Offering 48 hours course in Data Science & Gen AI for engineering & MBA students.

Course delivered by industry expert.

Course Curriculum

Module1: Python Basics: It will help learn the tool, Python to be used for working with data-2h

• Introduction to Python
• OOP: Object & Class
• Serialization: Pickle Library
• Variables
• Lists
• Tuples
• Dictionary
• Sets
• List and Dictionary Comprehensions
• Conditional Statements (If, If-else,elif)
• Loops (For, While)
• Functions
• Lambda Function
• Apply Function

Class Exercises

Module2: Python NUMPY Library: It is used to perform a wide variety of mathematical operations on arrays-1h

• Array Characteristics
• Array Creation (arrange, linspace, flatten)
• Array Indexing (Slicing)
• Array Manipulation
  • Reshape
  • Concatenate
  • Append
  • Insert
  • Delete
  • Transpose
• Class Exercises

Module3: Python PANDAS Library: It is used for data manipulation, data cleaning, data analysis-2h

• Series
• Data Frames
• Reading csv file
• Sub Setting / Filtering / Slicing Data
• Dropping rows & columns
• Adding/Deleting columns
• Binning
• Renaming columns or rows
• Sorting
• Data type conversions
• Handling duplicates /missing
• Broadcasting
• Group by Function
• Map Function
• Visualization (bar graph, histogram, box plot)
• Merging (Inner, Left, Right, Outer)
• EDA

Class Exercises

Module4: Python MATPLOTLIB Library: Data Visualization part1 -1h

• Bar Plot
• Stacked Bar Plot
• Histogram
• Line Chart
• Box plot
• Pie-Chart

Class Exercises

Module5: Python SEABORN Library: Data Visualization part2 -1h

• Bar Plot
• Histogram
• Pairwise Plots: Joint Plot, Pair Plot
• Categorical Scatter Plot: Strip-plot, Swarm-plot
• Box-Plot
• Violin Plot
• Cat Plot
• Facet Grid
• Pair Grid
• Line Plot

Class Exercises

Module6: Basic Statistics: For business analysis-1h

• Type of Data
• Statistics
• Type of Statistics
• Descriptive Statistics
• Mean, Median, Mode (Measures of Central Tendency)
• Standard Deviation, Variance (Measures of Dispersion)
• Normal Distribution
• Standard Normal Distribution
• Standard Error
• Sampling
• Probability

Class Exercises

Module7: Advance Statistics: For business analysis-1h

• Confidence Interval
• T-Test & Z-Test
• P-value
• Hypothesis Testing
• Type I Error & Type II Error
• Chi-Square Test
• ANOVA
• Covariance
• Correlation

Class Exercises

Module8: Supervised Machine Learning: Linear Regression (Solve business problems where we have to predict a value)-2h

• Introduction
• Assumptions (Linearity, Hetroskedasticity, Multivariate Normality, etc)
• Data Preparation (Outlier Treatment, Missing Value Imputation)
• Building Linear Regression Model
• Understanding model metrics (p-value, R-square/Adjusted R-square etc)
• Multicolinearity (VIF)
• Model Validation (MAPE,RMSE)
• Case study

Module9: Supervised Machine Learning: Logistic Regression (Used for binary classification business problems)-2h

• Introduction
• Linear Regression Vs. Logistic Regression
• Data Preparation (Outlier Treatment, Missing Value Imputation, Dummy Variable Creation)
• Building Logistic Regression Model
• Understanding model metrics (p-value)
• Multicolinearity (VIF)
• Model Validation (Confusion Matrix, ROC curve, AUC, etc)
• Case study

Module10: Supervised Machine Learning: Decision Tress (Used for multi-class classification business problems & regression business problems)-2h

• Introduction
• Types
• Entropy, Gini Index, Chi-Square
• Overfitting
• Pruning
• Cross – Validation
• Case study

Module11: Supervised Machine Learning: Ensemble (Used for multi-class classification business problems & regression business problems)-2h

• Introduction
• Bagging
  • Random forest
• Boosting
  • Gradient Boosting Machines (GBM)
• Case study

Module12: Supervised Machine Learning: KNN (Used for multi-class classification business problems & regression business problems)-2h

• Introduction
• Working of KNN
• Optimal value of K
• Case study

Module13: Unsupervised Machine Learning: Clustering (Used for segmenting data points into different groups)-1h

• Introduction
• K -Means Clustering
• Cluster Evaluation and Profiling
• Case study

Module14: Unsupervised Machine Learning: PCA (Used for segmenting data points into different groups)-1h

• Introduction
• Curse of dimensionality
• Process of working
• Case study

Module15: Unsupervised Machine Learning: Isolation Forest (Used for Anomaly detection/ Fraud detection)-1h

• Introduction
• Contamination Factor
• Case study

Module16: Time Series Forecasting: Used for inventory planning or forecasting future value-3h

• Introduction
• Time Series Components: Trend, Seasonality, Cyclicity
• Smoothening Techniques– Moving Averages, Exponential
• ARIMA
• Accuracy
• Neural Prophet
• Case study

Module17: Text Analytics: Used for text mining business problems working with unstructured data-3h

• Introduction
• Text Pre-processing
  • Noise Removal
  • Lemmatization
  • Stemming
  • Feature Engineering on Text Data
  • Bag of words
  • TF-IDF
• Case study

Module18: AI: Deep Learning, Keras-4h

• Introduction: Deep Learning
• Deep Learning vs Machine learning
• Neural Networks
• Activation Functions, hidden layers, hidden units
• Backpropagation
• Vanishing Gradient Problem
• Exploding Gradient Problem
• Perceptron & Multi-layer Perceptron
• CNN
• RNN
• Case study

Module19: Model Deployment: Using model for predicting output on new input values-2

• Flask
• Case study

Module20: Power BI: Data Visualization-7h

• Introduction
• Connection to Data Sources
• Power Query Editor
• Views: Report, Data, and Relationships
• Data Modelling
• Data Relationship
• DAX
• DAX queries: Calculated Columns & Calculated Measures
• Data Visualization Charts
• Slicers
• Dashboard
• Case study

Module21: Generative AI- 6h

• Introduction
• Large Language Models (LLM)-GPT
• Transformer Architecture
• Prompt Engineering
• Configuration
• Lang Chain Framework
• Use Cases

Module22: Project-1h

• Capstone Project

Course Duration: 48hours