DATASCIENCE & DATA ANALYTICS TRAINING IN BANGALORE

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Data Science and Data Analytics  – Python / R / SAS Training in Bangalore

Learn Data Science, Deep Learning, & Machine Learning  using Python / R  /SAS With Live Machine Learning & Deep Learning Projects 

Duration : 3 Months – Weekends 3 Hours on Saturday and Sundays

Real Time Projects , Assignments , scenarios are part of this course

Data Sets , Installations , Interview Preparations , Repeat the session until 6 months are all attractions of this particular course

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  • 25000+ Man-hours of Real-time projects & scenarios
  • 10 to 20+ year Experienced corporate trainers With Real Time Experience.
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Course description

Pre-requisites

There are no formal prerequisites for this course; however, previous operating system administration experience will be very beneficial.

Course Content

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

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