artificial neural network which is nothing but
    multiple layers of algorithms.

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Skills Required to Become a Data Scientist
  • Common Data Science Skills with Data Analytics
    Can be divided into Technical and Non-Technical
    Data Science Skills.
  • Common Data Science Skills (Technical)
    Programming, Data Visualization
  • Common Data Science Skills (Non-Technical)
    Presentation Communication Skills, Business
    Thinking
  • Data Science and Data Analytics are quite similar
    in broader perspective.
  • Unique Data Science Skills Can be divided into
    Technical and Non-Technical Data Science Skills.
  • Unique Data Science Skills (Technical) Database,
    Statistics Mathematics
  • Unique Data Science Skills (Non-Technical)
    Problem-solving, Data-driven Decision-Making

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Qualification Required to Become a Data Scientist
  • Bachelor's Degree in Computer Science, IT, or
    Related Field Earning a Bachelors Degree in CS,
    IT or any related field will make the journey a
    little easier.
  • Master's Degree in Computer Science or Related
    Field Earning a Masters Degree in Data Science
    will introduce a candidate to the high-level
    programming and integration.
  • Experience in Related Field Starting-off as an
    entry-level Data Analyst, or Data engineer, or
    Business Analyst will help you gain domain
    acumen, as well as technical expertise.

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What is Machine Learning?
Machine learning is a field that deals with
educating the machines to make them intelligent.
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Machine Learning Expert Major Responsibilities
  • Machine Learning Experiments To undertake
    various experiments and tests and run them. Fine
    tune the test results and implement them.
  • Train and Retain the System To develop models
    that are capable of learning continually from a
    stream a data.
  • Perform Statistical Analysis To select the
    appropriate datasets and data representation
    methods to run statistical analysis and fine-tune
    the test results.
  • Extend ML Frameworks To work towards extending
    the existing ML libraries and frameworks.

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Skills Required to Become a Machine Learning
Expert
  • Common Machine Learning Skills with Data Science
    Can be divided into Technical and Non-Technical
    Data Science Skills.
  • Common Machine Learning Skills (Technical)
    Programming, Statistics Mathematics
  • Common Machine Learning Skills (Non-Technical)
    Presentation Communication Skills
  • Machine Learning is quite different from Data
    Science. However, there are some shared
    attributes between these two domains.
  • Unique Machine Learning Skills Can be divided
    into Technical and Non-Technical Data Science
    Skills.
  • Unique Machine Learning Skills (Technical)
    Software Designing, Machine Learning Algorithm,
    Computer Science, Data Visualization
  • Unique Machine Learning (Non-Technical) Working
    in Teams, Time Management, Leadership

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Qualification Required to become a Data Analyst
  • Earn Qualification
  • A Data Analyst needs to acquire either a
    Bachelors Degree in
  • Business related fields.
  • Gain Work Experience
  • A Data Analyst needs to acquire either a
    Bachelors Degree in
  • Business related fields.

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Data Science vs Data Analytics Which One to
Choose?
Fit for Data Science Fit for Data Analytics
For professionals who are good at problem-solving. For professionals who are good at computation.
Suited for candidates with strong Programming and Data Visualization skills. Suited for candidates with strong Database and Programming skills.
Better suited for people who have worked as BI engineers, business analysts, IT application engineers, Architects, and Data analysts. Better suited for people who have worked as database administrators, data warehousing professionals, QA engineers, and associates in Sales, Marketing, etc.
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Data Science Vs Machine Learning Vs Data Analytics
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Conclusion 
Modern technology field is blurring the
industrial boundaries, thanks to the explosion of
data. As the dependence on data growing
immensely, the need for having distinctive fields
for separate uses has become imperative. Data
Science, Machine Learning, and Data Analytics are
three such fields, which have confused aspirants
to a great extent. These slides will give you a
clear picture about the three fields and why
should you choose either of them. Click here to
read the blog
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