Data Science & Artificial Intelligence

Enrolled (450+)

Welcome to the “Data Science & Artificial Intelligence” course! This comprehensive program is designed to empower engineering graduates with the knowledge and skills required to tackle real-world data science challenges and harness the power of artificial intelligence.

Course Eligibility: – BE/B.Tech/BCA/MCA

Course Overview

Welcome to the “Data Science & Artificial Intelligence” course! This comprehensive program is designed to empower engineering graduates with the knowledge and skills required to tackle real-world data science challenges and harness the power of artificial intelligence. With 15 modules that span from the basics to advanced concepts, this course will equip students with the tools and techniques needed to excel in the exciting field of data science.

Course Objective

  • Develop practical skills to analyze and interpret data to make data-driven decisions.
  • Introduce students to various machine learning and deep learning techniques.
  • Enable students to solve real-world problems using data science and AI methodologies.
  • Prepare students for careers in data science, machine learning, and artificial intelligence.

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Course Details

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 comments & escape sequences
Python Data Types
Strings, Decisions & 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
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

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