Data Science with AI & ML

Data Science Courses Information

Master the art of data-driven decision-making with our comprehensive Data Science Training program. Dive deep into the world of data analytics, machine learning, and predictive modeling with hands-on experience and real-world projects.

Whether you're a beginner or an experienced professional, our expert-led courses are designed to equip you with the skills needed to excel in the fast-growing field of data science. Join us and transform data into actionable insights that drive success.

Curriculum

Introduction of Data Science

Overview of data science
Roles and responsibilities of a data scientist

Case studies
Group discussion: What is data science?

Core Python

Introduction to Python programming
Data structures (lists, dictionaries, tuples)
Control structures (loops, conditional statements)
Exception handling
String handling
Python operators

User defined functions
Python datetime functions
Math and random functions
Modules and packages
Object oriented programming
Assignments: Python programming exercises

Python for Data Science (Advanced Python)

Advanced Python programming concepts
Working with popular libraries (NumPy, Pandas)
Data preparation and data cleaning

Data analysis using Pandas
Assignments: Python programming exercises
Project: Data analysis using Python

Reporting and Visualization

Data visualization using Matplotlib and Seaborn
Charts
Bar charts, pie charts, and histograms

Data visualization best practices
Assignments: Data preprocessing and visualization

Statistics and Probability

Descriptive statistics (mean, median, mode, variance)
Inferential statistics (hypothesis testing, confidence intervals)

Probability theory (Bayes' theorem, conditional probability)
Assignments: Calculations and problem-solving

Machine Learning with Scikit-learn - I

Introduction to machine learning
Supervised learning
Linear regression
Logistic regression

Decision trees
Random forests
Support vector machines (SVMs)
Model evaluation metrics (accuracy, precision, recall, F1 score)

Machine Learning with Scikit-learn - II

Unsupervised Learning
Introduction to unsupervised learning
K-Means clustering
Hierarchical clustering

Principal Component Analysis (PCA)
Model evaluation and selection
Assignments: Machine learning exercises
Project: Machine learning using Scikit-learn

Introduction to Artificial Intelligence (AI)

Overview of AI and its applications
AI vs Machine Learning vs Deep Learning

Brief history of AI and its evolution
Generative AI

Natural Language Processing (NLP)

Overview of NLP and its applications
Types of NLP tasks: Text classification, sentiment analysis
Brief history of NLP and its evolution
Introduction to text preprocessing

Tokenization: Splitting text into words and tokens
Stopword removal: Removing common words like "the", "and"
Stemming and lemmatization: Reducing words to their base form
Demo: Text preprocessing using NLTK and spaCy libraries

Deep Learning with TensorFlow/Keras

Introduction to deep learning
Types of Deep Learning: Supervised, Unsupervised, Reinforcement Learning
Brief history of Deep Learning and its evolution

Introduction to a popular Deep Learning library
Building a simple Deep Learning model for image classification or text analysis
Training and evaluating the model

Computer Vision

Introduction to computer vision
Fundamentals of computer vision

Applications of CV

Data Science with R

Introduction to R
Data manipulation using dplyr and tidyr
Data visualization using ggplot2

Statistics with R
Machine learning with caret
Assignments: Data analysis using R

Data Analytics with SQL

Overview of SQL
Data Definition Language (DDL)
Data Manipulation Language (DML)
Data Query Language (DQL)
Filtering data
WHERE clause
Filtering single conditions
Filtering multiple conditions
Sorting and grouping data
Joining tables
Subqueries
Aggregate functions

Views
Stored procedures
Functions
Triggers
Data Control Language (DCL)
Authentication and authorization
Access control and permissions
Transaction Control Language (TCL)
Operators
Project and case studies

Data Analytics & Visualization with Tableau

Downloading tableau public
Applying filters
Calculation in Tableau
Connecting with datasets
Table calculation
Plotting simple charts
Using inbuilt functions
Mapping in Tableau

Creating calculated fields
Joining datasets
Formatting and sorting data
Creating story and dashboard
Projects and case studies
Assignments: Data analysis using Tableau

Data Analytics & Visualization with PowerBI

Power BI introduction
Data preparation with Power Query Editor
Filter a column
Math and statistical functions

Visualizing data using charts
DAX function types
Assignments: Data analysis using Power BI

Big Data Analysis with Apache Spark

Overview of Big Data
Introduction to Apache Spark
Spark architecture and components
Setting up Spark environment
Introduction to Spark Core
RDDs (Resilient Distributed Datasets)
Data partitioning and serialization
Actions and transformations
Introduction to Spark SQL
DataFrames and Datasets
Querying DataFrames
DataFrames API

Introduction to Spark Streaming
Streaming architecture
Spark best practices
Lab sessions
Setting up Spark environment
Spark Core programming
Spark SQL and DataFrames
Spark Streaming

Data Analytics with Excel (Advanced)

Getting to know Excel
Logic functions
Math functions
Text functions
VLOOKUP and HLOOKUP
Importing data from a text file
Conditional formats with built-in rules
Data validation
Comments and notes

Cell formatting
Sparklines
Building charts
Power Query Editor
Pivot table
Assignments: Creating salary sheets, mark sheets, etc.
Project: Data analysis using Excel

Data Science with AI & ML

Data Science Courses Information

Curriculum

Introduction of Data Science

Core Python

Python for Data Science (Advanced Python)

Reporting and Visualization

Statistics and Probability

Machine Learning with Scikit-learn - I

Machine Learning with Scikit-learn - II

Introduction to Artificial Intelligence (AI)

Natural Language Processing (NLP)

Deep Learning with TensorFlow/Keras

Computer Vision

Data Science with R

Data Analytics with SQL

Data Analytics & Visualization with Tableau

Data Analytics & Visualization with PowerBI

Big Data Analysis with Apache Spark

Data Analytics with Excel (Advanced)

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