Advanced Certification Program in Data Science & Analytics

Python Essentials: Learn, Code, Create

• • Python Basics: Learn to Code

    • Introduction to Python Programming: 
      • Basic Data Types in Python
      • Variables and Naming Conventions
    • Data Types in Python: 
      • Data Structure
      • Python String Object
      • Python List and Tuple Objects
      • Python Dictionary Object
      • Python Set Object
    • Indexing & Slicing: 
      • Importance of indexing in Python
      • Introduction to Slicing
      • Indexing and Slicing in Strings
      • Indexing and Slicing in Lists and Tuples
    • Operators in Python: 
      • Arithmetic Operators
      • Comparison Operators
      • Logical Operators
      • Assignment Operators
      • Membership Operators
    • In-Built Functions & Methods: 
      • Exploring Built-in Functions
      • Use Built-in Functions with data types
      • Introducing Built-in Methods for specific data types

  • Python Control Flow Simplified

    • Statements, Indentation and Conditionals: 
      • Statements and Indentation
      • Introduction to Conditional Statements
      • Combining Multiple Conditions
    • Loops & Iterations: 
      • Conditional Loops (While Loop)
      • Conditional Loops (For Loop)
      • Master ‘for’ loops, Learn ‘while’ loops
      • Comprehend loop control statements
      • Explore nested loops

  • Building Smarter Programs

    • Custom Functions in Python: 
      • Introduction to Custom Functions
      • Defining and Calling Custom Functions in Python
      • Working with Arguments and Return Values
    • Advanced Looping Concepts: 
      • List Comprehension
      • Set Comprehension
      • Dictionary Comprehension
      • Lambda Functions
    • OOPs in Python: 
      • Introduction to Object-Oriented Programming (OOPs)
      • Creating and Working with Classes
      • Understanding Objects and Instances
      • Working with Attributes
      • Utilizing Methods in Classes
      • Embracing Polymorphism
      • Leveraging Inheritance
    • Exception Handling & Logging: 
      • Introduction to Exception Handling
      • Try-Except Block: Catching Exceptions
      • Handling Specific Exceptions
      • Finally Block: Cleaning Up After Exceptions
      • Raising Exceptions: Taking Control
      • Handling Multiple Exceptions: The Exceptional Mastery

Numerical Programming in Python

• • Python Data Structures and Algorithms

    • Data Structures Fundamentals: 
      • Introduction to Data Structures
      • Arrays
    • Efficient String Operations: 
      • Introduction to Strings
      • Combining strings using concatenation (+)
      • Searching and Finding
      • Introduction to regex
    • Recursion Fundamentals: 
      • Understanding Recursion: Introduction and Basics
      • Recursive Function Design
      • Implementing Recursion in Algorithms
      • Analyzing Recursive Calls
    • Mastering Recursion Concept: 
      • Time and space complexity
    • Algorithm Fundamentals: 
      • Introduction to Algorithms
      • Big O Notation
      • Sorting Algorithms
      • Searching Algorithms

  • Python Data Wrangling

    • Getting Started with Pandas & Numpy: 
      • Introduction to Pandas & Numpy
    • Mastering Data Wrangling: 
      • Series and dataframes
      • Indexing and slicing
      • Manipulating data
    • Advanced Data Wrangling Concepts: 
      • Introduction to Data Wrangling
      • Handling Missing Data
      • Merging and Joining Data
      • Grouping and Aggregation
      • Reshaping Data
      • Filtering Data
    • Data Wrangling – Code Optimisation: 
      • Techniques for Code Optimization
      • Strategies for Parallel and Distributed Data Wrangling
      • Best Practices for Efficient Data Wrangling
    • Data Wrangling on Different Data Formats: 
      • Data Wrangling on Different Data Formats
    • Data Management Libraries: 
      • File Management Libraries
      • Data Manipulation Libraries
      • Data Visualization Libraries
      • Web Scraping
      • Regular Expression Libraries
      • Date and Time Libraries
    • Web Scraping using Python: 
      • Introduction to Web Scraping

  • Python Visualization Libraries in Action

    • Data Visualization with Matplotlib & Seaborn: 
      • Introduction to Matplotlib and Seaborn Plots

Mastering Data Domain with SQL

• • Introduction to SQL: Learn the Lang. of Data

    • SQL Basics for Data Analysis: 
      • Introduction to SQL
      • Setting up the SQL environment
      • Basic SQL Commands
      • Creating and Deleting Databases and Tables
      • Importing and Exporting Data from CSV Files
    • Fundamentals of SQL Query: 
      • Anatomy of SQL Query
      • SQL Data Types and Operators
      • Filtering and Sorting Data in SQL
      • Aggregate functions in SQL
    • Dealing With Multiple Tables: 
      • Grouping Data – GROUP BY
      • HAVING
      • Subqueries
      • Joining tables using INNER JOIN, LEFT JOIN, RIGHT JOIN & FULL OUTER JOIN
      • Alias in SQL queries
      • Working with Multiple Tables Using Subqueries
      • Using Set Operators
      • Aggregating Data from Multiple Tables using GROUP BY and HAVING.
    • Advanced SQL Joins: 
      • Advanced Join Techniques
      • Joining multiple tables
      • Handling duplicate records and eliminating duplicates
      • Using UNION and UNION ALL to combine data from multiple tables 

  • SQL In-Built Functions

    • Type Casting & Math Functions: 
      • Mathematical Functions
      • Type Conversion Functions
      • Using CASE Statements to Perform Conditional Operations
    • DateTime & String Functions: 
      • Working with date/time data in SQL
      • Date/time functions
      • Formatting date/time data
      • String manipulation functions (e.g. UPPER, LOWER, LEFT, RIGHT, etc.)
      • Regular expressions in SQL for string operations
      • Using CONCAT_WS to concatenate strings with a separator
    • Window Functions: 
      • Syntax of Windows Function
      • Ranking functions (e.g. ROW_NUMBER, RANK, DENSE_RANK, etc.)
      • Aggregate functions using windows (e.g. SUM, AVG, MAX, MIN, etc.)
      • Partitioning data for window functions
      • Understanding the difference between row-based and aggregate-based window functions

  • SQL for Data Preparation

    • Complex queries using CTE & Pivoting: 
      • Common Table Expressions(CTE)
      • Recursive CTEs for Hierarchical Data
      • Combining CTEs with Window Functions and Subqueries
      • Understanding the Performance Implications of CTEs
    • Database Management & Schema Design: 
      • Understanding The Relational Model and Database Schema Design
      • Normalization and Denormalization of Database Tables
      • Database Administration Tasks
      • Implementing Indexes and Constraints for Data Integrity
      • Designing Efficient Database Queries for Performance Optimization

Data Visualization Tools

• • Data Analysis using Excel

    • Fundamentals of Excel: 
      • Introduction to Excel
      • Reading Data into Excel
      • Basic Data Manipulation in Excel
      • Arithmetic Manipulation in Excel
      • Basic Functions in Excel
      • Function Using Absolute and Relative Reference in Excel
      • Additional Useful Function in Excel
      • Conditional Statements in Excel
    • Data Exploration with In-Built Functions: 
      • Data Filtering in Excel
      • Data Validation in Excel
      • Use of Pivot Tables in Excel
      • Pivot Table Operations & Applications
      • Introduction to Charts in Excel
      • Excel Shortcuts
    • Storytelling with Excel: 
      • Introduction to storytelling with data
      • The importance of data visualization in storytelling
      • Choosing the right chart type for different data scenarios
      • Understanding the principles of good data visualization
      • How to use Excel to create compelling data visualizations
    • Advanced Dashboarding Concepts: 
      • Introduction to dashboards
      • The purpose and benefits of dashboards
      • Choosing the right chart types for different dashboard scenarios
      • Techniques for creating interactive dashboards in Excel
      • Creating dashboard templates for reuse
      • Sharing dashboards with stakeholders 

  • Tableau for Professionals

    • Getting Started with Tableau Ecosystem: 
      • Introduction to Tableau
      • Use of tableau for the company’s KPI
      • What are business KPIs?
    • Choosing the Right Chart: 
      • What Is a Chart and Types of Charts?
      • Line Chart
      • Bar Chart and Column Chart
      • Pie Chart
      • Area Chart
    • Dashboarding & Storytelling with Tableau: 
      • Best Practices for Dashboard Design
      • Define your goal: Start by identifying the key insights or metrics that you want to communicate through your dashboard
      • Use appropriate visualizations: Choose the right charts and graphs to best represent your data
      • Make it interactive: Use filters and other interactive elements to allow users to explore the data
      • Keep it simple: Avoid using too many elements that may confuse or distract the viewer

  • Data-Driven Business Analysis with Power BI

    • Dashboarding with Power Bl: 
      • Introduction to creating and formatting table visualization
      • Formatting Our First visualization
      • Creating Different Visualizations: Matrices and Charts
      • Adding More Control to Your Visualizations
      • Introduction to Mapping
      • Measure Performance by Using Kpis, Gauges, and Cards
    • Advanced Dashboarding Concepts with PowerBl: 
      • Introduction to Get and Transform Data
      • Introduction for Get Data – Home
      • Getting multiple files
      • Transform menu
    • Customer & Web Analytics: 
      • Introduction
      • Understanding customer behavior and preferences through website data
      • Key performance indicators (KPIs) for website analytics
      • Analyzing website traffic and user engagement using tools like Google Analytics
      • A/B testing and experimental design for website optimization
      • Creating user personas and segments for targeted marketing -Cro techniques
      • Identifying opportunities for upselling and cross-selling through website data
    • Advanced Charts: 
      • Introduction
      • Visualization best practices and chart selection for different data types and analysis goals
      • Advanced chart types and techniques, such as heatmaps, sankey diagrams, and sparklines
      • Tools for creating advanced charts, such as D3.js, Plotly, and Tableau
      • Dashboard design principles and user experience considerations
      • Creating interactive charts and dashboards for exploratory data analysis
    • Dashboarding with Business KPIs – Ecommerce: 
      • Key Performance Indicators (KPIs) for Ecommerce Businesses
      • Defining Business Goals and Objectives for E-commerce Dashboards
      • Selecting and Designing Appropriate Charts and Visualizations for E-commerce KPIs
      • Identifying Trends and Patterns in E-commerce Data Using Exploratory Analysis Techniques
      • Creating Data-Driven Recommendations for Improving Ecommerce Performance
      • Measuring the Impact of Marketing Campaigns and Other Initiatives on Ecommerce Metrics
      • Integrating Data from Multiple Sources into E-commerce Dashboards

Applied Statistics

• • Maths Essentials

    • Foundational Maths for DS: 
      • Introduction to Linear Algebra
      • Theory of Matrices
      • Determinant of a Matrix
      • Eigenvalues and Eigenvectors
    • Advanced Maths for DS: 
      • Calculus
      • Differentiation
      • Integration
      • Maxima and Minima using derivatives
      • Partial Derivatives

  • Descriptive Statistics

    • Probability Theory: 
      • Introduction to probability theory
      • Conditional probability and Bayes theorem
      • Random variables and properties
    • Data Summarization: 
      • Introduction to data summarization
      • Descriptive statistics and its uses
      • Measures of central tendency
      • Percentile and quartiles
      • Skewness and kurtosis
      • Outlier detection and treatment
    • Discrete Probability Distributions: 
      • Probability mass function
      • Cumulative distribution function
      • Distribution
    • Continuous Probability Distributions: 
      • Probability density function
      • Cumulative distribution function
      • Distribution
    • Joint Distribution Concept: 
      • Joint probability mass function
      • Joint probability density function
      • Covariance and correlation
      • Multivariate normal distribution

  • Mastering Inferential Statistics

    • Sampling & Statistical Inference:
      • Introduction to Sampling
      • Probability Sampling
      • Non-Probability Sampling
      • Sampling Bias and Estimation
      • Sample Size Determination and Sum of Independent Random Variables
    • Concept of Confidence: 
      • Introduction to Confidence Levels
      • Interpreting Confidence Intervals
      • Choosing Appropriate Confidence Levels
    • Hypothesis Testing: 
      • Hypothesis
      • Null and Alternative Hypotheses
      • Type I and Type II Errors
      • One-Tailed and Two-Tailed Tests
      • P-Values
    • Experimental Design: 
      • Experimental Design
      • Types of Experimental Design
      • Hypothesis Testing
      • Power Analysis
      • Ethical Considerations

Introduction to Machine Learning

• • Unlocking Machine Learning

    • Learning Objective: 
      • Introduction to ML
      • Supervised and Unsupervised Algorithms
      • Understand the Mechanisms Behind Machine Learning
    • Mechanisms Behind Machine Learning: 
      • Introduction to mechanism of Supervised
      • Unsupervised and Deep Learning
    • Supervised Learning – Regression: 
      • What are supervised models?
      • What is regression analysis?
      • Types of Regression Models
    • Supervised Learning – Classification: 
      • Introduction to Supervised Models – Classification
      • Logistic Regression
      • What is logistic regression?
      • Implementing logistic regression
      • Evaluation Metrics used for Classification
      • Defining the cost function
    • Decision Trees: 
      • What is a Decision Tree?
      • How to split?
      • Math Behind it
      • Simple Implementation
      • Advantages & Disadvantages
    • Unsupervised Learning: 
      • What is Unsupervised Learning?
      • Introduction to Clustering
      • K-Means Clustering
      • Simple Example
      • Expectation-Maximization
      • Silhouette Analysis
      • Elbow Method
      • Implementation of K-Means
      • Limitations

  • Optimizing Models for Accuracy

    • Data Preparation for ML Models: 
      • Introduction
      • Understanding the data: Data types, data quality, and data distribution
      • Handling missing values: Imputation techniques
      • Handling outliers: Detection and removal techniques
      • Dealing with categorical data: Techniques for encoding categorical variables
      • Data normalization and scaling: Techniques and their applicability
    • Optimizing Model Performance With Validation: 
      • Introduction
      • Why Cross-Validation?
      • K-Fold Cross-Validation
      • Stratified K-Fold Cross-Validation
      • Trade-off between Train-Test Split and Cross-Validation
      • Optimizing the number of folds: Bias-variance tradeoff
      • Hyperparameter Tuning
    • Feature Engineering for Interpretable Models: 
      • What is Feature Engineering?
      • Why do we need it?
      • What is feature understanding?
      • Basic EDA
      • Other methods for Feature Engineering
    • Customer Segmentation – Case Study: 
      • What is customer segmentation?
      • Why segment customers?
      • What are the benefits of customer segmentation
      • Analysis of the customer segments

  • Beyond Simplicity – Artificial Intelligence

    • Bagging & Boosting: 
      • What are ensembles?
      • Bagging
      • Boosting
      • Stacking
      • Bagging vs Boosting
    • Neural Networks: 
      • Understanding Neural Networks
      • Decoding how neural networks work?
      • Artificial Neural Networks
      • Implementing Neural Network
    • Introduction to NLP: 
      • Introduction to NLP
      • Basic Concepts of NLP
      • Sentiment Analysis
      • Tokenization
      • Lemmatization
      • Stemming vs Lemmatization
      • Vectorization
    • Image Processing (Self Paced): 
      • Introduction
      • Understanding digital images: Pixels, color spaces, and image representation
      • Image filtering: Techniques for enhancing image quality and removing noise
      • Object detection: Techniques for identifying and localizing objects in an image
      • Image segmentation: Techniques for separating the foreground and background regions in an image
      • Deep learning for image processing
    • Time Series Analysis: 
      • Introduction
      • What is a time series data?
      • Stationarity and its importance
      • Smoothing Technique: Moving Average
      • Forecasting Techniques: ARIMA & SARIMA
    • Recommender Systems: 
      • Introduction
      • Understanding recommendation systems: Collaborative filtering, Content-based filtering, and hybrid models
      • Understanding Recommendation Systems: Implementation of Collaborative Filtering, Content Based Filtering, Implementation of Content Based Filtering
      • Evaluation metrics for recommendation systems: Precision, recall, F1-score, and others
      • Real-world applications of recommendation systems: E-commerce, social media, and others.