Data Science

Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data

Category Data Science

What you'll learn

Master Machine Learning & AI
Hands-on Data Analysis
End-to-End Project Execution
Specialized Industry Applications

Course Syllabus

Python

a:3:{i:0;s:55:"Intro What is Python ? Why Python ? Features of Python.";i:1;s:175:"Installation of Jupyter Notebook / Introduction to Google Colab Indentation Variables Data Types Operators if if - else if - elif - else for loop while loop break and continue";i:2;s:69:"Strings Functions Function Parameters Lambda Function and Map Modules";}

Statistics

a:5:{i:0;s:46:"Core Libraries Pandas NumPy Seaborn Matplotlib";i:1;s:46:"Intro Types Population Vs Sample Types of Data";i:2;s:90:"Measures of Central Tendency - Mean - Median - Mode - Weighted Mean [L] - Trimmed Mean [L]";i:3;s:88:"Measure of Dispersion - Range - Variance - Standard Deviation - Coefficient of Variation";i:4;s:726:"Quantiles and Percentiles 5 number summary and BoxPlot Skewness Kurtosis [L] Plotting Graphs - Univariate Analysis - Bivariate Analysis - Multivariate Analysis Pearson Correlation Coefficient Spearman Correlation Coefficient [L] Correlation and Causation Random Variables What are Probability Distributions Why are Probability Distributions important Probability Distribution Functions and it's types Probability Mass Function (PMF) CDF of PMF Probability Density Function(PDF) CDF of PDF Density Estimation [L] What is Hypothesis Testing? Null and Alternate Hypothesis Steps involved in a Hypothesis Test Performing Z-test Rejection Region Approach Type 1 Vs Type 2 Errors One Sided vs 2 sided tests Statistical Power P-value";}

Data Analytics with Python

a:24:{i:0;s:101:"Data Analytics Introduction Types Importance in Today's World Project 1 Project 2 Project 3 Project 4";i:1;s:231:"Advanced Excel Introduction Applications Importance in Today's World Brief Tour Conditional Formatting Pivot table Sorting and Filtering Sumif and sumifs countif and countifs Functions Data Handling and Cleaning lookup and vlookup";i:2;s:173:"hlookup Descriptive Statistics EDA with conditional formatting Anomaly detection in your data category analysis continuous analysis Excel Dashboard Data Analytics with Excel";i:3;s:39:"SQL Introduction to MySQL Installation";i:4;s:18:"Importing Data DDL";i:5;s:3:"DML";i:6;s:3:"TCL";i:7;s:30:"unpivot and pivot Select Query";i:8;s:16:"Where Clause AND";i:9;s:2:"OR";i:10;s:13:"NOT Operators";i:11;s:22:"Like Operator Order By";i:12;s:5:"Limit";i:13;s:10:"Between IN";i:14;s:15:"NOT IN operator";i:15;s:33:"String Functions Data Aggregation";i:16;s:32:"Numeric Functions Date Functions";i:17;s:22:"Case Operator Group By";i:18;s:19:"Having Clause Joins";i:19;s:13:"Set Operators";i:20;s:10:"Subqueries";i:21;s:22:"Views Stored Procedure";i:22;s:81:"Window Functions Data Analytics with SQL Project 1 Project 2 Project 3 Project 4";i:23;s:409:"Power BI Introduction to BI Importance in Today's World Introduction to Power BI Introduction to Charts in Power BI Dashboard for Starters Dax Functions introduction Calculated columns and measures Aggregate functions Date and Time Functions Time Intelligence Functions Logical Functions Introduction to Business Queries Data Cleaning Data Modelling Dashboard Creation Project 1 Project 2 Project 3 Project 4";}

Artificial Intelligence Machine Learning

a:22:{i:0;s:60:"What is Machine Learning? Revise ML concepts and definitions";i:1;s:53:"typical Machine Learning flow Types of ML: Supervised";i:2;s:12:"Unsupervised";i:3;s:33:"Reinforcement Feature Engineering";i:4;s:37:"Encoding Linear Regressions - concept";i:5;s:75:"solution and implementation in core python Conditions for Linear Regression";i:6;s:18:"Evaluation metrics";i:7;s:173:"Introduction to Scikit Learn Machine Learning Module Linear Regression using the Scikit Learn module Predictions using Linear Regression Model Other Regression types - lasso";i:8;s:5:"ridge";i:9;s:45:"multiple regression Classification Techniques";i:10;s:241:"Overfitting and Regularization Instance of use of Regression in projects from Kaggle Instance of use of classification in projects from Kaggle What is Clustering? K-Means Clustering Hierarchical Clustering Dimensionality Reduction Techniques";i:11;s:34:"Understanding of NLP Tokentization";i:12;s:13:"Lemmatization";i:13;s:8:"Stemming";i:14;s:18:"Stop Words Removal";i:15;s:3:"PoS";i:16;s:54:"Cosine Similarity Embeddings - Vectorization - N-Grams";i:17;s:24:"TFIDF Analysis - Lexical";i:18;s:8:"Semantic";i:19;s:9:"Syntactic";i:20;s:49:"Pragmatic Text Classification(Sentiment Analysis)";i:21;s:72:"Topic Modeling Named Entity Recognition (NER) and Information Extraction";}

Deep Learning

a:22:{i:0;s:60:"What is Machine Learning? Revise ML concepts and definitions";i:1;s:53:"typical Machine Learning flow Types of ML: Supervised";i:2;s:12:"Unsupervised";i:3;s:33:"Reinforcement Feature Engineering";i:4;s:37:"Encoding Linear Regressions - concept";i:5;s:75:"solution and implementation in core python Conditions for Linear Regression";i:6;s:18:"Evaluation metrics";i:7;s:173:"Introduction to Scikit Learn Machine Learning Module Linear Regression using the Scikit Learn module Predictions using Linear Regression Model Other Regression types - lasso";i:8;s:5:"ridge";i:9;s:19:"multiple regression";i:10;s:231:"Classification Techniques Overfitting and Regularization Instance of use of Regression in projects from Kaggle Instance of use of classification in projects from Kaggle What is Clustering? K-Means Clustering Hierarchical Clustering";i:11;s:70:"Dimensionality Reduction Techniques Understanding of NLP Tokentization";i:12;s:13:"Lemmatization";i:13;s:8:"Stemming";i:14;s:18:"Stop Words Removal";i:15;s:3:"PoS";i:16;s:54:"Cosine Similarity Embeddings - Vectorization - N-Grams";i:17;s:24:"TFIDF Analysis - Lexical";i:18;s:8:"Semantic";i:19;s:9:"Syntactic";i:20;s:49:"Pragmatic Text Classification(Sentiment Analysis)";i:21;s:72:"Topic Modeling Named Entity Recognition (NER) and Information Extraction";}

Capstone Project

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Course Syllabus

Intro What is Python ? Why Python ? Features of Python.
Installation of Jupyter Notebook / Introduction to Google Colab Indentation Variables Data Types Operators if if - else if - elif - else for loop while loop break and continue
Strings Functions Function Parameters Lambda Function and Map Modules

Core Libraries Pandas NumPy Seaborn Matplotlib
Intro Types Population Vs Sample Types of Data
Measures of Central Tendency - Mean - Median - Mode - Weighted Mean [L] - Trimmed Mean [L]
Measure of Dispersion - Range - Variance - Standard Deviation - Coefficient of Variation
Quantiles and Percentiles 5 number summary and BoxPlot Skewness Kurtosis [L] Plotting Graphs - Univariate Analysis - Bivariate Analysis - Multivariate Analysis Pearson Correlation Coefficient Spearman Correlation Coefficient [L] Correlation and Causation Random Variables What are Probability Distributions Why are Probability Distributions important Probability Distribution Functions and it's types Probability Mass Function (PMF) CDF of PMF Probability Density Function(PDF) CDF of PDF Density Estimation [L] What is Hypothesis Testing? Null and Alternate Hypothesis Steps involved in a Hypothesis Test Performing Z-test Rejection Region Approach Type 1 Vs Type 2 Errors One Sided vs 2 sided tests Statistical Power P-value

Data Analytics Introduction Types Importance in Today's World Project 1 Project 2 Project 3 Project 4
Advanced Excel Introduction Applications Importance in Today's World Brief Tour Conditional Formatting Pivot table Sorting and Filtering Sumif and sumifs countif and countifs Functions Data Handling and Cleaning lookup and vlookup
hlookup Descriptive Statistics EDA with conditional formatting Anomaly detection in your data category analysis continuous analysis Excel Dashboard Data Analytics with Excel
SQL Introduction to MySQL Installation
Importing Data DDL
DML
TCL
unpivot and pivot Select Query
Where Clause AND
OR
NOT Operators
Like Operator Order By
Limit
Between IN
NOT IN operator
String Functions Data Aggregation
Numeric Functions Date Functions
Case Operator Group By
Having Clause Joins
Set Operators
Subqueries
Views Stored Procedure
Window Functions Data Analytics with SQL Project 1 Project 2 Project 3 Project 4
Power BI Introduction to BI Importance in Today's World Introduction to Power BI Introduction to Charts in Power BI Dashboard for Starters Dax Functions introduction Calculated columns and measures Aggregate functions Date and Time Functions Time Intelligence Functions Logical Functions Introduction to Business Queries Data Cleaning Data Modelling Dashboard Creation Project 1 Project 2 Project 3 Project 4

What is Machine Learning? Revise ML concepts and definitions
typical Machine Learning flow Types of ML: Supervised
Unsupervised
Reinforcement Feature Engineering
Encoding Linear Regressions - concept
solution and implementation in core python Conditions for Linear Regression
Evaluation metrics
Introduction to Scikit Learn Machine Learning Module Linear Regression using the Scikit Learn module Predictions using Linear Regression Model Other Regression types - lasso
ridge
multiple regression Classification Techniques
Overfitting and Regularization Instance of use of Regression in projects from Kaggle Instance of use of classification in projects from Kaggle What is Clustering? K-Means Clustering Hierarchical Clustering Dimensionality Reduction Techniques
Understanding of NLP Tokentization
Lemmatization
Stemming
Stop Words Removal
PoS
Cosine Similarity Embeddings - Vectorization - N-Grams
TFIDF Analysis - Lexical
Semantic
Syntactic
Pragmatic Text Classification(Sentiment Analysis)
Topic Modeling Named Entity Recognition (NER) and Information Extraction

What is Machine Learning? Revise ML concepts and definitions
typical Machine Learning flow Types of ML: Supervised
Unsupervised
Reinforcement Feature Engineering
Encoding Linear Regressions - concept
solution and implementation in core python Conditions for Linear Regression
Evaluation metrics
Introduction to Scikit Learn Machine Learning Module Linear Regression using the Scikit Learn module Predictions using Linear Regression Model Other Regression types - lasso
ridge
multiple regression
Classification Techniques Overfitting and Regularization Instance of use of Regression in projects from Kaggle Instance of use of classification in projects from Kaggle What is Clustering? K-Means Clustering Hierarchical Clustering
Dimensionality Reduction Techniques Understanding of NLP Tokentization
Lemmatization
Stemming
Stop Words Removal
PoS
Cosine Similarity Embeddings - Vectorization - N-Grams
TFIDF Analysis - Lexical
Semantic
Syntactic
Pragmatic Text Classification(Sentiment Analysis)
Topic Modeling Named Entity Recognition (NER) and Information Extraction

Requirements

Laptop
Good wifi

Description

Know about Data science

Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines statistics, machine learning, data analysis, and domain expertise to uncover patterns, trends, and correlations that can help organizations make informed decisions and drive business growth. Data scientists are skilled in programming languages such as Python and R, as well as in data visualization tools like Tableau and Power BI. They play a crucial role in transforming raw data into actionable insights that drive innovation and competitive advantage.

Core Foundations of AI & Data Science • Understand the fundamentals of Artificial Intelligence, Machine Learning, and Deep Learning. • Explore the data science lifecycle, from data collection to deployment. • Master essential tools li
Machine Learning & Deep Learning Techniques • Build and fine-tune machine learning models, including regression, classification, clustering, and ensemble methods. • Implement advanced algorithms like Random Forests, Gradient Boosting, and Ne
End-to-End Project Development • Work on real-world datasets to build complete projects from scratch. • Understand data preprocessing, feature engineering, and model evaluation. • Learn the intricacies of deploying machine learning mo
Data Visualization & Insights • Master storytelling with data through dashboards and visualizations. • Use tools like Matplotlib, Seaborn, and Power BI to present insights effectively. • Understand how to align data insights with busi
Specialized Topics • Forecasting and predictive analytics for marketing and business strategies. • Developing AI-powered solutions for industrial use cases like traffic analysis, crop identification, and marketing. Career Development Skills

Who this course is for:

For aspiring data science professionals, analysts, and anyone looking to build a career in data-driven roles
It's ideal for individuals who have some foundational knowledge of programming or analytics and want to deepen their expertise in data science tools and techniques

Meet your instructors

Rashmi Ranjan Mangaraj

AI & Data Sceince

View Profile

Rashmi Ranjan Mangaraj is a highly skilled AI/ML Engineer with 6 years of practical, hands-on experience in developing and deploying artificial intelligence and machine learning solutions. His expertise lies in creating data-driven models and AI-powered solutions tailored to solve complex business c...

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