Data Science with Machine learning and AI Pro+ Certification Course with Honeywell by Ivy Professional School: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study, Virtual Classroom +1 more	Mode of Delivery Video and Text Based	Frequency of Classes Weekends

Course overview

Data Science with Machine learning and AI Pro+ Certification Course with Honeywell is a 6 monthly course offered by Ivy Professional School in collaboration with Honeywell. The goal of the course is to develop a pool of Data Scientists who are capable of managing complicated data using risk analytics and advanced statistical modeling techniques.

The Data Science with Machine learning and AI Pro+ Certification Course with Honeywell syllabus provides a thorough background in statistics, which led to a natural progression into data management and analytic tool use. Data Science is a collection of algorithms, tools, and machine learning techniques aimed at uncovering hidden patterns in large amounts of data.

The Data Science with Machine learning and AI Pro+ Certification Course with Honeywell training is an advanced Data Science study targeted at developing a talent pool with a working understanding of the industry's most up-to-date tools and processes. Students who complete the program will not only learn how to utilize analytical tools, but they will also get significant hands-on experience as a result of the many projects and case studies they will complete as part of the curriculum.

Course and certificate fees

Fees information

₹ 64,000

Data Science with Machine learning and AI Pro+ Certification Course Fees

Particulars	Amount in INR
Classroom Fee	Rs. 77,000
Online mode Fee	Rs. 64,000

certificate availability

Yes

certificate providing authority

ivyproschool

Who it is for

Data analyst Data scientist Database architect

Candidates who want to become a data analyst, data scientist, database architect, machine learning engineer, and business intelligence professional will be benefited from this course.

Eligibility criteria

Certification Qualifying Details

To acquire the prestigious certification from Ivy Professional School, candidates must successfully complete the Data Science with Machine learning and AI Pro+ Certification Course with Honeywell.

What you will learn

Machine learning Data science knowledge Knowledge of artificial intelligence

After completing Data Science with Machine learning and AI Pro+ online training, Candidates will gain a thorough grasp of cutting-edge analytics technologies in order to address challenging issues in machine learning and data science. Students will be able to interact and network with prominent academics from the data analytics industry, as well as speakers from industry partner Honeywell.

The syllabus

Advanced Excel

Introduction to MS Excel, Cell Ref, Basic Functions and Usage
Sorting, Filtering, Advance Filtering, Subtotal
Pivot Tables and Slicers
Goal Seek and Solver
Different Charts Graphs Which one to use and when
Vlookup, Hlookup, Match, Index
Conditional Formatting
Worksheet & Workbook Reference, Error Handling
Logical Operators & Functions IF and Nested IF
Data Validation
Text Functions
Form Controls
Dashboard
6 Case Studies from App Cab Aggregators, Insurance, Sports, Sales, Marketing, Web Analytics Industry

SQL Queries & Relational Database Management

Relational Database Fundamentals
Steps to Design Efficient Relational Database Models
Case Studies on Designing Database Models
Case Study Implementation on Handling Data
Importing / Exporting Large Amount of Data into a database
SQL Statements DDL, DML, DCL, DQL
Writing Transactional SQL Queries, Merging, joining, sorting, indexing, co-related queries, etc.
Hands-on Exercises on Manipulating Data Using SQL Queries
Creating Database Models Using SQL Statements
Individual Projects on Handling SQL Statements
6 Case Studies from App Cab Aggregators, E-commerce, Sports Industry

Data Visualization using Tableau

Introduction to Data Visualization
What is Dashboard
Why do we need Dashboard
Introduction of Data Visualization using Tableau
Use of Tableau
Navigation in Tableau
Exporting Data
Connecting Sheets
Tableau Basics
Working with Dimension and Measures
Making Basic Charts like Line, Bar etc.
Adding Colours
Working in marks card
Working with Sorting and Filters
Creating Dual Axis and Combo Charts
Working with Tables
Creating Data Tables
Table Calculations
Calculated Field
Logical Calculations
If/Then
IIF
Case/When
Date Calculations
Date
DateAdd
DateDiff
DateParse
Today()
Now()
Parameters
Pre-defined Lists for Faster Filtering
Top N Filter
Reference Line Parameter
Swapping Dimensions or Measures in a View
Using Actions to Create Interactive Dashboards
Filter Actions
Highlight Actions
Advanced Charts
Heat maps, Tree Maps, Waterfall Charts etc.
Working with latitude and Longitude
Symbol and filled maps
Working with data
Joining multiple tables
Blending of Data
Sets
In/Out Sets
Combines Sets
Drilling Up/Down using Hierarchies
Grouping
Bins/Histograms
Analytics
Referencing lines
Clustering
Trend Line
Building dashboards
Layout and Formatting
Interactivity with Actions
Best Practices
Story Telling with Data
Working with story
Highlighting important insights
Data Interpreter
Data Preparation
Data Cleaning
Pivoting
4 Case Studies on Retail, Airline, Bank datasets

Business Statistics

Types of data, Graphical representation
Introduction of data
Types of data
Data Presentation
Charts & Diagrams
Assignment on Type of Data and Type of Charts
Correlation, Data Modeling & Index Numbers
Correlation
Data Modeling
Index Number
Measures of Central Tendency & Dispersion
Measures of Central Tendency
Measures of Central Dispersion
Measures of Central Dispersion (Variance)
Normal Distribution
Assignment of Central Tendency and Dispersion
Forecasting & Time Series Analysis
Forecasting
Components of Time Series
Measurement of Secular Trend
Forecasting Software
Probability, Bayesian Theory
Probability
Computing joint & marginal probabilities
Bayes’ Theorem
Probability Distribution and Mathematical Expectation
Random Variables
Probability Distribution (Discrete)
Probability Distribution (Continuous)
Finding Normal Probabilities
Sampling and Sampling Distribution
Sample, Types of sample
Sampling Distribution
Example of Sampling
Assignment on Probability Distribution, Binomial & Poisson, Normal Distribution
Theory of Estimation and Testing of Hypothesis
Theory of Estimation, Estimation Process, Statistical Inference
Test of Hypothesis, Decision Errors, OneLevel of Significance
Two-tail test, Testing of hypothesis
Degrees of freedom
9. Analysis of Variance
Anova
Hypothesis One way Anova
Two-way ANOVA
Assignment on Hypothesis Testing
Regression Models
Regression, Linear Regression, Multiple Linear Regression
Coefficient of Determination, R-square, Adjusted R-square
Example using Excel
Assignment on Correlation & Simple Regression

Predictive Modeling with R

Introduction to R
General introduction to R and R Packages
Installing R in Windows
Installing R packages through R using syntax
Basic syntaxes in R
Data Handling in R
Creating Dataframe
Variables in R
Creating columns with conditions AND, OR
Different numeric functions in R like exp, log, sqrt, sum, prod etc. Sorting in R. Ranking and concatenating strings in R.
Exercises on Import / Export of Data
Exercises on Data Handling in R
Overview of Analytics and Statistics
Types of data variables
What is Population
Mean, Median, or Mode – Their applications
Basic Statistics Exercises
String and character functions in R
Substring, string split
Change name of column and checking mode of variable
Dividing variables into different buckets
Creating user-defined functions in R
Loops in R
SQL in R using SQL
Scatter plot, Box plot, Histogram, pie chart in R T-Test in R
viii) Exercise: Data Summarization using Financial Retail Datasets
Overview of Analytics and Statistics
Standard deviation interpretation
Population vs Sample
Univariate & Bivariate Analysis
Normal distribution
What is Confidence Interval
Hypothesis Testing
In-Case Study: Academic Performance Case Study
viii) Self-Case Study: Health Care Case Study
Linear regression in R
Regression
Residual Analysis
Multiple Regression
Model Building
In-class Case Study: Predict Academic Performance of School Students
Self Case Study: Predict Customer Value for an Insurance Firm
Logistic Regression in R
Model theory, Model Fit Statistics
Reject Reference, Binning, Classing
Dummy Creation, Dummy Correlation
Model Development (Multicollinearity, WOE, IV, HLT, Gini KS, Rank Ordering, Clustering Check)
Model Validation (Rerun, Scoring)
Final Dashboard
In-class Case Study: Predict Customer Churn for a Telecom firm
viii) Self Case Study: Predict Propensity to Buy Financial Product among Existing Bank
Time Series theory discussion overview
ARIMA, Stationarity & Non-stationarity check concepts
forecasting
components of Time Series
Measurement of Circular Trend
Time Series codes overview
Exponential smoothening theory discussion
Case Study Random walk in Time Series
viii) Case Study Forecasting sales for retail
Clustering Concepts and Case Study
K-means Clustering
Types of Clustering
Centroids
Case Study Airline customer segmentation
Feature Engineering & Dimension Reduction and Case Study
Factor Analysis
PCA
Methods of Variable Reduction
Dimensionality Reduction
Decision Trees
Pre-reading on basics of segmentation and decision trees
Intro to Objective Segmentation
CHAID and CART concept, example, and exercise
Implement Decision Trees
Advantages and disadvantages of Decision Trees over Prediction
Multiple Decision Trees
Case Study – Predict earning of an individual

Python for Data Science

Python Essentials
Overview of PythonStarting with Python
Introduction to the 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, sci-kit-learn, Pandas, Matplotlib, etc)
Installing & loading Packages & Name Spaces
Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
viii) List and Dictionary Comprehensions
Variable & Value Labels – Date & Time Values
Basic Operations Mathematical string date
Reading and writing data
Simple plotting
xiii) Control flow & conditional statements
xiv) Debugging & Code profiling
How do create classes and modules and how to call them?
Scientific Distribution
Numpy, sci-fi, pandas, sci-kit learn, stat models, NLTK etc
Accessing / Importing and Exporting Data using Python modules
Importing Data from various sources (CSV, txt, excel, access etc)
Database Input (Connecting to the database)
Viewing Data objects subsetting, methods
Exporting Data to various formats
Important python modules: Pandas, beautiful soup
Data Manipulation
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
viii) Formatting data
Important Python modules for data manipulation (Pandas, Numpy, re, math, string, DateTime etc)
Visualization using Python
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 GraphsBar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy. stats etc)
Introduction to Predictive Modeling
Concept of the 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 Modeling
Modelling on Linear Regression
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.)
viii) Interpretation of Results Business Validation Implementation on new data
Modelling on Logistic Regression
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 Lemeshow 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
Time Series Forecasting
Introduction Applications
Time Series Components (Trend, Seasonality, Cyclicity, and Level) and Decomposition
Classification of Techniques (Pattern-based Patternless)
Basic Techniques Averages, Smoothening, etc
Advanced Techniques AR Models, ARIMA, etc
Understanding Forecasting Accuracy MAPE, MAD, MSE, etc

Machine Learning

Predictive Modeling Basics
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 Tradeoff) & Performance Metrics
Feature engineering & dimension reduction
Concept of optimization & cost function
viii) 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 )
Unsupervised Learning: Segmentation
What is segmentation & the 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)
Supervised learning: Decision Tree
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
viii) Case Study on Decision Tree
Supervised Learning: Ensemble Learning
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
viii) Gradient Boosting Machines (GBM)
XGBoost
Case Study on Random Forest
Supervised Learning: Artificial Neural Networks (ANN)
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 hyperparameters
Validating ANN models
Supervised Learning: Support Vector Machines
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 hyperparameters
Validating SVM models
Supervised Learning: KNN
What are KNN & Applications?
KNN for missing treatment
KNN For solving regression problems
KNN for solving classification problems
Validating KNN model
Model fine-tuning with hyperparameters
Supervised Learning : Naïve Bayes
Concept of Conditional Probability
Bayes Theorem and Its Applications
Naïve Bayes for classification
Applications of Naïve Bayes in Classifications
Text Mining and Analytics
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 Python
Text Analytics – Word cloud analysis using Python
Text Analytics Segmentation using K-Means/Hierarchical Clustering
Text Analytics Classification (Spam/Not spam)
viii) Applications of Social Media Analytics
Metrics(Measures Actions) in social media analytics
Examples & Actionable Insights using Social Media Analytics
Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk, etc)
Fine-tuning the models using Hyperparameters, grid search, piping etc.
xiii) Case Study on Text Analytics

Deep Learning

Introduction to TensorFlow
HelloWorld with TensorFlow
Linear Regression
Nonlinear Regression
Logistic Regression
Convolutional Neural Networks (CNN)
CNN Application
Understanding CNNs
Recurrent Neural Networks (RNN)
Intro to RNN Model
Long Short-Term Memory (LSTM)

Admission details

To get admission to the Data Science with Machine learning and AI Pro+ training, follow the steps mentioned below:

Step 1. By clicking on the link given below, candidates may access the official course info page. (https://ivyproschool.com/data-science-machine-learning-ai-certification-course-with-honeywell)

Step 2. Learners may get more information by going to the website and filling out the form.

Step 3. Students must finish the admissions procedure by approaching a course adviser and filling out the form in order to get admission.

How it helps

Candidates pursuing Data Science with Machine learning and AI Pro+ Certification Course with Honeywell will be benefited in the following ways:

A unique program created with the collaboration between academics and business, focusing on the most recent developments in the Data Science sector.
Opportunity to collaborate with Honeywell* and other big analytics businesses on the analyst team.
Concentrated hands-on training based on Honeywell and Ivy's joint curriculum, given on weekends by experienced industry practitioners.
Honeywell's experienced Machine Learning and Data Science practitioners helped create the whole course structure.

FAQs

Ivy Pro School provides Data Science with Machine learning and AI Pro+ tutorial in collaboration with which provider?

Ivy Pro School collaborated with Honeywell to provide Data Science with Machine learning and AI Pro+ tutorial.

Who is the course provider of Data Science with Machine learning and AI Pro+ Certification training?

Ivy Pro School is the course provider of Data Science with Machine learning and AI Pro+ Certification training.

What is the duration of the Data Science with Machine learning and AI Pro+ Certification online classes?

The duration of the Data Science with Machine learning and AI Pro+ Certification online classes is 6 months.

How many projects are covered in Data Science with Machine learning and AI Pro+ Classes?

More than 25 projects are covered in the Data Science with Machine learning and AI Pro+ Classes.

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Data Science with Machine learning and AI Pro+ Certification Course with Honeywell

Online

6 Months

₹ 64000

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