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

**Co-Created with Industry partner | 6 Months Weekend program | For Selected Few, Apply Now!**

The Data Science and Machine learning Pro+ Certification is a 6 month advanced certification program by Ivy Pro School co created with Honeywell aimed at creating a pool of Data Science with expertise in handling complex Data with the help of advanced level statistical modeling and risk analytics tools.

– 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

– 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, Ecommerce, Sports Industry

- - 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

- - 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 Probablity 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-sqare
- - Example using Excel
- - Assignment on Corelationa & Simple Regression

- - 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 variable into different buckets
- - Creating user defined functions in R
- - Loops in R
- - SQL in R using sqldf
- - 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 (Multicolinearity, 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 segmenation

- - 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 Essentials
- - Overview of Python- Starting with Python
- - Introduction to 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, scikit-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 to create class and modules and how to call them?

- - Scientific Distribution
- - Numpy, scify, pandas, scikitlearn, statmodels, nltk etc

- - Accessing / Importing and Exporting Data using Python modules
- - Importing Data from various sources (Csv, txt, excel, access etc)
- - Database Input (Connecting to database)
- - Viewing Data objects - subsetting, methods
- - Exporting Data to various formats
- - Important python modules: Pandas, beautifulsoup

- - 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 Graphs- Bar/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 model in analytics and how it is used?
- - Common terminology used in analytics & modeling process
- - Popular modeling algorithms
- - Types of Business problems - Mapping of Techniques
- - Different Phases of Predictive Modeling

- - Modeling 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

- - Modeling 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 Lemeshov 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 - Pattern less)
- - Basic Techniques - Averages, Smoothening, etc
- - Advanced Techniques - AR Models, ARIMA, etc
- - Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc

- - 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 Trade off) & 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-squre, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )

- - Unsupervised Learning : Segmentation
- - What is segmentation & 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 hyper parameters
- - 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 hyper parameters
- - Validating SVM models

- - Supervised Learning : KNN
- - What is KNN & Applications?
- - KNN for missing treatment
- - KNN For solving regression problems
- - KNN for solving classification problems
- - Validating KNN model
- - Model fine tuning with hyper parameters

- - 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 Hyper parameters, grid search, piping etc.
- xiii) - Case Study on Text Analytics

- - 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)

Rs 64,000 (Online Mode)

Join the industry endorsed program by Ivy Pro School co-created with Honeywell. A Unique Collaboration between academia and industry specifically focused on.

Opportunity to work in the Analytics team with Honeywell* and other large analytics firms.

Join the industry endorsed program by Ivy Pro School co-created with Honeywell. A Unique Collaboration between academia and industry specifically focused on.

Hands-on learning on real-life projects / case studies using advanced statistical tools ( R, Python, Tableau) and latest models used in Machine Learning and Data Visualization.

The entire course framework has been designed in close collaboration with Honeywell’s senior Data Science and Machine Learning practitioners. You’ll also get an opportunity to get mentored by them during the course.

Honeywell is a global Fortune 100 software-industrial company with technologies that help everything from aircraft, buildings, manufacturing plants, supply chains, and workers become more connected to make our world smarter, safer, and more sustainable

Honeywell as a Knowledge partner is involved in the Data Science training course through curriculum design, project reviews, guest lectures and mentorship. A partnership with such an industry leader ensures that the curriculum is industry relevant.

Honeywell has the right to hire students from the batch basis their performance in the internal assessments and Industry projects. However, the program does not guarantee placement with Honeywell.

Ivy is committed to assist all its students to get placed in top notch Analytics roles and we help our students in several ways

**Placement Preparation Workshops**

– Provide guidance in preparing professional data science resume followed by 1:1 interaction with Ivy’s directors and senior faculty members

– uploading the resume on online job portals like Naukri, Shine etc.

– Prepare you for business interviews like guesstimates, puzzles etc.

– Provide comprehensive topic wise interview questions and answers. This also includes company specific interview pattern and questions. Ivy has built a database of company-wise interview question bank basis its alumni interview feedback.

– For fresh college graduates, we organize Aptitude tests and Group Discussions before the college placements. This has helped a lot of students to get jobs from college before graduation.

**Corporate Alliances**

– Ivy is Official Learning Partner of large corporates such as Genpact, Honeywell, Capgemini, HSBC, ITC Limited, Tata Steel, etc. The corporates directly contact Ivy for their internship and full time requirements. Ivy’s alumni are working in more than 100+ companies.

**Alumni Network**

– Ivy’s 16000+ alumni working in 100+ companies provide Ivy with internal job referrals in data science.

**Faculty Access**: You get faculty access on the phone / Whatsapp to discuss your queries.

**Skilled Teaching Assistants**: We’ve full-time teaching assistants who help clear your doubts on 1 to 1 basis.

**Batch-wise Whatsapp Groups & Class Forums**: We have WhatsApp groups for all the batches. You can post your doubts there for faster answers. Also, you can ask questions in your batch groups on our course management platform.

**Recorded Video Access to Past Doubt Clearing Classes**: You get access to recorded video access to past doubt clearing classes held in similar data science course batches.

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