PGP in Data Science and Machine Learning

MS-Excel – 

1. 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 Table, Dashboards, Data And File Security 
• VBA Macros, Ranges and Worksheet in VBA 
• IF conditions, loops, Debugging, etc.

2. 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.  

3. 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

1. Python 

• Introduction to Python and IDEs – The basics of the python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc. 
• Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, 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. 

2. 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.

1. SQL Basics – 

• Fundamentals of Structured Query Language
• SQL Tables, Joins, Variables 

2. Advanced SQL –  

• SQL Functions, Subqueries, Rules, Views
• Nested Queries, string functions, pattern matching
• Mathematical functions, Date-time functions, etc. 

3. Deep Dive into User Defined Functions

• Types of UDFs, Inline table value, multi-statement table. 
• Stored procedures, rank function, triggers, etc. 

4. SQL Optimization and Performance

• Record grouping, searching, sorting, etc. 
• Clustered indexes, common table expressions

Hands-on exercise: 

Writing comparison data between past year to present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data,  and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

1. Descriptive Statistics – 

• Measure of central tendency, measure of spread, five points summary, etc. 

2. Probability 

• Probability Distributions, bayes theorem, central limit theorem. 

3. Inferential Statistics –  

• Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

1. Machine Learning libraries – You will learn about various libraries in python that supports machine learning like scikit-learn, keras, tensorflow, etc and other supporting libraries like pandas, numpy, pandas, matplotlib, seaborn, scipy, stats, etc. 

1. These libraries will help you grasp a good command over the various steps involved in the machine learning life cycle like data extraction, loading, transformation, manipulation, visualization, feature engineering, feature selection, standardization, creating machine learning models, optimization, performance metrics, etc. 
2. Other necessary verticals include statistical tests, hypothesis testing, linear algebra that covers the basics of any machine learning problem. 

2. Introduction to Machine learning 

1. Supervised, Unsupervised learning.
2. Introduction to scikit-learn, Keras, etc. 

3. Regression 

1. Introduction classification problems, Identification of a regression problem, dependent and independent variables. 
2. How to train the model in a regression problem. 
3. How to evaluate the model for a regression problem. 
4. How to optimize the efficiency of the regression model. 

4. Classification 

1. Introduction to classification problems, Identification of a classification problem, dependent and independent variables. 
2. How to train the model in a classification problem. 
3. How to evaluate the model for a classification problem. 
4. How to optimize the efficiency of the classification model. 

5. Clustering 

1. Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables. 
2. How to train the model in a clustering problem. 
3. How to evaluate the model for a clustering problem. 
4. How to optimize the efficiency of the clustering model.

1. 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 – Gradient descent algorithm that is an iterative optimization approach to finding 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. 

2. Unsupervised Learning 

• K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
• Dimensionality reduction – Handling multi dimensional 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 the multidimensional data.

3. 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, false positive/negative outcomes in the model. 
• r2, adjusted r2, mean squared error, etc.

4. Reinforcement Learning

• Mapping the human mind with deep neural networks (dnns)
• Several building blocks of artificial neural networks (anns)
• The architecture of dnn and its building blocks
• Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.

5. Text Mining, Cleaning, and Pre-processing

• Various Tokenizers, Tokenization, Frequency Distribution, Stemming, POS Tagging, Lemmatization, Bigrams, Trigrams & Ngrams, Lemmatization, Entity Recognition.

6. Text classification, NLTK, sentiment analysis, etc.

• Overview of Machine Learning, Words, Term Frequency, Countvectorizer, Inverse Document Frequency, Text conversion, Confusion Matrix, Naive Bayes Classifier.

7. Sentence Structure, Sequence Tagging, Sequence Tasks, and Language Modeling

• Language Modeling, Sequence Tagging, Sequence Tasks, Predicting Sequence of Tags, Syntax Trees, Context-Free Grammars, Chunking, Automatic Paraphrasing of Texts, Chinking.

8. AI Chatbots and Recommendations Engine 

• Using the NLP concepts, build a recommendation engine and an AI chatbot assistant using AI.

1. Introduction to MLOps 

• MLOps lifecycle
• MLOps pipeline 
• MLOps Components, Processes, etc.

2. Deploying Machine Learning Models 

• Introduction to Azure Machine Learning 
• Deploying Machine Learning Models using Azure

1. Power BI Basics

• Introduction to PowerBI, 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.

2. DAX 

• Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

3. 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

Hands-on Exercise:

Creating a dashboard to depict actionable insights in sales data.

The capstone project focuses on establishing a strong hold 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 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 various classification, regression problems using supervised/unsupervised machine learning algorithms.
• Assessment and monitoring of the model created using the machine learning models.

1. 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. 
2. 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.
3. Census – 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.
4. 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.
5. Object Detection –  A much more advanced yet simple case study that will guide you towards making a machine learning model that can detect objects in real time.
6. Stock Market Analysis – Using historical stock market data, you will learn about how feature engineering and feature selection can provide you some really helpful and actionable insights for specific stocks.
7. Banking Problem – A classification problem that predicts consumer behavior based on various features using machine learning models.
8. AI Chatbot – Using the NLTK python library, you will be able to apply machine learning algorithms and create an AI chatbot.
9. 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.
10. 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 time period(weeks, months, years, etc.)
11. HR Analytics – Based on the data provided by a firm, we will study the HR analytics data, and create actionable insights using various statistical tests and hypothesis testing.
12. Dimensionality Reduction – To understand the impact of multidimensional data, we will go through various dimensionality reduction techniques and optimize the computational time on the same that will eventually be used for various classification and regression problems.
13. 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.
14. 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.
15. 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 early detection of various diseases.
16. Image Classification – The case study will entail working with image data, and how simple machine learning techniques can be useful to recognize image data at the behest of well trained and optimized machine learning models.