• Course overview and objectives
• Importance of Data Science and AI in the industry
• Understanding the learning path and outcomes
• Tools introduction related to data
• Data and its impact on career opportunities
• Importance of data in research and development

• Why Python for Data Science
• Python Data Types
• Strings & Loop Control
• Functions and Modules
• File handling & Regular Expressions

• Data Analysis Using Numpy
• Array Creation, slicing and iterating
• Array shape and reshape
• Copy and View
• Numpy for Statistical Operation
• Data Manipulation Using Pandas
• Importing data into Python
• Creating DataFrames
• Data Wrangling
• Data Pre-processing and Cleaning
• Data Visualization Using Matplotlib & Seaborn
• Line plot & Subplot
• Pie and Bar chart
• Scatter Plot
• Visualizing statistical relationships
• Import and Prepare data
• Plotting with categorical data and Visualizing linear relationships

• Probability & Cumulative distributed function
• Descriptive Statistics (Central Tendency & Measures of Dispersion)
• Inferential Statistics (Central Limit Theorem, Z-distribution and T-Distribution)
• Hypothesis Testing
• Binomial Distribution (Normal Distribution & Central Limit Theorem)

• Matrices in Python
• Square Matrix, Triangular Matrix, Diagonal Matrix, Identity Matrix
• Scalar Multiplication, Matrix Multiplication, Matrix Transpose
• Analysis of variance (ANOVA)
• Analysis of Covariance (ANCOVA)

• Definition, Examples, Importance of Machine Learning
• ML Models Type: Supervised Learning, Unsupervised Learning and Reinforcement Learning
• Data Preprocessing (Data Encoding, Handle Missing values, Normalization, Standardization)
• Supervised Learning
• Linear Regression , Polynomial Regression
• Random Forest, Logistic Regression, Decision Trees, Support vector Machines
• Unsupervised Learning
• K-means clustering
• KNN (k-nearest neighbors)

• Difference between DBMS and RDBMS
• MySQL Installation Process
• SQL Commands (DDL, DML, DQL, TQL, TCL)
• SQL Operators (and, or, not)
• ORDER BY, GROUP BY and Distinct
• Joins (Left Join, Right Join, Inner Join, Self Join, Cross Join, Natural Join)
• Union and Union All

• Introduction to Mongo DB and NoSQL
• Mongo DB installation process
• Connecting to MongoDB database
• Object Ids in MongoDB
• MongoDB Shell vs MongoDB Server
• CRUD operation in MongoDB

• Introduction to Tableau
• Connecting to data source
• Creating dashboard pages
• Creating calculated columns
• Basic charts in Tableau
• Getting Started With Visual Analytics
• Sorting and grouping
• Worksheets & Dashboard Action
• Filters
• Forecasting and Clustering

Power BI Installation Process

• Connecting Data to Power BI Desktop
• Introduction to Power Query Editor
• Conditional Columns & Column From Examples
• Append & Merge Queries in Power Query Editor
• Combine multiple data sources
• Data Cleaning using Power Query Editor
• Creating visuals in Report View
• Bar Chart, Column Chart, Stacked Column Chart
• Pie Chart, Donut Chart, Maps, Ribbon Chart
• Tables v/s Matrix
• Scatter Plot, Waterfall Chart, Funnel Chart
• Creating Interactive Power BI Reports
• Deployment on Power BI Service

• Understanding Big Data and Hadoop
• Brief Overview of Distributed Architecture
• Map Reduce Architecture
• Introduction to Spark SQL and Data frames
• Using R-Spark for machine learning programming

• Time Series Analysis and Forecasting
• Method Selection in Forecasting
• Different Components of Time Series Data
• Introduction to ARIMA Models
• ARIMA Model Calculations
• Manual ARIMA Parameter Selection
• ARIMA with Explanatory Variables

• Introduction to Deep Learning and TensorFlow
• Simple Computation, Constants, and Variables
• Creating a Graph Visualization
• Creating Logistic Regression Model
• Basic of Neural Network
• Single Hidden Layer and Multiple Hidden Layer Model
• Understand Backpropagation Using Neural Network Example
• The architecture of a CNN
• Understanding and Visualizing a CNN

• Introduction to NLP
• NLP Libraries
• Textual Analytics Overview
• Distance Algorithms used in Text Analytics
• String Similarity
• Cosine Similarity Mechanism
• Levenshtein distance
• KNN for document retrieval
• K-Means for document retrieval
• Clustering for document retrieval

• Deploying trained models on AWS and Azure
• Monitoring model performance and managing versions