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It involves developing algorithms that can learn from and make predictions or decisions based on data. Familiarity with regression techniques, decisiontrees, clustering, neural networks, and other data-driven problem-solving methods is vital. Tools like Tableau, Matplotlib, Seaborn, or PowerBI can be incredibly helpful.
Other hierarchical tools are tree diagrams, sunburnt diagrams, decisiontrees, and flow charts. Network Network tools are tools that allow you to visualise data that’s hard to capture using a tree structure. When presenting data this way, you can give items multiple attributes.
Here is the tabular representation of the same: Technical Skills Non-technical Skills Programming Languages: Python, SQL, R Good written and oral communication Data Analysis: Pandas, Matplotlib, Numpy, Seaborn Ability to work in a team ML Algorithms: Regression Classification, DecisionTrees, Regression Analysis Problem-solving capability Big Data: (..)
What are the advantages and disadvantages of decisiontrees ? Yes, I am proficient in data visualisation tools such as Tableau, PowerBI, and Matplotlib in Python, which I use to create interactive and insightful visualisations for data analysis. Lifetime access to updated learning materials.
Key topics include: Supervised Learning Understanding algorithms such as linear regression, decisiontrees, and support vector machines, and their applications in Big Data. Visualisation Tools Familiarity with tools such as Tableau, PowerBI, and D3.js js for creating interactive visualisations.
Luckily, nothing too complicated is needed, as Tableau is user-friendly while matplotlib is the popular Python library for data visualization. PowerBI is surprisingly popular as well, possibly for its focus on business and applications, making it more commonly used by even non-tech-savvy individuals.
Dive Deep into Machine Learning and AI Technologies Study core Machine Learning concepts, including algorithms like linear regression and decisiontrees. Learn to use tools like Tableau, PowerBI, or Matplotlib to create compelling visual representations of data. Additionally, familiarity with cloud platforms (e.g.,
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