Data science Introduction

• Data Science motivating examples -- Money ball, okcupid, Nate Silver, Netfilx, LinkedIn,
• Introduction to Analytics, Types of Analytics,
• Introduction to Analytics Methodology
• Analytics Terminology, Analytics Tools
• Introduction to Big Data
• Introduction to Machine Learning

Data Science Using R Language

INTRODUCTION TO DATA SCIENCE USING R:

• What is Data Science?
• Common Terms in Analytics
• Types of problems and business objectives in various industries
• How leading companies are harnessing the power of analytics?
• Overview of analytics tools & their popularity
• List of steps in Analytics projects
• Identify the most appropriate solution design for the given problem statement
• Why R for data science?

Introduction to R

• Introduction to R
• Install R & R studio
• Perform basic operations in R using command line
• Learn the use of IDE R Studio
• Use the ‘R help’ feature in R

Introduction to R programming

• Variables in R
• Scalars
• Vectors
• Matrices
• List
• Data frames
• Using c, Cbind, Rbind, attach and detach functions in R
• Factors

Data Manipulation in R

• Data sorting
• Cleaning data
• Recoding data
• Merging data
• Slicing of Data
• Apply functions

Functions and operators in R

• Topics
• Numerical functions
• Character functions
• Operators in R
• Arithmetic operator
• Relational operator
• Logical operator
• Assignment operator

Decision making and looping statements in R

• If loop
• For loop
• While loop
• Break, next and pass statement

Data Import in R

• Reading Data from excel, csv and txt files
• Writing Data
• Basic SQL queries in R
• Connecting to the database
• Dealing with Date values

Exploratory Data Analysis

• Box plot
• Histogram
• Pareto charts
• Pie graph
• Line chart
• Scatterplot
• Developing Graphs

INTRODUCTION TO PREDICTIVE MODELING

• Concept of model in analytics and how it is used?
• Common terminology used in analytics & modeling process
• Popular modeling algorithms
• Different Phases of Predictive Modeling

DATA EXPLORATION FOR MODELING

• Need for structured exploratory data
• EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
• Identify missing data
• Identify outliers data
• Visualize the data trends and patterns

LINEAR REGRESSION: SOLVING REGRESSION PROBLEMS

• Introduction - Applications
• Assumptions of Linear Regression
• Building Linear Regression Model
• Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
• Assess the overall effectiveness of the model
• Interpretation of Results

LOGISTIC REGRESSION: SOLVING CLASSIFICATION PROBLEMS

• Introduction - Applications
• Building Logistic Regression Model
• Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
• Validation of Logistic Regression Models
• Standard Business Outputs (ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)

SUPERVISED LEARNING: DECISION TREES

• Decision Trees - Introduction - Applications
• Types of Decision Tree Algorithms
• Construction of Decision Trees through Simplified Examples; Choosing the "Best" attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
• Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
• Pruning a Decision Tree; Cost as a consideration
• Decision Trees - Validation
• Overfitting - Best Practices to avoid

SUPERVISED LEARNING: ENSEMBLE LEARNING

• Concept of Ensembling
• Random forest (Logic, Practical Applications)

SUPERVISED LEARNING: SUPPORT VECTOR MACHINES

• Motivation for Support Vector Machine & Applications
• Interpretation of Outputs and Fine tune the models with hyper parameters

SUPERVISED LEARNING: KNN

• What is KNN & Applications?
• KNN for missing treatment
• KNN For solving regression problems
• KNN for solving classification problems
• Validating KNN model
• Model fine tuning with hyper parameters

SUPERVISED LEARNING: NAÏVE BAYES

• Concept of Conditional Probability
• Bayes Theorem and Its Applications
• Naïve Bayes for classification
• Applications of Naïve Bayes in Classifications

TIME SERIES FORECASTING: SOLVING FORECASTING PROBLEMS

• Introduction - Applications
• Basic Techniques - Averages, Smoothening, etc
• Advanced Techniques - AR Models, ARIMA, etc
• Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc

UNSUPERVISED LEARNING: SEGMENTATION

• What is segmentation & Role of ML in Segmentation?
• K-Means Clustering
• Expectation Maximization
• Principle component Analysis (PCA)

Data Science Using Python

INTRODUCTION TO DATA SCIENCE USING PYTHON:

• What is Data Science?
• Common Terms in Analytics
• Types of problems and business objectives in various industries
• Overview of analytics tools & their popularity
• List of steps in Analytics projects
• Identify the most appropriate solution design for the given problem statement
• Why Python for data science?

PYTHON: ESSENTIALS

• Overview of Python- Starting with Python
• Introduction to installation of Python
• Introduction to Python IDE's
• Understand Jupyter notebook
• Concept of Packages/Libraries - Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
• Installing & loading Packages & Name Spaces
• Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
• List and Dictionary Comprehensions
• Basic Operations
• Reading and writing data
• Simple plotting
• Control flow & conditional statements
• How to create class and modules and how to call them?

SCIENTIFIC DISTRIBUTIONS USED IN PYTHON FOR DATA SCIENCE

• Numpy, pandas, matplotlib, scikitlearn etc

ACCESSING/IMPORTING AND EXPORTING DATA USING PYTHON MODULES

• Importing Data from various sources (Csv, txt, excel etc)
• Viewing Data objects - subsetting, methods
• Exporting Data to various formats
• Important python modules: Pandas

DATA MANIPULATION – CLEANSING – MUNGING USING PYTHON MODULES

• Cleansing Data with Python
• Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
• Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
• Normalizing data
• Formatting data
• Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)

DATA ANALYSIS – VISUALIZATION USING PYTHON

• Introduction exploratory data analysis
• Descriptive statistics, Frequency Tables and summarization
• Univariate Analysis (Distribution of data & Graphical Analysis)
• Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
• Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
• Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, Pandas etc)

INTRODUCTION TO STATISTICS

• Basic Statistics - Measures of Central Tendencies and Variance
• Building blocks - Probability Distributions - Central Limit Theorem
• Inferential Statistics -Sampling - Concept of Hypothesis Testing
• Statistical Methods - Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square
• Important modules for statistical methods: Numpy, Scipy, Pandas

INTRODUCTION TO PREDICTIVE MODELING

• Concept of model in analytics and how it is used?
• Common terminology used in analytics & modeling process
• Popular modeling algorithms
• Different Phases of Predictive Modeling

DATA EXPLORATION FOR MODELING

• Need for structured exploratory data
• EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
• Identify missing data
• Identify outliers data
• Visualize the data trends and patterns

LINEAR REGRESSION: SOLVING REGRESSION PROBLEMS

• Introduction - Applications
• Assumptions of Linear Regression
• Building Linear Regression Model
• Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
• Assess the overall effectiveness of the model
• Interpretation of Results

LOGISTIC REGRESSION: SOLVING CLASSIFICATION PROBLEMS

• Introduction - Applications
• Building Logistic Regression Model
• Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
• Validation of Logistic Regression Models
• Standard Business Outputs (ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)

SUPERVISED LEARNING: DECISION TREES

• Decision Trees - Introduction - Applications
• Types of Decision Tree Algorithms
• Construction of Decision Trees through Simplified Examples; Choosing the "Best" attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
• Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
• Pruning a Decision Tree; Cost as a consideration
• Decision Trees - Validation
• Overfitting - Best Practices to avoid

SUPERVISED LEARNING: ENSEMBLE LEARNING

• Concept of Ensembling
• Random forest (Logic, Practical Applications)

SUPERVISED LEARNING: SUPPORT VECTOR MACHINES

• Motivation for Support Vector Machine & Applications
• Interpretation of Outputs and Fine tune the models with hyper parameters

SUPERVISED LEARNING: KNN

• What is KNN & Applications?
• KNN for missing treatment
• KNN For solving regression problems
• KNN for solving classification problems
• Validating KNN model
• Model fine tuning with hyper parameters

SUPERVISED LEARNING: NAÏVE BAYES

• Concept of Conditional Probability
• Bayes Theorem and Its Applications
• Naïve Bayes for classification
• Applications of Naïve Bayes in Classifications

TIME SERIES FORECASTING: SOLVING FORECASTING PROBLEMS

• Introduction - Applications
• Basic Techniques - Averages, Smoothening, etc
• Advanced Techniques - AR Models, ARIMA, etc
• Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc

UNSUPERVISED LEARNING: SEGMENTATION

• What is segmentation & Role of ML in Segmentation?
• K-Means Clustering
• Expectation Maximization
• Principle component Analysis (PCA)