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Data science is one of todays top careers. Get the training you need to get ahead—or stay on top—in fields such as data analysis, mining, visualization, and big data, using tools like Excel, R, Hadoop, and Python.
By : Satyendra singh
Basics of machine learning,Linear Regression,Logistic Regression, Naïve Bayes ,KNN a...
4 1274
6:1:56 hrs 19 lectures Expert Level
By : Sekhar Metla (Microsoft Certified Professional) Sudha
Using MySQL Server RDBMS with Workbench to Become a SQL Expert on Queries for your Bu...
4 713
5:16:58 hrs 87 lectures All Level
By : John Hedengren
Data science introduction for scientists and engineers...
4.1 568668
1:12:54 hrs 17 lectures All Level
By : Arbaz Khan
Home Automation Using J.A.R.V.I.S AI Assistant With Arduino UNO Board...
4.3 8671
1:1:54 hrs 16 lectures Beginner Level
By : Juan Galvan
Become a professional Data Scientist and learn how to use NumPy, Pandas, Machine Lear...
4.7 7476
14:21:2 hrs 140 lectures All Level
By : Abdulhadi Darwish
Become an NLP Engineer by creating real projects using Python, semantic search, text ...
4.7 71953
2:38:12 hrs 71 lectures Beginner Level
By : Prince Patni
Answers with Detail Explanation to Actual Spotfire Interview Questions, beneficial fo...
4.2 92135
25 lectures All Level
By : Pruthviraja L
A Practical Approach To Learn Pandas From Basic To Advanced Level With 100 + Exercise...
4.8 45870
15:44:16 hrs 103 lectures All Level
By : Phikolomzi Gugwana
Histograms, Box Plot and Descriptive Statistics in R...
4.8 73096
31 lectures Intermedite Level
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Data Science is a multidisciplinary field that involves extracting insights and knowledge from structured and unstructured data. It combines expertise from statistics, mathematics, programming, and domain-specific knowledge to analyze and interpret complex data sets.
The Data Science process typically involves steps like data collection, cleaning, exploratory data analysis, feature engineering, model building, evaluation, and deployment. This iterative process aims to extract valuable insights and predictions from data.
Common programming languages in Data Science include Python and R. They offer extensive libraries and frameworks, such as NumPy, pandas, scikit-learn (Python), and tidyverse (R), supporting various aspects of data manipulation, analysis, and machine learning.
Machine Learning is a subset of Data Science that focuses on creating algorithms and models that enable systems to learn and make predictions or decisions without explicit programming. It involves supervised learning, unsupervised learning, and reinforcement learning.
Data Science plays a crucial role in business decision-making by providing data-driven insights. It helps businesses understand customer behavior, optimize processes, forecast trends, and make informed decisions based on patterns and trends identified in the data.