1. Introduction to Datascience/Analytics

• Why does companies need Datascientist/Analyst?
• Data Analytics: OLAP vs Data Mining
• What is Datascience? Why Datascience?
• Data driven product engineering
• How to become a Datascientist? and Skill-set of Datascientist?
• Career Opportunities & Hiring companies.

2. Business problems with Datascience

• Predictive Analytics Problems: Classification, Regression, Recommenders. (Supervised techniques)
• Descriptive Analytics Problems: Frequent Pattern Mining, Clustering, Outlier Detection. (Un Supervised techniques)
• Prescriptive Analytics problems: Predictive and Descriptive Problems.
• Types of Data:
• Structured data
• Unstructured data
• Semi structured data
• Time Series data
• Business Verticals: Retail, Banking, Financial, Auto mobile, Social, Web, Medical, Scientific, Logistics, Real Estate etc.

3. Required Tools/technologies for Data Science

• Datascience Life Cycle for Analysis
• Required technologies for each phase of Data Science life cycle.
• Single Machine Analytic Platforms: R, Python, SAS, etc...
• Distributed Analytical Platforms: Hadoop, Spark, H20

4. Mastering in Python Language

• Python introduction and Installation
• Python basic topics
• Variables
• Decision making
• Loops
• Functions, etc.
• Python advance topic
• Classes and OOPs Concepts
• Modules & packages
• File Handling
• Database handling, etc.
• Python advanced features
• Required Packages for Datascience in Python

Statistics and Mathematics for Datascientist/Analyst.

5. Statistics

• Descriptive stats for single variable
• Mean, Median, Mode, Quantiles, Percentiles
• Standard Deviation, Variance
• MAD, IQR
• Descriptive stats for two variables
• Covariance
• Correlation
• Chi-squared Analysis
• Hypothesis Testing
• Inferential Statistics

6. Linear Algebra.

• Ideas that need Linear Algebra
• Vector Algebra
• Ideas that map to vectors
• Understanding vector operations
• Matrix Algebra
• Ideas that map to matrices
• Understanding matrix operations
• Understanding eigen-values and eigen-vectors
• Concepts of basis
• Understanding factorization & Types
• Spectral factorization
• Eigen factorization
• SVD factorization

7. Probability

• Basic Probability
• Conditional Probability
• Bayes Rule/Reasoning
• Mapping Random process to Random variable
• Properties of Random variables
• Probability Expectation
• Entropy and cross-entropy
• Estimating probability of Random variable
• Understanding standard random processes
• Understanding on Probability Distributions

8. Calculus for data scientist

• Rate of change
• Concept of limit
• Concept of derivative
• Partial derivatives & gradient
• Significance of gradient
• Concept of integration, etc.

9. Data Visualizations

• Tabular form
• Using statistical methods – mean, medium, mode, range, frequency, multi-dimensional tables, etc.
• Graphical form
• Bar graphs
• Histograms graphs
• Pie graphs
• Area graphs
• Density graphs
• Scatter graphs
• Line graphs
• Whisker graphs
• Correlation graphs
• Facet plots, etc.

10. Overview of Machine Learning Algorithms

• What is Machine Learning?
• ML – Software Development Life Cycle
• ML-SDLC Phases
• Data Collection
• Data Preparation
• Feature Engineering
• Model Building,
• Model Evaluation
• Model Deployment
• Model Maintenance
• Type of Machine Learning Algorithms
• Supervised
• Unsupervised
• Semi-supervised
• Reinforcement Algorithms

11. Data Collection Techniques

• Collecting data from Excel/csv/txt files
• Collecting data from databases
• Collecting data from services
• Collecting data via scraping (from Web)

12. Data Preparation Techniques

• Structured Data Preparation
• Handling Missing Data
• Data Type Conversion
• Category to Numeric Conversion
• Numeric to Category Conversion
• Data Normalization:0-1, Z-Score
• Handling Skew Data: Box-Cox Idea
• Text Data Preparation/preprocessing

Noise removal (Stop word removal, URLs, punctuations, etc.)

Lexicon Normalization (Stemming & Lemmatization)

Object Standardization (Convert acronyms to dictionary words, grammar check, spell check etc.)

13. EDA (Numerical + Graphical) and Feature Engineering

• Exploring Individual Features
• Exploring Bi-Feature Relationships
• Exploring Multi-Feature Relationships
• Create new Features.
• Feature/Dimension Reduction: PCA
• Intuition behind PCA
• Covariance & Correlation
• Relating PCA to Covariance/Correlation
• Intuition to math
• Applications of PCA: Dimensionality Reduction, Image Compression

14. Model Building (ML Algorithm Building)

• Mathematical understanding for each model
• Limitations for each model
• Tuning for each model
• Model scope for type of problem: Classification, Regression Recommenders and Association.
• Pros and Cons of each model

Supervised learning Models

• Decision trees
• Probability learning (Naive Bayes)
• KNN Learning
• Linear regression
• Non-Linear regression
• Logistic regression
• Support Vector Machines(SVM)
• Ensemble Models
• Bagging
• Bagged trees
• Random Forest
• Extreme tress
• Boosting
• Ada boosting
• Gradient Boosting
• Extreme Gradient Boosting
• Voting (by Stacking aggregation)
• Soft voting
• Hard voting
• Stacking
• Neural Network Model
• Apriori Model

Unsupervised Learning

• Clustering Models
• K-Mean Model
• K-Medoid Model
• K-Centers Model
• Hierarchical Model
• Dimension reduction: Principal component analysis (PCA)
• Association Models.

Time Series Models

• Holt-Linear
• Holt-Winters (Extension for Holts Linear for Trend and Seasonality)
• ARIMA Model
• SARIMAX

15. Model Evaluations techniques

• Repeated holdouts (R.H)
• K-fold Cross-validation
• Bootstrap
• Metrix based evaluation.

17. Model Implementation

18. Distributed/BIGDATA Analytics overview

• Big data Analytics Overview
• Platforms for Distributed Analytics: Hadoop, Spark, H20
• Hoop and Spark Overview & Comparison