Certified Data Science Course in Pune

Introduction to Computer Programming (Python)

• Programming Foundation Concepts: Basic Terminology, Data Types and Variables, Operators and Expressions, Control Structures, Functions and Procedures, Recursion, Basic Algorithms

• Object-Oriented Concepts: Classes and Objects, Inheritance, Polymorphism, Encapsulation, Abstraction, Method Overriding, Constructor and Destructor

• Getting Started with Python: History of Python, Python Features, Python 2 vs Python 3, Installing Python, Running Python Scripts, Python Syntax and Semantics, Python Development Environments

• Python Installation and Setup: Python 3.X Installation Guide, Introduction to Anaconda Python, IDEs: Jupyter Notebook, PyCharm, and VS Code, Setting Up Virtual Environments, Package Management with pip and conda

• Python Built-in Objects: Numbers (int, float, complex), Strings, Lists, Tuples, Sets, Dictionaries, Type Conversion

• Control Flow in Python: Conditional Statements (if, elif, else), Looping Statements (for, while), Nested Loops, Loop Control Statements (break, continue, pass), Comprehensions (List, Dict, Set), Conditional Expressions

• Functions in Python: User Defined Functions, Lambda Functions, Function Arguments (default, keyword, arbitrary), Return Statement, Recursion, Built-in Functions, Higher-Order Functions

• String Handling in Python: String Creation and Operations, String Methods, String Formatting, Regular Expressions, String Slicing, Escape Characters, Unicode Strings

• Data Structures in Python: List Operations and Methods, Dictionary Operations and Methods, Set Operations and Methods, Tuple Operations and Methods, Working with Stacks and Queues, Comprehensions, Collections Module

• File Handling in Python: Reading and Writing Files, File Methods, File Modes, Handling Binary Files, Working with CSV Files, Context Managers, File Path Operations

• Python Libraries and Modules: Standard Library Overview, Commonly Used Libraries (os, sys, math, datetime), Importing Modules, Creating User Defined Modules, User Defined Packages, Working with init.py, Packaging and Distribution (whl Packaging)

• Advanced Python Concepts: Object Oriented Python, Exception Handling, Debugging Techniques, Iterators and Generators, Decorators, Context Managers, Metaprogramming

• Database Interface with Python: Introduction to Databases, SQLite in Python, CRUD Operations, Connecting to Sqlite, Database Connection Pooling, Handling Transactions

• Working with Data Formats: Introduction to JSON Data, Parsing JSON, Creating JSON, Working with XML, Using CSV Module, pickle Module for Serialization, YAML Format

• Web Programming with Python: Requests Module, Web Scraping with BeautifulSoup, Using Scrapy for Advanced Scraping, Working with APIs

• Numerical Computation with Numpy: Introduction to Numpy, Numpy Arrays, Array Indexing and Slicing, Array Operations, Broadcasting, Numpy Functions, Working with Matrices

• Data Manipulation with Pandas: Introduction to Pandas, Series and DataFrame, Reading and Writing Data, Data Cleaning, Data Transformation, Merging and Joining, Data Aggregation

Introduction to RDBMS Database (SQL)

• Introduction of RDBMS: Definition of RDBMS, Characteristics of RDBMS, Advantages of using RDBMS, Data Models in RDBMS, Keys in RDBMS (Primary Key, Foreign Key), RDBMS vs. NoSQL, Popular RDBMS Software

• Installing and Using MySQL: System Requirements for MySQL, Downloading and Installing MySQL, Configuring MySQL Server, MySQL Workbench Overview, Connecting to MySQL Server, Basic MySQL Commands, MySQL User Management

• Querying Single Table: SELECT Statement Basics, Filtering Data with WHERE Clause, Sorting Data with ORDER BY Clause, Using LIMIT to Restrict Rows, Aggregation Functions (COUNT, SUM, AVG), Grouping Data with GROUP BY, Handling NULL Values

• Modifying Data and Table Structures: Inserting Data (INSERT INTO), Updating Data (UPDATE), Deleting Data (DELETE), Modifying Table Structure (ALTER TABLE), Dropping Tables and Databases, Adding and Removing Columns, Renaming Tables and Columns

• Querying Multiple Tables: Introduction to Joins, Inner Join, Left Join, Right Join, Full Outer Join, Cross Join, Using Aliases for Table Names

• Constraints of SQL: Primary Key Constraints, Foreign Key Constraints, Unique Constraints, Not Null Constraints, Check Constraints, Default Constraints, Enforcing Constraints in MySQL

• Importing and Exporting Data: Importing Data from CSV Files, Exporting Data to CSV Files, Using MySQL Workbench for Import/Export, Using LOAD DATA INFILE, Using SELECT INTO OUTFILE, Data Formats for Import/Export, Handling Errors During Import/Export

• Working With Expressions: Arithmetic Expressions, String Expressions, Date and Time Expressions, Logical Expressions, Conditional Expressions (CASE), Using Functions in Expressions, Combining Expressions

• Database Normalization: Introduction to Normalization, First Normal Form (1NF), Second Normal Form (2NF), Third Normal Form (3NF), Boyce-Codd Normal Form (BCNF), Advantages of Normalization, Denormalization

• Grouping and Summarizing Data: Using GROUP BY Clause, Using HAVING Clause, Aggregate Functions (SUM, AVG, MAX, MIN), Counting Rows with COUNT, Grouping Sets and Rollup, Pivoting Data, Summarizing Data with Subqueries

• Triggers & Cursor: Introduction to Triggers, Creating Triggers in MySQL, BEFORE and AFTER Triggers, Row-Level and Statement-Level Triggers, Managing Trigger Execution Order, Debugging Triggers, Use Cases for Triggers. Introduction to Cursors, Declaring Cursors in MySQL, Opening and Closing Cursors, Fetching Data with Cursors, Cursor Looping, Handling Exceptions in Cursors, Use Cases for Cursors

• Functions: Built-in Functions in MySQL, String Functions (CONCAT, LENGTH), Numeric Functions (ROUND, CEIL), Date and Time Functions (NOW, DATE_ADD), Aggregate Functions (SUM, AVG), Creating User-Defined Functions, Using Functions in Queries

• Designing and Creating a Database: Database Design Principles, Entity-Relationship (ER) Modeling, Creating ER Diagrams, Defining Tables and Relationships, Choosing Data Types, Setting Up Primary and Foreign Keys, Implementing Business Rules with Constraints

• Exploring and Processing on Data: Data Exploration Techniques, Descriptive Statistics in SQL, Data Cleaning and Transformation, Data Integration from Multiple Sources, Using Subqueries for Data Processing, Analyzing Data Patterns, Using SQL for Data Reporting

• Advanced Querying Techniques: Subqueries and Nested Queries, Common Table Expressions (CTEs), Window Functions, Recursive Queries, Dynamic SQL, Query Optimization Techniques, Advanced Join Operations.

Mathematics & Statistics

• Introduction to Statistics: Definition and Importance of Statistics, Types of Statistics: Descriptive and Inferential, Applications of Statistics in Data Science, Basic Terminology: Population vs. Sample, Statistical Methods and Techniques, Role of Statistics in Decision Making
• Descriptive and Inferential Statistics: Overview of Descriptive Statistics, Data Summarization Techniques, Visualization Methods (Charts, Graphs), Measures of Central Tendency and Dispersion, Overview of Inferential Statistics, Concept of Statistical Inference, Estimation and Confidence Intervals, Hypothesis Testing Fundamentals
• Process of Statistical Study: Defining the Research Question, Data Collection Methods, Data Cleaning and Preparation, Data Analysis Techniques, Interpretation of Results, Reporting and Communicating Findings.
• Sampling Techniques: Probability Sampling Methods, Simple Random Sampling, Stratified Sampling, Cluster Sampling, Systematic Sampling, Non-Probability Sampling Methods, Convenience Sampling, Judgmental Sampling, Quota Sampling, Sampling Errors and Biases
• Measure of Central Tendencies: Mean (Arithmetic, Geometric, Harmonic), Median, Mode, Weighted Mean, Applications and Interpretation of Central Tendencies, Comparison of Different Measures
• Frequency Distribution: Types of Frequency Distributions, Ungrouped Frequency Distribution, Grouped Frequency Distribution, Constructing Frequency Tables, Histograms, Frequency Polygons, Cumulative Frequency Distribution, Relative Frequency
• Measure of Dispersion: Range, Variance, Standard Deviation, Mean Absolute Deviation, Quartiles and Interquartile Range (IQR), Coefficient of Variation
• Probability: Basic Concepts of Probability, Rules of Probability (Addition and Multiplication), Conditional Probability, Bayes’ Theorem, Independent and Dependent Events, Applications of Probability in Data Science
• Probability Density Function: Definition and Properties, Continuous Random Variables, Common Continuous Distributions (Normal, Exponential), Calculation of Probabilities using PDF, Examples and Applications, Relationship with Cumulative Distribution Function (CDF)
• Types of Data Distribution: Normal Distribution, Binomial Distribution, Poisson Distribution, Exponential Distribution, Uniform Distribution, Log-normal Distribution
• Skewness: Definition and Interpretation, Positive Skewness, Negative Skewness, Measuring Skewness (Pearson’s, Bowley’s, Kelly’s), Impact of Skewness on Data Analysis, Real-life Examples, Definition and Interpretation, Types of Kurtosis (Leptokurtic, Mesokurtic, Platykurtic), Measuring Kurtosis, Impact of Kurtosis on Data Analysis, Real-life Examples, Relationship with Skewness
• IQR (Interquartile Range): Definition and Calculation, Importance of IQR in Data Analysis, Detecting Outliers using IQR, Comparison with Range, Applications of IQR, Examples and Case Studies
• Hypothesis Testing: Definition and Importance, Null and Alternative Hypotheses, Type I and Type II Errors, P-value and Significance Level, Steps in Hypothesis Testing, Examples and Applications
• Chi-Square Test: Definition and Applications, Types of Chi-Square Tests (Goodness of Fit, Independence), Assumptions and Conditions, Calculation and Interpretation, Examples and Case Studies, Limitations of Chi-Square Test
• ANOVA (One-Way and Two-Way): Definition and Applications, Assumptions of ANOVA, One-Way ANOVA (Concept and Calculation, Interpretation of Results, Examples and Case Studies), Two-Way ANOVA (Concept and Calculation, Interaction Effects, Examples and Case Studies)
• Multicollinearity: Definition and Impact on Regression Analysis, Detecting Multicollinearity (VIF, Tolerance), Causes of Multicollinearity,Handling Multicollinearity (Ridge Regression, Lasso), Examples and Applications, Case Studies and Real-life Scenarios

Machine Learning (ML)

• Prerequisites to ML – Overfitting, Regularization, Feature Selection: Introduction to Overfitting and Underfitting, Techniques to Handle Overfitting, Regularization Techniques, Feature Selection Techniques, Feature Engineering Techniques, Dimensionality Reduction Techniques, Data Preprocessing
  
• Linear Regression: Introduction to Linear Regression, Ordinary Least Squares (OLS) Method, Model Evaluation Metrics, Assumptions of Linear Regression, Multiple Linear Regression, Regularization in Linear Regression, Polynomial Regression
  
• Logistic Regression: Introduction to Logistic Regression, Binary Classification, Model Evaluation Metrics, Multinomial Logistic Regression, Regularization in Logistic Regression, Handling Imbalanced Data, Model Interpretation
  
• Decision Tree: Introduction to Decision Trees, Tree Building Algorithms, Splitting Criteria, Pruning Techniques, Handling Continuous and Categorical Variables, Advantages and Disadvantages of Decision Trees, Visualizing Decision Trees
  
• Random Forest:Introduction to Random Forest, Building a Random Forest Model, Hyperparameter Tuning, Feature Importance, Out-of-Bag (OOB) Error, Advantages and Disadvantages of Random Forest, Applications of Random Forest
  
• AdaBoost: Introduction to AdaBoost, Working Principle, Algorithm Steps, Types of AdaBoost, Advantages and Disadvantages of AdaBoost, Hyperparameter Tuning, Applications of AdaBoost
  
• Gradient Boosting: Introduction to Gradient Boosting, Working Principle, Algorithm Steps, Types of Gradient Boosting Algorithms, Advantages and Disadvantages of Gradient Boosting, Hyperparameter Tuning, Applications of Gradient Boosting
  
• XG Boost: Introduction to XGBoost, Working Principle, Algorithm Steps, Hyperparameter Tuning, Evaluation Metrics, Advantages and Disadvantages of XGBoost, Applications of XGBoost
  
• LDA (Linear Discriminant Analysis): Introduction, Working Principle, Algorithm Steps, LDA for Classification, Advantages and Disadvantages, Comparison with Other Techniques, Applications of LDA
  
• PCA (Principal Component Analysis): Introduction to PCA, Working Principle, Algorithm Steps, Interpreting Principal Components, Advantages and Disadvantages of PCA, Comparison with Other Techniques, Applications of PCA
  
• KMeans and KNN:Introduction to KMeans Clustering, Working Principle, Algorithm Steps, Evaluation Metrics for KMeans, Advantages and Disadvantages of KMeans, Introduction to KNN (K-Nearest Neighbors), Working Principle of KNN
  
• SVM: Introduction to SVM, Working Principle, Algorithm Steps, Kernel Functions, Advantages and Disadvantages of SVM, Hyperparameter Tuning, Applications of SVM

Natural Language Processing & Deep Learning (NLP & DL)

• Forward Propagation: Definition and Purpose, Mathematical Formulation, Input Layer to Hidden Layer Transitions, Hidden Layer to Output Layer Transitions, Activation Functions in Forward Propagation, Computational Graphs, Example of Forward Propagation in Neural Networks
  
• Back Propagation: Definition and Importance, Chain Rule of Differentiation, Error Calculation, Weight Adjustment, Gradient Descent in Back Propagation, Back Propagation in Deep Networks, Example of Back Propagation
  
• Epochs: Definition of Epochs, Role of Epochs in Training, Impact on Model Performance, Epochs vs. Iterations, Monitoring Training Progress, Determining Optimal Number of Epochs, Overfitting and Underfitting Concerns
  
• Weights and Bias: Definition and Role of Weights, Initialization of Weights, Role of Bias in Neural Networks, Adjusting Weights and Biases, Impact on Model Accuracy, Regularization Techniques, Visualizing Weights and Biases
  
• Activation Function: Definition and Purpose, Types of Activation Functions, Sigmoid Activation Function, ReLU (Rectified Linear Unit), Tanh (Hyperbolic Tangent), Softmax Activation Function, Choosing the Right Activation Function
  
• Gradient Descent: Definition and Purpose, Types of Gradient Descent (Batch, Stochastic, Mini-Batch), Learning Rate and Its Impact, Convergence Criteria, Gradient Descent Algorithm Steps, Challenges and Solutions, Examples of Gradient Descent
  
• Loss Functions: Definition and Purpose, Mean Squared Error (MSE), Cross-Entropy Loss, Hinge Loss, Regularization in Loss Functions, Impact on Model Training, Selecting Appropriate Loss Function
  
• Optimizers: Definition and Role, Stochastic Gradient Descent (SGD), Adam Optimizer, RMSProp, Adagrad, Momentum-Based Optimization, Comparing Different Optimizers
  
• Finding patterns in text: Text Data Collection, Tokenization, Stop Words Removal, N-grams Analysis, Frequency Distribution, Part-of-Speech Tagging, Named Entity Recognition
  
• Text Analytics: Definition and Importance, Text Preprocessing Techniques, Sentiment Analysis, Topic Modeling, Text Classification, Text Summarization, Applications in Various Domains
  
• Applications of Social Media Analytics: Sentiment Analysis on Social Media, Trend Analysis, Social Network Analysis, Customer Feedback and Insights, Brand Monitoring, Crisis Management, Influencer Identification
  
• Fine tuning the models using Hyper parameters: Definition of Hyperparameters, Hyperparameter Tuning Techniques, Grid Search, Random Search, Bayesian Optimization, Hyperparameter Impact on Model Performance, Practical Examples
  
• Deep Learning for NLP: Overview of Deep Learning in NLP, RNNs for Sequence Modeling, CNNs for Text Classification, Transformer Models, Transfer Learning in NLP, Pre-trained Language Models, Applications and Case Studies
  
• Word Embeddings: Definition and Importance, Word2Vec, GloVe (Global Vectors for Word Representation), FastText, Contextualized Word Embeddings, Evaluation of Word Embeddings, Applications in NLP
  
• Taming big text, Unstructured vs. Semi-structured Data: Differences Between Unstructured and Semi-structured Data, Challenges with Big Text Data, Storage Solutions for Big Text, Data Cleaning and Transformation, Tools for Handling Big Text Data, Practical Examples, Case Studies
  
• Wordnet: Overview of WordNet, Synsets and Semantic Relations, WordNet for Synonym Identification, Applications in NLP, Integrating WordNet with Other Tools, Practical Examples, Case Studies
  
• Text Preprocessing: Tokenization, Lowercasing, Stop Words Removal, Stemming and Lemmatization, Handling Punctuation and Special Characters, Removing Duplicates, Practical Examples
  
• Lemmatization and Stemming: Definition and Differences, Stemming Algorithms (Porter, Snowball), Lemmatization with WordNet, Impact on Text Data, Choosing Between Lemmatization and Stemming, Practical Examples, Case Studies
  
• Feature Extraction from text:Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Word Embeddings, N-grams, Named Entity Recognition, Part-of-Speech Tagging, Practical Examples
  
• Vocabulary and Document: Building a Vocabulary, Document-Term Matrix, Term Frequency Calculation, Inverse Document Frequency Calculation, Handling Out-of-Vocabulary Words, Practical Examples, Case Studies