Become a Data Scientist

Session 1: Basic statistical concepts : This session introduces you with how statistics is used in business with basic statistical concepts like levels of data and measures of central tendencies.

• Measures of central tendencies
• Measures of variability
• Measures of shape
• Introduction to probability

Session 2: Inferential Statistics : You will start making statistical inferences about populations from samples

• Sampling
• Estimating the population Mean Using Z-statistic and T- statistic
• Hypothesis testing
• Confidence Intervals

Session 1: Basic concepts of R programming

This session covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, organizing, and commenting R code.

Session 2: Continuation of R concepts and execute statistical concepts using R

• Pre-processing techniques: Binning, Filling missing values, Standardization & Normalization, type conversions, train-test data split, ROCR1
• Other R concept
•  Exploratory Data Analysis

Session 3: Introduction to ML Algorithms : Preparing data as an input for machine learning algorithms

• Case Study
•  Assignment on R understandings

Session 4: Execute all machine algorithms in R

Session 1: Linear Regression

• Simple Linear Regression
• Coefficient of determination
• Significance Tests
• Residual Analysis
• Confidence & Prediction intervals
• Multiple linear regression
• Coefficient of Determination
• Interpretation of regression coefficients
• Categorical variables in regression
• Heteroscedasticity, Multi-co linearity outliers
• R-square and goodness of fit
• Hypothesis testing of Regression Model
• Transformation of variables
• Polynomial Regression

Followed by a Case Study

Session 2: Logistic Regression

Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.

• Logistic function
• Estimation of probability using logistic regression
• Model Evaluation
• Confusion Matrix

Followed by a Case Study

Session 3: Time series data : The focus is on analyzing and understanding Time Series with financial markets as the case study

• Trend Analysis
• Cyclical & Seasonal Analysis
• Smoothing; Moving Averages
• Auto-Correlation
• ARIMA; ARIMAX
• Applications of Time Series in Financial Markets

Followed by a Case Study

• Session 1: Clustering
• What is Clustering   
• Clustering examples in Business Verticals
• Solution Strategies for Clustering
• Finding pattern and Fixed Pattern Approach
• Limitations of Fixed Pattern Approach
• Machine Learning Approaches for Clustering
• Iterative based K-Means & K-Medoid Approaches
• Hierarchical Agglomerative Approaches
• Density based DB-SCAN Approach       
• Evaluation Metrics for Clustering
• Cohesion, Coupling Metric       
• Correlation Metric

Followed by a Case Study

Session 2: Decision Tree (In Python & R)

• Introduction
• Building a Decision Tree
• Entropy, Information Gain
• Regression using Decision Tress
• Bias- Variance trade off
• Limitations

Followed by a Case Study

Session 3: Support Vector machines (In Python)

• Loss function based  interpretation
• Linear svm
• Non linear svm and kernel function

Followed by a Case Study

Session 4: KNN (In Python & R)

• KNN learning
• Limitation
• KNN regression
• Applying KNN and parameter  tuning

Session 5: Neural Networks (In Python)

• Introduction
• Perceptrons
• Self organizing maps
• Auto encoders
• Back propagation and typical feed forward algorithm
• Vanishing gradient problem

Followed by a Case Study

Session 6: Association Rules (In R)

• Apriori Model
• Pros and cons of the model
• Recommemder Systems
  
  • User-user
  • Item-item
  • Content based

Followed by a Case Study

Session 7:Feature Engineering (In Python & R)

• Dimensionality Reduction
• PCA and EDA
• Eigen values and eigen vectors