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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>DataTech</title>
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<nav>
<div class="container nav__container grad">
<a href="index.html"><h4>DATA SCIENCE</h4></a>
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<li><a href="#home" class="nav__link">Home</a></li>
<li><a href="#about" class="nav__link">About</a></li>
<li><a href="#courses" class="nav__link">Courses</a></li>
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<h1 class="pge1 title-font sm:text-4xl text-3xl mb-4 font-medium text-white"> <b> Learn <br><span>Data Science</span>
<br class="hidden lg:inline-block"> Interactively</b>
</h1>
<p class="para1 mb-8">Build projects, practice and learn to code from scratch - without leaving your browser.</p>
<a href="./Form/logsig.html" class="bttn bttn-prrimary"><span>Get Started</span></a>
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<h1 class="vision grad3">What is Data Science ?</h1>
<p>
Data science is a deep study of the massive amount of data, which involves extracting meaningful insights from raw, structured, and unstructured data that is processed using the scientific method, different technologies, and algorithms.
<br><br>Data science, or data-driven science, combines different fields of work in statistics and computation in order to interpret data for the purpose of decision making.
<br><br> Data science uses the most powerful hardware, programming systems, and most efficient algorithms to solve the data related problems. It is the future of artificial intelligence.
</p>
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<!----================ END OF What is Data Sc ===-->
<section class="achievement">
<h2 class="headingh2">Achievements</h2>
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<article class="achievement__card">
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<p>Courses</p>
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<!----================ END OF CARDS ===-->
<section class="courses" id="courses">
<h2 class="headingh2">Our Popular Courses</h2>
<div class="container courses__container">
<article class="course">
<div class="course__image">
<img src="./images/3.jpg">
</div>
<div class="course__info">
<h4 class="course_heading">Data Science</h4>
<p class="course_description">
Foundations using R <br />
Johns Hopkins University<br />
Launch Your Career in Data Science. A ten-course introduction to data science, developed and taught by leading professors.
</p>
<a href="https://www.coursera.org/specializations/data-science-foundations-r" class="btn btn-primary">Learn More</a>
</div>
</article>
<article class="course">
<div class="course__image">
<img src="./images/3.jpg">
</div>
<div class="course__info">
<h4 class="course_heading">IBM Data Science</h4>
<p class="course_description">
IBM Skills Network<br />
Kickstart your career in data science & ML. Build data science skills, learn Python & SQL, analyze & visualize data, build machine learning models. No degree or prior experience required.
</p>
<a href="https://www.coursera.org/professional-certificates/ibm-data-science" class="btn btn-primary">Learn More</a>
</div>
</article>
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<div class="course__image">
<img src="./images/3.jpg">
</div>
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<h4 class="course_heading">Deep Learning Specification</h4>
<p class="course_description">
Hawdra University<br />
Become a Machine Learning expert. Master the fundamentals of deep learning and break into AI. Recently updated with cutting-edge techniques!
</p>
<a href="https://www.coursera.org/specializations/deep-learning" class="btn btn-primary">Learn More</a>
</div>
</article>
</div>
</section>
<!--====================== END OF COURSES =====================-->
<section class="faqs" id="faq">
<h2 class="headingh2">Frequently Asked Questions</h2>
<div class="container faqs__container">
<article class="faq">
<div class="faq__icon"><i class="uil uil-plus"></i></div>
<div class="question__answer">
<h4>How do I know the right courses for me?</h4>
<p>
Finding the right courses for yourself can be an important step in shaping your education and
career path.Remember, choosing the right courses is a personal decision, and it's okay to
explore and make adjustments along the way. Be open to new opportunities and remain flexible as
your interests and goals may evolve over time.
</p>
</div>
</article>
<article class="faq">
<div class="faq__icon"><i class="uil uil-plus"></i></div>
<div class="question__answer">
<h4>What is the benefit of Data Science</h4>
<p>
Data science offers numerous benefits and has become a highly sought-after field in recent
years. Here are some key benefits of data science: <br>1- Informed decision-making <br>2-
Improved business outcomes <br>3- Predictive analytics <br>4- Personalized experiences <br>5-
Efficient operations <br>6- Innovation and product development <br>7- Career opportunities
<br>These are just a few of the benefits of data science. As technology advances and data
continues to play a central role in decision-making, the importance of data science is expected
to increase further, making it a valuable field to explore.
</p>
</div>
</article>
<article class="faq">
<div class="faq__icon"><i class="uil uil-plus"></i></div>
<div class="question__answer">
<h4>What is IBM Data Science</h4>
<p>
IBM Data Science refers to the data science offerings and solutions provided by IBM, a leading
technology company. IBM offers a comprehensive suite of tools, platforms, and services that
enable organizations to harness the power of data science for various applications.IBM has a
wide range of data science solutions tailored for different industries, including finance,
healthcare, retail, and more. The company's data science portfolio aims to empower organizations
with the tools and capabilities needed to extract insights from data, drive innovation, and make
data-driven decisions.
</p>
</div>
</article>
<article class="faq">
<div class="faq__icon"><i class="uil uil-plus"></i></div>
<div class="question__answer">
<h4>What is the meaning of Deep Learning</h4>
<p>
Deep learning is a subset of machine learning that focuses on training artificial neural
networks to perform complex tasks by mimicking the structure and functioning of the human brain.
It is a type of learning algorithm that can automatically learn representations of data through
multiple layers of interconnected nodes, also known as artificial neurons or units. <br>Deep
learning algorithms utilize a technique called backpropagation, which involves iteratively
adjusting the weights and biases of the neural network based on the calculated errors or
discrepancies between the predicted outputs and the actual outputs. This iterative learning
process helps the network improve its performance over time and make more accurate predictions
or classifications.
</p>
</div>
</article>
<article class="faq">
<div class="faq__icon"><i class="uil uil-plus"></i></div>
<div class="question__answer">
<h4>Difference b/w Deep learning and Machine learning</h4>
<p>
Deep learning is a subset of machine learning. <br> In traditional machine learning, feature
engineering plays a significant role. This involves manually selecting and engineering relevant
features from the input data. On the other hand, deep learning learns representations
automatically. Deep learning models have the ability to learn hierarchical representations and
extract features from raw data without the need for explicit feature engineering. <br> Deep
learning models typically consist of multiple layers of interconnected artificial neurons,
forming deep neural networks. These networks can be much larger and more complex compared to
traditional machine learning models, which often consist of simpler algorithms like decision
trees, support vector machines, or linear regression. Deep learning architectures allow for the
learning of intricate patterns and representations in the data. <br>It's important to note that
deep learning and traditional machine learning are not mutually exclusive but rather
complementary. Depending on the problem at hand, the available data, and the desired outcomes,
either approach or a combination of both can be utilized.
</p>
</div>
</article>
<article class="faq">
<div class="faq__icon"><i class="uil uil-plus"></i></div>
<div class="question__answer">
<h4>How to kick start career in Data science</h4>
<p>
To kick-start your career in data science, consider the following steps: <br>1- Acquire the
necessary skills and knowledge <br>2- Earn a relevant degree or certification <br>3- Build a
strong portfolio <br>4- Network and engage with the data science community <br>5- Apply for data
science positions and prepare for interviews <br>Remember that building a career in data science
takes time and dedication. Be persistent, keep learning, and actively seek opportunities to
apply your skills. Data science is a multidisciplinary field, so cultivating a diverse skill set
and being adaptable will help you succeed in this exciting and rapidly growing field.
</p>
</div>
</article>
<article class="faq">
<div class="faq__icon"><i class="uil uil-plus"></i></div>
<div class="question__answer">
<h4> What are the essential knowledge required to study data science?</h4>
<p>
To study data science, it is crucial to have a strong foundation in mathematics, including statistics and linear algebra.
Additionally, proficiency in programming languages like Python or R is essential for data manipulation and analysis.
Familiarity with machine learning algorithms and techniques, data visualization, and database querying is also beneficial.
Lastly, having a problem-solving mindset and a curiosity for exploring and interpreting data are valuable traits for a data science learner.
</p>
</div>
</article>
<article class="faq">
<div class="faq__icon"><i class="uil uil-plus"></i></div>
<div class="question__answer">
<h4> What are some popular tools and technologies used in the field of data science?</h4>
<p>
There are several popular tools and technologies used in data science.
Some widely used programming languages include Python and R, which offer extensive libraries and frameworks for data analysis and machine learning.
Data visualization tools like Tableau and Matplotlib help in creating insightful visual representations.
For handling big data, technologies like Apache Hadoop and Spark are commonly used.
Additionally, SQL databases, Jupyter Notebooks, and cloud computing platforms such as
AWS and Google Cloud are widely employed in data science workflows.
</p>
</div>
</article>
<article class="faq">
<div class="faq__icon"><i class="uil uil-plus"></i></div>
<div class="question__answer">
<h4> What are the different types of data used in data science?</h4>
<p>
Data science utilizes various types of data for analysis. Structured data refers to well-organized and formatted data,
typically stored in databases, with fixed fields and rows. Unstructured data, on the other hand, lacks a predefined structure
and includes text documents, social media posts, images, and videos. Semi-structured data lies in between, containing some organizational
elements but without a rigid structure. Time series data captures information collected at regular intervals over time, such as stock prices
or weather data. Geographic data includes information with spatial coordinates, like GPS data or maps. By leveraging these different data types,
data scientists can gain insights and make informed decisions.
</p>
</div>
</article>
<article class="faq">
<div class="faq__icon"><i class="uil uil-plus"></i></div>
<div class="question__answer">
<h4>What are the main challenges faced in the field of data science?</h4>
<p>
Data science faces several challenges in its implementation. One major challenge is dealing with large volumes of data,
known as "big data," which requires scalable infrastructure and efficient processing techniques. Ensuring data quality and
handling missing or inconsistent data is another significant challenge. Extracting meaningful insights from complex and unstructured
data sources can be difficult. Additionally, privacy and ethical concerns surrounding data collection and usage require careful consideration.
Lastly, staying up-to-date with rapidly evolving technologies and techniques in the field poses a continuous learning challenge for data scientists.
</p>
</div>
</article>
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