Data Science or Cyber Security: Which Should I Choose? 🤔🤔 | Simplilearn

🔥 Post Graduate Program In Data Science: https://www.simplilearn.com/pgp-data-science-certification-bootcamp-program?utm_campaign=11May2023DataScienceorCyberSecurity&utm_medium=DescriptionFF&utm_source=youtube
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This video, "Data Science or Cyber Security: Which Should I Choose" by Simplilearn, will help you gain perspective towards your future. In this video, you will understand which career suits you in cybersecurity and data science. Both Data Science and Cybersecurity are growing at a very rapid and unrealistic pace, and the jobs in this field are high in number. This video will help you gain perspective toward building your career in any of the two most booming areas of this decade. Below are the topics covered in this Data science or Cybersecurity video.

00:00 Data Science Or Cyber Security: Which should i choose?
01:42 Introduction to Data Science
02:12 Introduction to Cybersecurity
02:34 Reasons To Learn Data Science
03:47 Reasons to learn Cybersecurity
05:21 Post Graduate Program in Data Science
06:52 Post Graduate Program in Cybersecurity

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✅ About Post Graduate Program In Data Science:
This in-depth Data Science program is designed for working professionals, and covers job-critical topics like R, Python programming, Machine Learning techniques, NLP notions, and Data Visualization with Tableau, using an active learning model that includes live sessions from global professionals, practical labs, IBM Hackathons, and corporate ready projects

✅ Key Features:
- Post Graduate Data Science certificate and Purdue alumni association membership
- 3 Capstone and 25+ Projects with industry data sets from Amazon, Uber, Walmart, Comcast, and many more
- Masterclasses delivered by Purdue faculty and IBM experts
- Exclusive hackathons and Ask Me Anything sessions by IBM
- 8X higher engagement in live online classes by seasoned academics and industry professionals
- & More

✅ Skills Covered:
- Exploratory Data Analysis
- Descriptive Statistics
- Inferential Statistics
- Model building and fine tuning
- Supervised and unsupervised learning
- Natural Language Processing
- Ensemble Learning

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