🔥 Top 10 Data Science Skills | Skills Required for Data Science 2024 | Simplilearn

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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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