Data Science Course in Bangalore

1. Using a Spread sheet
   ● What is Excel?
   ● Why Use Excel?
   ● Excel Overview
   ● Excel Ranges, Selection of Ranges
   ● Excel Fill, Fill Copies, Fill Sequences, Sequence of Dates
   ● Excel adds, move, and delete cells
   ● Excel Formulas
   ● Relative and Absolute References

2. Functions
   ● SUM
   ● AVERAGE
   ● COUNT
   ● MAX & MIN
   ● RANDBETWEEN
   ● TRIM
   ● LEN
   ● CONCATENATE
   ● TODAY & NOW

3. Advanced Functions
   ● Excel IF Function
   ● Excel If Function with Calculations
   ● How to use COUNT, COUNTIF, and COUNTIFS Function?

4. Data Visualization
   ● Excel Data Analysis – Data Visualization
   ● Visualizing Data with Charts
   ● Chart Elements and Chart Styles
   ● Data Labels
   ● Quick Layout

   ● An Introduction to RDBMS & SQL
   ● Data Retrieval with SQL
   ● Pattern matching with wildcards
   ● Basics of sorting
   ● Order by clause
   ● Aggregate functions
   ● Group by clause
   ● Having clause
   ● Nested queries
   ● Inner join
   ● Multi join
   ● Outer join
   ● Adding and Deleting columns
   ● Changing column name and Data Type
   ● Creating Table from existing Table
   ● Changing Constraints Foreign key

1. An Introduction to Python
   ● Why Python , its Unique Feature and where to use it?
   ● Python environment Setup/shell
   ● Python Identifiers, Keywords

2. Conditional Statement ,Loops and File Handling
   ● Python Data Types and Variable
   ● Condition and Loops in Python
   ● Decorators
   ● Python Files and Directories manipulations

3. Python Core Objects and Functions
   ● String/List/Dictionaries/Tuple
   ● Python built in function
   ● Python user defined functions

4. Introduction to NumPy
   ● Array Operations
   ● Arrays Functions
   ● Array Mathematics
      o Mean
      o Standard Deviation
      o Max
      o Min
   ● Array Manipulation
      o Reshaping
      o Resizing
   ● Random function
   ● Transpose

5. Data Manipulation with Pandas
   ● Data Frames
   ● Series
   ● Creating Pandas DataFrame
   ● Selection in DFs
   ● Data Describe
   ● Data info
   ● Retrieving in DFs
   ● Reshaping the DFs – Pivot
   ● Combining DFs
      o Merge
      o Concatenation

6. Visualization with Matplotlib
   ● Matplotlib Installation
   ● Matplotlib Basic Plots & it’ s Containers
   ● Matplotlib components and properties
   ● Scatter plots
   ● Histograms
   ● Bar Graphs
   ● Pie Charts
   ● Box Plots

7. SciPy
   ● Hypothesis Testing using Scipy
   ● Shapiro Test
   ● Spearmaman Test
   ● T-Test of Independents
   ● Chi-Square Test

1. Exploratory Data Analysis
   ● Data Exploration
   ● Missing Value handling
   ● Outliers Handling
   ● Feature Engineering
   ● Train-Test Split
   ● Standard Scaler
   ● Min-Max Scaler
   ● Data Pre-processing
   ● Resampling
      o Up-Sampling
      o Down-Sampling

2. Machine Learning: Supervised Algorithms
   ● Introduction to Machine Learning
   ● Linear Regression
   ● Model Evaluation and performance
      o R2 Score and Adjusted R2 Score
      o Mean Squared Error
      o Root Mean Squared Error
   ● Gradient Descent
   ● Logistic Regression

3. Model Evaluation and performance
   ● Accuracy ,Precision
   ● Recall
   ● F1 Score
   ● Confusion Matrix
   ● Classification Report
   ● K-Fold Cross Validation
   ● ROC, AUC etc…
   ● K-Nearest Neighbor Algorithm
   ● Decision Tress
   ● Random Forest
   ● Support Vector Machines
   ● Hyper parameter tuning

4. Machine Learning: Unsupervised Learning Algorithms
   ● Similarity Measures
   ● K-Means Clustering
      o Elbow Method

5. Ensemble algorithms
   ● Bagging
   ● Boosting
   ● Principal Components Analysis

1. Artificial Intelligence
   ● An Introduction to Artificial Intelligence
   ● History of Artificial Intelligence
   ● Future and Market Trends in AI

2. Natural Language Processing
   ● Tokenization
   ● Part of Speech Tagging (POS Tagging)
   ● Named Entity Recognition
   ● Semantic Analysis
   ● Sentiment Analysis

3. Artificial Neural Network
   ● Understanding Artificial Neural Network
   ● The Activation Function ReLU and Softmax
   ● Building an ANN
   ● Evaluation the ANN

4. Conventional Neural Networks
   ● CNN Intuition
   ● Convolution Operation
   ● Filtering operation
   ● Padding on image
   ● Pooling Layer
      o Max Pooling
   ● Fully Connected Dense Layer
   ● Building a CNN
   ● Evaluating the CNN

5. Recurrent Neural Network
   ● RNN Intuition
   ● Building an RNN
   ● Evaluating the RNN
   ● LSTM in RNN

6. Time Series Data
   ● Introduction to Time series data
   ● Data cleaning in time series
   ● Pre-Processing Time-series Data
   ● Prediction in Time Series using LSTM
   ● Prediction in Time Series using ARIMA

1. Foundations of Artificial Intelligence

• Explore the evolution of Artificial Intelligence (AI) from the 1950s to today, covering key milestones like the Turing Test and Deep Blue.
• Understand core AI concepts: Machine Learning (ML), Deep Learning (DL), Neural Networks, Perceptrons, and Transformers (e.g., BERT, GPT).
• Learn about AI types: Narrow, General, and Superintelligent.
• Discover real-world AI applications across industries like customer service, marketing, and finance.
  
  

2. Introduction to Generative AI

• What is Generative AI
• Evolution from Traditional AI → Gen AI
• Overview of Generative AI models Large Language Models (LLMs)
• GPT, Gemini, Claude (comparison & use cases)
  
  

3. Prompt Engineering & Task Automation

• What is Prompt Engineering & why it matters
• Prompt structure: Context → Task → Output
• Prompting Techniques
• Zero-shot prompting
• Few-shot prompting
• Chain-of-Thought prompting
• ReAct prompting (Reason + Act) Role-based prompting
• Common prompt mistakes & how to fix them
• Reusable prompt templates
• Get hands-on experience using ChatGPT and Claude for task automation

1. Introduction to Git & Distributed Version Control
2. Life Cycle
3. Create clone & commit Operations
4. Push & Update Operations
5. Stash, Move, Rename & Delete Operations.

Module 1: Introduction to Power BI

1. Introduction to Business Intelligence & Power BI
   ● Need for Business Intelligence
   ● Evolution of Power BI
   ● What is Power BI? Features & Components

2. Power BI Ecosystem
   ● Power BI Desktop
   ● Power BI Service
   ● Power BI Mobile
   ● Power BI Report Builder vs Paginated Reports

3. Installation & Setup
   ● Downloading Power BI Desktop
   ● Installing and configuring settings
   ● Exploring the start screen and workspace

4. Power BI Interface Overview
   ● Ribbon and Navigation Pane
   ● Report, Data, and Model views
   ● Fields Pane and Visualizations Pane

5. Supported Data Sources
   ● Excel, CSV, SQL Server, Web APIs
   ● Cloud sources: Azure, SharePoint, OneDrive
   ● Folder as a data source

Module 2: Data Loading and Transformation with Power Query
1. Connecting to Data
   ● Import vs DirectQuery
   ● Loading from Excel, CSV, Web, SQL Server
   ● Data Preview and Load options

2. Column-Level Transformations
   ● Split column by delimiter/position
   ● Merge columns
   ● Change data types
   ● Rename columns
   ● Add column from examples

3. Row-Level Transformations
   ● Filter rows based on conditions
   ● Remove or keep rows
   ● Sorting data
   ● Grouping data with aggregations

4. Data Cleaning & Shaping
   ● Handling missing values: Replace, Fill up/down
   ● Remove duplicates
   ● Pivot and Unpivot operations
   ● Creating conditional columns

Module 3: Visualizations in Power BI
1. Core Visual Elements
   ● Bar/Column charts, Line charts, Pie/Donut charts
   ● Matrix and Table visuals
   ● Cards and Multi-row cards
   ● Maps: Shape map, Filled map

2. Slicers and Filters
   ● Basic Slicers
   ● Date and Range slicers
   ● Sync Slicers across pages
   ● Drill-down and Drill-through

3. Formatting and Interactions
   ● Title, label, legend customization
   ● Tooltips, data labels, axis formatting
   ● Visual interaction controls
   ● Custom themes and color palettes

Project Work and Case Studies ML

❖ Profit prediction on Startups data using Multiple Linear Regression.

❖ Diabetes, Pre-Diabetes and Non-Diabetes Classification using Multiclass

❖ Logistic Regression

❖ Spam Mail Detection using Gradient Boost ,XGBoost and Random Forest.

❖ Drug classifications using K-Nearest Neighbours

❖ Loan Defaulter Classification using SVM

❖ Customer Grouping using Kmeans and Agglomerative Clustering

❖ Product associations using Association rule mining.

Capstone Project 1 : Delivery Duration Prediction

Capstone Project 2 : Machine Failure Prediction

Project Work and Case Studies AI

❖ PowerPlant Energy predictions using ANN.

❖ CIFAR10 Image Classification using CNN

❖ Handwritten Digit Image classification using CNN.

❖ IMDB Movie reviews sentiment analysis using RNN

❖ AIR Passenger Prediction using ARIMA Time Series Analysis

❖ Next Word Generator using NLP and LSTM Text Generation

Capstone Project : Delivery Duration Prediction

Project Work and Case Studies Power BI
❖ Project: Retail Sales Dashboard
   ● Sales vs Target KPIs
   ● Product category and region-wise breakdown
❖ Project: HR Analytics Dashboard
   ● Attrition rate, hiring trends
   ● Department-level analysis
❖ Project: Financial Performance Report
   ● P & L view, trend analysis, YoY comparison
❖ Project: Supply Chain and Inventory Dashboard
   ● Stock availability
   ● Supplier performance tracking

Project Domains: Finance

   ● The insurance company wants to decide on the premium using various

      parameters of the client.
   ● It’ s an important problem to keep the clients and attract new ones.

By completing this Project you will learn:
   ● How to collect data?, how to justify the right features? , Which ML / DL model is

      best in this situation? How much data is enough?
   ● How to have CI/CD in the project?
   ● How to do Deployment of Project to cloud?

Project Domain: Image Processing in Health care

   ● A hospital wants to automate the Detection of pneumonia in X-rays using image processing.

By completing this Project you will learn:
   ● How to handle image data? How to preprocess and augment image data?
   ● How to choose the right model for the image process?
   ● How to apply transfer learning in image processing?
   ● How to do incremental learning & CI/CD in the project?
   ● How to do Deployment of Project to cloud?

Natural Language Processing

   ● One of the companies wants to automate applicant’ s level in English

      communication.
   ● Create a ML/DL model for this task.

By completing this Project you will learn:
   ● How do convert text to the right representation?
   ● How to preprocess text data? How to select the right ML/DL model for text data?
   ● How to do transfer learning in Text Analytics?
   ● How to do CI/CD in a text analytics project? How to do Deployment of Project to cloud?

Mechanical

   ● A mechanical company wants to perform predictive maintenance of engineparts.
   ● This enables the company to efficiently change parts before the machine fails.

By completing this Project you will learn:
   ● How to handle time-series data?
   ● How to preprocess time series data?
   ● How to create ML/DL model for Time-series Data?
   ● How to do CI/CD in a text analytics project?
   ● How to do Deployment of Project to cloud?

Sales / Demand Forecasting

   ● Predict the sales/demand of a product of a company.
   ● Sales / Demand forecasting of the product will help the company efficiently manage the resources.
   ● Create a ML/DL model for this problem.

By completing this Project you will learn:
   ● How to handle time-series data?
   ● How to preprocess time series data?
   ● How to create ML/DL model for Time-series Data?
   ● How to do CI/CD in a text analytics project? How to do Deployment of Project to cloud?