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    Data Science Course Program

    Data Science Course prepares you for the Data Science Certification exam and for the role of Data Scientist by making you an expert in Statistics, Analytics, Data Science, Big Data, AI, Machine Learning and Deep Learning. The Data science master program has been designed by the industry expert to help you master data mining, management, exploration, and carry out several industry-relevant projects. Enroll & Get Certified now!

    • ✔ Course Duration : 56 hrs
    • ✔ Training Options : Live Online / Self-Paced / Classroom
    • ✔ Certification Pass : Guaranteed

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    UPCOMING-BATCHES OVERVIEW LEARNING OBJECTIVES PRE-REQUISITES CURRICULUM FAQ CERTIFICATE

    Instructor-led Training live online classes

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    Why Learn Data Science?

    Data science is a “concept to unify statistics, data analysis, machine learning & their related methods” in order to “understand & analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, data mining, databases, and visualization. Data Science Turing award winner Jim Gray imagined data science as a ” fourth paradigm” of science (empirical, theoretical, computational and now data-driven) and asserted that ” everything about science is changing because of the impact of information technology” and the data deluge.

    What you will Learn

    Data science course program will help you master skills and tools like Statistics, Hypothesis testing, Clustering, Decision trees, Linear and Logistic regression, R Studio, Data Visualization, Regression models, Hadoop, Spark, PROC SQL, SAS Macros, Statistical procedures,Advanced analytics, Matplotlib, Excel analytics functions, Hypothesis testing, Zookeeper, Kafka interfaces. You will master TensorFlow, Machine Learning, and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems. These skills will help you prepare for the role of a Data Scientist.

    PREREQUISITES

    Following are some prerequisites to learn Anaplan:

    1. Fundamentals of Python programming
    2. Basic knowledge of statistics

    If you don’t know about python, statistics, machine learning etc. don’t worry we will provide training for that to you so, that you can analyze and statistical techniques, it will take hardly 15-20 hours.

    CURRICULUM

    Learning Objectives: Analytics Overview Introduction
    Introduction to Business Analytics
    Types of Analytics
    Areas of Analytics
    Analytical Tools
    Analytical Techniques Introduction to SAS Introduction
    What is SAS
    Navigating in the SAS Console
    SAS Language Input Files
    DATA Step
    PROC Step and DATA Step
    DATA Step Processing
    SAS Libraries
    Importing Data
    Exporting Data Combining and Modifying Datasets Introduction
    Why Combine or Modify Data
    Concatenating Datasets
    Interleaving Method
    One – to – one Reading
    Data Manipulation
    Modifying Variable Attributes PROC SQL Introduction
    What is PROC SQL
    Retrieving Data from a Table
    Selecting Columns in a Table
    Retrieving Data from Multiple Tables
    Selecting Data from Multiple Tables
    Concatenating Query Results
    Activity SAS Macros Introduction
    Need for SAS Macros
    Macro Functions
    Macro Functions Examples
    SQL Clauses for Macros
    The % Macro Statement
    The Conditional Statement Basics of Statistics Introduction to Statistics
    Statistical Terms
    Procedures in SAS for Descriptive Statistics
    Descriptive Statistics
    Hypothesis Testing
    Variable Types
    Hypothesis Testing
    Process
    Parametric and Non – parametric Tests
    Parametric Tests
    Non – parametric Tests
    Parametric Tests – Advantages and Disadvantages Statistical Procedures Introduction o Statistical Procedures
    PROC Means
    PROC FREQ
    PROC UNIVARIATE
    PROC CORR
    PROC CORR Options
    PROC REG
    PROC REG Options
    PROC ANOVA Data Exploration Introduction
    Data Preparation
    General Comments and Observations on Data Cleaning
    Data Type Conversion
    Character Functions
    SCAN Function
    Date/Time Functions
    Missing Value Treatment
    Various Functions to Handle Missing Value
    Data Summarization Advanced Statistics Introduction
    Introduction to Cluster
    Clustering Methodologies
    K Means Clustering
    Decision Tree
    Regression
    Logistic Regression Working with Time Series Data Introduction
    Need for Time Series Analysis
    Time Series Analysis — Options
    Reading Date and DDateTimeValues
    White Noise Process
    Stationarity of a Time Series
    Stages of ARIMA Modelling
    Transform Transpose and Interpolating Time Series Data Designing Optimization Models Introduction
    Need for Optimization
    Optimization Problems
    PROC OPTMODEL

    Learning Objectives: Introduction to Business Analytics Introduction
    Objectives
    Need of Business Analytics
    Business Decisions
    Introduction to Business Analytics
    Features of Business Analytics
    Types of Business Analytics
    Descriptive Analytics
    Predictive Analytics
    Prescriptive Analytics
    Supply Chain Analytics
    Health Care Analytics
    Marketing Analytics
    Human Resource Analytics
    Web Analytics
    Application of Business Analytics
    Business Decisions
    Business Intelligence (BI)
    Data Science
    Importance of Data Science
    Data Science as a Strategic Asset
    Big Data
    Analytical Tools Introduction to R Introduction
    Objectives
    An Introduction to R
    Comprehensive R Archive Network (CRAN)
    Cons of R
    Companies Using R
    Understanding R
    Installing R on Various Operating Systems
    Installing R on Windows from CRAN Website
    Install R
    IDEs for R
    Installing RStudio on Various Operating Systems
    Install R-Studio
    Steps in R Initiation
    Benefits of R Workspace
    Setting the Workplace
    Functions and Help in R
    Access the Help Document
    R Packages o Installing an R Package
    Install and Load a Package R Data Structure Introduction
    Objectives
    Types of Data Structures in R
    Vectors
    Create a Vector
    Scalars
    Colon Operator
    Accessing Vector Elements
    Matrices
    Accessing Matrix Elements
    Create a Matrix
    Arrays
    Accessing Array Elements
    Create an Array
    Data Frames
    Elements of Data Frames
    Create a Data Frame
    Factors
    Create a Factor
    Lists
    Create a List
    Importing Files in R
    Importing an Excel File
    Importing a Minitab File
    Importing a Table File
    Importing a CSV File
    Read Data from a File
    Read Data from a File
    Exporting Files from R Apply Functions Introduction
    Objectives
    Types of Apply Functions
    Apply() Function
    Lapply() Function
    Sapply() Function
    Tapply() Function
    Vapply() Function
    Mapply() Function
    Dplyr Package
    Installing the Dplyr Package
    Functions of the Dplyr Package
    Functions of the Dplyr Package – Select()
    Use the Select() Function
    Functions of Dplyr-Package – Filter()
    Use the Filter() Function
    Use Select Function
    Functions of Dplyr Package – Arrange()
    Use Arrange Function
    Functions of Dplyr Package – Mutate()
    Functions of Dply Package – Summarise()
    Use Summarise Function Data Visualization Introduction
    Objectives
    Graphics in R
    Types of Graphics
    Bar Charts
    Creating Simple Bar Charts
    Editing a Simple Bar Chart
    Create a Stacked Bar Plot and Grouped Bar Plot
    Pie Charts
    Editing a Pie Chart
    Create a Pie Chart
    Histograms
    Creating a Histogram
    Kernel Density Plots
    Creating a Kernel Density Plot
    Create Histograms and a Density Plot
    Line Charts
    Creating a Line Chart
    Box Plots
    Creating a Box Plot
    Create Line Graphs and a Box Plot
    Heat Maps o Creating a Heat Map
    Create a Heatmap
    Word Clouds
    Creating a Word Cloud
    File Formats for Graphics Outputs
    Saving a Graphic Output as a File
    Save Graphics to a File
    Exporting Graphs in RStudio
    Exporting Graphs as PDFs in RStudio
    Save Graphics Using RStudio Introduction to Statistics Introduction
    Objectives
    Basics of Statistics
    Types of Data
    Qualitative vs. Quantitative Analysis
    Types of Measurements in Order
    Nominal Measurement
    Ordinal Measurement
    Interval Measurement
    Ratio Measurement
    Statistical Investigation
    Normal Distribution
    Example of Normal Distribution
    Importance of Normal Distribution in Statistics
    Use of the Symmetry Property of Normal Distribution
    Standard Normal Distribution
    Use Probability Distribution Functions
    Distance Measures
    Distance Measures – A Comparison
    Euclidean Distance
    Example of Euclidean Distance
    Manhattan Distance
    Minkowski Distance
    Mahalanobis Distance
    Cosine Similarity
    Correlation
    Correlation Measures Explained
    Pearson Product Moment Correlation (PPMC)
    Pearson Correlation
    Dist() Function in R
    Perform the Distance Matrix Computations Hypothesis Testing I Introduction
    Objectives
    Hypothesis
    Need of Hypothesis Testing in Businesses
    Null Hypothesis
    Alternate Hypothesis
    Null vs. Alternate Hypothesis
    Chances of Errors in Sampling
    Types of Errors
    Contingency Table
    Decision Making
    Critical Region
    Level of Significance
    Confidence Coefficient
    Bita Risk
    Power of Test
    Factors Affecting the Power of Test
    Types of Statistical Hypothesis Tests
    Upper Tail Test
    Test Statistic
    Factors Affecting Test Statistic
    Critical Value Using Normal Probability Table Hypothesis Testing II Introduction
    Objectives
    Parametric Tests
    Z-Test
    Z-Test in R
    T-Test
    T-Test in R
    Use Normal and Student Probability Distribution Functions
    Testing Null Hypothesis
    Objectives of Null Hypothesis Test
    Three Types of Hypothesis Tests
    Hypothesis Tests About Population Means
    Decision Rules
    Hypothesis Tests About Population Means
    Hypothesis Tests About Population Proportions
    Chi-Square Test
    Steps of Chi-Square Test
    Degree of Freedom
    Chi-Square Test for Independence
    Chi-Square Test for Goodness of Fit
    Chi-Square Test for Independence
    Chi-Square Test in R
    Use Chi-Squared Test Statistics
    Introduction to ANOVA Test
    One-Way ANOVA Test
    The F-Distribution and F-Ratio
    F-Ratio Test
    F-Ratio Test in R
    One-Way ANOVA Test
    One-Way ANOVA Test in R
    Perform ANOVA Regression Analysis Introduction
    Objectives
    Introduction to Regression Analysis
    Use of Regression Analysis
    Types Regression Analysis
    Simple Regression Analysis
    Multiple Regression Models
    Simple Linear Regression Model
    Perform Simple Linear Regression
    Correlation
    Correlation Between X and Y
    Find Correlation
    Method of Least Squares Regression Model
    Coefficient of Multiple Determination Regression Model
    Standard Error of the Estimate Regression Model
    Dummy Variable Regression Model
    Interaction Regression Model
    Non-Linear Regression
    Non-Linear Regression Models
    Perform Regression Analysis with Multiple Variables
    Non-Linear Models to Linear Models
    Algorithms for Complex Non-Linear Models Classification Objectives
    Introduction to Classification
    Examples of Classification
    Classification vs. Prediction
    Classification System
    Classification Process
    Classification Process – Model Construction
    Classification Process – Model Usage in Prediction
    Issues Regarding Classification and Prediction
    Data Preparation Issues
    Evaluating Classification Methods Issues
    Decision Tree
    Decision Tree – Dataset
    Classification Rules of Trees
    Overfitting in Classification
    Tips to Find the Final Tree Size
    Basic Algorithm for a Decision Tree
    Statistical Measure – Information Gain
    Calculating Information Gain for Continuous-Value Attributes
    Enhancing a Basic Tree
    Decision Trees in Data Mining
    Model a Decision Tree
    Naive Bayes Classifier Model
    Features of Naive Bayes Classifier Model
    Bayesian Theorem
    Naive Bayes Classifier
    Applying Naive Bayes Classifier
    Naive Bayes Classifier – Advantages and Disadvantages
    Perform Classification Using the Naive Bayes Method
    Nearest Neighbor Classifiers
    Computing Distance and Determining Class
    Choosing the Value of K
    Scaling Issues in Nearest Neighbor Classification
    Support Vector Machines
    Advantages of Support Vector Machines
    Geometric Margin in SVMs
    Linear SVMs
    Non-Linear SVMs
    Support a Vector Machine Clustering Introduction
    Objectives
    Introduction to Clustering
    Clustering vs. Classification
    Use Cases of Clustering
    Clustering Models
    K-means Clustering
    K-means Clustering Algorithm
    Pseudo Code of K-means
    K-means Clustering Using R
    K-means Clustering
    Perform Clustering Using K-means
    Hierarchical Clustering
    Hierarchical Clustering Algorithms
    Requirements of Hierarchical Clustering Algorithms
    Agglomerative Clustering Process
    Perform Hierarchical Clustering
    DBSCAN Clustering
    Concepts of DBSCAN
    DBSCAN Clustering Algorithm
    DBSCAN in R
    DBSCAN Clustering Association Introduction
    Objectives
    Association Rule Mining
    Application Areas of Association Rule Mining
    Parameters of Interesting Relationships
    Association Rules
    Association Rule Strength Measures
    Limitations of Support and Confidence
    Apriori Algorithm
    Applying Apriori Algorithm
    Step 1 – Mine All Frequent Item Sets
    Algorithm to Find Frequent Item Set
    Ordering Items
    Candidate Generation
    Step 2 – Generate Rules from Frequent Item Sets
    Perform Association Using the Apriori Algorithm
    Perform Visualization on Associated Rules
    Problems with Association Mining

    Learning Objectives: Introduction to Big data and Hadoop Ecosystem Introduction
    Overview to Big Data and Hadoop
    Hadoop Ecosystem HDFS and YARN Introduction
    HDFS Architecture and Components
    Block Replication Architecture
    YARN Introduction MapReduce and Sqoop Introduction
    Why Mapreduce
    Small Data and Big Data
    Data Types in Hadoop
    Joins in MapReduce
    What is Sqoop Basics of Hive and Impala Introduction
    Interacting with Hive and Impala
    Working with Hive and Impala
    Data Types in Hive
    Validation of Data
    What is Catalog and Its Uses Types of Data Formats Introduction
    Types of File Format
    Data Serialization
    Importing MySql and Creating hive to
    Parquet With Sqoop Advanced Hive Concept and Data File Partitioning Introduction
    Overview of the Hive Query Language Apache Flume and HBase Introduction
    Introduction to HBase Pig Introduction
    Getting Datasets for Pig Development Basics of Apache Spark Introduction
    Spark – Architecture, Execution, and Related Concepts
    RDD Operations
    Functional Programming in Spark RDDs in Spark Introduction
    RDD Data Types and RDD Creation
    Operations in RDDs
    Implementation of Spark Applications Introduction
    Running Spark on YARN
    Running a Spark Application
    Dynamic Resource Allocation
    Configuring Your Spark Application Spark Parallel Processing Introduction
    Parallel Operations on Partitions Spark RDD Optimization Techniques Introduction
    RDD Persistence Spark RDD Optimization Techniques Spark Algorithm
    Introduction
    Spark: An Iterative Algorithm
    Introduction To Graph Parallel System
    Introduction To Machine Learning
    Introduction To Three C’s Spark SQL Introduction
    Interoperating with RDDs Apache Kafka Core Java

    Learning Objectives: Data Science Introduction to Data Science
    Different Sectors Using Data Science
    Purpose and Components of Python Data Analytics Data Analytics Process
    Exploratory Data Analysis(EDA)
    EDA-Quantitative Technique
    EDA – Graphical Technique
    Data Analytics Conclusion or Predictions
    Data Analytics Communication
    Data Types for Plotting
    Data Types and Plotting Statistical Analysis and Business Applications Introduction to Statistics
    Statistical and Non-statistical Analysis
    Major Categories of Statistics
    Statistical Analysis Considerations
    Population and Sample
    Statistical Analysis Process
    Data Distribution
    Dispersion o Histogram
    Testing Python Environment Setup and Essentials Anaconda
    Installation of Anaconda Python Distribution (contd.)
    Data Types with Python
    Basic Operators and Functions Mathematical Computing with Python (NumPy) Introduction to Numpy
    Activity-Sequence it Right
    Creating and Printing an array
    Class and Attributes of array
    Basic Operations
    Activity-Slice It
    Copy and Views
    Mathematical Functions of Numpy Scientific computing with Python (Scipy) Introduction to SciPy
    SciPy Sub Package – Integration and Optimization
    SciPy sub package
    Calculate Eigenvalues and Eigenvector
    SciPy Sub Package – Statistics, Weave and IO Data Manipulation with Pandas Introduction to Pandas
    Understanding DataFrame
    View and Select Data
    Missing Values
    Data Operations
    File Read and Write Support
    Pandas Sql Operation Machine Learning with Scikit–Learn Machine Learning Approach
    How it Works
    Supervised Learning Model Considerations
    Scikit-Learn
    Supervised Learning Models – Linear Regression
    Supervised Learning Models – Logistic Regression
    Unsupervised Learning Models
    Pipeline
    Model Persistence and Evaluation Natural Language Processing with Scikit Learn NLP Overview
    NLP Applications
    NLP Libraries-Scikit
    Extraction Considerations
    Scikit Learn-Model Training and Grid Search Data Visualization in Python using matplotlib Introduction to Data Visualization
    Line Properties
    (x,y) Plot and Subplots
    Types of Plots Web Scraping with BeautifulSoup Web Scraping and Parsing
    Understanding and Searching the Tree
    Navigating options
    Navigating a Tree
    Modifying the Tree
    Parsing and Printing the Document Python integration with Hadoop MapReduce and Spark Why Big Data Solutions are Provided for Python
    Hadoop Core Components
    Python Integration with HDFS using Hadoop Streaming
    Using Hadoop Streaming for Calculating Word Count
    Python Integration with Spark using PySpark
    Using PySpark to Determine Word Count
    Python Basics

    Learning Objectives: Introduction to Business Analytics Introduction
    What Is in It for Me
    Types of Analytics
    Areas of Analytics Formatting Conditional Formatting and Important Functions Introduction
    What Is in It for Me
    Custom Formatting Introduction
    Conditional Formatting Introduction
    Logical Functions
    Lookup and Reference Functions
    VLOOKUP Function
    HLOOKUP Function
    MATCH Function
    INDEX and OFFSET Function
    Statistical Function
    SUMIFS Function
    COUNTIFS Function
    PERCENTILE and QUARTILE
    STDEV, MEDIAN and RANK Function Analyzing Data with Pivot Tables Introduction
    What Is in It for Me
    Pivot Table Introduction
    Concept Video of Creating a Pivot Table
    Grouping in Pivot Table Introduction
    Custom Calculation
    Calculated Field and Calculated Item
    Slicer Intro
    Creating a Slice
    Dashboarding
    Introduction
    What Is in It for Me
    What is a Dashboard
    Principles of Great Dashboard Design
    How to Create Chart in Excel
    Chart Formatting
    Thermometer Chart
    Pareto Chart
    Form Controls in Excel
    Interactive Dashboard with Form Controls
    Chart with Checkbox
    Interactive Chart Business Analytics With Excel Introduction
    What Is in It for Me
    Concept Video Histogram
    Concept Video Solver Addin
    Concept Video Goal Seek
    Concept Video Scenario Manager
    Concept Video Data Table
    Concept Video Descriptive Statistics Data Analysis Using Statistics Introduction
    Moving Average
    Hypothesis Testing
    ANOVA
    Covariance
    Correlation
    Regression
    Normal Distribution Power BI Introduction
    Power Pivot
    Power View
    Power Query
    Power Map Microsoft Power BI Desktop Microsoft Power BI Recipes

    Learning Objectives: Machine Learning Introduction Techniques of Machine Learning
    Supervised Learning
    Unsupervised Learning
    Semi-supervised Learning and Reinforcement Learning
    Some Important Considerations in Machine Learning
    Data Preprocessing Data Preparation
    Feature engineering
    Feature scaling
    Datasets
    Dimensionality reduction
    Math Refresher Eigenvalues, Eigenvectors, and Eigendecomposition
    Concepts of Linear Algebra
    Introduction to Calculus
    Probability and Statistics
    Regression Regression and Its Types
    Linear Regression: Equations and Algorithms
    Classification Logistic regression
    K-nearest neighbours
    Support Vector Machines
    Kernel SVM
    Naive Bayes
    Decision tree classifier
    Random forest classifier
    Unsupervised learning – Clustering K-Means Clustering
    Clustering Algorithms
    Introduction to Deep Learning Meaning and importance of deep learning
    Artificial Neural networks
    TensorFlow
    Introduction to Artificial Intelligence and Machine Learning Artificial Intelligence
    Machine Learning o Machine Learning algorithms o Applications of Machine Learning
    Python Programming for Beginners Python Django From Scratch

    Learning Objectives: Introduction to Deep Learning with TensorFlow Introduction to TensorFlow
    Intro to TensorFlow
    Computational Graph
    Key highlights
    Creating a Graph
    Regression example
    Gradient Descent
    TensorBoard
    Modularity
    Sharing Variables
    Keras
    Perceptrons What is a Perceptron
    XOR Gate
    Activation Functions Sigmoid
    ReLU
    Hyperbolic Fns
    Softmax
    Artificial Neural Networks Introduction
    Perceptron Training Rule
    Gradient Descent Rule
    Gradient Descent and Backpropagation Gradient Descent
    Stochastic Gradient Descent
    Backpropagation
    Some problems in ANN
    Optimization and Regularization Overfitting and Capacity
    Cross-Validation
    Feature Selection
    Regularization
    Hyperparameters
    Intro to Convolutional Neural Networks Intro to CNNs
    Kernel filter
    Principles behind CNNs
    Multiple Filters
    CNN applications
    Intro to Recurrent Neural Networks Intro to RNNs
    Unfolded RNNs
    Seq2Seq RNNs
    LSTM
    RNN
    Deep Learning applications Image Processing
    Natural Language Processing
    Speech Recognition
    Video Analytics

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