Data science

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Data Science Data Science

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Data Science Google Trends GoogleNGRAM Data Science: interest by country Data Science Data Scientist Data Science Roadmap Data Science Programming Language

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Data Science 0 20 40 60 80 100 120 Data Science:(Worldwide)

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Data Science

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Data Science 0 10 20 30 40 50 60 70 80 90 100 Data Science:interest by country

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Data Science Data Engineer Data Analyst Software Math and Algorithms Computer Science Data

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Data Science Mathematics StatisticalAnalysis Machine Learning Software Engineering Data Scientist

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Data Science Python RJulia Java Scala C++

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Data Science Fundamentals Statistics Programming Machine Learning TextMining / Natural Language Processing Data Visualization Big Data Data Ingestion Data Munging Toolbox

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Data Science  Matrix Linear Algebra  Binary Tree  Hash functions  Database basic  Multidimensional Data model  Relational Algebra 05%

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Data Science  Descriptivestatistics  Probability theorem  Random variables  Bayes theorem  Histograms  Continues distributions  ANOVA  Monte Carlo method  Kernel density  Regressions  Correlations  Euclidian distance  Least Fit 10%

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Data Science  Variables Vectors  Matrices  Array  List  Functions  Data frames  Python/R Basics  Manipulate Data frames  Install Packages 15%

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Data Science  Supervisedlearning  Unsupervised learning  Training / Testing data  Classification  Linear Classifiers  Decision tree  Boosting  Naïve Bayes classifier 30%

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Data Science  Regression Linear Regression  Multiple Regression  Ridge Regression  Kernel Regression  Logistic Regression  Clustering  Nearest Neighbor  k-means  Hierarchical Clustering  Precision-Recall 30%

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Data Science  Textanalysis  Name entity recognition  Corpus  Market based analysis  Feature extraction  Using Mahout/UIMA  Using Weka  Using NLTK  Classify text 40%

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Data Science  DataExploration in R/Python  Uni, Bi & Multivariate Viz  ggplot2  Histogram & Pie (Uni)  Tree & Tree map  Scatter plot (Bi)  Line Charts (Bi) 50%

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Data Science  SpecialCharts  Time line  Decision Tree  D3.js  Tableau  Matplotlib, vispy, bokeh, seaborn, pygal, folium, and networkx 50%

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Data Science  Mapreduce Fundamentals  Hadoop Components  HDFS  Data replication principles  Name and data nodes  Using Mahout  Cassandra  MongoDB 70%

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Data Science  Dataformats  Data discovery  Data sources  Data integration  Data fusion  Transformation  Data survey  Google OpenRefine 75%

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Data Science  DimensionalityReduction  Normalization  Data scrubbing  Handling missing values  Denoising  Feature Extraction  Sampling  Principle Component analysis 80%

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Data Science  MSExcel  Python, R packages  Weka, Knime, RapidMiner  Hadoop, Spark, Storm  D3.js, ggplot2, Shiny, Matplotlib, Tableau  NLTK  WebScraper  scikit-learn, Theano, Keras  Cassandra, Mongo DB 100%

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Data Science NumPy SciPy Matplotlib Pandas ScikitLearn Statsmodels Seaborn Scrapy Keras

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Data Science facebook.com/mloey mohamedloey@gmail.com twitter.com/mloey linkedin.com/in/mloey mloey@fci.bu.edu.eg

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