Data Science

What you'll learn

This comprehensive Data Science course is designed to take learners from foundational concepts to advanced, real-world applications. You will develop a strong understanding of statistics, data analysis, and machine learning while gaining hands-on experience with industry-standard tools and technologies. The program focuses on transforming raw data into meaningful insights that drive smart business decisions.

Throughout the course, you will work on practical projects involving data cleaning, exploratory data analysis, predictive modeling, and data visualization. You will learn how to use Python, SQL, and modern data science libraries to analyze complex datasets and build intelligent solutions. Real case studies ensure you understand how data science is applied across various domains.

By the end of this course, you will be confident in collecting, processing, analyzing, and interpreting data to solve real-world problems, making you job-ready for roles such as Data Analyst, Data Scientist, and Business Intelligence professional.

  • Fundamentals of Data Science, statistics, and analytical thinking.
  • Data cleaning, preprocessing, and feature engineering techniques.
  • SQL for data extraction, querying, and database management.
  • Exploratory Data Analysis (EDA) using Python libraries.
  • Machine Learning algorithms for prediction and classification.
  • Natural Language Processing (NLP) for text analysis and insights.
  • Data visualization and storytelling using real-world datasets.
  • Deploying data science models into practical, business-ready solutions.

Course Content

  • About Python
  • How to Python Work
  • Python setup & environment
  • Python Virtual Machine
  • Installing Python (Anaconda / Standard)
  • Running Python scripts
  • Python Output/print function
  • Syntax, Comments
  • Variables, Data Types, Built in Data Types
  • Type Casting & Type Checking
  • Python Literal, Operators, Keywords and Identifiers, User Input
  • If, Elif, Else
  • Nested Conditions
  • Loops (For, While)
  • Break, Continue, Pass
  • Comprehensions
  • String : String indexing, String slicing, Edit and delete a string, Operations on String, Common String functions
  • List : Create and access a add items, Edit items in a list, Deleting items from a list, Arithmetic, membership and loop operations on a List, Various List functions, List comprehension, Zip() function
  • Tuple : Create and access a tuple, Can we edit and add items to a tuple?, Deletion, Operations on tuple, Tuple functions, List vs tuple, Tuple unpacking, Zip () on tuple
  • Set : Create and access a set, Can we edit and add items to a set?, Deletion, Operations on set, set functions, Frozen set (immutable set), Set comprehension
  • Dictionary : Create dictionary, Accessing items, Add, remove, edit key-value pairs, Operations on dictionary, Dictionary functions, Nested comprehension
  • Defining & Calling Functions
  • Parameters & Arguments
  • Return Values
  • Default & Keyword Arguments
  • *args and **kwargs
  • Lambda Functions
  • Scope (local, global)
  • Nested functions
  • Introduction to OOP
  • Classes & Objects
  • Attributes & Methods
  • Constructor (__init__), self Keyword
  • Encapsulation, Abstraction, Inheritance, Polymorphism
  • Special Methods (__str__, __add__, etc.)
  • How objects access attributes
  • Iterators and Iterable Objects in Python
  • Decorators for Code Reusability and Abstraction
  • Generators for Efficient Data Processing
  • Text Processing Using Regular Expressions (re Module)
  • Package Installation (pip)
  • Importing Modules
  • Standard Library (os, sys, math, random, datetime)
  • Virtual Environments (venv, pipenv, conda)
  • Reading & Writing Files (txt, json)
  • File modes (r, w, a, rb, wb)
  • Exception Handling with Files
  • JSON Handling (json module)
  • Pickle
  • Try, Except, Finally
  • Raise Exceptions
  • Introduction to Arrays
  • List vs Arrays
  • Array Operations
  • What is Time Complexity?
  • Big-O Notation
  • Common Complexity Examples
  • Introduction to Numpy
  • Numpy Fundamentals
  • Numpy Basics
  • Array Indexing & Slicing
  • Array Operations
  • Mathematical Functions
  • Array Shape & Reshaping
  • Stacking & Splitting
  • Random Module
  • Advanced Numpy
  • Introduction to Pandas
  • Pandas Data Structures
  • Data Inspection
  • Data Selection & Indexing
  • Data Cleaning
  • Data Transformation
  • Sorting & Ranking
  • Grouping & Aggregation
  • Combining & Merging Data
  • Reshaping & Pivoting
  • Advanced Pandas
  • Matplotlib : 2D Plot / 3D Plot (Line plot, Scatter plot, Bar plot, Stem plot, Step plot, Histogram plot, Box plot, Pie chart, Subplots, Violin plot, Stacked bar chart, Contour plot, Contourf plot, Heatmap)
  • Seaborn : Line plot, Scatter plot, Dis plot, Cat plot, Rel plot, Hist plot, Kde plot, Rug plot, Bar plot, Violin plot, Heatmap, Cluster map, Strip plot, Joint plot
  • Plotly : Plotly Go, Plotly Express, Dash, Line plots, Scatter plots, Bar charts, Pie charts, Donut charts, Bubble charts, Histogram, Box plot, Violin plot, Strip plot, Waterfall charts
  • Dashboards with Plotly : Basic Dash app structure, Linking Plotly figures to Dash, Advance Dash app
  • All functions of plots
  • All changing styles of plots
  • Project using Dashboard with Plotly
  • Requests
  • BeautifulSoup
  • Selenium
  • Introduction to Statistics
  • Types of Statistics
  • Basics of Data : Population vs. Sample, Raw data vs. Grouped data, Types of data, Frequency distribution
  • Measures of Central Tendency : Mean, Median, Mode, Weighted Mean, Trimmed Mean (robust measure)
  • Measures of Dispersion (Spread of Data) : Range, Standard Deviation, Variance, Coefficient of Variation, Interquartile Range (IQR)
  • Measures of Position (Relative Standing) : Percentiles, Quartiles (Q1, Q2, Q3), Z-scores (Standard scores)
  • Measures of Shape : Skewness
  • Data Visualization in Descriptive Statistics : Categorical – Categorical, Numerical – Numerical, Categorical – Numerical
  • Hypothesis Testing
  • Null and alternate hypothesis
  • Type I & Type II errors
  • p-value interpretation
  • One-tailed vs. Two-tailed tests
  • Z-test, T-test, Chi-square test
  • ANOVA
  • 2D density plots
  • Normal Distribution
  • Standard Normal Variate
  • Properties of Normal Distribution
  • Skewness
  • CDF of Normal Distribution
  • QQ plot
  • Uniform Distribution
  • Log-normal distribution
  • Power Transformer
  • Log Transform
  • Box-Cox Transform
  • Yeo-Johnson Transformation
  • Data Understanding
  • Data Collection & Import
  • Data Cleaning
  • Feature Engineering
  • Statistical Testing
  • Visualization & Reporting
  • Feature Transformation : Standardization, Normalization, Log transformation, Power transformations, K-Nearest Neighbors
  • Feature Selection : Filter methods, Wrapper methods, Embedded methods
  • Feature Extraction : PCA, t-SNE
  • Handling Missing Values : Mean, Median, Mode, KNN imputer, Handling duplicates
  • Encoding Categorical Variables : One-Hot Encoding, Label Encoding, Target Encoding
  • Outlier detection & treatment : IQR, Z-Score, Percentile
  • Project
  • Introduction to Machine Learning
  • Types of ML
  • ML vs AI vs DL vs Data Science
  • Regression Algorithms : Linear Regression, Multiple Linear Regression, Polynomial Regression, Lasso Regression (L1 Regularization), Ridge Regression (L2 Regularization), Support Vector Regression (SVR), Decision Tree Regression, Random Forest Regression, Gradient Boosting Regression, XGBoost, CatBoost
  • Classification Algorithms : Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes Classifier, Decision Tree Classifier, Random Forest Classifier, Support Vector Machines (SVM), Gradient Boosting Classifier, XGBoost, CatBoost, AdaBoost, Bagging
  • Clustering : K-Means Clustering, Hierarchical Clustering, DBSCAN, Gaussian Mixture Models (GMM)
  • Dimensionality Reduction : Principal Component Analysis (PCA), t-SNE (t-distributed Stochastic Neighbor Embedding)
  • Self-Training
  • Label Propagation
  • Introduction to RL
  • Markov Decision Process (MDP)
  • Value-Based Methods
  • Regression: MSE, RMSE, MAE, R²
  • Classification: Accuracy, Precision, Recall, F1-Score
  • Confusion Matrix & Cross-Validation
  • Data collection
  • Overfitting and Underfitting
  • What is Git?
  • What is VCS/SCM?
  • How Git works?
  • Installing git
  • Creating and Cloning repository
  • add, commit, add, gitignore
  • seeing commits
  • Nonlinear Development (Branching)
  • Merging branches
  • Working with a remote repository
  • Introduction to Deep Learning : What is Deep Learning?, Difference between ML, DL, AI, Perceptron & Biological Neuron Analogy
  • Neural Network Fundamentals : Perceptron Model, Activation Functions, Loss Functions, Forward Propagation, Backpropagation Algorithm, Gradient Descent
  • Training Deep Neural Networks : Weight Initialization, Optimization Algorithms, Learning Rate Scheduling, Dropout (Regularization)
  • Deep Learning Frameworks : TensorFlow, Keras
  • ANN : Biological neuron vs Artificial neuron, Perceptron model, Inputs, Weights, Bias, Weighted sum, Activation function, Single-layer perceptron, Multi-layer perceptron (MLP), Input layer, Hidden layers, Output layer, Fully connected layers, Loss / Cost Functions, Backpropagation, Improving ANN Performance, Mathematical Foundations, Training ANN Models
  • CNN : Convolution layers, Convolution Operation, Pooling layers, Activation & Loss Functions,Training CNN Models, Regularization Techniques, Model Evaluation, Fully connected layers
  • RNN : RNN cell, Types of RNN, Mathematical Foundations, Backpropagation Through Time, Training RNN Models
  • LSTM : LSTM Architecture Components, Gates in LSTM, LSTM Cell Equations, Types of LSTM Architectures, Training LSTM Models, Data Preparation for LSTM, LSTM for Speech & Audio, Advanced LSTM Concepts, Model Evaluation
  • GRU : Introduction to GRU, GRU Architecture, GRU Mathematical Formulation, Forward Pass in GRU, Training GRU Models
  • Introduction to Databases & SQL : What is a Database?, CRUD operations, Types of Database, DBMS
  • SQL : Database, Table, Row, Column concepts, SQL Data Types, Creating & Dropping Databases, Creating & Dropping Tables, Altering Tables
  • CRUD Operations : INSERT INTO, SELECT, UPDATE, DELETE
  • Filtering & Sorting : WHERE, Logical Operators, Comparison Operators, Sorting Data, Limiting Results
  • SQL Functions : Aggregate Functions, String Functions, Date & Time Functions, Mathematical Functions
  • Grouping Data, GROUP BY, HAVING
  • SQL Joins : INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, CROSS JOIN, SELF JOIN
  • SQL Subqueries : Subquery in SELECT, Subquery in WHERE, Nested Queries
  • SQL Set Operations : UNION vs. UNION ALL, EXCEPT, INTERSECT
  • Keys : Primary Key, Foreign Key, Unique Key, Candidate Key, Super Key, Composite Key, Surrogate Key
  • Triggers : What are Triggers?, BEFORE & AFTER Triggers
  • Advanced Functions : Window Functions, Pivot, Case Statements
  • SQL Projects
  • Introduction to NLP
  • Text Preprocessing : Tokenization, Stopword Removal, Lowercasing, normalization, Stemming, Lemmatization, Handling Punctuation, Numbers, Case Normalization, Handling Emojis & Special Characters, Remove URLs, Spelling correction, Keyword extraction
  • Text Representation (Feature Engineering) : Bag of Words, One Hot Encoding, N-grams/Bi-grams/Tri-grams, TF-IDF, Word2Vec
  • Classical NLP Algorithms : Text Classification, Language Modeling, Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA)
  • Sequence Modeling : Recurrent Neural Networks (RNNs), LSTMs, GRUs, Seq2Seq Models
  • Model evaluation : Accuracy, Precision, Recall, F1-score, Confusion Matrix
  • NLTK
  • Advanced Architectures : Encoder-Decoder models, Sequence-to-sequence learning, Speech-to-text, Text-to-speech
  • NLP Project
  • Introduction to Power BI
  • Data Sources & Connectivity
  • Data Cleaning with Power Query
  • Data Modeling
  • DAX (Data Analysis Expressions)
  • Data Visualization
  • Dashboard Design
  • Advanced Analytics
  • Power BI Project
  • Introduction to Data Visualization & Tableau
  • Data Connections
  • Data Preparation
  • Data Modeling
  • Calculations in Tableau
  • Data Visualization
  • Dashboard Design
  • Advanced Analytics
  • Tableau Project
  • Introduction to FastAPI
  • Background Processing
  • Creating your first FastAPI app
  • Path parameters
  • Query parameters
  • Request body
  • Response models
  • GET, POST, PUT, DELETE
  • Flow Diagram
  • Databases
  • Request Handling
  • Testing FastAPI
  • Introduction to MongoDB
  • MongoDB Basics : Database, collection, document structure, CRUD operations
  • Querying Documents
  • Data Modeling in MongoDB
  • Aggregation
  • Advanced MongoDB
  • Total Duration : 350+ hours
  • Lectures : 175+
  • Skill Level : Expert
  • Language : Hindi
  • Industry Based Course Curriculum
  • Weekdays & Weekend Classes
  • Resume & LinkedIn Profile Building
  • Regular Assessments
  • Quiz
  • Hands-on Practice & Projects
  • Globally Recognized Certification
  • Interview Preparation & Mock Interview Session
  • 100% Placement & Job Assistance
  • 24/7 Student Support
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