1. Introduction to python- why Python?
2. Installing the IDE's and related libraries.
3. Python hands on
4. Running python scripts
5. Data Visualization with MatplotLib & Seaborn
6. Introduction to Numpy
7. Introduction to Pandas
8. Advanced Pandas - A practical approach

1. Types of data
2. Mean, Median , Mode using python
3. Understanding Standard Deviation and Variance
4. Probability Density function and Probability mass function
5. Common data distribution, PDF, PMF, Lorenz Curves and Poisson's
6. Percentiles and Moments.
7. Central Limit Theorem
8. Introduction to Descriptive and Inferential Statistics.
9. Hands On Activity Covariance and Correlation
10. Introduction to Probability
11. Problems on Conditional Probability
12. Solution Conditional Probability
13. Baye’s Theorem

1. Supervised vs. Unsupervised Learning, and Train/Test
2. Linear Regression
3. Polynomial Regression
4. Multiple Linear Regression
5. Logistic Regression with hands on activity
6. Preventing Over-fitting of a Hypothesis
7. Bayesian Methods
8. Implementing a Spam Classifier with Naive Bayes
9. K-Means Clustering
10. Hands in Activity Clustering people based on income and Education
11. Measuring the chaos in data: Entropy
12. Decision Trees
13. Random Forest
14. Project: Apply Decision Trees concepts
15. Ensemble Learning

In this module we will understand the practical problems of data science and how to ensure that we can set up the problem statement correctly. We will also learn how to clean the data and apply different techniques to process the data for best results.

1. Data preprocessing
2. Bias Variance Tradeoff
3. Hands On Activity K-Fold Cross-Validation to avoid over fitting Data
4. Cleaning and Normalization
5. Hands On Activity Cleaning web log data
6. Normalizing numerical data
7. Hands On Activity Detecting outliers
8. Feature Engineering

1. Meditations on reference systems(Cartesian, spherical etc) and linear algebra
2. Support Vector Machines(SVM)
3. Project: Apply SVM concepts using Scikit
4. Principal Component Analysis(PCA) and Dimensionality Reduction
5. PCA example with the iris data set
6. Data Warehousing: ETL and ELT concepts

1. User-Based Collaborative Filtering
2. Item-Based Collaborative Filtering
3. Project: Finding Similar Movies
4. Improving the Results of Movie Similarities
5. Project: Movie Recommendations Engine
6. Improve the recommenders results

1. Spark and RDD
2. Introduction to MLLib
3. Exercise Decision Trees in Spark
4. K-Means Clustering in Spark
5. TF -IDF : Term frequency and Inverse
6. Searching documents with Spark

1. Neural Network Game
2. Deep Learning concepts
3. Biological Motivation of Neural Networks and Historical
4. Deep Learning using Tensorflow on Google's tensorflow playground
5. Tensor Flow
6. Keras
7. Convolution Neural Networks(CNN’s)
8. CNN for pattern recognition in hand writing recognition
9. Recurrent Neural Networks(RNN)
10. Natural Language Processing
11. Sentiment Analysis.

1. AB Testing
2. T-Tests and P-Values.
3. Hands-on With T-Tests
4. K-Means Clustering in Spark
5. When to stop an Experiment
6. AB Testing Summary