SAS Training

 

DATA SCIENCE

 

 

INTRODUCTION TOPICS

      1. Data Science
      2. Machine Learning.
      3. Data Mining.
      4. Deep Learning.
      5. Artificial Intelligence.
      6. Descriptive Analysis.
      7. Predictive Analysis.
      8. Python

 

 

PYTHON BASICS REQUIRED FOR DATA SCIENCE AND MACHINE LEARNING

      1. Basics Python.
          1. Types of Variables
              1. Numbers
              2. Strings
              3. Lists
              4. Dictionaries
              5. Tuples
          2. Statements
          3. Looping
          4. Function
          5. Database Connectivity in Python
      2. Installation of Anaconda Distribution
      3. Working Framework Jupyter Notebook

 

 

 

 

 

 

PYTHON LIBRARIES

      1. Introduction to Libraries Required for Data Science
          1. NumPy
          2. Pandas
          3. MatplotLib
          4. SciPy
          5. Seaborn

 

 

NUMPY

      1. NdArray In NumPy
                  1. ndim, shape, size, dtype, itemsize, data
                  2. Array Creation
                  3. Printing Arrays
                  4. Basic Operations
                  5. Universal Functions
                  6. Indexing, Slicing and Iterating

 

      1. Shape Manipulation Using NumPy
                  1. Changing the shape of an array
                  2. Stacking together different arrays
                  3. Splitting one array into several smaller ones
      1. Copies and Views In NumPy
                  1. No Copy at All
                  2. View or Shallow Copy
                  3. Deep Copy

 

      1. Fancy indexing and index tricks
                  1. Indexing with Arrays of Indices
                  2. Indexing with Boolean Arrays
                  3. The ix_() function
      1. Linear Algebra In NumPy
                  1. Simple Array Operations

 

      1. Tricks and Tips
                  1. Automatic Reshaping
                  2. Vector Stacking

 

 

PANDAS

      1. Pandas Basics
          1. Object Creation
          2. Viewing Data
          3. Selection
          4. Missing Data
          5. Operations
          6. Merge
          7. Grouping
          8. Reshaping

 

      1. Essential Basic Functionality
          1. Head and Tail
          2. Attributes and Underlying Data
          3. Accelerated operations
          4. Flexible binary operations
          5. Descriptive statistics
          6. Function application
          7. Reindexing and altering labels
          8. Iteration
          9. .dt accessor
          10. Vectorized string methods
          11. Sorting
          12. Copying
          13. dtypes
          14. Selecting columns based on dtype
      1. Intro to Data Structures
          1. Series
          2. DataFrame
          3. Panel

 

 

MATPLOTLIB

      1. Introduction of Matplot Library
      2. Intermediate
      3. Advanced
      4. Colors
      5. Text
      6. Toolkits

 

 

SCIPY

      1. Introduction Of SciPy Library
      2. Basic functions
      3. Special functions
      4. Integration
      5. Optimization
      6. Interpolation
      7. Signal Processing
      8. Linear Algebra
      9. Spatial data structures and algorithms
      10. Statistics

 

 

SEABORN

      1. Plotting functions
          1. Visualizing statistical relationships
                  1. Relating variables with scatter plots
                  2. Emphasizing continuity with line plots
                  3. Showing multiple relationships with facets

 

          1. Plotting with categorical data
                  1. Categorical scatterplots
                  2. Distributions of observations within categories
                  3. Statistical estimation within categories
                  4. Plotting “wide-form” data
                  5. Showing multiple relationships with facets
          1. Visualizing the distribution of a dataset
                  1. Plotting univariate distributions
                  2. Plotting bivariate distributions
                  3. Visualizing pairwise relationships in a dataset

 

          1. Visualizing linear relationships
                  1. Functions to draw linear regression models
                  2. Fitting different kinds of models
                  3. Conditioning on other variables
                  4. Controlling the size and shape of the plot
                  5. Plotting a regression in other contexts
      1. Multi-plot grids
          1. Building structured multi-plot grids
                  1. Conditional small multiples
                  2. Using custom functions
                  3. Plotting pairwise data relationships

 

      1. Plot aesthetics
          1. Controlling figure aesthetics
                  1. Seaborn figure styles
                  2. Removing axes spines
                  3. Temporarily setting figure style
                  4. Overriding elements of the seaborn styles
                  5. Scaling plot elements
          1. Choosing color palettes
                  1. Building color palettes
                  2. Qualitative color palettes
                  3. Sequential color palettes
                  4. Diverging color palettes
                  5. Setting the default color palette

 

 

DATA PREPROCESSING AND DATA ANALYSIS

      1. Data Cleaning or Data Cleansing.
      2. Data Integration.
      3. Data Transformation.
      4. Data Reduction.
      5. Data Discretisation.
      6. Data Visualisation.
      7. Feature Scaling

 

 

MYSQL DATABASE

    1. Introduction to Database
    2. Data Types
    3. SQL Operators
    4. SQL Functions
    5. SQL DDL Commands
        1. Create database
        2. Drop database
        3. Create table
        4. Drop table
        5. Alter table 
    6. SQL DML Commands
        1. Select
        2. Where
        3. Group By
        4. Order By

 

    1.  Joining In SQL
        1. Inner Join
        2. Left Outer Join
        3. Right Outer Join
        4. Full Outer Join
    2. Examples

 

 

STATISTICS

      1. Types Of Statistics
      2. Descriptive Statistics.
          1. Mean, Median, Mode, Variance etc.

 

      1. Inferential Statistics.
          1. Linear Regression.
          2. Binomial Distribution.
          3. Normal Distribution.
          4. Chi-Squared Test.
          5. Probability.
          6. Permutation and Combination.
          7. Least Square etc.

 

 

DATA STRUCTURES

      1. Algorithms.
          1. Aysymptotic Notations
          2. Greedy Algorithm
          3. Divide And Conquer
          4. Pointer
          5. Time Complexity
          6. Array

 

      1. Linked Lists.
          1. Linked Lists.
          2. Doubly Linked Lists.
          3. Circular Linked Lists.
          4. Circular Doubly Linked Lists.
      1. Stack and Queue.
          1. Stack Implementation
          2. Queue Implementation

 

      1. Searching Techniques.
          1. Linear Search
          2. Binary Search
          3. Interpolation Search
          4. Hash Table
      1. Sorting Techniques
          1. Insertion Sort
          2. Selection Sort
          3. Bubble Sort
          4. Quick Sort
          5. Merge Sort
          6. Heap Sort

 

      1. Graph.
          1. Graph Traversal Algorithm
              1. Depth First Search ( DFS )
              2. Breadth First Search ( BFS )

 

      1. Tree.
          1. Tree Traversal
          2. Binary Search Tree
          3. AVL Tree
          4. Spanning Tree
          5. B Tree
          6. B+ Tree
      1. Recursion.
          1. Tower Of Hanoi
          2. Fibonacci Seres

 

 

TYPES OF MACHINE LEARNING

  1. Supervised Learning
  2. Unsupervised Learning
  3. Reinforcement Learning

 

 

SUPERVISED LEARNING

 

    1. Factors in Machine Learning
      1. What is Dependent variable ?
      2. What is Independent variable ?
      3. What is Sample And Population ?
      4. Least squares
      5. Regularization
      6. Correlation.
      7. Training Data and Test Data.
      8. Cross Validation.
      9. Type I and type II error.
      10. Interpolation and Extrapolation.
      11. False Positive and False Negative.
      12. Bias and Variance.
      13. File Format.
      14. Outliers
      15. Underfitting and Overfitting
      16. Dimensionality Reduction
    1. Regression Algorithms.
                  1. Linear Regression With One Variable.
                  2. Gradient Descent.
                  3. Cost Function.
                  4. Hypothesis function
                  5. Linear Regression With Multiple Variable.
                  6. Polynomial Regression.
                  7. Logistic Regression.
                  8. Decision Tree Regression.
                  9. Random Forest Regression.

 

    1. Classification Algorithms.
                  1. Support Vector Machine (SVM).
      1. Hyper Planes
      2. Support Vectors
      3. Small margin
      4. Large margin

 

                  1. Time Series Forecasting.
      1. Trends
      2. Linear Trend and Non Linear Trend
      3. Seasonal Trend
      4. Cyclical Trend
      5. Irregular Trend
      6. Autoregressive ( AR )
      7. Moving Average ( MA )
      8. Autoregressive Moving Average ( ARMA )
      9. Autoregressive Integrated Moving Average ( ARIMA )
                  1. Naive Bayes.
                  2. K Nearest Neighbours.

 

 

UNSUPERVISED LEARNING

          1. What is Clustering ?
          2. Why Clustering ?
          3. Applications of Clustering
          4. Types of Clustering Algorithms
                  1. K means Clustering.
                  2. Hierarchical Clustering.
                  3. Mean Shift Clustering.