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Data Science

Data Science

Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data.

Data science is related to data mining, machine learning and big data.

  • What is Data Science?
  • What is Machine Learning?
  • What is Deep Learning?
  • What is AI?
  • Data Analytics & it’s types
  • What is Python?
  • Why Python?
  • Installing Python
  • Python IDEs
  • Jupyter Notebook Overview
  • Python Basic Data types
  • Lists
  • Slicing
  • IF statements
  • Loops
  • Dictionaries
  • Tuples
  • Functions
  • Array
  • Selection by position & Labels
  • Pandas
  • Numpy
  • Sci-kit Learn
  • Mat-plot library
  • Reading CSV files
  • Saving in Python data
  • Loading Python data objects
  • Writing data to csv file
  • Selecting rows/observations
  • Rounding Number
  • Selecting columns/fields
  • Merging data
  • Data aggregation
  • Data munging techniques
  • Mean
  • Median
  • Mode
  • Skewness
  • Normal Distribution
  • Probability Basics:
  • What does mean by probability?
  • Types of Probability
  • ODDS Ratio?
  • Standard Deviation:
  • Data deviation & distribution
  • Variance
  • Bias variance Trade off:
  • Underfitting
  • Overfitting
  • Distance metrics:
  • Euclidean Distance
  • Manhattan Distance
  • Outlier analysis:
  • What is an Outlier?
  • Inter Quartile Range
  • Box & whisker plot
  • Upper Whisker
  • Lower Whisker
  • catter plot
  • Cook’s Distance
  • Missing Value treatments:
  • What is a NA?
  • Central Imputation
  • KNN imputation
  • Dummification
  • Correlation
  • Pearson correlation
  • Positive & Negative correlation
  • Error Metrics Duration-3hr:
  • Classification
  • Confusion Matrix
  • Precision
  • Recall
  • Specificity
  • F1 Score
  • Regression:
  • MSE
  • RMSE
  • MAPE
  • Linear Regression:
  • Linear Equation
  • Slope
  • Intercept
  • R square value
  • Logistic regression:
  • ODDS ratio
  • Probability of success
  • Probability of failure
  • ROC curve
  • Bias Variance Tradeoff
  • K-Means
  • K-Means ++
  • Hierarchical Clustering
  • K – Nearest Neighbour
  • Naïve Bayes Classifier
  • Decision Tree – CART
  • Decision Tree – C50
  • Random Forest