Start Date Class Timing Duration Mode of Training Trainer Profile
26-Apr 9am to 2pm 90 Days Onsite

    Why Choose Midhunit


  • Real-Time Expert Trainers (more than 10 years experience in particular
  • technology)
  • Flexible timings
  • Do not worry about your timings because we are always with your timings.
  • Industry Specific Scenarios
  • Students are provided with all the Real-Time and Relevant Scenarios. With Real time workshops
  • live online Training courses
  • Industry Specific Scenarios
  • Video Recording Sessions
  • Soft Copy of Materials
  • Resume Preparation for interviews
  • Interview Preparation Tips
  • 100 %Free job assistance

    Online Training Features

    1. Real-Time Expert Trainers

    We believe to provide our students the Best interactive experience as part of their learning

    Flexible timings

    Do not worry about your timings because we are always with your timings.

    Industry Specific Scenarios

    Students are provided with all the Real-Time and Relevant Scenarios.  With Real time workshops

    • Live online Training courses
    • Real-Time Expert Trainers
    • Industry Specific Scenarios
    • Video Recording Sessions
    • Soft Copy of Materials
    • Resume Preparion for interviews
    • Interview Preparation Tips interviews
    • 100 %Free job assistance

Who can do this course

This course is also suitable for graduation of (UG and PG) university and as well as who is having passion in system knowledge, Business and would like to enter this course.

Keywords: AWS training, CCNA training, DEVOPS training, Linux training, Kali Linux training, Ethical Hacking training, Phython training, Soft Skills training, Midhunit AWS training, Midhunit CCNA training, Midhunit DEVOPS training, Midhunit Ethical Hacking training, Midhunit Linux training, Midhunit Kali Linux training, Midhunit Phython training, Midhunit Soft Skills training

What does it covers

After course completion

  • Explain and use the basic functions of technology
  • Make the necessary settings for consumption-based planning
  • Utilize procurement optimization techniques
  • Camptia training in India
  • Release procedures
  • Process invoices and manage discrepancies
  • Enter goods movements in the system and make the relevant settings for special function.

Job Opportunities

  • (After passing the certification exam)
  • Business Process Owner
  • Team Lead
  • Power User and End User

Certification Exam

  • This course will lead you towards the following certification
  • Associate Level
  • 80 questions for the exam
  • 3 Hours duration
  • Passing Score is 80%


Course Content

    Data Science training in india.
    Data Science training in India has become one of the most popular courses, due to demand in innovation of existing jobs. Midhunit, India offers you complete training in data science course your aim towards becoming a Data Scientist. As the technological area is growing so are the new fields in IT in sector growing. And specially when coming to the profession of data scientist it has got the demand on course.
    Data Science Course at Midhunit, India ensures to provide the training with top industry experts and well-trained data scientist who will help you throughout the completion of your data science course.
    Data Science training in India and also training uncountable batches for years, Midhunit has always been forward to come up with new courses for the learners.

    Introduction to Data Analytics
    Data Science training in India
    Introduction to Business Analytics
    Understanding Business Applications
    Data types and data Models
    Type of Business Analytics
    Evolution of Analytics
    Data Science Components
    Data Scientist Skillset
    Univariate Data Analysis
    Introduction to Sampling

    Data Handling in R Programming
    Basic Operations in R – Expressions, Constant Values, Arithmetic, Function Calls, Symbols
    Sub-setting Data
    Selecting (Keeping) Variables
    Excluding (Dropping) Variables
    Selecting Observations and Selection using Subset Function
    Merging Data
    Sorting Data
    Adding Rows
    Visualization using R
    Data Type Conversion
    Built-In Numeric Functions
    Built-In Character Functions
    User Built Functions
    Control Structures
    Loop Functions
    Introduction to Statistics
    Basic Statistics
    Measure of central tendency
    Types of Distributions
    Central Limit Theorem & applications
    Types of variables
    Relationships between variables
    Central Tendency
    Measures of Central Tendency
    Arithmetic Mean / Average
    Merits & Demerits of Arithmetic Mean
    Mode, Merits & Demerits of Mode
    Median, Merits & Demerits of Median
    Concept of Quantiles, Quartiles, percentile
    Standard Deviation
    Calculate Variance
    Introduction to Statistics – 2
    Hypothesis Testing
    Multiple Linear Regression
    Logistic Regression
    Market Basket Analysis
    Clustering (Hierarchical Clustering & K-means Clustering)
    Classification (Decision Trees)
    Time Series Analysis (Simple Moving Average, Exponential smoothing, ARIMA+)
    Introduction to Machine Learning
    Overview & Terminologies
    What is Machine Learning?
    Why Learn?
    When is Learning required?
    Data Mining
    Application Areas and Roles
    Types of Machine Learning
    Supervised Learning
    Unsupervised Learning
    Reinforcement learning
    Machine Learning Concepts & Terminologies
    Steps in developing a Machine Learning application

    Key tasks of Machine Learning
    Modelling Terminologies
    Learning a Class from Examples
    Probability and Inference
    PAC (Probably Approximately Correct) Learning
    Noise and Model Complexity
    Triple Trade-Off
    Association Rules
    Association Measures
    Regression Techniques
    Concept of Regression
    Best Fitting line
    Simple Linear Regression
    Building regression models using excel
    Coefficient of determination (R- Squared)
    Multiple Linear Regression
    Assumptions of Linear Regression
    Variable transformation
    Reading coefficients in MLR
    Methods of building Linear regression model in R
    Model validation techniques
    Cooks Distance
    Q-Q Plot
    Durbin- Watson Test
    Kolmogorov-Smirnof Test
    Homoskedasticity of error terms
    Logistic Regression
    Applications of logistic regression
    Concept of odds
    Concept of Odds Ratio
    Derivation of logistic regression equation
    Interpretation of logistic regression output
    Model building for logistic regression
    Model validations
    Confusion Matrix
    Concept of ROC/AOC Curve
    KS Test

    Basic Operations in R Programming
    Introduction to R programming
    Types of Objects in R
    Naming standards in R
    Creating Objects in R
    Data Structure in R
    Matrix, Data Frame, String, Vectors
    Understanding Vectors & Data input in R
    Lists, Data Elements
    Creating Data Files using R

    Introduction to Probability
    Standard Normal Distribution
    Normal Distribution
    Geometric Distribution
    Poisson Distribution
    Binomial Distribution
    Parameters vs. Statistics
    Probability Mass Function
    Random Variable
    Conditional Probability and Independence
    Unions and Intersections
    Finding Probability of dataset
    Probability Terminology
    Probability Distributions
    Data Visualization Techniques
    Bubble Chart
    Waterfall chart
    Box Plot
    Line Charts
    Frequency Chart
    Bimodal & Multimodal Histograms
    Scatter Plot
    Pie Chart
    Bar Graph
    Line Graph
    Market Basket Analysis

    Applications of Market Basket Analysis
    What is association Rules
    Overview of Apriori algorithm
    Key terminologies in MBA
    Model building for MBA
    Transforming sales data to suit MBA
    MBA Rule selection
    Ensemble modelling applications using MBA
    Time Series Analysis (Forecasting)

    Model building using ARIMA, ARIMAX, SARIMAX
    Data De-trending & data differencing
    KPSS Test
    Dickey Fuller Test
    Concept of stationarity
    Model building using exponential smoothing
    Model building using simple moving average
    Time series analysis techniques
    Components of time series
    Prerequisites for time series analysis
    Concept of Time series data
    Applications of Forecasting
    Decision Trees using R

    Understanding the Concept
    Internal decision nodes
    Terminal leaves.
    Tree induction: Construction of the tree
    Classification Trees
    Selecting Attribute
    Information Gain
    Partially learned tree
    Causes for over fitting
    Overfitting Prevention (Pruning) Methods
    Reduced Error Pruning
    Decision trees – Advantages & Drawbacks
    Ensemble Models
    K Means Clustering

    Parametric Methods Recap
    Direct Clustering Method
    Mixture densities
    Classes v/s Clusters
    Hierarchical Clustering
    Dendogram interpretation
    Non-Hierarchical Clustering
    Distance Metrics
    K-Means Algorithm
    K-Means Objective
    Color Quantization
    Vector Quantization
    Tableau Analytics

    Tableau Introduction
    Data connection to Tableau
    Calculated fields, hierarchy, parameters, sets, groups in Tableau
    Various visualizations Techniques in Tableau
    Map based visualization using Tableau
    Reference Lines
    Adding Totals, sub totals, Captions
    Advanced Formatting Options
    Using Combined Field
    Show Filter & Use various filter options
    Data Sorting
    Create Combined Field
    Table Calculations
    Creating Tableau Dashboard
    Action Filters
    Creating Story using Tableau
    Analytics using Tableau

    Clustering using Tableau
    Time series analysis using Tableau
    Simple Linear Regression using Tableau
    R integration in Tableau

    Integrating R code with Tableau
    Creating statistical model with dynamic inputs
    Visualizing R output in Tableau