PGP in Data Science and Machine Learning Course

• Regular Test & Performance Monitoring by Technical Mentors
• Interview Preparation Sessions
• Hackathons / Projects Evaluation & Solutioning

• Regular Test & Performance Monitoring by Technical Mentors
• Interview Preparation Sessions
• Hackathons / Projects Evaluation & Solutioning

• Regular Test & Performance Monitoring by Technical Mentors
• Interview Preparation Sessions
• Hackathons / Projects Evaluation & Solutioning

• Regular Test & Performance Monitoring by Technical Mentors
• Interview Preparation Sessions
• Hackathons / Projects Evaluation & Solutioning

• Introduction to Python and IDEs
• The basics of the Python programming language
• How you can use various IDEs for Python development like Jupyter, Colab, etc.
• Python Basics
• Variables, Data Types, Loops, Conditional Statements, functions, lambda functions, file handling, exception handling ,etc.
• Object Oriented Programming
• Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
• Hands-on Sessions And Assignments for Practice
• The culmination of all the above concepts with real-world problem statements for better understanding.
• Case Study – Polygon Area Calculator Using Python Programming

• Introduction To Python Libraries
• Introduction to NumPy, Pandas, Matplotlib ,Seaborn, etc
• Importance of Data Analysis
• Preprocessing Of Data – Do’s and Don’ts
• NumPy
• What is NumPy?
• Why NumPy over Lists?
• What is NumPy Array?
• Array Manipulations
• Matrices
• NumPy Linear Algebra
• Pandas
• What is Pandas?
• Pandas Series
• Pandas Dataframes
• Working with Various Data Sources using Pandas
• Data Manipulation and Wrangling Using Pandas
• Matplotlib and Seaborn
• What is Data Visualization?
• Why Data Visualization?
• What are the various types of Plots
• Plotting Categorical Data
• Inferences from the Plots
• Case Study – HR Analytics Case Study

• Descriptive Statistics
• The measure of Central Tendency
• Measure of Spread
• Five Points Summary
• Probability Theory
• Central Limit Theorem
• Bayes Theorem
• Conditional Probability
• Inferential Statistics
• Correlation
• Covariance
• Confidence intervals
• Hypothesis testing
• F-test, Z-test, t-test, ANOVA, chi-square test, etc.
• Case Study
• Advanced HR Analytics Case Study
• EDA Case Study – Purchase Data

• Introduction to Machine Learning
• Supervised, Unsupervised learning.
• Introduction to scikit-learn, Keras, etc.
• Regression
• Introduction to regression problems, Identification of a regression problem, dependent and independent variables.
• How to train the model in a regression problem.
• How to evaluate the model for a regression problem.
• How to optimize the efficiency of the regression model.
• Classification
• Introduction to classification problems, Identification of a classification problem, dependent and independent variables.
• How to train the model in a classification problem.
• How to evaluate the model for a classification problem.
• How to optimize the efficiency of the classification model.
• Clustering
• Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
• How to train the model in a clustering problem.
• How to evaluate the model for a clustering problem.
• How to optimize the efficiency of the clustering model.
• Supervised Learning
• Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
• Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.
• Decision Tree – Creating decision tree models on classification problems in a tree-like format with optimal solutions.
• Random Forest – Creating random forest models for classification problems in a supervised learning approach.
• Support Vector Machine – SVM or support vector machines for regression and classification problems.
• Gradient Descent – The gradient descent algorithm is an iterative optimization approach to finding the local minimum and maximum of a given function.
• K-Nearest Neighbors – A simple algorithm that can be used for classification problems.
• Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.
• Unsupervised Learning
• K-means – The K-means algorithm that can be used for clustering problems in an unsupervised learning approach.
• Dimensionality reduction – Handling multidimensional data and standardizing the features for easier computation.
• Linear Discriminant Analysis –  LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
• Principal Component Analysis – PCA follows the same approach in handling multidimensional data.
• Performance Metrics
• Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.
• Confusion matrix – To evaluate the true positive/negative, and false positive/negative outcomes in the model.
• r2, adjusted r2, mean squared error, etc.
• Case Studies
• Concrete Data Case Study
• Insurance Data And Scrap Price Regression Case Study
• Weather Prediction Case Study
• Banking Case Study
• Heart Disease Prediction
• Recruitment and Factory Salary Case Study
• Customer Acquisition Cost Case Study
• Airline Delay Prediction Case Study
• Customer Churn Case Study
• Online Retail Case Study
• MNIST Digit Data Case Study For Dimensionality Reduction
• Basketball Case Study
• AirPassengers Influx Case Study
• Average Temperature Case Study

• Power BI Basics
• Introduction to Power BI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI.
• SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
• Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.
• DAX
• Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features
• Data Visualization with Analytics 
• Slicers, filters, Drill Down Reports
• Power BI Query, Q & A and Data Insights
• Power BI Settings, Administration and Direct Connectivity
• Embedded Power BI API and Power BI Mobile
• Power BI Advance and Power BI Premium

Artificial Intelligence Basics 

• Introduction to Keras API and tensorflow

Neural Networks

• Neural networks
• Multi-layered Neural Networks
• Artificial Neural Networks 

Deep Learning 

• Deep neural networks
• Convolutional Neural Networks 
• Recurrent Neural Networks
• GPU in deep learning
• Autoencoders, restricted Boltzmann machine 

• The Data Science capstone project focuses on establishing a stronghold of analyzing a problem and coming up with solutions based on insights from the data analysis perspective. The capstone project will help you master the following verticals:
  • Extracting, loading and transforming data into a usable format to gather insights.
  • Data manipulation and handling to pre-process the data.
  • Feature engineering and scaling the data for various problem statements.
  • Model selection and model building on regression problems using supervised/unsupervised machine learning algorithms.
  • Assessment and monitoring of the model created using the machine learning models.

• Recommendation Engine – The case study will guide you through various processes and techniques in machine learning to build a recommendation engine that can be used for movie recommendations, restaurant recommendations, book recommendations, etc.
• Rating Predictions – This text classification and sentiment analysis case study will guide you towards working with text data and building efficient machine learning models that can predict ratings, sentiments, etc.
• Census Income – Using predictive modeling techniques on the census data, you will be able to create actionable insights for a given population and create machine learning models that will predict or classify various features like total population, user income, etc.
• Housing – This real estate case study will guide you towards real-world problems, where a culmination of multiple features will guide you towards creating a predictive model to predict housing prices.
• Stock Market Analysis – Using historical stock market data, you will learn about feature engineering and feature selection and how they offer some really helpful and actionable insights for specific stocks.
• Customer Churn – The case study involves studying the customer data for a given XYZ company, and using statistical tests and predictive modeling, we will gather insights to efficiently create an action plan for the same.
• Sales Forecasting – By studying the various patterns and sales data for a firm/store, we will use the time series forecasting method to forecast the number of sales for the next given period(weeks, months, years, etc.)
• Customer Segmentation – Using unsupervised learning techniques, we will learn about customer segmentation, which can be quite useful for e-commerce sectors, stores, marketing funnels, etc.
• Inventory Management – In this case study, you will learn about how meaningful insights can be used to drive a supply chain, using predictive modeling and clustering techniques.
• Disease Prediction – A medical endeavor that is achieved through machine learning will give you an insight into how the predictive model can prove to be a great marvel in the early detection of various diseases.

• Job Search Strategy
• Resume Building
• Linkedin Profile Creation
• Interview Preparation Sessions by Industry Experts
• Mock Interviews
• Placement opportunities with 400+ hiring partners upon clearing the Placement Readiness Test.

• Excel Fundamentals
• Reading the Data, Referencing in formulas , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering
• Working with Charts in Excel, Pivot Tables, Dashboards, Data and File Security
• VBA Macros, Ranges and Worksheet in VBA
• IF conditions, loops, Debugging, etc.
• Excel For Data Analytics
• Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.
• Data Visualization with Excel
• Charts, Pie charts, Scatter and bubble charts
• Bar charts, Column charts, Line charts, Maps
• Multiples: A set of charts with the same axes, Matrices, Cards, Tiles
• Excel Power Tools
• Power Pivot, Power Query and Power View
• Classification Problems using Excel
• Binary Classification Problems, Confusion Matrix, AUC and ROC curve
• Multiple Classification Problems
• Information Measure in Excel
• Probability, Entropy, Dependence
• Mutual Information
• Regression Problems Using Excel
• Standardization, Normalization, Probability Distributions
• Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation
• Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression

• Linux
  • Introduction to Linux  – Establishing the fundamental knowledge of how Linux works and how you can begin with Linux OS.
  • Linux Basics – File Handling, data extraction, etc.
  • Hands-on Sessions And Assignments for Practice – Strategically curated problem statements for you to start with Linux.