Data Science for Professionals

Data Science for Professionals

Introduction to Data Science
    • What is data science?
    • Importance of Data Science in real world
    • Difference between Data science and Reporting?
    • Who is data scientist?
    • Pre-requisite for data science
data science professional
R programming
  1. Introduction to R
  2. Data Types in R
  3. Objects in R
    1. Vector
    2. Matrices
    3. Data Frames
    4. Lists
    5. Arrays, etc.
  4. Variables creation and manipulation
  5. Conditional statement, Loops, Function
  6. Data Frame Creation and manipulation
  7. Data retrieval from Data Frame and aggregation using different packages like dplyr
  8. Join/ Merge data frame
  9. Date Time data manipulation
  10. Plotting data using various packages
  11. Reading data from file and Data base
  12. Practice Example
Python
  • Introduction to Python
  • Data Types in Python
  • Objects in Python
  • Introduction to Modules
  • Programming using Python
Probability
  • Introduction Probability
  • Binomial Distribution
  • Different Examples
Statistics
  1. What is Statistics?
  2. Datatypes in statistics
    1. Continuous variables
    2. Interval
    3. Ordinal Variables
    4. Ratio
  3. Descriptivestatistics
    1. Understanding descriptive statistics
    2. Example using R
  4. Sampling
    1. Different types ofSampling
    2. Simple randomsampling
    3. Systematicsampling
    4. StratifiedSampling
    5. Example using R
  5. Inferential statistics
    1. Data distributions
      • Normal Distribution
      • Importance of Normal distribution
      • Standard Normal Distribution
      • Visualizing using R
    2. Central Limit Theorem and Standard Error
    3. Hypothesistesting
      • Null and alternatehypothesis
      • Type Ierror
      • Type IIerror
      • Reject or acceptancecriterion
      • Example using R
    4. Confidence Interval
    5. Z and T distribution
    6. Z, t, Anova, chi-square test with example using R
  6. Co variance and correlation
    1. Understanding using R
Exploratory data analysis
  • Loading data into R
  • Preprocessing of data (Cleaning and preparing the data)
  • Missing Value imputation
  • Summary of data (5point summary)
  • Distribution of Data using plots in R (ggplot2)
  • Finding outliers using plots
  • Correlation
  • Feature engineering
Confirmatory data analysis
  • Hypothesis of Data based on data types

 

Machine Learning
  1. Introduction to machine learning
  2. Supervised and Unsupervised Learning
  3. Supervised learning
    1. Linear Regression
      • Understanding linear Regression
      • Assumptions in Linear regression
      • Model development and interpretation
      • Model validation
      • Model optimization
      • Gradient decent Algorithm
      • Case study using R
    2. Logistic Regression
      • Introduction logistic regression
      • Understanding Odds ratio
      • Understanding Logit link function
      • Model development and interpretation
      • Model validation using Confusion Matrix, ROC curve
      • Measuring sensitivity and specificity
      • Gradient decent Algorithm
      • Case study using
    3. Decision Tree
      • Introduction
      • Types of Decision tree (C5.0, CART)
      • Information Gain Theory and Gini Index
      • Model creation and validation
      • Over fitting and pruning tree
      • Case study using R
    4. Random Forest
      • Introduction random forest
      • Understanding algorithm
      • Advantage over decision tree
      • Model development and validation
      • Model optimization
      • Finding best features
      • Case study using R
    5. K Nearest Neighbors (KNN)
      • Understanding algorithm
      • Model development and validation
      • Model optimization
      • Finding best K
      • Example using R
    6. Support Vector Machine (SVM)
      • Introduction
      • Understanding algorithm
      • Regression and classification
      • Different Kernels
      • Model development and validation
      • Model optimization
      • Finding best cost and gamma value
      • Example R
      • Un-Supervised learning
  4. Cluster analysis
      • Hierarchical clustering
      • K-Means clustering
  5. PCA and Dimension reduction techniques
  6. Market basket analysis
Deep Learning
  • Introduction
  • Artificial Neural Network (ANN)
  • Multi-Layer perceptron
  • Feed forward and back propagation
  • Regression and classification using ANN
  • Convolution neural network (CNN)
  • Image classification
  • Digit classification
  • Recurrent Neural Network (RNN)
Natural Language processing
  • Introduction
  • NLTK, genism,
  • Word embeddings
  • Text mining, text classification
  • Sentiment Analysis

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