Introduction to Data Science with Python

• What is analytics & Data Science?
• Common Terms in Analytics
• Analytics vs. Data warehousing, OLAP, MIS Reporting
• Relevance in industry and need of the hour
• Types of problems and business objectives in various industries
• How leading companies are harnessing the power of analytics?
• Critical success drivers
• Overview of analytics tools & their popularity
• Analytics Methodology & problem solving framework
• List of steps in Analytics projects
• Identify the most appropriate solution design for the given problem statement
• Project plan for Analytics project & key milestones based on effort estimates
• Build Resource plan for analytics project

• Why Python for data science?
• Overview of Python- Starting with Python
• Introduction to installation of Python
• Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
• Understand Jupyter notebook & Customize Settings
• 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
• Variable & Value Labels –  Date & Time Values
• Basic Operations – Mathematical – string – date
• Reading and writing data
• Simple plotting
• Control flow & conditional statements
• Debugging & Code profiling
• How to create class and modules and how to call them?

Scientific Distributions Used In Python For Data Science

NumPy, pandas, scikit-learn, stat models, nltk

• Importing Data from various sources (Csv, txt, excel, access etc)
• Database Input (Connecting to database)
• Viewing Data objects – subsetting Data, methods
• Exporting Data to various formats
• Important python modules: Pandas, beautiful soup

• 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)
• Python Built-in Functions (Text, numeric, date, utility functions)
• Python User Defined Functions
• Stripping out extraneous information
• Normalizing data
• Formatting data
• Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)

• 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, seaborn, Pandas and SciPy. Stats etc)

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

• Concept of model in analytics and how it is used?
• Common terminology used in analytics & Modelling process
• Popular modelling algorithms
• Types of Business problems – Mapping of Techniques
• Different Phases of Predictive Modelling

• 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

• Need of Data preparation
• Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
• Variable Reduction Techniques – Factor & PCA Analysis

• Introduction to Segmentation
• Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
• Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
• Behavioural Segmentation Techniques (K-Means Cluster Analysis)
• Cluster evaluation and profiling – Identify cluster characteristics
• Interpretation of results – Implementation on new data

• 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
• Validation of Models (Re running Vs. Scoring)
• Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
• Interpretation of Results – Business Validation – Implementation on new data

• Introduction – Applications
• Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
• Building Logistic Regression Model (Binary Logistic Model)
• Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
• Validation of Logistic Regression Models (Re running Vs. Scoring)
• Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
• Interpretation of Results – Business Validation – Implementation on new data

• Introduction – Applications
• Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
• Classification of Techniques(Pattern based – Pattern less)
• Basic Techniques – Averages, Smoothening, etc
• Advanced Techniques – AR Models, ARIMA, etc
• Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

• Introduction to Machine Learning & Predictive Modelling
• Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
• Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
• Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation)
• Overfitting (Bias-Variance Trade off) & Performance Metrics
• Feature engineering & dimension reduction
• Concept of optimization & cost function
• Overview of gradient descent algorithm
• Overview of Cross validation(Bootstrapping, K-Fold validation etc)
• Model performance metrics (R-square, Adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )

• What is segmentation & Role of ML in Segmentation?
• Concept of Distance and related math background
• K-Means Clustering
• Expectation Maximization
• Hierarchical Clustering
• Spectral Clustering (DBSCAN)
• Principle component Analysis (PCA)

• 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; Unwrapping Trees as Rules
• Decision Trees – Validation
• Overfitting – Best Practices to avoid

• Concept of Ensembling
• Manual Ensembling Vs. Automated Ensembling
• Methods of Ensembling (Stacking, Mixture of Experts)
• Bagging (Logic, Practical Applications)
• Random forest (Logic, Practical Applications)
• Boosting (Logic, Practical Applications)
• Ada Boost
• Gradient Boosting Machines (GBM)
• XGBoost

• Motivation for Neural Networks and Its Applications
• Perceptron and Single Layer Neural Network, and Hand Calculations
• Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
• Neural Networks for Regression
• Neural Networks for Classification
• Interpretation of Outputs and Fine tune the models with hyper parameters
• Validating ANN models

• What is analytics & Data Science?
• Common Terms in Analytics
• Analytics vs. Data warehousing, OLAP, MIS Reporting
• Relevance in industry and need of the hour
• Types of problems and business objectives in various industries
• How leading companies are harnessing the power of analytics?
• Critical success drivers
• Overview of analytics tools & their popularity
• Analytics Methodology & problem solving framework
• List of steps in Analytics projects
• Identify the most appropriate solution design for the given problem statement
• Project plan for Analytics project & key milestones based on effort estimates
• Build Resource plan for analytics project
• Why R for data science?

• Introduction R/R-Studio – GUI
• Concept of Packages – Useful Packages (Base & Other packages)
• Data Structure & Data Types (Vectors, Matrices, factors, Data frames,  and Lists)
• Importing Data from various sources (txt, dlm, excel, sas7bdata, db, etc.)
• Database Input (Connecting to database)
• Exporting Data to various formats)
• Viewing Data (Viewing partial data and full data)
• Variable & Value Labels –  Date Values

• Data Manipulation steps
• Creating New Variables (calculations & Binning)
• Dummy variable creation
• Applying transformations
• Handling duplicates
• Handling missings
• Sorting and Filtering
• Subsetting (Rows/Columns)
• Appending (Row appending/column appending)
• Merging/Joining (Left, right, inner, full, outer etc)
• Data type conversions
• Renaming
• Formatting
• Reshaping data
• Sampling
• Data manipulation tools
• Operators
• Functions
• Packages
• Control Structures (if, if else)
• Loops (Conditional, iterative loops, apply functions)
• Arrays
• R Built-in Functions (Text, Numeric, Date, utility)
• Numerical Functions
• Text Functions
• Date Functions
• Utilities Functions
• R User Defined Functions
• R Packages for data manipulation (base, dplyr, plyr, data.table, reshape, car, sqldf, etc)

• 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)
• R Packages for Exploratory Data Analysis(dplyr, plyr, gmodes, car, vcd, Hmisc, psych, doby etc)
• R Packages for Graphical Analysis (base, ggplot, lattice,etc)

• Basic Statistics – Measures of Central Tendencies and Variance
• Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
• Inferential Statistics -Sampling – Concept of Hypothesis Testing
• Statistical Methods – Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square

• Concept of model in analytics and how it is used?
• Common terminology used in analytics & modelling process
• Popular modelling algorithms
• Types of Business problems – Mapping of Techniques
• Different Phases of Predictive Modelling

   Data Preparation

•  Need of Data preparation
• Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
• Variable Reduction Techniques – Factor & PCA Analysis

• Introduction to Segmentation
• Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
• Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
• Behavioral Segmentation Techniques (K-Means Cluster Analysis)
• Cluster evaluation and profiling – Identify cluster characteristics
• Interpretation of results – Implementation on new data

• 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
• Validation of Models (Re running Vs. Scoring)
• Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
• Interpretation of Results – Business Validation – Implementation on new data

 Introduction – Applications

• Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
• Building Logistic Regression Model (Binary Logistic Model)
• Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
• Validation of Logistic Regression Models (Re running Vs. Scoring)
• Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
• Interpretation of Results – Business Validation – Implementation on new data

• Introduction – Applications
• Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
• Classification of Techniques(Pattern based – Pattern less)
• Basic Techniques – Averages, Smoothening, etc
• Advanced Techniques – AR Models, ARIMA, etc
• Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

• Introduction to Machine Learning & Predictive Modeling
• Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
• Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
• Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
• Overfitting (Bias-Variance Trade off) & Performance Metrics
• Feature engineering & dimension reduction
• Concept of optimization & cost function
• Overview of gradient descent algorithm
• Overview of Cross validation(Bootstrapping, K-Fold validation etc)
• Model performance metrics (R-square, Adjusted R-squre, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )

• What is segmentation & Role of ML in Segmentation?
• Concept of Distance and related math background
• K-Means Clustering
• Expectation Maximization
• Hierarchical Clustering
• Spectral Clustering (DBSCAN)
• Principle component Analysis (PCA)

• 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; Unwrapping Trees as Rules
• Decision Trees – Validation
• Overfitting – Best Practices to avoid

• Concept of Ensembling
• Manual Ensembling Vs. Automated Ensembling
• Methods of Ensembling (Stacking, Mixture of Experts)
• Bagging (Logic, Practical Applications)
• Random forest (Logic, Practical Applications)
• Boosting (Logic, Practical Applications)
• Ada Boost
• Gradient Boosting Machines (GBM)
• XGBoost

• Motivation for Neural Networks and Its Applications
• Perceptron and Single Layer Neural Network, and Hand Calculations
• Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
• Neural Networks for Regression
• Neural Networks for Classification
• Interpretation of Outputs and Fine tune the models with hyper parameters
• Validating ANN models

• Motivation for Support Vector Machine & Applications
• Support Vector Regression
• Support vector classifier (Linear & Non-Linear)
• Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
• Interpretation of Outputs and Fine tune the models with hyper parameters
• Validating SVM models

• 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

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

• Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
• Finding patterns in text: text mining, text as a graph
• Natural Language processing (NLP)
• Text Analytics – Sentiment Analysis using R
• Text Analytics – Word cloud analysis using R
• Text Analytics – Segmentation using K-Means/Hierarchical Clustering
• Text Analytics – Classification (Spam/Not spam)
• Applications of Social Media Analytics
• Metrics(Measures Actions) in social media analytics
• Examples & Actionable Insights using Social Media Analytics
• Important R packages for Machine Learning (caret, H2O, Randomforest, nnet, tm etc)
• Fine tuning the models using Hyper parameters, grid search, piping etc.

Introduction To Analytics

• Analytics World
  • Introduction to Analytics
  • Concept of ETL
  • S-A-S in advanced analytics
• Global Certification: Induction and walk through
  • Getting Started
  • Software installation
  • Introduction to GUI
  • Different components of the language
  • All programming windows
  • Concept of Libraries and Creating Libraries
  • Variable Attributes – (Name, Type, Length, Format, In format, Label)
  • Importing Data and Entering data manually
• Understanding Datasets
  • Descriptor Portion of a Dataset (Proc Contents)
  • Data Portion of a Dataset
  • Variable Names and Values
  • Data Libraries

 Understanding Data Step Processing

• • Data Step and Proc Step
  • Data step execution
  • Compilation and execution phase
  • Input buffer and concept of PDV
• Importing Raw Data Files
  • Column Input and List Input and Formatted methods
  • Delimiters, Reading missing and non standard values
  • Reading one to many and many to one records
  • Reading Hierarchical files
  • Creating raw data files and put statement
  • Formats / Informat
• Importing and Exporting Data (Fixed Format / Delimited)
• Proc Import / Delimited text files
• Proc Export / Exporting Data
• Datalines / Cards;
• Atypical importing cases (mixing different style of inputs)
  • Reading Multiple Records per Observation
  • Reading “Mixed Record Types”
  • Sub-setting from a Raw Data File
  • Multiple Observations per Record
  • Reading Hierarchical Files
  •  
• Concept of SAS library and SAS Catalog
• Variable Types in SAS
• Reading Data stored external to SAS
• Importing Data by using Proc Import
• Data Step SAS statements
• SAS Functions
• Appending and Merging using SAS
• SAS Procedures like proc means, proc Univariate, proc append, proc freq and proc export.
• SAS SQL
• SAS Macros

• One Sample t-test of comparing means
• Two Sample t-test of comparing means
• One Way ANOVA
• Assumptions of ANOVA Modeling
• n-way ANOVA
• ANOVA Post Hoc Studies

• Apply the principles of honest assessment to model performance measurement
• Assess classifier performance using the confusion matrix
• Model selection and validation using training and validation data
• Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
• Establish effective decision cut-off values for scoring

•  Understanding and Exploration Data
  • Introduction to basic Procedures – Proc Contents, Proc Print
• Understanding and Exploration Data
  • Operators and Operands
  • Conditional Statements (Where, If, If then Else, If then Do and select when)
  • Difference between WHERE and IF statements and limitation of WHERE statements
  • Labels, Commenting
  • System Options (OBS, FSTOBS, NOOBS etc…)
• Data Manipulation
  • Proc Sort – with options / De-Duping
  • Accumulator variable and By-Group processing
  • Explicit Output Statements
  • Nesting Do loops
  • Do While and Do Until Statement
  • Array elements and Range
• Combining Datasets (Appending and Merging)
  • Concatenation
  • Interleaving
  • Proc Append
  • One To One Merging
  • Match Merging
  • IN = Controlling merge and Indicator

• Engineering/Management Graduate or Post-graduate Fresher Students who want to make their career in Data Science Industry or want to be future Data Scientist.
• Engineers who want to use a distributed computing engine for batch or stream processing or both
• Analysts who want to leverage Spark for analyzing interesting datasets
• Data Scientists who want a single engine for analyzing and modelling data as well as productionizing it.
• MBA Graduates or business professionals who are looking to move to a heavily quantitative role.
• Engineering Graduate/Professionals who want to understand basic statistics and lay a foundation for a career in Data Science
• Working Professional or Fresh Graduate who have mostly worked in Descriptive analytics or not work anywhere and want to make the shift to being  data scientists
• Professionals who’ve worked mostly with tools like Excel and want to learn how to use R for statistical analysis