Please refer below for more info on our Modules and Pricing
MODULES
Module 1: Introduction to Data Science
1.1 What is Data Science?
Definition and Scope of Data Science: Extracting insights and knowledge from data to make informed decisions.
1.2 Evolution of Data in the 21st Century
Historical Perspective: From traditional data analysis to the emergence of data science.
1.3 Rise of Big Data and the Internet
Impact of the Internet and technological advancements on data generation, collection, and storage.
1.4 Types of Software and Data Roles
Overview of Data-Related Roles: Analyst, Engineer, Scientist, Business Analyst.
Comparison of Software Tools: R, Python, SQL, Excel, Tableau, etc.
1.5 Utilization of Data Across Domains
Applications of Data Science: Healthcare (diagnosis, drug discovery), Finance (risk analysis, fraud detection), Marketing (customer segmentation, campaign optimization), Transportation (route optimization, demand forecasting), and more.
1.6 Life of a Data Scientist
Understanding the daily tasks, challenges, and responsibilities of data scientists.
Collaboration with cross-functional teams and continuous learning in a dynamic field.
Module 2: Understanding Different Types of Data and Exploratory Data Analysis (EDA)
2.1 Introduction to Data Types
Overview of Data Types: Tabular, Text, Image, Video, Time Series, Spatial, etc.
Understanding the Characteristics and Formats of Each Data Type.
2.2 Types of Variables
Numerical Variables: Continuous vs. Discrete, Examples, and Characteristics.
Categorical Variables: Nominal vs. Ordinal, Examples, and Characteristics.
2.3 Exploratory Data Analysis (EDA) for Numerical Variables
Techniques for Analyzing Numerical Variables:
Distribution Analysis: Histograms, Kernel Density Estimates (KDE), Boxplots.
Summary Statistics: Mean, Median, Mode, Variance, Standard Deviation.
Count of Unique Values.
2.4 Exploratory Data Analysis (EDA) for Categorical Variables
Techniques for Analyzing Categorical Variables:
Frequency Tables: Count of Each Category.
Bar Charts: Visualization of Frequency Distribution.
Mean and Count for Each Category.
2.5 Exploratory Data Analysis (EDA) for Two Variables
Understanding Relationships between Variables:
Numerical-Numerical: Scatter Plots, Correlation Analysis.
Numerical-Categorical: Boxplots, Violin Plots.
Categorical-Categorical: Cross-tabulation, Heatmaps.
2.6 Exploring Relationships with More than Two Variables
Introduction to Advanced Techniques:
Multiple Regression: Modeling relationships between multiple numerical and/or categorical variables.
Decision Trees: Visualizing and understanding complex relationships between multiple variables.
Neural Nets : Deep Learning Models
Module 3: MS-EXCEL Basics and Functions
3.1 Introduction to Excel
Understanding the Role of Excel in Data Analysis.
Introduction to Excel Interface: Workbooks, Worksheets, Cells.
3.2 Essential Excel Functions
Overview of Basic Excel Functions: SUM, AVERAGE, COUNT, MAX, MIN.
Conditional Functions: IF, SUMIF, COUNTIF.
Lookup Functions: VLOOKUP, HLOOKUP, INDEX, MATCH.
3.3 Advanced Excel Functions
Exploring Advanced Excel Functions: OFFSET, CONCATENATE, TEXTJOIN, INDIRECT, etc.
Statistical Functions: STDEV, VAR, CORREL, etc.
Date and Time Functions: DATE, TODAY, MONTH, YEAR, etc.
3.4 Pivot Tables and Data Analysis
Understanding Pivot Tables: Creating Pivot Tables, Rows, Columns, Values.
Analyzing Data with Pivot Tables: Summarizing, Filtering, Sorting Data.
Module 4: SQL Basics and Querying
4.1 Introduction to SQL
Understanding Structured Query Language (SQL) and its Importance.
Overview of SQL Syntax: SELECT, FROM, WHERE, GROUP BY, HAVING, ORDER BY.
4.2 Data Manipulation with SQL
Retrieving Data with SELECT Statement.
Filtering Data with WHERE Clause.
Sorting Data with ORDER BY Clause.
4.3 Aggregation and Grouping
Using Aggregate Functions: SUM, COUNT, AVG, MIN, MAX.
Grouping Data with GROUP BY Clause.
Filtering Grouped Data with HAVING Clause.
4.4 Joins and Subqueries
Performing Inner, Outer, Left, and Right Joins.
Understanding Subqueries and Their Applications.
Using Common Table Expressions (CTEs) for Complex Queries.
Module 5: Introduction to Python Programming
5.1 Overview of Python
Introduction to Python as a High-Level, Interpreted Programming Language.
Understanding Python's Versatility and Popularity in Data Science and Software Development.
5.2 Python Basics: Syntax and Variables
Python Syntax: Indentation, Comments, Statements.
Variables and Data Types: Integers, Floats, Strings, Booleans.
5.3 Conditional Statements: If-Else
Making Decisions with If-Else Statements.
Nested If-Else Statements for Complex Conditions.
5.4 Loops: For and While
Iterating Over Sequences with For Loops.
Executing Code Repeatedly with While Loops.
Loop Control Statements: Break, Continue.
5.5 Functions and Modules
Defining and Calling Functions in Python.
Passing Arguments to Functions: Positional, Keyword, Default.
Overview of Built-in Python Modules and Importing External Modules.
5.6 Introduction to Pandas
Overview of Pandas Library for Data Manipulation and Analysis.
Introduction to Series and DataFrame Objects.
Basic Operations with Pandas: Loading Data, Viewing Data, Indexing, Slicing.
Module 6: Data Visualisation
6.1 Introduction to Data Visualization
Importance of Data Visualization in Data Analysis and Communication.
Overview of Data Visualization Libraries in Python: Matplotlib, Seaborn.
6.2 Matplotlib Basics
Introduction to Matplotlib: History, Features, and Applications.
Understanding the Matplotlib Figure and Axes Structure.
6.3 Basic Plotting with Matplotlib
Creating Line Plots, Scatter Plots, and Bar Plots with Matplotlib.
Customizing Plot Appearance: Labels, Titles, Colors, and Styles.
6.4 Seaborn Introduction
Overview of Seaborn: High-Level Data Visualization Library Built on Matplotlib.
Advantages of Seaborn over Matplotlib for Statistical Data Visualization.
6.5 Advanced Data Visualization with Seaborn
Creating Complex Plots with Seaborn: Pair Plots, Heatmaps, Box Plots.
Visualizing Relationships Between Variables with Seaborn's Categorical Plots
Module 7: Statistical Modelling and Hypothesis Testing
7.1 Introduction to Statistical Modeling
Understanding the Role of Statistics in Data Analysis and Decision Making.
Overview of Statistical Models: Descriptive vs. Inferential Statistics.
7.2 Formulating Hypotheses
Understanding Hypothesis Testing: Null Hypothesis vs. Alternative Hypothesis.
Formulating Null and Alternative Hypotheses for Different Scenarios.
7.3 T-Test
Introduction to T-Test: One-Sample T-Test, Two-Sample T-Test (Independent and Paired).
Assumptions of T-Test and Interpretation of Results.
7.4 F-Test
Understanding F-Test: One-Way ANOVA, Two-Way ANOVA.
Application of F-Test in Comparing Variability Across Groups.
7.5 Chi-Square Test
Introduction to Chi-Square Test: Goodness of Fit Test, Test of Independence.
Performing Chi-Square Test for Categorical Data Analysis.
7.6 ANOVA (Analysis of Variance)
Understanding Analysis of Variance (ANOVA): One-Way ANOVA, Two-Way ANOVA.
Interpretation of ANOVA Results and Post-Hoc Testing.
7.7 Model Evaluation and Interpretation
Techniques for Evaluating Statistical Models: P-Values, Confidence Intervals, Effect Sizes.
Interpreting Statistical Results and Drawing Conclusions.
Module 8: Linear Models and Regression Techniques
8.1 Introduction to Linear Models
Understanding Linear Models and Their Applications in Data Analysis.
Overview of Regression Analysis: Simple Linear Regression vs. Multiple Linear Regression.
8.2 Simple Linear Regression
Formulating and Fitting Simple Linear Regression Models with One Independent Variable.
Interpretation of Regression Coefficients and Assessment of Model Fit.
8.3 Multiple Linear Regression
Extending Simple Linear Regression to Include Multiple Independent Variables.
Assumptions of Multiple Linear Regression and Techniques for Model Evaluation.
8.4 Polynomial Regression
Modeling Nonlinear Relationships using Polynomial Regression.
Understanding the Concept of Polynomial Degrees and Its Impact on Model Flexibility.
8.5 Logistic Regression
Introduction to Logistic Regression for Binary Classification Problems.
Modeling the Probability of a Binary Outcome using Logistic Regression.
8.6 Generalized Additive Models (GAM)
Understanding Generalized Additive Models (GAMs) for Modeling Nonlinear Relationships.
Incorporating Smoothing Functions (Splines) to Capture Complex Relationships.
8.7 Model Evaluation and Selection
Techniques for Evaluating Regression Models: R-squared, Adjusted R-squared, Residual Analysis.
Cross-Validation and Regularization Methods for Model Selection and Validation.
Module 9: Tree Based Models and Ensemble Learning
9.1 Introduction to Tree-Based Models
Understanding the Concept of Decision Trees and Their Hierarchical Structure.
Overview of Tree-Based Models as Powerful Algorithms for Both Classification and Regression Tasks.
9.2 Decision Trees
Construction of Decision Trees using Recursive Partitioning based on Feature Splits.
Criteria for Splitting Nodes such as Gini Impurity and Information Gain.
9.3 Random Forest
Ensemble Learning Technique combining Multiple Decision Trees to Improve Predictive Performance.
Bagging Approach for Building Random Forest Models with Bootstrap Sampling and Feature Randomness.
9.4 Bagging and Boosting
Understanding Bagging (Bootstrap Aggregating) and Boosting Techniques for Ensemble Learning.
Introduction to Boosting Algorithms such as AdaBoost and Gradient Boosting Machines (GBM).
9.5 Extreme Gradient Boosting (XGBoost)
Introduction to XGBoost as an Efficient and Scalable Implementation of Gradient Boosting.
Understanding Key Hyperparameters and Optimization Strategies in XGBoost.
9.6 Model Evaluation and Tuning
Techniques for Evaluating Tree-Based Models including Cross-Validation and Performance Metrics.
Hyperparameter Tuning using Grid Search or Randomized Search to Optimize Model Performance.
Module 10: Introduction to Deep Learning Models
10.1 Understanding Deep Learning
Overview of Deep Learning as a Subset of Machine Learning that Emphasizes Learning Representations from Data.
Importance of Deep Learning in Various Domains including Computer Vision, Natural Language Processing, and Robotics.
10.2 Introduction to Perceptrons
Basics of Perceptrons: The Fundamental Building Blocks of Neural Networks.
Understanding the Structure and Functionality of Perceptrons in Learning from Data.
10.3 Single-Layer Perceptron
Concept of Single-Layer Perceptron: Simplest Form of Neural Network with One Layer of Neurons.
Learning Process of Single-Layer Perceptron: Training Data, Activation Function, and Weight Updates.
10.4 Limitations of Single-Layer Perceptron
Perceptrons' Inability to Learn Non-Linear Relationships and Complex Patterns.
Understanding the Need for Multi-Layer Perceptrons to Address Limitations.
10.5 Introduction to Multi-Layer Perceptrons (MLPs)
Extending Perceptrons to Multi-Layer Perceptrons: Introducing Hidden Layers.
Overcoming Limitations of Single-Layer Perceptrons through Multiple Layers of Neurons.
10.6 Activation Functions in Deep Learning
Overview of Activation Functions: Sigmoid, ReLU, Tanh, etc.
Role of Activation Functions in Introducing Non-Linearity to Neural Networks.
