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Syllabus:

ADVANCED FULL STACK DATA SCIENCE
with Projects Development & Deployment

Topics:

Introduction to Data Science

• What is Data Science
• Importance of Data Science
• Why Data Science is needed
• Use Cases
• Problems and Solutions
• Different Roles

Project Life Cycle

• Macine Learning Project Life Cycle
• Problem
• Collecting the data
• EDA
• Cleaning
• Transformation
• Partition
• Model fitting
• Cross validation
• Metrics
• Deployment of Project

Fundamentals of Statistics

• Sample
• Population
• Data types continous
• discrete
• Central tendency
• Spread
• Shape of the data such histogram
• Skewness
• Kurtosis

Statistical Visualisation

• Bargraph
• Pie Graph
• Box plot IQR
• Whisker lengths
• outliers
• Scatter plot Positive
• Negative
• Neutral
• Correlation examples

Working Environments

• Introduction to Python Language
• Software Installation
• Python
• Anaconda Jupyter
• Visual Studio Code
• Google Colab

Introduction of Python

• What is Python
• Features
• Identifiers
• Characteristics
• Indentation
• Quotations
• Reserved Words
• Variables
• Data Types
• Operators
• Different Types of Operators
• String Indexing
• Slicing
• String Functions
• Expressions

Python Data Structure

• Introduction to Data Structures
• Methods such as append
• extend
• insert
• remove
• pop
• clear
• index
• count
• sort
• reverse
• Tuples
• Dictionary
• Set

Control Flow Structure

• Conditional Statement
• if
• if-else
• if-elif-else
• nested if
• Looping Statement
• For loop
• While Loop
• Break
• Ccontinue
• Functions
• Different Types of Functions
• Lambda function
• use cases
• scenario
• examples

Exception Handling

• What is Exception
• Different Types of Exceptions
• Use Cases
• Scenario
• use cases
• scenario
• examples

File Handling

• What is file
• Creation of files
• Different types of file handling
• Orders of files
• Different operations
• Examples
• Use Cases
• Scenarios
  

NumPy

• Numpy – Installation and Usage
• Importing Numpy
• scalar
• array
• vector
• one dimension
• two dimension
• random int

Exploring Pandas

• Pandas Installation
• usage
• giving column names
• Importing pandas
• read_csv
• head
• tail
• describe
• Pandas info
• selecting columns
• dropping columns
• groupby
• concat
• merge
• removing duplicates
• filling blanks with mean
• preprocessing
• use cases
• scenarios
• examples

Exploratory Data Analysis

• EDA showing graphs
• as histogram
• boxplot
• bargraph
• scatter plot
• heat map using matplotlib
• seaborn using Google collab with examples.
  

Advanced Statistics - Probability Normal Distribution

• Probability
• Normal distribution theory
• standardization
• zscore
• z tables
• applications
• confidence Interval Working on Case Study

Advanced Statistics - Hypothesis Testing

• Level of significance
• Hypothesis Testing One sample Z test
• Two sample Z test
• T-test
• Working on case study

Introduction to Machine Learning

• What is Machine Learning?
• Different Types of Machine Learning
• use cases
• Scenarios
• Real Time Examples

Supervised Machine Learning Linear Regression

• Simple Linear Regression
• metrics such RMSE and R square - Working on case study, examples, use cases, scenarios

Multiple Linear Regression

• Introduction to Regression models
• Multiple Linear Regression
• Assumptions of Linear Regression
• Variable selection
• Multicollinearity
• VIF
• Use Cases
• Scenarios with Real Time Example

Logistic Regression

• what is meant by classification models ?
• When do we choose Logistic regression
• modelfitting
• confusion matrix
• accuracy score - Working on case study

Metrics

• Other metrics Sensitivity
• Specificity
• precision
• F1 score
• ROC curve
• AUC score Working on case Study
• use case
• examples

Data Transformation

• What is Data Transformation
• Standardard scaler
• minmax scaler
• label encoding
• one hot encoding and Data partition
• Training and Testing

Modal Validation Techniques

• Cross validation Stratified K-Fold
• K-Fold cross validation
• Shuffle Split Cross - Validation real time examples

Under fitting to Over fitting

• Variance Biased Trade-off under fitting-causes-Lack of training
• best fit
• over fitting - causes - Noise in training data
• Too many training epochs or iterations
• Ttoo many variables
• Visualizations Underfitting
• bestfit
• Overfitting and Feature Engineering - Working on case study

Regularization Techniques

• Techniques such as Lasso
• Ridge
• ElasticNet - Working on case study.

Classifiers – Support Vector Machine

• Support vector machine Hyperplane
• Maximum margin classifier
• Support Vectors
• SVM for Linear Classification
• SVM for Non-Linear Classification polynomial
• RBF
• Sigmoid Function

Decision Tree

• Decision Tree Structure
• Root node
• Internal nodes
• terminal nodes
• Gini Impurity
• Entropy and Information Gain for classification
• Working on case study

Ensembled Techniques

• Ensemble Methods: Bagging and Random forests
• working on hyper parameters to control overfitting
• Real Time Examples

Boosting methods

• Sequential methods: Gradient Boosting
• Ada Boost
• using Grid search CV
• XG Boost
• LightGBM use cases
• Real Time Examples

Deployment - Project Discussion

• Project with Deployment

Unsupervised Machine Learning

• What are DImensional Reduction Techniques?
• Purpose of PCA
• Working on case study

Clustering

• Introduction to Clustering
• Distance Metrics
• Clustering Algorithms (K mean, dbscan)
• Choosing the Right Number of Clusters
• Elbow Method
• Silhouette Analysis working on case study

Recommendation system

• What is Recommendation and why it is important
• What is Collaborative Filtering (CF) and Content-Based Filtering

Time series Analysis

• Time series Concepts
• Components
• Visualization
• Data partition
• ARIMA models
• Real Time Examples

Deep Learning - Artifical Neural Network

• Perceptron
• Single Layer Network
• Aactivation functions
• Back propagation method
• Simple ANN code
• Multilayer Neural network
• Gradient Descent method
• Optimizers
• Learning rate
• Tensorflow

Deep Learning - Recurrent Neural Networks

• Working model
• RNN
• LSTM
• GRU
• Real Time Examples

Natural Language Processing:

• What is Text Data
• Various forms
• Applications
• Text pre processing
• Tokenization
• Normalization
• Stopwords
• Lemmatization
• stemming
• Visualization on preprocessed text data
• Text Representation
• Bag of Words (BoW)
• Term Frequency-Inverse Document Frequency (TF-IDF)
• Sentiment Analysis
• Classification model using Machine Learning
• Named Entity Recognition (NER)
• What is Word Embedding?
• What are pre-trained word Embeddings
• Word2Vec
• Skip gram
• CBOW
• real time applications.
• Language Modeling
• N-gram Models
• Neural Language Models
• Working on case study
• use cases and real time examples.

Project Development

• Real Time Project Development & Deployment

GitHub:

• Account Creation
• New Repository
• Uploading Developed Project
• Uploading Use Cases with Coding Examples

Resume:

• Resume Building Discussion and Guidance