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<!DOCTYPE html>
<html>
<head>
<link rel="stylesheet" href="mysite.css">
</head>
<body>
<!-- Menu Bar -->
<div class="topnav">
<a href="index.html">Home</a>
<a href="projects.html">Projects</a>
<a href="resources.html">Resources</a>
<a class="active" href="topics.html">Topics</a>
<a href="talks.html">Talks</a>
<a href="articles.html">Articles</a>
<a href="about.html">About</a>
</div>
<h1>Topics</h1>
<center>Key topics I've learned (or I'm in the process of learning) on my journey to becoming a Data Scientist.</center>
<br>
<h2>Tools</h2>
<p>
<ul>
<li><a href="/topic_notes/shell.html">Bash Shell</a></li>
<li>Hadoop/MapReduce</li>
<li>Matlab</li>
<li>MongoDB</li>
<li>MPI</li>
<li>OpenMP</li>
<li><a href="/topic_notes/python.html">Python</a></li>
<li>R</li>
<li><a href="/topic_notes/sas.html">SAS</a></li>
<li><a href="/topic_notes/sql.html">SQL</a></li>
<li><a href="/topic_notes/weka.html">Weka</a></li>
</ul>
</p>
<h2>Data Visualisation</h2>
<a href="/topic_notes/vis.html">See Notes</a><br><br>
<ul>
<li>Bar Chart</li>
<li>Grouped Bar Chart</li>
<li>Stacked Bar Chart</li>
<li>Diverging Bar Chart</li>
<li>Lollipop Chart</li>
<li>Diverging Lollipop Chart</li>
<li>Histogram</li>
<li>Scatter Plot</li>
<li>Bubble Plot</li>
<li>Dumbbell Plot</li>
<li>Dot Plot</li>
<li>Box Plot</li>
<li>Tufte Box Plot</li>
<li>Violin Plot</li>
<li>Quantile-Quantile Plot (Q-Q Plot)</li>
<li>Diverging Dot Plot</li>
<li>Pie Chart</li>
<li>Cumulative Distribution Plot</li>
<li>Stem and Leaf Plot</li>
<li>Heat Map</li>
<li>Calendar Heat Map</li>
<li>Correlogram</li>
<li>Area Chart</li>
<li>Stacked Area Chart</li>
<li>Slope Chart</li>
<li>Density Plot</li>
<li>Population Pyramid</li>
<li>Waffle Chart</li>
<li>Big Ol' Number</li>
<li>Treemap</li>
<li>Time Series Plot</li>
<li>Hierarchical Dendogram</li>
<li>Map Plot</li>
<li>Sankey Diagram</li>
<li>Word Cloud</li>
<li>Network Diagram</li>
<li>Misrepresentations of Data</li>
</ul>
<br>
<h2>Statistics</h2>
<p>
Probability:
<br><br>
<a href="/topic_notes/probability.html">See Notes</a>
<ul>
<li>Complementary Events</li>
<li>Permutations</li>
<li>Combinations</li>
<li>Independent Events</li>
<li>Mutually Exclusive Events</li>
<li>The Addition Law of Probability</li>
<li>Tree Diagrams</li>
<li>Conditional Probability</li>
<li>Higher Order Conditional Probability</li>
<li>Total Probability</li>
<li>Bayes Theorem</li>
<li>Discrete Probability Distributions</li>
<li>Properties of Probability Distributions</li>
<li>Mean, Variance and Standard Deviation</li>
<li>Markov's Inequality</li>
<li>Chebychev's Theorem</li>
<li>Binomial Distribution</li>
<li>Poisson Distribution</li>
<li>Normal Distribution</li>
<li>Distribution of the Sample Mean</li>
<li>Central Limit Theorem</li>
<li>Distribution of Sample Total</li>
<li>Inverse CDF</li>
</ul>
<br><br><br>
Sampling:
<ul>
<li>What is sampling and why do it?</li>
<li>Random sampling</li>
<li>Stratified Sampling</li>
<li>Cluster Sampling</li>
<li>Quota Sampling</li>
<li>Systematic Sampling</li>
</ul>
<br><br>
Descriptive Statistics:
<ul>
<li>Frequencies</li>
<li>Median</li>
<li>Mean</li>
<li>Geometric Mean and Geometric Mean Rate of Return</li>
<li>Mode</li>
<li>Quartiles</li>
<li>Percentiles</li>
<li>Min, Max, and Range</li>
<li>Interquartile Range</li>
<li>5 Number Summary</li>
<li>Variance</li>
<li>Standard Deviation</li>
<li>Outliers</li>
<li>Coefficient of Variation</li>
<li>Sharpe Ratio</li>
<li>Z Score</li>
<li>Skewness and Kurtosis</li>
<li>Empirical Rule</li>
<li>Covariance</li>
<li>Coefficient of Correlation</li>
</ul>
<br><br>
Statistical Tests & Measures of Uncertainty:
<ul>
<li>Hypothesis Testing</li>
<li>Type 1 and Type 2 Errors</li>
<li>P Values</li>
<li>Sample Size Determination</li>
<li>Confidence Intervals</li>
<li>Significance Tests</li>
<li>Single, Paired, and Unpaired Samples</li>
<li>Normal and T Tests</li>
<li>F test</li>
<li>Normal Probability Plot</li>
<li>One-Way Analysis of Variance</li>
<li>Hartley's Test</li>
<li>Bartlett's Test</li>
<li>ANOVA</li>
<li>Prediction Intervals</li>
<li>Correlation and Rank Correlation</li>
<li>Chi-Square Test</li>
<li>Non-Parametric Tests - Wilcoxon's Signed Rank Test, Mann-Whitney-Wilcoxson Test, Kruskal-Wallis Test, Friedmann Test</li>
</ul>
</p>
<br>
<h2>Maths Revision</h2>
<br>
<a href="/topic_notes/maths_revision.html">See Notes</a><br><br>
<p>
<ul>
<li>Fractions</li>
<li>Standard Form</li>
<li>Surds</li>
<li>Fractional Indices</li>
<li>Algebra</li>
<li>Quadratic Equations</li>
<li>Trigonometry</li>
<li>Vectors</li>
<li>Factorials</li>
</ul>
</p>
</details>
<br>
<h2>Data Science & Machine Learning</h2>
<p>
<ul>
<li>Data Science Process</li>
<li>Approaching Data Science Projects</li>
<li>Data Cleansing</li>
<li>Model Selection</li>
<li>Overfitting and Occam's Razor</li>
<li>Model Evaluation</li>
<li>Cross Validation</li>
<li>Model Types</li>
<li>Clustering</li>
<li>K-means</li>
<li>Association Rules</li>
<li>Fuzzy Systems</li>
<li><a href="/topic_notes/nlp.html">Natural Language Processing</a></li>
<li>Linear Regression</li>
<li>Decision Trees</li>
<li>Random Forests</li>
<li>Gradient Boosting Decision Trees</li>
<li>Naive Bayes</li>
<li>Nearest Neighbours</li>
<li>Logistic Regression</li>
<li>Support Vector Machines</li>
<li>Neural Networks and Deep Learning</li>
<li>Interpretability</li>
<li>Reproducible Code</li>
<li>Ethical, Legal and Societal Aspects of Data Science</li>
</ul>
</p>
<br>
<h2>Data Management</h2>
<p>
<ul>
<li>Data Modelling</li>
<li>Graph Data Modelling</li>
<li>OLAP</li>
<li>Data Warehousing and ETL</li>
<li>Data Management Planning</li>
<li>GDPR</li>
<li>Parallel and Distributed Processing</li>
<li>Cloud Computing</li>
<li>High Performance Computing</li>
<li>Dealing with Big Data</li>
</ul>
</p>
<br>
<br><br>
</body>
</html>