Save 37% off PRO during our Black Friday Sale! »

Data Science Course

754f65920170f44b83e0f27677cf73b1?s=47 skillslash
November 23, 2021

Data Science Course

"Skillslash – not just another data science institute.
We not only provides multiple online data science certification courses but we also provide you an opportunity to learn by working in live projects under Industry experts. To fetch a job in Data Science field your skills matters more than just certification.
It doesn’t matter whether you are a college student or a professional, we help you grow in this field of data science through our courses on:
Artificial Intelligence and machine learning for professional
Data Science and Artificial Intelligence program for students
We believe in delivering the best, with all our trainers and instructor being data scientists with industry experience. They will provide you with an environment to learn few of the most industry-oriented tool like R, Python, Power BI, AWS, Hadoop, Scala, Hive, Tableau, SQL and many more.
We provide you 3yr subscription to program with belief that even after getting/transitioning job you will need to keep learning and upskilling yourself. Cherry on top is the BYOC (Build Your Own Course track) program. So, as per your experience and current job role, we counsel you for a track that can get you in the field of data science.
This program will give guidance to those who are looking to switch their domain by providing an experience in multiple domains like
Banking
E-commerce
Manufacturing
Healthcare
Finance
Insurance
Retail etc.
It doesn’t end here, Skillslash also prepares you for data science jobs through
Resume Building
Mock Interviews
Job Referrals
In the end of your training program, as many institutes provide you a completion certificate of their respective institute, but We will provide you project experience certificate from Industry experts along with our course completion certificate. Project certificate has found to be more valuable in career transition for professionals.
We are living in the world of Steve Jobs, Elon Musk and Zuckerberg, who have proven that skills have taken over the degree. Skillslash have decided to make you future ready, so step out of your comfort zone and Apply Now!
"

754f65920170f44b83e0f27677cf73b1?s=128

skillslash

November 23, 2021
Tweet

Transcript

  1. None
  2. 1 2 3 SUB-TOPIC Data science: An untapped resource for

    machine learning How data science is transforming business? How data science is conducted?
  3. Data science: An untapped resource for machine learning Data science

    is one of the most exciting fields out there today. But why is it so important?
  4. Data science: An untapped resource for machine learning Because companies

    are sitting on a treasure trove of data. As modern technology has enabled the creation and storage of increasing amounts of information, data volumes have exploded. It’s estimated that 90 percent of the data in the world was created in the last two years. For example, Facebook users upload 10 million photos every hour.
  5. Data science: An untapped resource for machine learning The wealth

    of data being collected and stored by these technologies can bring transformative benefits to organizations and societies around the world—but only if we can interpret it. That’s where data science comes in.
  6. Data science: An untapped resource for machine learning Data science

    reveals trends and produces insights that businesses can use to make better decisions and create more innovative products and services. Perhaps most importantly, it enables machine learning (ML) models to learn from the vast amounts of data being fed to them, rather than mainly relying upon business analysts to see what they can discover from the data.
  7. How data science is transforming business? Organizations are using data

    science to turn data into a competitive advantage by refining products and services. Data science and machine learning use cases include:
  8. How data science is transforming business? Determine customer churn by

    analyzing data collected from call centers, so marketing can take action to retain them. Improve efficiency by analyzing traffic patterns, weather conditions, and other factors so logistics companies can improve delivery speeds and reduce costs.
  9. How data science is transforming business? Improve patient diagnoses by

    analyzing medical test data and reported symptoms so doctors can diagnose diseases earlier and treat them more effectively. Optimize the supply chain by predicting when equipment will break down.
  10. How data science is conducted? The process of analyzing and

    acting upon data is iterative rather than linear, but this is how the data science lifecycle typically flows for a data modeling project:
  11. How data science is conducted? Planning: Define a project and

    its potential outputs. Deploying a model: Taking a trained, machine learning model and getting it into the right systems is often a difficult and laborious process. This can be made easier by operationalizing models as scalable and secure APIs, or by using in- database machine learning models.
  12. How data science is conducted? Building a data model: Data

    scientists often use a variety of open source libraries or in-database tools to build machine learning models. Often, users will want APIs to help with data ingestion, data profiling and visualization, or feature engineering. They will need the right tools as well as access to the right data and other resources, such as compute power.
  13. How data science is conducted? Evaluating a model: Data scientists

    must achieve a high percent of accuracy for their models before they can feel confident deploying it. Model evaluation will typically generate a comprehensive suite of evaluation metrics and visualizations to measure model performance against new data, and also rank them over time to enable optimal behavior in production. Model evaluation goes beyond raw performance to take into account expected baseline behavior.
  14. How data science is conducted? Explaining models: Being able to

    explain the internal mechanics of the results of machine learning models in human terms has not always been possible—but it is becoming increasingly important. Data scientists want automated explanations of the relative weighting and importance of factors that go into generating a prediction, and model- specific explanatory details on model predictions.
  15. @SKILLSLASH We are so lucky to have amazing supporters like

    you. s k i l l s l a s h . c o m Thanks! NEXT TOPIC: Lorem Ipsum To learn more, consider following!