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Data Science

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 Skill Development
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
  • 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
  • Intro What is Python ? Why Python ? Features of Python.
  • Strings Functions Function Parameters Lambda Function and Map Modules
Statistics
  • Core Libraries Pandas NumPy Seaborn Matplotlib
  • Intro Types Population Vs Sample Types of Data
  • Measure of Dispersion - Range - Variance - Standard Deviation - Coefficient of Variation
  • Measures of Central Tendency - Mean - Median - Mode - Weighted Mean [L] - Trimmed Mean [L]
  • 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
  • Between IN
  • Case Operator Group By
  • DML
  • Having Clause Joins
  • Importing Data DDL
  • Like Operator Order By
  • Limit
  • NOT IN operator
  • NOT Operators
  • Numeric Functions Date Functions
  • OR
  • Set Operators
  • String Functions Data Aggregation
  • Subqueries
  • TCL
  • Views Stored Procedure
  • Where Clause AND
  • Window Functions Data Analytics with SQL Project 1 Project 2 Project 3 Project 4
  • unpivot and pivot Select Query
  • 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
  • Data Analytics Introduction Types Importance in Today's World 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
  • SQL Introduction to MySQL Installation
  • hlookup Descriptive Statistics EDA with conditional formatting Anomaly detection in your data category analysis continuous analysis Excel Dashboard Data Analytics with Excel
Artificial Intelligence Machine Learning
  • Cosine Similarity Embeddings - Vectorization - N-Grams
  • Encoding Linear Regressions - concept
  • Evaluation metrics
  • Lemmatization
  • PoS
  • Pragmatic Text Classification(Sentiment Analysis)
  • Reinforcement Feature Engineering
  • Semantic
  • Stemming
  • Stop Words Removal
  • Syntactic
  • TFIDF Analysis - Lexical
  • Unsupervised
  • multiple regression Classification Techniques
  • ridge
  • solution and implementation in core python Conditions for Linear Regression
  • typical Machine Learning flow Types of ML: Supervised
  • Introduction to Scikit Learn Machine Learning Module Linear Regression using the Scikit Learn module Predictions using Linear Regression Model Other Regression types - lasso
  • 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
  • Topic Modeling Named Entity Recognition (NER) and Information Extraction
  • Understanding of NLP Tokentization
  • What is Machine Learning? Revise ML concepts and definitions
Deep Learning
  • Cosine Similarity Embeddings - Vectorization - N-Grams
  • Encoding Linear Regressions - concept
  • Evaluation metrics
  • Lemmatization
  • PoS
  • Pragmatic Text Classification(Sentiment Analysis)
  • Reinforcement Feature Engineering
  • Semantic
  • Stemming
  • Stop Words Removal
  • Syntactic
  • TFIDF Analysis - Lexical
  • Unsupervised
  • multiple regression
  • ridge
  • solution and implementation in core python Conditions for Linear Regression
  • typical Machine Learning flow Types of ML: Supervised
  • 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
  • Introduction to Scikit Learn Machine Learning Module Linear Regression using the Scikit Learn module Predictions using Linear Regression Model Other Regression types - lasso
  • Topic Modeling Named Entity Recognition (NER) and Information Extraction
  • What is Machine Learning? Revise ML concepts and definitions
Capstone Project

Course Syllabus

  • 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
  • Intro What is Python ? Why Python ? Features of Python.
  • Strings Functions Function Parameters Lambda Function and Map Modules

  • Core Libraries Pandas NumPy Seaborn Matplotlib
  • Intro Types Population Vs Sample Types of Data
  • Measure of Dispersion - Range - Variance - Standard Deviation - Coefficient of Variation
  • Measures of Central Tendency - Mean - Median - Mode - Weighted Mean [L] - Trimmed Mean [L]
  • 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

  • Between IN
  • Case Operator Group By
  • DML
  • Having Clause Joins
  • Importing Data DDL
  • Like Operator Order By
  • Limit
  • NOT IN operator
  • NOT Operators
  • Numeric Functions Date Functions
  • OR
  • Set Operators
  • String Functions Data Aggregation
  • Subqueries
  • TCL
  • Views Stored Procedure
  • Where Clause AND
  • Window Functions Data Analytics with SQL Project 1 Project 2 Project 3 Project 4
  • unpivot and pivot Select Query
  • 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
  • Data Analytics Introduction Types Importance in Today's World 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
  • SQL Introduction to MySQL Installation
  • hlookup Descriptive Statistics EDA with conditional formatting Anomaly detection in your data category analysis continuous analysis Excel Dashboard Data Analytics with Excel

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

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

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

image

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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Course Syllabus

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
  • Intro What is Python ? Why Python ? Features of Python.
  • Strings Functions Function Parameters Lambda Function and Map Modules
Statistics
  • Core Libraries Pandas NumPy Seaborn Matplotlib
  • Intro Types Population Vs Sample Types of Data
  • Measure of Dispersion - Range - Variance - Standard Deviation - Coefficient of Variation
  • Measures of Central Tendency - Mean - Median - Mode - Weighted Mean [L] - Trimmed Mean [L]
  • 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
  • Between IN
  • Case Operator Group By
  • DML
  • Having Clause Joins
  • Importing Data DDL
  • Like Operator Order By
  • Limit
  • NOT IN operator
  • NOT Operators
  • Numeric Functions Date Functions
  • OR
  • Set Operators
  • String Functions Data Aggregation
  • Subqueries
  • TCL
  • Views Stored Procedure
  • Where Clause AND
  • Window Functions Data Analytics with SQL Project 1 Project 2 Project 3 Project 4
  • unpivot and pivot Select Query
  • 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
  • Data Analytics Introduction Types Importance in Today's World 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
  • SQL Introduction to MySQL Installation
  • hlookup Descriptive Statistics EDA with conditional formatting Anomaly detection in your data category analysis continuous analysis Excel Dashboard Data Analytics with Excel
Artificial Intelligence Machine Learning
  • Cosine Similarity Embeddings - Vectorization - N-Grams
  • Encoding Linear Regressions - concept
  • Evaluation metrics
  • Lemmatization
  • PoS
  • Pragmatic Text Classification(Sentiment Analysis)
  • Reinforcement Feature Engineering
  • Semantic
  • Stemming
  • Stop Words Removal
  • Syntactic
  • TFIDF Analysis - Lexical
  • Unsupervised
  • multiple regression Classification Techniques
  • ridge
  • solution and implementation in core python Conditions for Linear Regression
  • typical Machine Learning flow Types of ML: Supervised
  • Introduction to Scikit Learn Machine Learning Module Linear Regression using the Scikit Learn module Predictions using Linear Regression Model Other Regression types - lasso
  • 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
  • Topic Modeling Named Entity Recognition (NER) and Information Extraction
  • Understanding of NLP Tokentization
  • What is Machine Learning? Revise ML concepts and definitions
Deep Learning
  • Cosine Similarity Embeddings - Vectorization - N-Grams
  • Encoding Linear Regressions - concept
  • Evaluation metrics
  • Lemmatization
  • PoS
  • Pragmatic Text Classification(Sentiment Analysis)
  • Reinforcement Feature Engineering
  • Semantic
  • Stemming
  • Stop Words Removal
  • Syntactic
  • TFIDF Analysis - Lexical
  • Unsupervised
  • multiple regression
  • ridge
  • solution and implementation in core python Conditions for Linear Regression
  • typical Machine Learning flow Types of ML: Supervised
  • 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
  • Introduction to Scikit Learn Machine Learning Module Linear Regression using the Scikit Learn module Predictions using Linear Regression Model Other Regression types - lasso
  • Topic Modeling Named Entity Recognition (NER) and Information Extraction
  • What is Machine Learning? Revise ML concepts and definitions
Capstone Project

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