Certification in Data Science

9

200 Hours of Training delivered through live webinar sessions

9

Course coverage in R, SAS, Python and Tableau in the technology dimensions

9

Learning includes deep rooted sectorial pain points and their customized solutions

9

Live Project coverage by certified industry professionals

9

Focus across entire Data Analytics Landscape

Online Instructor – Led Training
Price : Rs. 80,000/-

Learn More About The Program

– Data Science

  • Intro to Data Science
    • Prerequisites of DS landscape
    • Data Science Implementation
    • Approaches & best practices
    • Live examples & case studies
    • Tools, technologies and the benchmarks
  • Intro to Basics of Statistics
    • Test of normality
    • Distribution Analysis
    • Bi variate analysis
    • Hypothesis Testing:
    • T Test: Activity
    • ANOVA: Activity
    • Design of Experiment
    • Correlation / Pearson Coefficient
  • Intermediate Statistics
  • Equation Based Algorithms
  • Regression
  • Variable Selection
  • Best fit variables
  • Champion model
  • Problems of Modeling
  • Chi Square Test
  • Logistic Regression
  • Advance Analytics & modern ML algorithms
  • Decision Tree
  • Random Forest
  • Ensemble Trees
  • Bagging & Boosting
  • Pruning & optimization
  • Deep learning & Self -learning models
  • Neural Network
  • SVM
  • KNN
  • Bayesian Distribution
  • Gradient Boosting & XG Boost

– SAS

  • Assaying and accessing data
    • Introduction to SAS Program
    • Submitting a SAS Program
    • SAS Program Syntax
    • Examining SAS Datasets
    • Assaying and accessing data
    • Sub setting Report Data
    • Sorting & Grouping Report Data
  • Creating & enhancing reports
    • Enhancing Reports
    • Using SAS Formats
    • User Defined Formats
    • Reading a SAS Dataset
    • Customizing a SAS Dataset
    • Introduction to reading raw data files
    • Reading standard delimited data
    • Reading nonstandard delimited data
    • Handling missing data
  • Manipulating data using Advance Programming
    • Using SAS Functions
    • Conditional Processing
    • Concatenating Data Sets
    • Merging data sets
    • Writing observations explicitly
    • Writing to multiple SAS data sets
    • Selecting variables and observations
    • Creating an Accumulating Total Variable
    • Accumulating Totals for a group of data
  • Data Transformations using Advance Programming
      • Manipulating character values
      • Manipulating Numeric Values
      • Converting variable type
      • Do loop processing
      • Conditional do loop processing
      • SAS Array Processing
      • Using SAS Arrays
    • SAS SQL & SAS Macro
      • Introduction to SAS SQL
      • Specifying columns
      • Specifying rows
      • Presenting data
      • Summarizing data
      • Live hands on for different scenarios / use cases
      • Introduction to SAS Macro

    – R

    • Data Management with R studio
      • Installing & loading R packages
      • R data structures
      • Vectors
      • Factors (Lists, Data frames, Matrixes & arrays)
      • Managing data with R
      • Exploring the structure of data
      • Exploring numeric variables
      • Exploring categorical variables
      • Exploring relationships between variables
    • Machine Learning with R studio & Rattle
      • Lazy Learning – Classification Using Nearest Neighbors
      • Probabilistic Learning: Classification Using Naïve Bayes
      • Classification Using Decision Trees & Rules
      • Black Box Methods- Neural Networks & Support Vector Machines
      • Finding Patterns – Market Basket Analysis Using Association Rules
      • Finding Groups of Data- Clustering with k-mean

     

    • Forecasting using R Studio
      • Understanding the components of Time Series forecasting
      • Box-Jenkins method
      • Moving Average Method
      • ARMA (Auto Regressive Moving Average)
      • ARIMA (Auto Regressive Integrated Moving Average)
      • ADF Test
      • Auto ARIMA
    • Social Media Analysis, Text Mining & Sentiment Analysis
      • Web scrapping
      • Preparing the data for text mining
      • Tagging
      • Text Mining
      • Sentiment Analysis

    – Python

    • Data Management with Python
      • Introduction to Python
      • Installing Anaconda
      • Lists, Tuples, Variables, Dictionaries
      • Spyder Integrated Development Environment (IDE)
      • Introduction to Numpy Arrays
      • Creating and playing with Arrays
    • Advance Data Management
      • Indexing
      • Data Processing using Arrays
      • Input & Output to files
      • Introduction to Pandas & getting started
      • Selection & Filtering
      • Sorting & summarizing
      • Descriptive Statistics
      • Combining & merging data frames
      • Removing duplicates
      • Binning & discretization
      • String manipulation
    • Machine Learning with Python
      • Lazy Learning – Classification Using Nearest Neighbors
      • Probabilistic Learning: Classification Using Naïve Bayes
      • Classification Using Decision Trees & Rules
      • Black Box Methods- Neural Networks & Support Vector Machines
      • Finding Patterns – Market Basket Analysis Using Association Rules
      • Finding Groups of Data- Clustering with k-means
    • Forecasting using Python
      • Understanding the components of Time Series forecasting
      • Box-Jenkins method
      • Moving Average Method
      • ARMA (Auto Regressive Moving Average)
      • ARIMA (Auto Regressive Integrated Moving Average)
      • ADF Test
      • Auto ARIMA
    • Social Media Analysis, Text Mining & Sentiment Analysis
      • Web scrapping
      • Preparing the data for text mining
      • Tagging
      • Text Mining

    – Core Programming with Python (Part 1)

    • Introduction to Python
    • Writing the first python program
    • Datatypes in Python
    • Operators in Python
    • Input & Output
    • Control Statements
    • Arrays in Python
    • Strings & characters
    • Functions                    
    • Lists & Tuples
    • Dictionaries
    • Introduction to OOP 

    – Core Programming with Python (Part 2)

    • Classes & objects
    • Inheritance & Polymorphism
    • Abstract classes & interfaces
    • Exceptions
    • Files in python
    • Regular Expressions in Python
    • Data Structures in Python
    • Date & time
    • Threads
    • Graphical User Interface of Python
    • Networking in Python
    • Python’s Database Connectivity
    • Data Science using Python

    – Visualization with Tableau

    • Tools available in the Visualization space.
    • Choosing the right visualization.
    • Base Measure vs KPI
    • Prerequisites to dashboarding /visualization
    • Creating graphs, charts, KPIs in Tableau
    • Interactive graphs and dashboards
    • Creating maps using Tableau

    Announcements

    -Registration closes on 12th July 2019 for the batch

    -Book Appointment today to speak to our consultants

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