Difference between revisions of "CV - Courses"

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''[https://www.udemy.com/course/linux-academy-red-hat-certified-systems-administrator-prep/ Red Hat Certified Systems Administrator Prep]''
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'''Content overview'''
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NumPy and Pandas
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Python for Data Visualization:
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Matplotlib, Seaborn, Pandas Built-in-Data Visualization
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Plotly and Cufflinks
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Geographical Plotting
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Linear Regression
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Cross Validation and Bias-Variance Trade-Off
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K Nearest Neighbors
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Decision Tress and Random Forests
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Support Vector Machines
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K Means Clustering
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Principal Component Analysis
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Recommender Systems
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Natural Language Processing
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See the complete course content at https://www.udemy.com/course/linux-academy-red-hat-certified-systems-administrator-prep/
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Revision as of 02:05, 15 January 2021

Adelo Vieira,  Developer / Data Scientist

47-A Phibsborough Rd, Dublin

  +353 852 40 72 08

  adeloaleman@gmail.com

 Github          
 Linkedin        
 Portfolio        
 My Wiki         Download a pdf version of my CV

BSc. (Hons) in Information Technology, Geophysical Engineer, and MSc in Petroleum Geosciencewith strong mathematical, problem-solving, and analytical skills. I'm currently particularly interested in Data Analytics and Software Development.

Proficient in multiple programming languages, including Python, Java, JavaScript, SQL, and R. I have a huge interest in Machine Learning and Natural Language Processing. I've been recently working in areas such as Text classification and Sentiment Analysis. I have solid knowledge in several ML algorithms (Naive Bayes, Decision Trees, K-Nearest Neighbour) and in Time Series Analysis. I have experience working with Python (Pandas, NLPTK, Scikit-learn, SciPy, Plotly, TextBlob, Vader Sentiment), R, and RapidMiner.

Solid experience in Object-oriented programming and Web Development. I have developed several projects using Java, Python, React, Node.js (Express.js), and Dash.

I also have advanced experience with Linux (including Shell Scripting) and I have worked with Relational databases (SQL, MySQL, PostgreSQL) and Cloud computing (AWS and Google Cloud).




Content overview

  • NumPy and Pandas
  • Python for Data Visualization:
  • Matplotlib, Seaborn, Pandas Built-in-Data Visualization
  • Plotly and Cufflinks
  • Geographical Plotting
  • Linear Regression
  • Cross Validation and Bias-Variance Trade-Off
  • K Nearest Neighbors
  • Decision Tress and Random Forests
  • Support Vector Machines
  • K Means Clustering
  • Principal Component Analysis
  • Recommender Systems
  • Natural Language Processing
  • .




  • AWS Academy - Cloud Foundations

Content overview

  • Introduction to AWS Cloud
  • Essential Characteristics of cloud computing, Service Model, Deployment Models
  • AWS Global Infrastructure: Regions, Availability zones, Edge Locations
  • .

  • AWS foundation services:
  • Compute: EC2, AWS Lambda, ECS, Auto Scaling
  • Networking: VPC, Elastic Load Balancer, Route 53
  • Storage: Amazon EBS, Amazon S3, Amazon EFS, Amazon Relational Database Service (RDS)


  • AWS Academy - Cloud Architecting

Content overview

  • Designing a cloud environment
  • Designing for High Availability
  • Configuring VPS, Availability zones, NAT Gateway, Route Table, Load Balancer, Auto Scaling Group.
  • Automating your Infrastructure
  • Infrastructure as code
  • AWS CloudFormation Templates
  • .

  • Decoupling your Infrastructure
  • Loose coupling Strategies
  • Designing Web Scale Media
  • Storing Web-Accessible Content with Amazon S3
  • Caching with Amazon CloudFront
  • Storing relational data in Amazon RDS, Managing NoSQL databases
  • Multi-region failover with Amazon Route 53

.

.

.

.

.


Content overview

  • NumPy and Pandas
  • Python for Data Visualization:
  • Matplotlib, Seaborn, Pandas Built-in-Data Visualization
  • Plotly and Cufflinks
  • Geographical Plotting
  • Linear Regression
  • Cross Validation and Bias-Variance Trade-Off
  • K Nearest Neighbors
  • Decision Tress and Random Forests
  • Support Vector Machines
  • K Means Clustering
  • Principal Component Analysis
  • Recommender Systems
  • Natural Language Processing
  • .