Best Data Science (OnlineCourse) in India

• Introduction to programming
• R or Python?
• Why Python for Data Science?
• Different job roles with Python
• Different Python IDEs
• Downloading and setting up python environment
Hands-On – Installing Python – IDLE

• Python input and output operations.
• Comments
• Variables, rules for naming variables
• Basic Data Types in Python
• Typecasting in python
Hands-On – Using comments, variables, data types, and typecasting in python program

● Arithmetic operators
● Assignment operators
● Comparison operators
● Logical operators
● Identity operators
● Membership Operators
● Bitwise Operators
Hands-On – Working with different data types in a program

• Creating strings
• String formatting
• Indexing
• Slicing
• String methods
Hands-On – Performing string operations

● Creating lists
● Properties of lists
● List indexing
● List slicing
● List of lists
● List Methods
● Adding, Updating & removing elements from lists
Hands-On – Slicing, Indexing, and using methods on lists

• Syntax to create tuples
• Tuple properties
• Indexing on tuples
• Slicing on tuples
• Tuple methods
Hands-On – Working with tuples

• Syntax for creating sets
• Updating sets
• Set operations and methods
• Difference between sets, lists and tuples
Hands-On – Performing set operations in a program

• Syntax for creating Dictionaries
• Storing data in dictionaries
• Dictionaries keys and values
• Accessing the elements of dictionaries
• Dictionary methods
Hands-On – Creating dictionaries and using dictionaries methods

• Setting logic with conditional statements
• If statements
• If -else statements
• If-elif-else statements
Hands-On – Setting logic in programs using conditional statements

• Iterating with python loops
• while loop
• for loop
• range
• break
• continue
• pass
• enumerate
• zip
• assert
Hands-On – Iterating with loops in python

● Solving Level by Level Challenges
● Assignments to acquire Bronze and Silver Level badges

• Why List comprehension
• Syntax for list comprehension
• Syntax for dict comprehension
Hands-On – Using List and Dictionary comprehension

• What are Functions
• Modularity and code reusability
• Creating functions
• Calling functions
• Passing Arguments
• Positional Arguments
• Keyword Arguments
• Variable length arguments (*args)
• Variable Keyword length arguments (**kargs)
• Return keyword in python
• Passing function as argument
• Passing function in return
• Global and local variables
• Recursion
Hands-On – Creating our own functions,passing arguments and performing operations

• Lambda
• Lambda with filter
• Lambda with map
• Lambda with reduce
Hands-On – Working with lambda, filter,map and reduce in python

● Creating and using generators
Hands-On – Creating and using generators

• Creating modules
• Importing functions from different module
• Importing Variables from different modules
• Python builtin modules
Hands-On – Creating and importing Modules

• Creating packages
• Importing modules from package
• Different ways of importing modules and packages
• Working on Numpy,Pandas and Matplotlib
Hands-On – Creating and importing packages

• Syntax errors
• Logical errors
• Handling errors using try,except and finally
Hands-On – Handling Errors with try and except

• Creating classes & Objects
• Attributes and methods
• Understanding __init__ constructor method
• Class and instance attributes
• Different types of of methods
• Instance methods
• Class methods
• Static methods
• Inheritance
• Creating child and parent class
• Overriding parent methods
• The super() function
• Understanding Types of inheritance
• Single inheritance
• Multiple inheritance
• Multilevel inheritance
• Polymorphism
• Operator overloading
Hands-On – Creating classes, objects. Creating methods and attributes. Working with different methods. Using inheritance and polymorphism.

• date module
• time module
• datetime module
• time delta
• formatting date and time
• strftime()
• striptime()
Hands-On – working with date and time

• Understanding the use of regex
• re.search()
• re.compile()
• re.find()
• re.split()
• re.sub()
• Meta characters and their use
Hands-On – using a regular expression to search patterns

• Opening file
• Opening different file types
• Read,write,close files
• Opening files in different modes
Hands-On – Reading, Writing, Appending, opening and closing files.

● Introduction to APIs
● Accessing Public APIs
Hands-on – Accessing Public Weather APIs and People in Space API

• Installing BeautifulSoup
• Understanding web structures
• Chrome devtools
• request
• Scraping data from web using beautifulsoup
• scraping static websites
• Scraping dynamic websites using beautiful soup.
Hands-On – Scraping static and dynamic websites using beautifulsoup and selenium

• Introduction to Pandas, a Python library for data manipulation and analysis.
• Overview of NumPy, a fundamental package for scientific computing with Python.
• Explanation of key data structures in Pandas: Series and DataFrame.
• Hands-on exploration of data using Pandas to summarize, filter, and transform data.
• Data cleaning techniques, handling missing values, and dealing with outliers.
• Statistical analysis of data using NumPy functions.

• Introduction to data visualization and its importance in data analysis.
• Overview of Matplotlib, a popular plotting library in Python.
• Exploring different types of plots: line plots, scatter plots, bar plots, histogram, etc.
• Customizing plots with labels, titles, colors, and styles.
• Introduction to Seaborn, a Python data visualization library based on Matplotlib.
• Advanced plotting techniques with Seaborn: heatmaps, pair plots, and categorical plots.
• Introduction to Plotly, an interactive plotting library for creating web-based visualizations.
• Creating interactive and dynamic visualizations with Plotly.

Hands-on: lnstagram Reach Analysis

• Introduction to data bases.
• WhySQL?
• Execution of an SQL statement.
• Installing MySQL
• Load data.
• Use, Describe, Show table.
• Select.
• Limit, Offset.
• Order By.
• Distinct.
• Where, Comparison Operators, NULL.
• Logic Operators.
• Aggregate Functions: COUNT, MIN, MAX,AVG, SUM.
• Group By.
• Having.
• Order of Keywords.
• Join and Natural Join.
• Inner, Left, Right, and Outer Joins.
• Sub Queries/Nested Queries/Inner Queries.
• DML: INSERT
• DML: UPDATE, DELETE
• DML: CREATE,TABLE
• DDL: ALTER, ADD, MODIFY, DROP
• DDL: DROP TABLE, TRUNCATE, DELETE
• Data Control Language: GRANT, REVOKE

Hands-on – Storing and Analysing Scraped Dataset Using SQL

• Excel Introduction
• Workbook Window
• Create & Open Workbooks
• MS Excel Online
• Excel vs Google Sheets
• Office Button
• Ribbon and Tabs
• Features of Tabs
• Quick Access Toolbar
• Mini Toolbar
• Title, Help, Zoom, View

• Worksheet, Row, Column
• Moving on Worksheet
• Enter Data
• Select Data
• Delete Data
• Move Data
• Copy Paste Data
• Spell Check Insert Symbols

• Addition
• Sigma Addition
• Subtraction
• Calculate Average
• Sigma Average

• Fill Handle
• Fill Handle with Text
• Text with Numbers
• Fill Handle with Dates

• Create Formula open link
• Fill Handle in Formula
• Relative Referencing
• Absolute Referencing
• Instruction for Typing

• Excel IF
• If Function
• If with Calculations Excel COUNTIF Advanced If
• WHAT IF Analysis

• Introduction to Excel Charts
• Dynamic Advanced Charts
• Pivot Table with Dashboard
• Advanced Pivot Table Tips & Tricks

• Excel Macros
• Excel sumif
• Excel vlookup
• Excel ISNA
• Find & Remove Duplicates
• Create drop-down List
• Merge cells in Excel

• Building bar charts and line charts
• Creating pie charts and scatter plots
• Designing basic maps and geographic visualizations
• Using filters to subset data
• Sorting data by different criteria
• Applying quick filters for interactive exploration
• Adding labels, tooltips, and colors to visualizations
• Formatting axes and gridlines
• Customizing visual elements for better presentation
• Combining multiple visualizations into a dashboard
• Adding interactivity with filters and actions
• Arranging and organizing dashboard elements
• Publishing dashboards to Tableau Public or Tableau Server
• Embedding dashboards in websites or presentations
• Presenting and sharing dashboards effectively

• Overview of Power Bl and its features
• Understanding the Power Bl interface
• Connecting to data sources
• Importing and transforming data
• Creating bar charts and line charts
• Designing pie charts and scatter plots
• Building basic tables and matrices
• Using filters and slicers to subset data
• Adding interactivity to visualizations
• Sorting and formatting data
• Building interactive dashboards with multiple visualizations
• Adding filters and slicers for user interactivity
• Formatting and organizing dashboard elements
• Publishing reports to the Power Bl Service
• Sharing reports and dashboards with others
• Configuring security and access controls

Hands-on: lnstagram Reach Analysis

• Data- types of data
• A measure of central tendency – Mean-Median-Mode
• A measure of shape – Variance- Standard deviation, Range, IQR
• The measure of shape – Skewness, and kurtosis
• Covariance
• Correlation – Pearson correlation & Spearman’s rank correlation
• Probability – Events, Sample Space, Mutually exclusive events, Mutually exclusive events
• Classical and Conditional Probability
• Probability distribution – Discrete and Continuous
• Uniform Distribution
• Expected values, Variance, and means
• Gaussian/Normal Distribution
• Properties, mean, variance, empirical rule of normal distribution
• Standard normal distribution and Z-score

• Central Limit Theorem
• Hypothesis testing – Null and Alternate hypothesis Type – I and Type – II error
• Critical value, significance level, p-value
• One-tailed and two-tailed test
• T-test – one sample, two-sample, and paired t-test f-test
• One way and two way ANOVA
• Chi-Square test

• Introduction to Machine Learning and its types (supervised, unsupervised, reinforcement learning)
• Setting up the development environment {Python, Jupyter Notebook, libraries: NumPy, Pandas, Scikit-learn)
• Overview of the Machine Learning workflow and common data preprocessing techniques

• Definition of data science and its role in various industries.
• Explanation of the data science lifecycle and its key stages.
• Overview of the different types of data: structured, unstructured, and semi-structured.
• Discussion of the importance of data collection, data quality, and data preprocessing..

• Introduction to Data Engineering: Data cleaning, transformation, and integration
• Data cleaning and Handling missing values: Imputation, deletion, and outlier treatment
• Feature Engineering techniques: Creating new features, handling date and time variables, and encoding categorical variables
• Data Scaling and Normalization: Standardization, min-max scaling, etc.
• Dealing with categorical variables: One-hot encoding, label encoding, etc.

• Cross-validation and model evaluation techniques
• Hyperparameter tuning using
• GridSearchCV and RandomizedSearchCV Model selection and comparison

• Introduction to Regression: Definition, types, and use cases
• Linear Regression: Theory, cost function, gradient descent, residual analysis, Q-Q Plot, Interaction Terms, and assumptions
• Polynomial Regression: Adding polynomial terms, degree selection, and overfitting
• Lasso and Ridge Regression: Regularization techniques for controlling model complexity
• Evaluation metrics for regression models: Mean Squared Error (MSE), R-squared, and Mean Absolute Error (MAE)

Hands-On – House Price Prediction

• Introduction to Classification: Definition, types, and use cases
• Logistic Regression: Theory, logistic function, binary and multiclass classification
• Decision Trees: Construction, splitting criteria, pruning, and visualization
• Random Forests: Ensemble learning, bagging, and feature importance
• Evaluation metrics for classification models: Accuracy, Precision, Recall, Fl-score, and ROC curves
• Implementation of classification models using scikit-learn library

Hands-On – Heart Disease Detection & Food Order Prediction

• Support Vector Machines (SVM): Study SVM theory, different kernel functions (linear, polynomial, radial basis function), and the margin concept. Implement SVM classification and regression, and evaIuate the models.
• K-Nearest Neighbors (KNN): Understand the KNN algorithm, distance metrics, and the concept of Kin KNN. Implement KNN classification and regression, and evalu­ ate the models.
• Naive Bayes: Learn about the Naive Bayes algorithm, conditional probability, and Bayes1 theorem. Implement Naive Bayes classification, and evaluate the model1s performance

Hands-On – Contact Tracing & Sarcasm Detection

• Ada Boost: Boosting technique, weak learners, and iterative weight adjustment Gradient Boosting {XGBoost): Gradient boosting algorithm, Regularization, and hyper para meter tuning
• Evaluation and fine-tuning of ensemble models: Cross-validation, grid search, and model selection
• Handling imbalanced datasets: Techniques for dealing with class imbalance, such as oversampling and undersampling

Hands-On – Medical Insurance Price Prediction

• Introduction to Clustering: Definition, types, and use cases
• K-means Clustering: Algorithm steps, initialization methods, and elbow method for determining the number of clusters
• DBSCAN (Density-Based Spatial Clustering of Applications with Noise): Core points, density reachability, and epsilon-neighborhoods
• Evaluation of clustering algorithms: Silhouette score, cohesion, and separation met-rics

Hands-On – Credit Card Clustering

• Introduction to Dimensionality Reduction: Curse of dimensionality, feature ex­ traction, and feature selection
• Principal Component Analysis {PCA): Eigenvectors, eigenvalues, variance explained, and dimensionality reduction
• Implementation of PCA using scikit-learn library

Hands-On – MNIST Data

• Introduction to Recommendation Systems: Understand the concept of recommen- dation systems, different types (collaborative filtering, content-based, hybrid), and evaluation metrics.
• Collaborative Filtering: Explore collaborative filtering techniques, including
• user-based and item-based approaches, and implement a collaborative filtering model.
• Content-Based Filtering: Study content-based filtering methods, such as TF-1DF and cosine similarity, and build a content-based recommendation system.
  Deployment and Future Directions: Discuss the deployment of recommendation systems and explore advanced topics in NLP and recommendation systems.

Hands-On – News Recommendation System

• Introduction to Reinforcement Learning: Agent, environment, state, action, and reward
• Markov Decision Processes {MOP): Markov property, transition probabilities, and vaIue functions
• Q-Learning algorithm: Exploration vs. exploitation, Q-table, and learning rate Hands-on reinforcement learning projects and exercises

Hands-On – Working with OpenAI Gym

• Introduction to Flask/ Stream lit web framework
• Creating a Flask/ Streamlit application for ML model deployment Integrating data preprocessing and ML model
• Designing a user-friendly web interface

Hands-On – Iterating with loops in python

• Building a web application for Machine Learning models: Creating forms, handling user input, and displaying results
• Deployment using AWS (Amazon Web Services): Setting up an AWS instance, con­ figuring security groups, and deploying the application
• Deployment using PythonAnywhere: Upleading Flask application files, configuring WSGI, and launching the application

• Work on a real-world Machine Learning project: Identify a problem, gather data, and define project scope
• Apply the learned concepts and algorithms: Data collection, preprocessing, model building, and evaluation
• Deployment of the project on AWS or PythonAnywhere: Showcase the developed application and share the project with others
• Presentation and discussion of the project: Demonstrate the project, explain design decisions, and receive feedback

• Introduction to NLP: Understand the basics of NLP, its applications, and chaIlenges.
• Named Entity Recognition (NER): Understand the various approaches and tools used for NER, such as rule-based systems, statistical models, and deep learning.
• Text Preprocessing: Learn about tokenization, stemming, lemmatization, stop word removal, and other techniques for text preprocessing.
• Text Representation: Explore techniques such as Bag-of-Words (BoW), TF-IDF, and word embeddings (e.g., Word2Vec, GloVe) for representing text data.
• Sequential Models: Introduction to RNN, LSTM, Hands on Keras LSTM
• Sentiment Analysis: Study sentiment analysis techniques, build a sentiment analysis model using supervised learning, and evaluate its performance.

Hands-On – Real Time Sentiment Analysis

• Introduction
• History of Deep Learning Perceptrons
• Multi-Level Perceptrons Representations
• Training Neural Networks Activation Functions

• Introduction
• Deep Learning
• Understanding Human Brain
• In-Depth Perceptrons
• Example for perceptron
• Multi Classifier
• Neural Networks
• Input Layer
• Output Layer
• Sigmoid Function
• Introduction to Tensorflow and Keras
• CPU vs GPU
• Introduction to Google collaboratory
• Training Neural Network
• Understanding Notations
• Activation Functions
• Hyperparameter tuning in keras
• Feed-Forward Networks
  Online offline mode
• Bidirectional RNN
• Understanding Dimensions
• Back Propagation
• Loss function
• SGD
• Regularization
• Training for batches

Hands-On – Facial Emotion Recognition

• Introduction to CNN
• Applications of CNN
• Idea behind CNN
• Understanding Images
• Understanding Videos
• Convolutions
• Striding and Padding
• Max Pooling
• Edges, Gradients, and Textures
• Understanding Channels
• Formulas
• Weight and Bias
• Feature Map
• Pooling
• Combining

• Introduction to RNNs
• Training RNNs
  RNN Formula
• Architecture
• Batch Data
• Simplified Notations
• Types of RNNs
• LSTM
• GRUs
• Training RNN
• One to many
• Vanishing Gradient problem

Hands-On – COVID-19 Cases Prediction

• Introduction to Generative Models:
• Understanding GANs {Generative Adversarial Networks)
• GAN Architecture
• GAN Training
• Evaluating GAN Performance
• GAN Variants and Applications

• Intro to OpenCV
• Reading and Writing Images
• Saving images
• Draw shapes using OpenCV
• Face detection and eye detection using OpenCV
• CNN with Keras
• VGG
  
  

Hands-On – Real Time Pose Estimator

• Dataset collection
• Data preprocessing
• Feature extraction
• Labeling
• Model selection
• Model training
• Model evaluation
• Real-time implementation
• Alert mechanism
• Continuous improvement

• Identify a reliable source for house price data
• Understand the website structure
• Perform web scraping
• Preprocess the scraped data
• Explore and preprocess additional data sources (if applicable)
• Define the problem
• Split the data
• Train the model
• Evaluate the model
• Fine-tune the model (optional)
• Deploy the model
• Continuously update the dataset and retrain the model

• Define chatbot goals and scope
• Gather training data
• Data preprocessing
• API integration
• Model customization
• User input handling
• Response generation
• Post-processing and filtering
• Error handling and fallback mechanisms
• Continuous improvement

• Data collection
• Data preprocessing
• Dataset augmentation
• Model architecture
• Training
• Model evaluation
• Fine-tuning
• Real-time inference
• Thresholding and alerts
• Model optimization