Data Science & Analytics With AI

Understanding Data & Data Science
8 Lessons
- What is data?
  Start
- Data science buzzwords
  Start
- Data analysis vs analytics
  Start
- Data analyst vs scientist
  Start
- Use cases of data science
  Start
- Data collection techniques
  Start
- Data cleaning & preparation
  Start
- Data visualization
  Start
Maths & Statistics for data science
10 Lessons
- Linear algebra
  Start
- Calculus
  Start
- Probability basics
  Start
- Combinatorics
  Start
- Uniform distribution
  Start
- Binominal distribution
  Start
- Standard normal distribution
  Start
- Inferential statistics
  Start
- Sampling techniques
  Start
- hypothesis testing
  Start
DBMS
8 Lessons
- Understanding Relational Model
  Start
- Relation & Tuple
  Start
- Business Entity/Object Attributes
  Start
- ER Modelling
  Start
- Degree Of Relationship
  Start
- Cardinality & Keys
  Start
- DBMS VS RDBMS
  Start
- Normalization
  Start
ANSI SQL
12 Lessons
- Introduction to SQL
  Start
- DDL Commands
  Start
- DML Commands
  Start
- DCL Commands
  Start
- DQL commands
  Start
- SQL Functions
  Start
- SQL Constraints
  Start
- Joins
  Start
- Sub Queries
  Start
- Views
  Start
- Index
  Start
- Sequence
  Start
PL-SQL
9 Lessons
- Introduction to PL-SQL
  Start
- PL-SQL Variables
  Start
- Control Structures
  Start
- Cursors
  Start
- Stored Procedures & Functions
  Start
- Exceptions
  Start
- Collections
  Start
- Packages
  Start
- Triggers
  Start
Big Data Basics
7 Lessons
- What is big data?
  Start
- Structured, semi-structured & unstructured data
  Start
- 5-V's of big data
  Start
- Problems with big data
  Start
- Solutions for big data
  Start
- Data Science & big data
  Start
- ML & big data
  Start
Python Programming
19 Lessons
- Introduction to python
  Start
- Variables & data types
  Start
- List & working with list
  Start
- Tuple
  Start
- Set
  Start
- Dictionary
  Start
- Operators
  Start
- Dicision Control Statements
  Start
- Loop Control Statements
  Start
- Functions & Modules
  Start
- Lambda, Filter, Map, Reduce
  Start
- Object oriented programming
  Start
- Abstraction
  Start
- Inheritance
  Start
- Polymorphism
  Start
- File handling
  Start
- Exception handling
  Start
- Regular expression
  Start
- Web scraping
  Start
NumPy
12 Lessons
- Introduction to NumPy
  Start
- NumPy Arrays
  Start
- NumPy Attributes
  Start
- Indexing & Slicing
  Start
- Array Operations - (Arithmetic, Aggregation, broadcasting)
  Start
- NumPy Functions
  Start
- Mathematical functions
  Start
- Linear Algebra
  Start
- Working with multiple dimensions
  Start
- Reshaping Arrays
  Start
- Concatenation
  Start
- Spiliting
  Start
Pandas
11 Lessons
- Introduction to Pandas
  Start
- Series & data frames
  Start
- Data types & missing values
  Start
- Indexing & selecting data
  Start
- Indexing methods - Ioc, iloc
  Start
- Boolean Indexing
  Start
- Selection based on conditions
  Start
- Data manipulation
  Start
- Handing missing data
  Start
- Grouping & aggregation
  Start
- Merging & joining data frames
  Start
Matplotlib
13 Lessons
- Introduction to matplotlib
  Start
- Installing matplotlib
  Start
- Basic plotting
  Start
- Line plots
  Start
- Scatter plots
  Start
- Bar plots
  Start
- Histograms
  Start
- Box plots
  Start
- Adding titles, labels & legends
  Start
- Changing colors
  Start
- Line & marker styles
  Start
- Adjusting axis limits and ticks
  Start
- Adding annotations
  Start
Seaborn
13 Lessons
- Introduction to seaborn
  Start
- Installation & setup
  Start
- Visualizing relationships between variables
  Start
- Correlation plots
  Start
- Scatterplot matrices
  Start
- Kernel density estimation (KDE)
  Start
- Visualizing distributions
  Start
- Confidence intervals
  Start
- Scatter plots
  Start
- Line plots
  Start
- Box plots
  Start
- Violin plots
  Start
- KDE plots
  Start
Supervised ML
14 Lessons
- Introduction to machine learning
  Start
- ML fundamentals & EDA
  Start
- ML modeling flow
  Start
- Types of ML
  Start
- Outliers & skewness
  Start
- Linear regression
  Start
- Logistic regression
  Start
- Decision tree & random forest
  Start
- Ensemble techniques
  Start
- K nearest neighbors
  Start
- KNN algorithm
  Start
- Working with KNN
  Start
- Support vector machine (SVM)
  Start
- Naïve Bayes
  Start
Unsupervised ML
9 Lessons
- Introduction to clustering
  Start
- K-means clustering
  Start
- K-means clustering algorithms
  Start
- choosing the optimum k value (Elbow Method)
  Start
- Introduction to Hierarchical clustering
  Start
- Dendrogram
  Start
- Types of Hierarchical clustering
  Start
- Principle component analysis (PCA)
  Start
- Dimensionality reduction
  Start
Deep Learning (DL)
7 Lessons
- AI vs ML vs DL
  Start
- Applications of AI
  Start
- Conventional AI vs GenAI
  Start
- Future of AI
  Start
- Introduction to deep learning
  Start
- Perceptron model
  Start
- Introduction to tenseflow & keras
  Start
Neural Networks (NN)
8 Lessons
- Activation functions in NN
  Start
- Loss functions
  Start
- Optimization techniques
  Start
- Semantic segmentation
  Start
- Working with images
  Start
- Working with videos
  Start
- OCR
  Start
- Media pipe - FaceMesh, Pose Detection
  Start
Natural Language Processing(NLP)
8 Lessons
- Introduction to NLP
  Start
- Text preprocessing
  Start
- Text vectorization & classification
  Start
- Embedding in NLP
  Start
- Sentiment analysis
  Start
- Sequence model
  Start
- Machine translation
  Start
- Introduction to LLM
  Start
Softskills
10 Lessons
- Introduction to Softskills
  Start
- SWOT Analysis
  Start
- Communication skills
  Start
- Group Discussion
  Start
- Goal Setting
  Start
- Resume Writing
  Start
- Interview Skills
  Start
- Time Management
  Start
- Presentation skills
  Start
- Mock Interviews
  Start

Data Scientist & AI

Course Summary

Course Curriculum

Understanding Data & Data Science

Maths & Statistics for data science

DBMS

ANSI SQL

PL-SQL

Big Data Basics

Python Programming

NumPy

Pandas

Matplotlib

Seaborn

Supervised ML

Unsupervised ML

Deep Learning (DL)

Neural Networks (NN)

Natural Language Processing(NLP)

Softskills

Pradeep

John Smith

Highly Recommended Course. Easy to Understand, Informative, Very Well Organized. The Course is Full of Practical and Valuable for Anyone who wants to Enhance their Skills. Really Enjoyed it. Thank you!!

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