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Data Science 6 Months

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Data science is a multidisciplinary field combining statistics, computer science, and domain-specific knowledge to extract insights from data....

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Course Content

Python Basics

Introduction to Python

  • Environment Setup
  • Basic Rules
  • History of Python
  • Features of Python
  • Application of Python & Uses

Python Syntax and Variables

  • Declaration of Syntax
  • Initialization

Data Types and Operators

  • Introduction to Data Types
  • Uses of Data Types in Programming
  • Operations on Data Types
  • Types of Operator in python
  • Operands and Operators in Expression

Conditional Statements

  • Introduction to Conditional Statements
  • Use and Scope of Conditional Statements
  • Indentation in Python
  • Flow Structure of Control Statements
  • If-else in Python
  • If-else Ladder
  • Break and Continue Statements

Loops in Python

  • Introduction to Loops
  • Uses of Loops
  • Types of loops (for, while)
  • Range function in loops
  • Practice Problems on loops

Python Data Structures

Lists and Tuples

  • Introduction and syntax of list and tuple
  • Declaration and Initialization
  • Various Types of Lists
  • Problems Based on List and Tuples

Dictionaries and Sets

  • Introduction to Dictionaries and Sets
  • Declaration of Dictionaries and Sets
  • Practice Programs on Dictionaries and Sets

Functions in Python

  • Introduction of Function
  • Use of Function
  • Types of Function

Lambda Functions

  • Introduction to Lambda Function
  • Declaration of Lambda Function
  • Programs on Lambda Function

Exception Handling

  • Introduction to Exception Handling
  • Use of Exception Handling
  • try-catch block
  • Finally Block

Data Manipulation and Pandas

Pandas

  • Introduction to Pandas Library
  • Importing Pandas Library

DataFrames and Series

  • Implementing Pandas to DataSet
  • Creating Own Dataset
  • Operations on Dataset using Pandas
  • Methods of Pandas

Data Cleaning Techniques

  • Handling Duplicate Values
  • Data Transformation
  • Outlier Detection
  • Validation Techniques

Data Aggregation and GroupBy

  • Aggregation Functions
  • GroupBy Operations
  • Aggregation in Time Series Data
  • Handling Non Linear Relationships

Working with Missing Data

  • Types of Missing Data
  • Impact of Missing Values
  • Deletion Method
  • Imputation Techniques
  • Evaluation Metrics for Imputation

Data Visualization with Matplotlib & Seaborn

Data Visualization

  • Introduction To Data Visualization

Matplotlib Basics

  • Line Plots
  • Bar Charts
  • Histograms
  • Scatter Plots

Advanced Matplotlib Techniques

  • Multiple Subplots
  • Annotations and Text
  • Plotting Time Series Data

Seaborn Basics

  • Load Dataset using Seaborn
  • Bar Plot
  • KDE Plot
  • Box Plot
  • Heat Map

Exploratory Data Analysis(EDA)

  • Feature Engineering
  • Detecting Anomalies
  • Descriptive Statistics
  • Model Selection and Optimization

Introduction to Statistics

Descriptive Statistics

  • Central Tendency
  • Variability
  • Distribution Shape
  • Univariate Analysis
  • Bivariate Analysis
  • Multivariate Analysis

Measures of Central Tendency

  • Mean, Median and Mode
  • Application of Central Tendency
  • Weighted Average
  • Comparing Central Tendencies

Variability

  • Range, Varience and Standard Deviation
  • Interquartile Range
  • Coefficient of Variation
  • Skewness and Kurtosis

Data Distribution and Visualization

  • Normal and Skewed Distribution
  • Box Plot and Violin Plots
  • Categorical vs Continuous Data Visualizations

Probability Basics

  • Classical and Empirical Probability
  • Independent and Dependent Events
  • Conditional Probability
  • Discrete vs Continuous Probability Distributions

Probability & Distributions

Probability Rules

  • Addition Rule of Probability
  • Multiplication Rule of Probability
  • Complement Rule
  • Conditional Probability and Baye's Theorem

Probability Distributions

  • Binomial Distribution
  • Poission Distribution
  • Uniform and Exponential Distribution

Hypothesis Testing Basics

  • Null and Alternate Hypotheses
  • One-Tailed and Two-Tailed Test
  • Type-1 and Type-2 Errors
  • Steps of Hypothesis Testing

Confidence Intervals

  • Understanding Confidence levels
  • Calculations of Confidence Intervals
  • Understanding Confidence Interval in Decision Making

Statistical Significance

  • P-Value and Significance levels
  • Factors Affecting Significance
  • Interpreting Results

Data Analysis with Excel

Introduction to Excel for Data Analysis

  • Layout, Essential Features and Shortcuts
  • Importing and Exporting Data
  • Basic Data Operations
  • Using Excel Tables

Data Cleaning in Excel

  • Ensuring Data Quality
  • Data Validation Tools
  • Text Functions for Cleaning
  • Find and Replace Techniques

Pivot Tables and Charts

  • Creating Pivot Tables for Analysis
  • Using Pivot Charts for Visualization
  • Custom Calculations in Pivot Tables

Advanced Excel Formulas

  • Lookup Functions
  • Array Formulas and Dynamic Arrays
  • Logical and Conditional Formulas
  • Text and Date Manipulations

Excel for Statistical Analysis

  • Descriptive Statistics in Excel
  • Regression Analysis
  • Hypothesis Testing in Excel
  • Correlation and Trend Analysis

Introduction to Machine Learning

What is Machine Learning?

  • Definition and Key Concepts
  • Machine Learning Workflow
  • Relation to AI and Data Science

Types of Machine Learning

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Semi-Supervised Learning

Supervised vs Unsupervised Learning

  • Key Differences
  • Examples of Algorithms
  • Use Cases

Model Evaluation Metrics

  • Classification Metrics( Accuracy, Precision, Recall)
  • Regression Metrics (MAE, MSE, R Square)
  • Confusion Matrix
  • Cross Validation

Bias-Variance Tradeoff

  • Understanding Bias and Variance
  • Underfitting vs. Overfitting
  • Balancing the Tradeoff

Supervised Learning - Regression

Linear Regression

  • Overview of Linear Regression
  • Assumptions of Linear Regression
  • Gradient Descent in Linear Regression
  • Applications of Linear Regression

Polynomial Regression

  • Introduction to Polynomial Regression
  • Fitting Higher-Degree Polynomials
  • Comparison with Linear Regression
  • Applications of Polynomial Regression

Ridge and Lasso

  • Introduction to Regularization
  • Ridge Regression
  • Lasso Regression
  • Ridge vs. Lasso

Model Evaluation for Regression

  • Key Metrics (MAE, MSE, RMSE, and R² explained)
  • Understanding errors and checking assumptions
  • Cross-Validation for Regression Models

Regression Based Project

  • House Price Prediction
  • Sales Price Prediction

Ensemble Learning

Introduction to Ensemble Learning

  • What is Ensemble Learning?
  • Types of Ensembles (Bagging, Boosting)
  • Advantages of Ensembles Methods
  • Application of Ensembles Learning(Classification, Regression)

Bagging and Boosting

  • Understanding Bagging (Bootstrap Aggregating)
  • Concept of Boosting
  • Key Differences (Parallel vs Sequential Learning)
  • Use Cases and Comparison

AdaBoost

  • What is AdaBoost?
  • Algorithm Overview
  • Strengths and Limitations
  • Applications of AdaBoost

Gradient Boosting

  • Introduction to Gradient Boosting
  • Key Features (Learning rate, number of estimators)
  • Differences from AdaBoost
  • Applications of Gradient Boosting

XGBoost

  • What is XGBoost?
  • Key Features of XGBoost(Regularization and Handling Missing Values)
  • Performance Optimization

Natural Language Processing(NLP)

Introduction to NLP

  • What is NLP?
  • Applications of NLP
  • Challenges in NLP
  • NLP Tools and Libraries

Text Preprocessing

  • Lowercasing and Normalization
  • Removing Special Characters and Punctuation
  • Stemming and Lemmatization
  • Handling Missing or Noisy Text Data

Tokenization and Stop Words

  • What is Tokenization?
  • Types of Tokenization
  • Stop Words in NLP
  • Advanced Tokenization Techniques

TF-IDF and Bag of Words

  • Bag of Words (BoW)
  • TF-IDF (Term Frequency-Inverse Document Frequency)
  • Comparison of BoW and TF-IDF
  • Feature Representation for Machine Learning Using TF-IDF and Bag of Words

Sentiment Analysis

  • What is Sentiment Analysis?
  • Lexicon-Based vs. Machine Learning Approaches
  • Building a Sentiment Classifier
  • Applications of Sentiment Analysis

Data Visualization (Power BI and Tableau)

Power BI

  • Getting Started with PowerBI
  • Building Interactive Dashboards
  • DAX (Data Analysis Expression)
  • Data Modeling in Power BI
  • Publishing and Sharing Dashboards

Tableau

  • Getting Started with Tableau
  • Building Visualizations in Tableau
  • Tableau Calculations and Analytics
  • Data Blending and Joins
  • Sharing and Publishing in Tableau

Big Data & Cloud Computing

Introduction To Big Data

  • Hadoop and HDFS
  • MapReduce
  • Spark Basics
  • PySpark for Data Processing
  • Spark SQL

Cloud for Data Science

  • AWS Basics
  • Google Cloud Platform Basics
  • Azure Basics
  • Deploying Basics
  • Deploying Models on Cloud

Capstone Project & Portfolio Building

Capstone Project

  • Complete End-to-End Capstone Projects
  • Focus on Model Deployment
  • Portfolio Building
  • Resume Building
  • LinkedIn Profile Optimization

Deep Learning

Neural Network

  • Introduction to Neural Network
  • Perceptron and Mathematics
  • Activation Function
  • Forward and backward propagation
  • Implementation of ANN in Keras
  • Callback Functions
  • Regression Using ANN
  • Loss Functions
  • Batch Normalization
  • Regularization
  • Weight Initialization
  • Optimizers

Convolutional Neural Network

  • CNN Foundation
  • CNN Architecture
  • Lenet
  • AlexNet
  • VGG
  • ResNet
  • InceptionNet

Computer Basics

Introduction to Computers & Operating System

  • What is a Computer?
  • Computer Components
  • Number System Basics
  • Operating Systems

MS Office

  • MS Word
  • MS Excel
  • MS PowerPoint
  • Google Workspace basics

Internet & Communication

  • Internet Basics
  • Email & Communication

Advanced Basics & Practical Skills

  • Computer Networks
  • Database Basics
  • Software Installation
  • Software Installation