Data Science with AI masters program

₹ 38,000

Join Our Free Master Class Session

Next Cohort Starts: 12 Aug, 2023
Only 10 learners per batch

Program Duration: 4-5 Months
At 10-15 hours/week

Online / Classroom
Learning Format

Data Science with AI Program Overview

Our course provides a comprehensive curriculum that covers everything from foundational to advanced concepts techniques. Gain practical experience with real-world projects, get certified and make a unique mark in the competitive job market. Elevate your expertise to new dimensions in the world of digital development.

Key Features

Advanced learning plans to get strong base on skills needed for a successful career

Practice in live coding environment and receive feedbacks on your code that builds coding potential.

Personalised Mentorship: Boost your confidence with mentorships, mock interviews and guidance

Engage with our community of learners and experts to receiving valuable support and insights

From basic to advanced level of coding will be explained until you reach an expertise level

Access to class Recordings that offers you to learn & practice at your comfortable times.

Course Plan

  • Introduction to Data Science
  • Life cycle of Data Science
  • Skills required for Data Science
  • Applications of Data Science in different Industries

Introduction to Python programming

  • Installing Python and setting up the development environment
  • Variables, data types, and type conversions
  • Operators: arithmetic, comparison, logical, and assignment
  • Control structures: if, else, elif, nested conditionals
  • Loops: for loop, while loop, nested loops
  • Functions and their role in code organization
  • Handling errors with try-except blocks
  • Working with files: reading, writing, and appending data

Introduction to NumPy

  • What is NumPy?
  • Advantages of using NumPy for data science
  • Installing NumPy
  • NumPy Arrays
  • Creating NumPy arrays
  • Data types in NumPy arrays
  • Array indexing and slicing
  • Array operations (arithmetic, broadcasting, etc.)

 

Working with Multi-dimensional Arrays

  • Creating multi-dimensional arrays
  • Shape, size, and dimensions of arrays
  • Array reshaping and resizin

 

Array Operations

  • Element-wise operations
  • Aggregation and reduction functions
  • Array broadcasting and vectorization

 

Array Manipulation

  • Adding and removing elements in arrays
  • Stacking and concatenating arrays
  • Splitting arrays

 

Indexing and Slicing

  • Boolean indexing and masking
  • Fancy indexing and advanced slicing

 

Array Math and Statistics

  • Mathematical functions in NumPy
  • Statistical functions (mean, median, variance, etc.)
  • Linear algebra with NumPy (dot product, matrix operations)

 

Random Number Generation

  • Generating random data with NumPy
  • Seeds and reproducibility

 

Handling Missing Data with NumPy

  • Dealing with NaN values in NumPy arrays

 

File Input and Output

  • Loading and saving NumPy arrays from/to files (CSV, text, binary)

Introduction to Pandas

  • What is Pandas and why it is essential for Data Science
  • Installing Pandas
  • Importing the Pandas library
  • Data structures in Pandas: Series and DataFrame 

 

Getting Started with DataFrames

  • Creating DataFrames from different data sources (CSV, Excel, etc.)
  • Inspecting the DataFrame: head(), tail(), info(), describe()
  • Selecting and accessing data: loc, iloc
  • Conditional data selection using boolean indexing

 

Data Cleaning and Preparation

  • Handling missing data: isnull(), dropna(), fillna()
  • Removing duplicates: drop_duplicates()
  • Data transformation: applying functions, mapping, and replacing values
  • Data type conversions

 

Data Manipulation with Pandas

  • Indexing and slicing DataFrames
  • Filtering rows and columns based on conditions
  • Aggregation and grouping data: groupby()
  • Pivot tables and cross-tabulations
  • Merging, joining, and concatenating DataFrames

Understanding the importance of data visualization in data science Overview of popular Python visualization libraries: Matplotlib and Seaborn

Matplotlib Basics

  • Installing Matplotlib and getting started
  • Creating basic plots: Line plot, Scatter plot, Bar plot
  • Customizing plots: Labels, Titles, Colors, Markers, and Linestyles
  • Working with multiple plots: Subplots

 

Advanced Matplotlib

  • Plotting categorical data: Bar plots, Pie charts
  • Plotting time series data
  • Working with different plot types: Histograms, Box plots, Violin plots
  • Adding annotations and text to plots

 

Seaborn Introduction

  • Installing Seaborn and understanding its advantages over Matplotlib
  • Seaborn vs. Matplotlib: When to use each library

 

Seaborn Plots

  • Creating basic Seaborn plots: Scatter plot, Line plot, Bar plot
  • Visualizing distributions: Histograms, KDE plots
  • Plotting categorical data: Bar plots, Box plots, Violin plots
  • Heatmaps and cluster maps for correlation analysis

 

Data Exploration and Visualization

  • Combining Matplotlib and Seaborn for more complex visualizations
  • Exploring relationships between variables using joint plots and pair plots
  • Using FacetGrids for multi-plot visualizations

 

Styling and Aesthetics

  • Customizing Seaborn styles and themes
  • Setting plot aesthetics: Colors, Fonts, Grids
  • Incorporating external color palettes

Introduction to Power BI

  • What is Power BI?
  • Key features and benefits
  • Understanding the Power BI ecosystem

 

Getting Started with Power BI

  • Downloading and installing Power BI Desktop
  • Creating a Power BI account
  • Overview of the Power BI interface

 

Importing and Transforming Data

  • Connecting to data sources (e.g., Excel, databases, online services)
  • Data loading and transformation using Power Query Editor

 

Data Modeling in Power BI

  • Understanding tables and relationships
  • Creating calculated columns and measures

 

Introduction to DAX (Data Analysis Expressions) language

  • Creating Visualizations
  • Building basic visualizations (e.g., bar charts, pie charts, tables)
  • Applying formatting and customization to visuals
  • Utilizing interactive features (e.g., drill-down, cross-filtering)

 

Designing Reports and Dashboards

  • Combining multiple visuals to create reports
  • Organizing and arranging visuals on a report page
  • Creating interactive dashboards for data exploration

Introduction to Data Science and Descriptive Statistics

  • Overview of data science and its applications
  • Types of data: categorical, numerical, continuous, and discrete
  • Data visualization: histograms, box plots, scatter plots, etc.
  • Measures of central tendency: mean, median, mode
  • Measures of dispersion: variance, standard deviation, range

 

Probability Basics

  • Fundamentals of probability theory
  • Probability rules: addition and multiplication rules
  • Conditional probability and Bayes’ theorem
  • Probability distributions: discrete and continuous
  • Expectation and variance of random variables

 

Sampling and Estimation

  • Simple random sampling
  • Point estimation and interval estimation
  • Sampling distributions
  • Central Limit Theorem and its applications

 

Statistical Inference

  • Hypothesis testing: null and alternative hypotheses
  • Type I and Type II errors
  • Commonly used significance levels
  • One-sample and two-sample t-tests
  • Chi-square test for independence
  • Analysis of Variance (ANOVA)

 

Regression Analysis

  • Linear regression
  • Multiple regression
  • Model evaluation and selection
  • Residual analysis

 

Probability Distributions for Data Science

  • Binomial distribution
  • Poisson distribution
  • Normal distribution and its applications
  • Exponential distribution

 

Bayesian Statistics

  • Bayesian inference
  • Bayesian updating
  • Bayesian regression

Introduction to Data Science and Machine Learning

  • Understanding the data science workflow
  • Overview of machine learning and its applications
  • Types of machine learning: supervised, unsupervised,
  • and reinforcement learning

 

Data Preprocessing

  • Data cleaning and handling missing values.
  • Feature engineering and selection
  • Data transformation and normalization

 

Supervised Learning Algorithms

  • Linear regression
  • Logistic regression
  • Decision trees and random forests
  • Support Vector Machines (SVM)
  • k-Nearest Neighbors (k-NN)
  • Naive Bayes
  • Gradient Boosting algorithms (e.g., XGBoost, LightGBM)

 

Unsupervised Learning Algorithms

  • K-means clustering
  • Hierarchical clustering
  • Dimensionality reduction techniques (e.g., Principal Component
  • Analysis – PCA)

 

Model Evaluation and Validation

  • Cross-validation techniques
  • Performance metrics: accuracy, precision, recall, F1-score, etc.
  • Overfitting and underfitting

 

Model Selection and Hyperparameter Tuning

  • Grid search and randomized search for hyperparameter optimization

 

Ensemble Methods

  • Bagging and bootstrap aggregating
  • Stacking and blending

 

Introduction to Recommender Systems

  • Collaborative filtering
  • Content-based filtering

 

Introduction to Time Series Analysis

  • Time series data handling
  • Time series forecasting using machine learning
  • Basics of neural networks: Neurons, activation functions, and backpropagation

  • Introduction to TensorFlow and Keras libraries

  • Building and training deep learning models for image classification and

  • regression tasks

  • Convolutional Neural Networks (CNN) for image analysis

  • Recurrent Neural Networks (RNN) for sequential data processing

 

Natural Language Processing (NLP)

  • Introduction to NLP and its applications

  • Text preprocessing: Tokenization, stop word removal, stemming, and lemmatization

  • Bag-of-Words (BoW) and TF-IDF representation

  • Sentiment analysis and text classification using NLP techniques

Data Cleaning and Preparation

  • Removing duplicates
  • Handling missing values
  • Data validation and data integrity
  • Text-to-columns and data formatting
  • Data transformation techniques

 

Advanced Formulas and Functions

  • Logical functions (IF, AND, OR, etc.)
  • Lookup and reference functions (VLOOKUP, HLOOKUP, INDEX-MATCH)
  • Text functions (CONCATENATE, LEFT, RIGHT, MID, etc.)
  • Date and time functions
  • Statistical functions (AVERAGEIF, COUNTIF, SUMIF, etc.)

 

Data Analysis Tools

  • PivotTables and PivotCharts
  • Data filtering and sorting
  • Subtotals and grouping data
  • What-If analysis (Data tables, Goal Seek, Scenario Manager)

 

Data Visualization

  • Creating charts and graphs (line charts, bar charts, pie charts, etc.)
  • Customizing chart elements
  • Advanced charting techniques (combination charts, sparklines, etc.)

 

Data Analysis with Excel

  • Descriptive statistics (mean, median, standard deviation, etc.)
  • Histograms and frequency distributions
  • Correlation and regression analysis
  • Data analysis using Excel’s Data Analysis ToolPak add-in

 

Data Import and Export

  • Importing data from external sources (CSV, text files, databases, etc.)
  • Exporting data to different file formats (CSV, PDF, etc.)

 

Automating Tasks with Macros

  • Introduction to VBA (Visual Basic for Applications)
  • Recording and running macros
  • Creating custom functions and procedures

 

Data Integration with Excel

  • Combining data from multiple sources
  • Data consolidation techniques

 

Excel and Data Visualization Tools Integration

  • Linking Excel data with Power BI or Tableau
  • Importing and exporting data between Excel and Python/R

 

Excel Efficiency and Productivity

  • Keyboard shortcuts and time-saving tips
  • Excel add-ins and productivity tools
 
 
 

Introduction to SQL

  • Overview of SQL and its applications in data science.
  • Relational databases and their components.
  • Basic SQL syntax and commands.

 

Querying Data

  • SELECT statement and retrieving data from tables.
  • Filtering data with the WHERE clause.
  • Sorting data with the ORDER BY clause.
  • Limiting results with the LIMIT clause.

 

Filtering and Combining Data

  • Using logical operators (AND, OR, NOT) to create complex queries.
  • Working with NULL values.
  • Combining data from multiple tables with JOIN operations
  • (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN).

 

Data Aggregation

  • Understanding aggregate functions (SUM, AVG, COUNT, MIN, MAX).
  • GROUP BY clause and grouping data based on specific criteria.
  • HAVING clause for filtering aggregated data.

 

Data Manipulation

  • Inserting, updating, and deleting data in a database.
  • Transactions and their importance.

 

Data Analysis with SQL

  • Subqueries and their applications.
  • Common table expressions (CTEs).
  • Window functions for advanced data analysis.

 

Advanced SQL Techniques

  • Working with date and time data.
  • String manipulation and regular expressions.
  • Conditional expressions and CASE statements.

 

Performance Optimization

  • Understanding indexes and their impact on query performance.
  • Query optimization techniques.

 

Data Import and Export

  • Importing data into a SQL database.
  • Exporting query results to external files.

 

Working with Large Datasets

  • Partitioning data to improve performance.
  • Using temporary tables and table variables.
 
 

Sentiment Analysis for Social Media Comments

Summary: Implement a deep learning model to perform sentiment analysis on social media comments. The model will classify comments as positive, negative, or neutral, enabling businesses to gauge customer sentiments effectively.

Domain: Natural Language Processing (NLP).

 

Predicting Customer Churn in Telecom Industry

Summary: Leveraging machine learning algorithms, this project predicts customer churn in the telecom industry. By analysing customer behaviour and usage patterns, it helps identify potential churners, allowing businesses to take proactive measures to retain valuable customers. Domain: Customer Analytics, Telecom.

 

Stock Price Prediction using Time Series Analysis

Summary: Employing time series forecasting techniques and historical stock data, this project predicts future stock prices. It can aid investors and traders in making informed decisions and managing their portfolios effectively.

Domain: Finance, Stock Market.

 

Image Classification for Medical Diagnostics

Summary: Using deep learning and convolutional neural networks (CNNs), this project aims to classify medical images into different diagnostic categories. It can assist medical professionals in making accurate and timely diagnoses, leading to improved patient outcomes. 

Domain: Medical Imaging, Healthcare.

 

Personalized Movie Recommendation System

Summary: This project builds a recommendation system using collaborative filtering and matrix factorization techniques to suggest movies based on users’ preferences and viewing history. It enhances user experience on streaming platforms and boosts user engagement. 

Domain: Recommender Systems, Entertainment.


Crop Yield Prediction for Precision Agriculture

Summary: Leveraging machine learning and historical agricultural data, this project predicts crop yields for different regions and seasons. It aids farmers in optimizing resource allocation and improving overall crop productivity. 

Domain: Agriculture, Remote Sensing.


Recommender System for E-commerce

Summary: Implement a personalized recommender system that suggests products to users based on their past behaviour and preferences. This project aims to enhance user engagement and increase sales in e-commerce platforms. 

Domain: Recommendation Systems.


Air Quality Prediction

Summary: Develop a machine learning model to predict air quality levels in each area based on historical data and environmental factors. This project aims to provide early warnings for potential air pollution events.
Domain: Environmental Sciences.


Natural Language Generation for Chatbots

Summary: Implement a natural language generation model to improve the conversational abilities of chatbots. The system will generate human-like responses, enhancing user interactions.
Domain: Natural Language Processing (NLP) and Chatbots.


Language Translation using Sequence-to-Sequence Models

Summary: This project involves implementing a sequence-to-sequence model for language translation tasks. By leveraging recurrent neural networks or transformers, the model can translate text from one language to another, facilitating cross-lingual communication in the natural language processing domain.


Climate Change Impact Analysis using Machine Learning

Summary: Build a machine learning model to analyse climate data and predict the potential impact of climate change on specific regions. The project contributes to climate research, environmental planning, and disaster management. 

Domain: Environmental Science, Climate Change Analysis

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