TGx:LinkedIn Learning - Data Science Foundations Fundamentals [CoursesGhar]

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LinkedIn Learning - Data Science Foundations Fundamentals [CoursesGhar]

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File	Size
!! Info !!.txt	1.10 kB
!! Join Us On Telgram !!.txt	287.00 B
!!! Please Support !!! [CoursesGhar.Com].txt	197.00 B
00. Websites You May Like/A1movies.com.pk.url	114.00 B
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[10] 9. Acting on Data Science/[1] Interpretability.mp4	9.30 MB
[10] 9. Acting on Data Science/[1] Interpretability.srt	5.77 kB
[10] 9. Acting on Data Science/[2] Actionable insights.mp4	8.24 MB
[10] 9. Acting on Data Science/[2] Actionable insights.srt	4.88 kB
[11] Conclusion/[1] Next steps.mp4	9.54 MB
[11] Conclusion/[1] Next steps.srt	4.90 kB
[1] Introduction/[1] The fundamentals of data science.mp4	11.81 MB
[1] Introduction/[1] The fundamentals of data science.srt	2.45 kB
[2] 1. What Is Data Science/[1] Supply and demand for data science.mp4	8.28 MB
[2] 1. What Is Data Science/[1] Supply and demand for data science.srt	6.83 kB
[2] 1. What Is Data Science/[2] The data science Venn diagram.mp4	8.64 MB
[2] 1. What Is Data Science/[2] The data science Venn diagram.srt	7.90 kB
[2] 1. What Is Data Science/[3] The data science pathway.mp4	23.22 MB
[2] 1. What Is Data Science/[3] The data science pathway.srt	9.72 kB
[2] 1. What Is Data Science/[4] Roles and teams in data science.mp4	14.03 MB
[2] 1. What Is Data Science/[4] Roles and teams in data science.srt	8.11 kB
[3] 2. The Place of Data Science in the Data Universe/[1] Artificial intelligence.mp4	29.55 MB
[3] 2. The Place of Data Science in the Data Universe/[1] Artificial intelligence.srt	14.22 kB
[3] 2. The Place of Data Science in the Data Universe/[2] Machine learning.mp4	29.96 MB
[3] 2. The Place of Data Science in the Data Universe/[2] Machine learning.srt	13.72 kB
[3] 2. The Place of Data Science in the Data Universe/[3] Deep learning neural networks.mp4	30.53 MB
[3] 2. The Place of Data Science in the Data Universe/[3] Deep learning neural networks.srt	12.29 kB
[3] 2. The Place of Data Science in the Data Universe/[4] Big data.mp4	14.91 MB
[3] 2. The Place of Data Science in the Data Universe/[4] Big data.srt	9.52 kB
[3] 2. The Place of Data Science in the Data Universe/[5] Predictive analytics.mp4	12.04 MB
[3] 2. The Place of Data Science in the Data Universe/[5] Predictive analytics.srt	8.52 kB
[3] 2. The Place of Data Science in the Data Universe/[6] Prescriptive analytics.mp4	13.98 MB
[3] 2. The Place of Data Science in the Data Universe/[6] Prescriptive analytics.srt	13.71 kB
[3] 2. The Place of Data Science in the Data Universe/[7] Business intelligence.mp4	11.08 MB
[3] 2. The Place of Data Science in the Data Universe/[7] Business intelligence.srt	6.98 kB
[4] 3. Ethics and Agency/[1] Legal, ethical, and social issues of data science.mp4	12.59 MB
[4] 3. Ethics and Agency/[1] Legal, ethical, and social issues of data science.srt	7.81 kB
[4] 3. Ethics and Agency/[2] Agency of algorithms and decision-makers.mp4	12.62 MB
[4] 3. Ethics and Agency/[2] Agency of algorithms and decision-makers.srt	9.06 kB
[5] 4. Sources of Data/[1] Data preparation.mp4	15.00 MB
[5] 4. Sources of Data/[1] Data preparation.srt	9.48 kB
[5] 4. Sources of Data/[2] In-house data.mp4	6.05 MB
[5] 4. Sources of Data/[2] In-house data.srt	3.75 kB
[5] 4. Sources of Data/[3] Open data.mp4	20.52 MB
[5] 4. Sources of Data/[3] Open data.srt	8.75 kB
[5] 4. Sources of Data/[4] APIs.mp4	10.55 MB
[5] 4. Sources of Data/[4] APIs.srt	4.65 kB
[5] 4. Sources of Data/[5] Scraping data.mp4	29.71 MB
[5] 4. Sources of Data/[5] Scraping data.srt	8.54 kB
[5] 4. Sources of Data/[6] Creating data.mp4	14.73 MB
[5] 4. Sources of Data/[6] Creating data.srt	10.33 kB
[5] 4. Sources of Data/[7] Passive collection of training data.mp4	13.21 MB
[5] 4. Sources of Data/[7] Passive collection of training data.srt	6.93 kB
[5] 4. Sources of Data/[8] Self-generated data.mp4	13.97 MB
[5] 4. Sources of Data/[8] Self-generated data.srt	5.81 kB
[6] 5. Sources of Rules/[1] The enumeration of explicit rules.mp4	12.66 MB
[6] 5. Sources of Rules/[1] The enumeration of explicit rules.srt	7.07 kB
[6] 5. Sources of Rules/[2] The derivation of rules from data analysis.mp4	23.83 MB
[6] 5. Sources of Rules/[2] The derivation of rules from data analysis.srt	7.58 kB
[6] 5. Sources of Rules/[3] The generation of implicit rules.mp4	9.18 MB
[6] 5. Sources of Rules/[3] The generation of implicit rules.srt	6.14 kB
[7] 6. Tools for Data Science/[1] Applications for data analysis.mp4	15.15 MB
[7] 6. Tools for Data Science/[1] Applications for data analysis.srt	7.90 kB
[7] 6. Tools for Data Science/[2] Languages for data science.mp4	12.86 MB
[7] 6. Tools for Data Science/[2] Languages for data science.srt	6.59 kB
[7] 6. Tools for Data Science/[3] Machine learning as a service.mp4	11.26 MB
[7] 6. Tools for Data Science/[3] Machine learning as a service.srt	5.67 kB
[8] 7. Mathematics for Data Science/[1] Algebra.mp4	14.83 MB
[8] 7. Mathematics for Data Science/[1] Algebra.srt	13.29 kB
[8] 7. Mathematics for Data Science/[2] Calculus.mp4	8.66 MB
[8] 7. Mathematics for Data Science/[2] Calculus.srt	7.83 kB
[8] 7. Mathematics for Data Science/[3] Optimization and the combinatorial explosion.mp4	13.91 MB
[8] 7. Mathematics for Data Science/[3] Optimization and the combinatorial explosion.srt	11.63 kB
[8] 7. Mathematics for Data Science/[4] Bayes' theorem.mp4	10.58 MB
[8] 7. Mathematics for Data Science/[4] Bayes' theorem.srt	7.82 kB
[9] 8. Analyses for Data Science/[10] Aggregating models.mp4	15.04 MB
[9] 8. Analyses for Data Science/[10] Aggregating models.srt	6.86 kB
[9] 8. Analyses for Data Science/[1] Descriptive analyses.mp4	23.19 MB
[9] 8. Analyses for Data Science/[1] Descriptive analyses.srt	12.36 kB
[9] 8. Analyses for Data Science/[2] Predictive models.mp4	24.51 MB
[9] 8. Analyses for Data Science/[2] Predictive models.srt	13.73 kB
[9] 8. Analyses for Data Science/[3] Trend analysis.mp4	19.19 MB
[9] 8. Analyses for Data Science/[3] Trend analysis.srt	11.67 kB
[9] 8. Analyses for Data Science/[4] Clustering.mp4	18.41 MB
[9] 8. Analyses for Data Science/[4] Clustering.srt	10.46 kB
[9] 8. Analyses for Data Science/[5] Classifying.mp4	13.01 MB
[9] 8. Analyses for Data Science/[5] Classifying.srt	10.32 kB
[9] 8. Analyses for Data Science/[6] Anomaly detection.mp4	18.19 MB
[9] 8. Analyses for Data Science/[6] Anomaly detection.srt	9.38 kB
[9] 8. Analyses for Data Science/[7] Dimensionality reduction.mp4	19.46 MB
[9] 8. Analyses for Data Science/[7] Dimensionality reduction.srt	9.74 kB
[9] 8. Analyses for Data Science/[8] Feature selection and creation.mp4	16.90 MB
[9] 8. Analyses for Data Science/[8] Feature selection and creation.srt	10.98 kB
[9] 8. Analyses for Data Science/[9] Validating models.mp4	18.20 MB
[9] 8. Analyses for Data Science/[9] Validating models.srt	8.33 kB
[TGx]Downloaded from torrentgalaxy.to .txt	564.00 B

Description

LinkedIn Learning - Data Science Foundations Fundamentals [CoursesGhar]

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 703 MB | Duration: 3h 42m
Please Support Us By Giving A Click On ADS on CoursesGhar

Data science is driving a world-wide revolution that touches everything from business automation to social interaction. It’s also one of the fastest growing, most rewarding careers, employing analysts and engineers around the globe. This course provides an accessible, nontechnical overview of the field, covering the vocabulary, skills, jobs, tools, and techniques of data science. Instructor Barton Poulson defines the relationships to other data-saturated fields such as machine learning and artificial intelligence. He reviews the primary practices: gathering and analyzing data, formulating rules for classification and decision-making, and drawing actionable insights. He also discusses ethics and accountability and provides direction to learn more. By the end, you’ll see how data science can help you make better decisions, gain deeper insights, and make your work more effective and efficient.
Learning objectives

Assess the skills required for a career in data science.
Evaluate different sources of data, including metrics and APIs.
Explore data through graphs and statistics.
Discover how data scientists use programming languages such as R, Python, and SQL.
Assess the role of mathematics, such as algebra, in data science.
Assess the role of applied statistics, such as confidence intervals, in data science.
Assess the role of machine learning, such as artificial neural networks, in data science.
Define the components of effective data visualization.

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