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Master Certification Program in
Analytics, Machine Learning & AI

16 Data Science Modules Live Projects and Doubt Sessions Assignment & Case Studies

India’s Only Data Science Training Program was created to help you to build a successful career in data science from scratch. You will solve 75+ projects and assignments across the project duration working on Stats, Advanced Excel, SQL, Python Libraries, Tableau, Advanced Machine Learning & Deep Learning algorithms to solve day- day industry data problems in healthcare, manufacturing, sales, media, marketing, education sectors making you job ready for 30+ roles.

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    Module 1: Introduction to Data Science

    • Introduction to the Industry & Buzzwords
    • Industrial application of data science
    • Introduction to different Data Science Techniques
    • Important Software & Tools
    • Career paths & growth in data science

    Module 2: Introduction to Excel

    • Introduction to Excel- Interface, Sorting & Filtering,
    • Excel Reporting- Basic & Conditional Formatting
    • Essential Excel Formulae
    • Layouts, Printing and Securing Files

    Module 3: Introduction to Stats

    • Introduction to Statistics & It’s Applications
    • Different types of Data
    • Population vs Sample
    • Sampling Techniques
    • Intro: Inferential vs. descriptive statistics

    Module 4: Descriptive Stats Using Excel Datasets

    • Categorical Variables Visualization Using Excel Charts- FDT, Pie Charts, Bar Charts & Pareto
    • Numerical Variables Visualization of Frequency & Absolute Frequency- Using Histogram, Cross Table & Scatter Plot
    • Measure of Spread ( Mean, Mode , Median)
    • Measure of Variance( Skewness, SD, Variance,
    • Range, Coef. Of Variance, Bivariate Analysis, Covariance & Correlation)

    Module 5: Inferential Stats Using Excel Datasets

    • Introduction to Probability
    • Permutation & Combinations
    • Types of events
    • Normal distribution
    • Standard Normal distribution
    • Normal vs. Standard Normal distribution
    • Confidence Intervals & Z-Score
    • Hypothesis Testing & It’s Types

    Module 6: Database Design & MySQL

    • Relational Database theory & Introduction to SQL
    • MySQL Installation
    • Database Creation in the MySQL Workbench
    • Querying in MySQL
    • Joins and Set Operations
    • SQL Practice Case Study
    • Window Functions
    • Case Statements, Stored Routines and
      Ø Query Optimisation and Best Practices
      Ø Problem-Solving Using SQL

    Module 7: Data Visualization Using Advanced Excel

    • Introduction
    • LOOKUP functions
    • Pivot Tables
    • WHATIF Analysis
    • Dashboard Creation
    • Recording Macros
    • Advanced Visualizations- PIVOT Charts, Sparklines, Waterfall Charts
    • Data Analysis ToolPak – Regression in Excel

    Module 8: Data Visualization Using Tableau

    Introduction to Tableau

    • Introduction
    • What is Data Analytics?
    • Why Data Visualisation?
    • What is Tableau?
    • Why Tableau?
    • Tableau vs Excel and PowerBI
    • Exploratory and Explanatory Analysis
    • Getting started with Tableau
    • Summary

    Visualizing and Analyzing data with Tableau – I

    • Introduction
    • Bar Charts
    • Line Charts and Filters
    • Area Charts
    • Box plots and Pivoting
    • Maps and Hierarchies
    • Pie Charts
    • Treemaps and Grouping
    • Dashboards
    • Summary

    Visualizing and Analyzing Data with Tableau – II

    • Introduction
    • Joins and Splits
    • Numeric and String functions
    • Logical and Date functions
    • Histograms and parameters
    • Scatter Plots
    • Dual Axis Charts
    • Top N Parameters and Calculated Fields
    • Stacked bar Charts
    • Dashboards – II and Filter Actions
    • Storytelling
    • Summary

    Module 9: Python Programming

    Installing Anaconda & Basics of Python

    • Introduction to programming languages
    • Compiler vs Interpreter
    • Getting Started With Python
    • Introduction to jupyter Notebooks
    • Identifiers, Keywords
    • Print function
    • Comment, Indentation
    • Data Types Functions
    • Understanding what are functions
    • Defining and calling functions
    • Local and global variables
    • Different types of arguments
    • Map,reduce,filter,lambda and recursive functions

    Data Structures in Python

    • Introduction
    • Lists
    • Tuples
    • Sets
    • Dictionaries
    • Practice Exercise
    • Summary

    Operator Input and Output

    • Different Arithmetic , logical and Relational operators
    • Input, Output function
    • Eval function
    • Format Function

    Control Flow

    • If elif else statement
    • For and while loops
    • Break , continue and Pass statement
    • List and dictionary comprehensions


    • Understanding what are functions
    • Defining and calling functions
    • Local and global variables
    • Different types of arguments
    • Map,reduce,filter,lambda and recursive functions

    File Handling

    • Purpose of file handling
    • Different function in file handling (open,read, write,close)
    • Different modes (r,w,a,r+,w+,a+)
    • With block

    Exception Handling, OOPX & Regex

    • What is exception handling
    • Try, except, else and finally block
    • Different types of Exception
    • Concept of Oops
    • Different functions in Regex
    • Metacharacters in Regex

    Module 10: Python For Data Science


    • Introduction to NumPy
    • Basics of NumPy
    • Operations Over 1-D Arrays
    • Practice Exercise I
    • Multidimensional Arrays
    • Creating NumPy Arrays
    • Mathematical Operations on NumPy
    • Mathematical Operations on NumPy II
    • Computation Times in NumPy vs Python Lists
    • Practice Exercise II


    •  Introduction to Pandas
    • Basics of Pandas
    • Pandas – Rows and Columns
    • Describing Data
    • Indexing and Slicing
    • Operations on Dataframes
    • Groupby and Aggregate Functions
    • Merging DataFrames
    • Pivot Tables
    • Practice Exercise

    Module 11: Data Visualization Using Python- Matplotlib & Seaborn

    Introduction to Data Visualisation with Matplotlib

    • Introduction to Matplotlib
    • The Necessity of Data Visualisation
    • Visualisations – Some Examples
    • Facts and Dimensions
    • Bar Graph
    • Scatter Plot
    • Line Graph and Histogram
    • Subplots
    • Choosing Plot Types
    • Summary

    Data Visualisation: Case Study

    • Introduction
    • Case Study: Mind Map
    • Case Study Overview
    • Data Handling and Cleaning: I
    • Data Handling and Cleaning: II
    • Sanity Checks
    • Outliers Analysis with Boxplots
    • Histograms
    • Summary
    • Practice Questions

    Data Visualization with Seaborn

    • Introduction
    • Distribution Plots
    • Styling Options
    • Pie – Chart and Bar Chart
    • Scatter Plots
    • Pair Plots
    • Revisiting Bar Graphs and Box Plots
    • Heatmaps
    • Line Charts
    • Stacked Bar Charts
    • Case Study Summary
    • Plotly
    • Practice Questions

    Module 12: Exploratory Data Analysis

    Data Sourcing

    • Module Introduction
    • Introduction to EDA
    • Public and Private Data
    • Private Data
    • Public Data
    • Web Scraping-I
    • Web Scraping-II
    • Summary

    Data Cleaning

    • Introduction
    • Data Types
    • Fixing the Rows and Columns
    • Impute/Remove Missing Values
    • Handling Outliers
    • Standardising Values
    • Fixing Invalid Values and Filter Data
    • Practice Questions
    • Summary

    Univariate Analysis

    • Introduction to Univariate Analysis
    • Categorical Unordered Univariate Analysis
    • Categorical Ordered Univariate Analysis
    • Statistics on Numerical Features
    • Graded Questions
    • Summary

    Bivariate and Multivariate Analysis

    • Introduction
    • Numeric – Numeric Analysis
    • Correlation vs Causation
    • Numerical – Categorical Analysis
    • Categorical – Categorical Analysis
    • Multivariate Analysis
    • Graded Questions
    • Summary
    • Module Summary

    Module 13: Supervised Learning Model - Regression

    Introduction to Simple Linear Regression

    • Introduction to Simple Linear Regression
    • Introduction to machine learning
    • Regression line
    • Best fit line
    • Strength of simple linear regression

    Simple linear regression in python

    • Assumptions of simple linear regression
    • Reading and understanding the data
    • Hypothesis testing in linear regression
    • Building a linear model
    • Residue analysis and predictions
    • Linear Regression using SKLearn

    Multiple Linear Regression

    • Motivation-when one variable is not enough
    • Moving from SLR to MLR-new considerations
    • Multi collinearity
    • Dealing with categorical variables
    • Model assessment in comparison
    • Feature selection

    Multiple Linear Regression in Python

    • Reading and understanding the data
    • Data preparation
    • Initial steps
    • Building the model I & II
    • Residue analysis and predictions
    • Variable selection using RFE

    Industry Relevance of Linear Regression

    • Linear regression revision
    • Prediction versus projection
    • Media company case study
    • Exploratory data analysis
    • Model building – I, II & III
    • Assessing the model
    • Interpreting the results

    Module 14: Supervised Learning Model - Classification

    Univariate Logistic Regression

    • Binary classification
    • Sigmoid curve
    • Finding the best fit sigmoid curve – I
    • Finding the best fit sigmoid curve – II
    • Odds and log Odds

    Multivariate Logistic Regression – Model Building

    • Multivariate Logistic Regression – Model Building
    • Multivariate logistic regression with telecom churn example
    • Data cleaning and preparation – I & II
    • Building your first model
    • Feature elimination using RFE
    • Confusion metrics and accuracy
    • Manual feature elimination

    Multivariate Logistic Regression – Model Evaluation

    • Multivariate Logistic Regression – Model Evaluation
    • Metrics beyond accuracy-sensitivity and specificity
    • Sensitivity and specificity in Python
    • Understanding ROC curve
    • ROC curve in python
    • Finding the optimal threshold
    • Model evaluation metrics – exercise
    • Precision and recall
    • Making predictions

    Logistic Regression – Industry Applications – Part I

    • Getting familiar with logistic regression
    • Nuances of logistic regression-sample selection
    • Nuances of logistic regression-segmentation
    • Nuances of logistic impression-variable transformation-I, II & III
    • Logistic Regression: Industry Applications – Part II
    • Model evaluation – A second look
    • Model validation and importance of stability
    • Tracking of model performance over time

    Logistic Regression – Industry Applications -Part II

    • Commonly face challenges in implementation of logistic regression
    • Model evaluation – A second look
    • Model validation and importance of stability
    • Tracking of model performance over time

    Module 15: Advanced Machine Learning

    Unsupervised Learning: Clustering

    • Introduction to Clustering
    • K Means Clustering
    • Executing K Means in Python
    • Hierarchical Clustering

    Business Problem Solving

    • Introduction to Business Problem Solving
    • Case Study Demonstration churn example
    • Practice Questions

    Tree Models

    • Introduction to Decision Trees
    • Algorithms for Decision Tree Construction
    • Hyperparameter Tuning in Decision Trees
    • Ensembles and Random Forests

    Time Series Forecasting – II (BA)

    • Introduction to AR Models
    • Building AR Models

    Model Selection

    • Principles of Model Selection
    • Model Building and Evaluation

    Time Series Forecasting – I (BA)

    • Introduction to Time Series
    • Smoothing Techniques

    Module 16: AI- NLP, Neural Networks & Deep Learning

    Introduction to NLP

    • What is NLP?
    • History and evolution of NLP
    • Applications of NLP
    • Challenges in NLP
    • Overview of NLP pipeline
    • Corpus and Corpus Linguistics

    NLTK Toolkit

    • Introduction to the NLTK toolkit
    • Preprocessing text data with NLTK
    • Basic NLP tasks using NLTK (e.g., Part-of-Speech Tagging, Named Entity Recognition)
    • Stemming and Lemmatization
    • WordNet in NLTK
    • Chunking and Chinking
    • Sentiment Analysis with NLTK

    Tokenization and Topic Modeling

    • Tokenization in NLP
    • Bag-of-Words representation
    • Topic Modeling with LDA
    • Latent Semantic Analysis
    • Word Embeddings

    Sentiment Analysis Project

    • Introduction to Sentiment Analysis
    • Sentiment Analysis using supervised and unsupervised methods
    • Building a Sentiment Analysis model with Python
    • Evaluating Sentiment Analysis models

    AI vs Deep Learning vs ML

    • Introduction to Artificial Intelligence (AI),
    • Machine Learning (ML) and Deep Learning (DL)
    • Applications of AI, ML, and DL
    • Differences between AI, ML and DL

    The Concept of Neural Networks

    • Introduction to Neural Networks
    • Types of Neural Networks
    • Layers in Neural Networks
    • Activation Functions

    Neural Networks – Feed-forward, Convolutional, Recurrent

    • Feed-forward Neural Networks
    • Convolutional Neural Networks
    • Recurrent Neural Networks
    • Applications of Neural Networks

    Deep Learning Project

    • Building a Deep Learning model with Python
    • Image Classification with Convolutional Neural Networks
    • Natural Language Processing with Recurrent Neural Networks
    • Hyperparameter Tuning in Deep Learning

    Theory, Live Practical & Implementation

    Who Can Attend?

    IT Professionals to Business Professionals, Statisticians and Mathematicians, Graduates and Post-Graduates and Professionals looking to switch careers. With millions of worldwide job openings, data scientist has become the hottest job of the decade.

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    Why Learn Data Science?

    Graduation Degree is Not Enough
    Graduation Degree is Not Enough
    Since the curriculum is outdated and the number of graduates is huge, students must learn some specific skills with hands-on experience to stand out from the crowd.
    Data Science Roles in High Demand
    Data Science Roles in High Demand
    According to analysts, India will see more than 11 million job openings in the field of data science in the next five years. It could witness a whopping 200% rise by 2026.
    Embark on a Rewarding Career
    Embark on a Rewarding Career
    Average salaries for fresher roles in data science and analytics vary between 9-12 lacs per annum. Plus the YOY growth is higher in analytics roles.

    Job Roles You Can Apply For

    Data Scientist
    Data Scientist
    Data Analyst
    Data Analyst
    Machine Learning Engineer
    Machine Learning Engineer
    Data Engineer
    Data Engineer
    Data Architect
    Data Architect
    Analytics Manager
    Analytics Manager

    Mastering Data Science Tools

    1. Python:

    2. Data Visualization Tools:

    3. Database Management Systems:

    4. Version Control Tools:

    5. Text Editors/IDEs::

    Our Student's Are Working At

    1. Healthcare Customer Feedback Analysis
    2. Management Teams Dashboard Creation
    3. Retails Store Sales Report Analysis
    4. Software Firm Employee Data Analysis
    5. Industrial Data Sets Classification & Comparison
    6. Charts & Graphs: Frequency Distribution Table, Pie-charts, Pareto Diagram, Histogram, Scatter Plots, Heatmaps, Bar Graphs & Many More
    7. Patient Disease Probability Analysis Using Healthcare Data
    8. Car Model & Menu Item Data Combination & Configuration Probability Analysis
    9. Manufacturing & Product Launch Data Classification & Analysis
    10. Customer Complaint Resolution Analysis Using Normal Distribution Curves
    11. Product Rating & Employee Productivity Analysis Usign Z-Score
    12. New Product Need Analysis Using Hypothesis Testing
    13. Inventory Management & Customer Segmentation Systems Using Vlook up & Hlook Lookup
    14. Sales Trend & Staffing Plan Creation using Pivot Tables
    15. Pricing Strategy & Financial Model Creation Using What if Analysis
    16. Sales & Operations Dashboard Creation
    17. Healthcare & Construction Reporting Automation Using Macros
    18. Retail Sales Opportunity Analysis Using PIVOT Charts
    19. Accounting Firm Statement Analysis Using Sparklines & Waterfall Chart
    20. FMCG Marketing spend To Sales Revenue Impact Analysis Using Regression Analysis
    21. Transportation Pricing Model Using Regression Analysis

    1. Customer Lifetime Value Calculation:

    The project involves calculating the customer lifetime value using SQL to understand the revenue generated by a customer over their lifetime.

    2. Customer Churn Prediction:

    This project involves building a predictive model using SQL to identify customers who are likely to churn based on their behavior and transaction history.

    3. Interactive Dashboard for E-Commerce Sales:

    The project involves creating an interactive dashboard using Tableau & SQL to analyze retail sales data, identify trends, and make data-driven decisions.

    4. Customer Segmentation Dashboard:

    This project involves creating a customer segmentation dashboard using Tableau to identify customer groups based on demographics, behavior, and purchasing patterns.

    5. Movie Recommendation System:

    The project involves building a movie recommendation system using Python and its libraries such as Pandas, NumPy, and Scikit-Learn. The recommendation system will suggest movies based on user preferences and ratings.

    6. Sentiment Analysis on Twitter Data:

    This project involves analyzing Twitter data using Python and its libraries such as NLTK and TextBlob to perform sentiment analysis and understand the overall sentiment of a particular topic.

    7. Visualizing COVID-19 Data:

    The project involves visualizing COVID-19 data using Python and its libraries such as Matplotlib, Seaborn, and Plotly to understand the impact of the pandemic on different countries and regions.

    8. Visualizing Stock Market Data:

    This project involves visualizing stock market data using Python and its libraries such as Pandas, Matplotlib, and Bokeh to understand the trends and patterns in stock prices over time.

    9. Airbnb Data Analysis:

    The project involves performing exploratory data analysis on Airbnb data to understand the patterns in the pricing, availability, and quality of Airbnb listings in different cities.

    10. Bike Sharing Data Analysis:

    This project involves performing exploratory data analysis on bike sharing data to understand the usage patterns of bikes in different cities and identify factors that influence bike usage.

    11. House Price Prediction:

    The project involves building a regression model using Python and its libraries such as Scikit-Learn to predict the prices of houses based on their features such as location, size, and amenities.

    12. Credit Risk Prediction:

    This project involves building a classification model using Python and its libraries such as Scikit-Learn to predict the credit risk of loan applicants based on their credit history and other factors.

    13. Time Series Forecasting for Sales Data:

    The project involves building a time series forecasting model using advanced machine learning algorithms such as ARIMA and LSTM to predict future sales trends and identify factors that influence sales.

    14. Sentiment Analysis on Product Reviews:

    The project involves building a sentiment analysis model using NLP techniques such as Word Embeddings and Recurrent Neural Networks (RNN) to analyze product reviews and understand the sentiment of customers towards different products.

    15. Segmentation using Deep Learning:

    This project involves using advanced deep learning techniques such as Fully Convolutional Networks (FCN) and U-Net to perform image segmentation and identify objects in images.

    16. Machine Translation using Transformers:

    This project involves building a machine translation model using advanced deep learning techniques such as Transformers to translate text from one language to

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