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    • Introduction to the Data Science and Data types
    • Python -Core , Advance, Analytics
    • Introduction to R Programming
    • Statistics and Probability – Business Analytics
    • Exploratory Data Analysis – Story telling using Visualization
    • Machine Learning – Statistics Decision making , Methods and Algorithms
    • Tableau For Story Telling
    • Natural Language Processing – Text Mining
    • Artificial Intelligence – Deep learning and Computer vision
    • Applying ML to Big Data using Hadoop and Spark
    • Deployment
    • Classroom Session and Lab Session
    • Capstone Projects: 4 – 5 on Retail, Banking, Insurance, Health, Image Processing, and Text Mining.
    • Introduction to data science
    • Application of Data Science
    • Life cycle of data science projects
    • Data and its various forms

Module – 1: Basics of Python Data Structures

    • Introduction to Python basic syntax
    • Basics of Datatypes
      • Numbers, Variables, Strings
      • List, Tuple, Set, Dictionary

Module – 2: Python Statements, Methods, Functions and Expressions

    • Python Statements
      • If Elif and Else Statements
      • For Loops, While Loops, Useful Operators
    • Methods and Functions in Python
    • Expression
      • Lambda Expression
      • Map, Filter Functions, Nested Statements and Scope

Module – 3: Advanced Python

    • Modules and Packages
    • File Handling, Errors, and Exceptions Handing

Module – 4: More Advanced Python

    • Object-Oriented Programming
      • Inheritance, Polymorphism, Abstraction, Encapsulation
    • Regular Expression

Module – 5: Python for Data Analysis

    • Introduction of NumPy
      • Arrays, Indexing, Array processing
      • Array Input and Output
    • Introduction to Pandas
      • Series, DataFrames, Indexing and slicing, Groupby
      • Concatenating, Merging Joining, Missing Values
      • Operations, Data Input, and Output
      • Pivot, Cross tab

Module – 1: Basics of R

    • Data types, Variables, Operators
    • Strings, List
    • Decision making
    • Loops, Vectors
    • Arrays, Functions

Module – 2: R for Data Analysis

    • Importing Data from texts and Spreadsheets
    • Data Frames
    • Packages, Libraries
    • Data Manipulation and reshaping
    • Data Visualization using R
    • Data Transformation

Module -1: Descriptive Statistics

    • Understanding the properties of attribute
      • Central tendencies (Mean, Median, Mode)
      • Measure of Spread (Range Variance Standard Deviation)
      • Basics of Probability
      • Expectation and Variance of a variable
      • Z- test
    • Probability theory
      • Random Variables
      • Probability theory
      • Conditional Probability
      • Bayes theorem
    • Deeper into probability distribution
      • Discrete Probability Distribution:
        • Bernoulli, Binomial, Geometric, Poison and properties of each.
    • Continuous Probability Distribution:
      • Exponential
      • Normal distribution and t- distribution

Module -2: Inferential Statistics

    • Judgments and Conclusion from samples.
      • Inferential Statistics:
        • Population from a sample and vice versa
        • Central Limit Theorem
        • Sampling Distribution
        • Confidence Interval, Hypothesis Testing.
    • More Statistical testing:
      • chi-square test,
      • t-test, ANOVA

Module -1:

    • Data Preprocessing using Python with usecase
      • Data-point, vector, observation
      • Dataset
      • Input variables/features/dimensions/independent variable
      • Output Variable/Class Label/ Response Label/ dependent variable
      • Objective: Classification.
      • Standardization and Normalization
      • Label encoding (Type conversion)
      • Splitting data

Module -2:

    • EDA with Visualization using Seaborn and Matplotlib
      • Scatter-plot: 2D, 3D.
      • Pair plots.
      • PDF, CDF, Univariate analysis.
        • Histogram and PDF
        • Univariate analysis using PDFs.
        • Cumulative distribution function (CDF)
      • Mean, Variance, Std-dev
      • Median, Percentiles, Quantiles, IQR, MAD and Outliers.
      • Box-plot with whiskers,
      • Violin plots.
      • Summarizing plots.
      • Univariate, Bivariate and Multivariate analysis.Multivariate probability density, contour plot.

Module-1: Predictive Analysis

    • Linear Regression
      • Relationship between variables: Regression (Linear, Multivariate Linear Regression) in prediction.
      • Understanding the summary output of Linear Regression
      • Residual Analysis
      • Identifying significant features, feature reduction using AIC, multi-collinearity check, observing influential points, etc.
      • Hypothesis testing of Regression Model
      • Confidence intervals of Slope
      • R-square and goodness of fit
      • Influential Observation – Leverage
      • Multiple Linear Regression and Polynomial Regression
      • Categorical Variable in Regression
    • Logistic Regression (Logit function) and interpretation
      • Hands-on Python Session on Logistic Regression using business case.
      • ROC
    • Naive Bayes classifier
      • Review probability distributions, Joint and conditional probabilities
      • Model Assumptions, Probability estimation
      • Required data processing
      • Feature Selection
      • Classifier
  • Time Series Analysis:
    • Trend analysis
    • Cyclical and Seasonal analysis
    • Smoothing; Moving averages; Auto-correlation; ARIMA
    • Application of Time Series in financial marke

Module-2: Algorithms in Machine Learning

    • Rule based in Supervised Learning
      • Classification Rules
        • Decision Tree – Indirect
        • Direct: Sequential covering
        • Decision nodes and leaf nodes
        • Variable Selection, Parent and child nodes branching
        • Stopping Criterion, Tree pruning, Depth of tree, Over fitting
        • Metrics for decision trees-Gini impurity, Information Gain, Variance Reduction
        • Regression using decision tree
        • Interpretation of a decision tree using If-else
        • Accuracy estimation using cross-validation
    • Distance Based Approach (k-NN)
      • What is KNN and why do we use it?
      • KNN-algorithm and regression
      • Curse of dimensionality and brief introduction to dimension reduction
      • KNN-outlier treatment and anomaly detection
      • Cross-Validation
      • Pros and cons of KNN
    • Mathematical Approach – Support Vector Machine (SVM)
      • Linear learning machines and Kernel space, making kernels and working in feature space
    • Ensemble Models
      • Introduction to Ensemble
      • Bias and Tradeoff
      • Bagging & boosting and its impact on bias and variance
      • Random forest
      • Gradient Boosting Machines and XGBoost

Module-3: Unsupervised Learning

    • Clustering
      • Different clustering methods
      • review of several distance measures
      • Iterative distance-based clustering
      • Dealing with continuous, categorical values in K-Means
      • Constructing a hierarchical cluster, and density-based
      • Test for stability check of clusters
    •  
    • Recommendation System
      • Association rules
        • How to combine clustering and classification;
        • A mathematical model for association analysis
        • Apriori: Constructs large item sets with mini sup by iterations
        • Metrics of rules-Lift, Support, Confidence, Conviction
      • Collaborative filtering and its applications areas

Module – 1: Tableau Basics

    • The Business Challenge
    • Connecting Tableau to a Data File
    • Navigating Tableau, Creating Calculated Fields
    • Adding Color, Adding Labels and Formatting,Exporting Your Worksheet

Module – 2: Time Series, Aggregating, and Filters

    • Working with Data Extracts in Tableau
    • Working with Time Series
    • Understanding, Aggregating, Granularity and Level and Detail
    • Creating an Area Chart & Learning About Highlighting
    • Adding a Filter and Quick Filter

Module – 3: Dashboard

    • Joining Data in Tableau
    • Creating a Map, working with Hierarchies
    • Creating a Scatter Plot, Applying Filters to Multiple Worksheets
    • Create First Dashboard
    • Adding an Interactive Action – Filter
    • Adding an Interactive Action – Highlighting

Module – 4: Calculations, Advanced Dashboards, Storytelling

    • Downloading the Dataset and connecting to Tableau
    • Creating Table Calculations
    • Creating Bins and distributions
    • Leveraging the Power of Parameter
    • How to Create a Tree Map Chart
    • Creating a Customer Segmentation Dashboard
    • Advance Dashboard Interactivity
    • Creating a Storyline
    • Introduction to Text Mining and its Application
      • Introduction to NLTK, Spacy libraries in python
    • Structured and Unstructured Data
    • Extracting Unstructured text from files and Websites
    • Processing with Raw Text
      • Regular Expression for Detecting Word Patterns
      • Normalizing Text
      • Tokenizing Text
      • Segmentation
        • Stemming and Lemma
    • Categorizing and Tagging
      • Automatic Tagging
      • N-Gram Tagging
      • Transformation based tagging
    • Introduction to the Fundaments of information retrieval
      • TF and IDF
      • Bag-of-words
      • Thinking about the math behind text; Properties of words; Vector Space Model
      • Named Entity Recognition
      • Relation Extraction
    • Matrix factorization: Singular Value Decomposition (SVD)
    • Text Indexing
      • Inverted Indexes
      • Boolean query processing
      • Handling phrase queries, proximity queries
      • Latent Sematic Analysis
    • Text classification
    • Sentiment analysis

Deep Learning:

Module – 1: Introduction to Neural Networks

    • Introduction to Neural Network
    • Introduction to Perceptron
    • Activation Functions
    • Cost Functions
    • Gradient Decent
    • Stochastic Gradient Descent
    • Back propagation

Module – 2: Tensorflow

    • Tensorflow Basic Syntax
    • Tensorflow Graphs
    • Variables and Placeholder
    • Saving and Restoring Models
    • Tensorboard

Module – 3: Building Neural Network With TensorFlow

    • Neural Network for Regression
    • Neural Network for Classification
    • Evaluating the ANN
    • Improving and tuning the ANN

Module -4: Convolutional Neural Networks (CNN)

    • Convolution Operation
    • ReLU Layer
    • Pooling
    • Flattening
    • Full Connection
    • Softmax and Cross Entropy

Module -5: Building Convolution Neural Network in Python

    • Computer Vision:
      • OpenCV library in Python
    • Getting Started with Images/Videos
      • Operations on Images
    • Image Processing in OpenCV
      • Geometric Transformation of Images
        • Rotation
        • Affine Transformation
        • Perspective Transformation
      • Image Thresholding
      • Contours
      • Edge detections
      • Morphological Transformation
      • Harris Corner Detection
    • Reshaping Images
    • Normalizing Images
    • Building Convolutional Network with Tensorflow
    • Training CNN for Image Classification

Module -6: Keras (Backend Tensorflow)

    • Keras vs Tensorflow
    • Introduction to Keras
    • Building Artificial Neural Network with Keras
    • Building Convolution Neural Network with Keras

Module -7: Recurrent Neural Networks (RNN)

    • The Idea behind Recurrent Neural Networks
    • Vanishing Gradient Problem
    • LSTM (Long Short Term Memory)

Module -8: Building Recurrent Neural Networks with Tensorflow and Keras

    • Time Series
      • Time Series Forecasting with LSTM
    • Structured Vs Unstructured data
    • 4 Vs if Big Data
    • Hadoop Ecosystem
    • Applications of Big Data
    • Introduction to Map-Reduce
    • Hadoop Ecosystem (Pig, Hive etc.)
    • Introduction to Spark and Scala
      • Spark ML
      • Spark SQL

At the end of any Data Science project end with Deploying.

    • Creating pickle and frozen files
    • Cloud Deploying Machine Learning and Deep Learning model for production
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