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The video on Top 10 Datascience Skills by Simplilearn is focused on industry-relevant skills that will help you to grow a better career according to the current IT standards. This video on Top 10 Datascience Skills to Learn in 2024 will cover some of the most sought skills like programming skills, machine learing, Data visualisation, and many more. By the end of this video, you will be able to understand and learn the best skills for your Data science professional life.

Below are the topics covered in this video:
00:00 Top 10 DS Skills
05:04 Programming Skills
05:25 Statistics
05:57 Machine Learning
06:24 Data Cleaning
06:46 Data Visualization
07:07 Big Data Technologies
07:34 SQL Data Bases
07:50 Deep Learning
08:17 Teamwork & collaboration
08:33 Communication skills

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➡️ About Caltech Post Graduate Program In Data Science

This Post Graduation in Data Science leverages the superiority of Caltech's academic eminence. The Data Science program covers critical Data Science topics like Python programming, R programming, Machine Learning, Deep Learning, and Data Visualization tools through an interactive learning model with live sessions by global practitioners and practical labs.

✅ Key Features
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Caltech PG program in Data Science completion certificate
- Earn up to 14 CEUs from Caltech CTME
- Masterclasses delivered by distinguished Caltech faculty and IBM experts
- Caltech CTME Circle membership
- Online convocation by Caltech CTME Program Director
- IBM certificates for IBM courses
- Access to hackathons and Ask Me Anything sessions from IBM
- 25+ hands-on projects from the likes of Amazon, Walmart, Uber, and many more
- Seamless access to integrated labs
- Capstone projects in 3 domains
- Simplilearn’s Career Assistance to help you get noticed by top hiring companies
- 8X higher interaction in live online classes by industry experts

✅ Skills Covered

- Exploratory Data Analysis
- Descriptive Statistics
- Inferential Statistics
- Model Building and Fine Tuning
- Supervised and Unsupervised Learning
- Ensemble Learning
- Deep Learning
- Data Visualization
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