Data is the new science. big data holds the answers

    Topic
    Learnings
  
    Introduction
    Introduction to Data Science, Benefit, Data Science Tools
  
    Foundation Statistics
    Types Data, Sample Vs Population, Probability, Mean, median Mode, Standard Deviation, Variance, Dealing with Missing Data, Mean Squared Deviation, Root Mean Square Deviation
  
    Foundation Statistics: Distribution
    Normal, Binomial, Poisson, Confidence Intervals, T Distribution, Hypothesis, Z-test and T-Test
  
    Data Visualization
    Data visualization in excel with sample data.
  
    Python For Data Science
    Python Basics, NumPy, Pandas, matplotlib, Exploratory Data Analysis, Data Visualization using Python
  
    Data Analysis
    Data Visualization using Python for Public Data Sets in Kaggle.
  
    Machine Learning
    Intro to ML, Supervised and Unsupervised Learning, When to Use ML, ML Categories
  
    ML Model
    Supervised Model (K Nearest Neighbor, Linear, Bayes Classifiers, Decision Trees, Support Vector Machines) Unsupervised Learning: K-Means Clustering Model Evaluation: Cross Validation, Grid Search, Evaluation Matrix and Scoring.
  
    Machine Learning
    Ensemble Techniques – Boosting, Bagging, XGBoost, Stacking Models
  
    ML Application
    Process of ML Application Building.
  
    Python for ML
    building Python Application using Python Libraries such as SciPy, Scikit, Thaeno, Keras, TensorFlow, using Public Data Sets in Kaggle or UCI
Data Science Technology
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