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Full Stack Data Science Program

Course Overview

The Online Training course presents a comprehensive perspective on data science and AI, merging theoretical principles with hands-on applications. Classes are designed to furnish participants with a thorough grasp of the complete data science pipeline, covering aspects from data gathering and scrubbing to analysis, modeling, and interpretation. Additionally, the program delves into artificial intelligence, examining a spectrum of techniques and algorithms vital for tasks like natural language processing, computer vision, and predictive analytics.

Learn software skills with real experts, either in live classes with videos or without videos, whichever suits you best.

Description

Naresh IT's Full Stack Data Science and AI Online Training is structured to cater to individuals with varying levels of expertise, from beginners to experienced professionals looking to upskill or transition into the field of data science and AI. The curriculum is meticulously crafted to cover essential concepts, tools, and techniques employed in real-world data science projects and AI applications. Participants will have the opportunity to work on hands-on projects and case studies, gaining practical experience and building a robust portfolio of work.

Course Objectives

  • To provide a comprehensive understanding of data science and AI concepts, methodologies, and tools.
  • To equip participants with the practical skills needed to collect, clean, analyze, and interpret data effectively.
  • To familiarize learners with popular programming languages and libraries used in data science and AI, such as Python, R, TensorFlow, and scikit-learn.
  • To explore advanced topics in machine learning and artificial intelligence, including deep learning, neural networks, reinforcement learning, and more.
  • To enable participants to apply their knowledge and skills to solve real-world problems and develop innovative AI solutions.
  • To prepare learners for careers in data science, AI research, machine learning engineering, and related fields.

Prerequisites
Course Curriculum

  • Introduction to Data Science
    • Introduction to Data Science
    • Discussion on Course Curriculum
    • Introduction to Programming
  • Python – Basics
    • Introduction to Python: Installation and Running (Jupyter Notebook, .py file from terminal, Google Colab)
    • Data types and type conversion
    • Variables
    • Operators
    • Flow Control : If, Elif, Else
    • Loops
    • Python Identifier
    • Building Funtions (print, type, id, sys, len)
  • Python - Data Types & Utilities
    • List, List of Lists and List Comprehension
    • List creation
    • Create a list with variable
    • List mutable concept
    • len() || append() || pop()
    • insert() || remove() || sort() || reverse()
    • Forward indexing
    • Backward Indexing
    • Forward slicing
    • Backward slicing
    • Step slicing
  • Set
    • Set creation with variable
    • len() || add() || remove() || pop()
    • union() | intersection() || difference()
  • Tuple
    • Tuple Creation
    • Create Tuple with variable
    • Tuple Immutable concept
    • len() || count() || index()
    • Forward indexing
    • Backward Indexing
  • Dictionary and Dictionary comprehension
    • Create a dictionary using variable
    • keys:values concept
    • len() || keys() || values() || items()
    • get() || pop() || update()
    • Comparision of datastructure
    • Introduce to range()
    • Pass range() in the list
    • Range() arguments
    • For loop introduction using range()
  • Functions
    • Inbuilt vs User Defined
    • User Defined Function
    • Function Argument
    • Types of Function Arguments
    • Actual Argument
    • Global variable vs Local variable
    • Anonymous Function | LAMBDA
  • Packages
  • Map Reduce
  • OOPs
  • Class & Object
    • What is mean by inbuild class
    • How to creat user class
    • Crate a class & object
    • __init__ method
    • Python constructor
    • Constructor, self & comparing objects
    • Instane variable & class variable
  • Methods
    • What is instance method
    • What is class method
    • What is static method
    • Accessor & Mutator
  • Python Decorator
    • How to use decorator
    • Inner class, Outerclass
    • Inheritence
  • Polymorphism
    • Duck typing
    • Operator overloading
    • Method overloading
    • Method overridding
    • Magic method
    • Abstract class & Abstract method
    • Iterator
    • Generators in python
  • Python - Production Level
    • Error / Exception Handling
    • File Handling
    • Docstrings
    • Modularization
  • Pickling & Unpickling
  • Pandas
    • Introduction, Fundamentals, Importing Pandas, Aliasing, DataFrame
    • Series – Intro, Creating Series Object, Empty Series Object, Create series from List/Array/Column from DataFrame, Index in Series, Accessing values in Series
    • NaN Value
    • Series – Attributes (Values, index, dtypes, size)
    • Series – Methods – head(), tail(), sum(), count(), nunique() etc.,
    • Date Frame
    • Loading Different Files
    • Data Frame Attributes
    • Data Frame Methods
    • Rename Column & Index
    • Inplace Parameter
    • Handling missing or NaN values
    • iLoc and Loc
    • Data Frame – Filtering
    • Data Frame – Sorting
    • Data Frame – GroupBy
    • Merging or Joining
    • Data Frame – Concat
    • DataFrame - Adding, dropping columns & rows
    • DataFrame - Date and time
    • DataFrame - Concatenate Multiple csv files
  • Numpy
    • Introduction, Installation, pip command, import numpy package, ModuleNotFoundError, Famous Alias name to Numpy
    • Fundamentals – Create Numpy Array, Array Manipulation, Mathematical Operations, Indexing & Slicing
    • Numpy Attributes
    • Important Methods- min(),max(), sum(), reshape(), count_nonzero(), sort(), flatten() etc.,
    • Adding value to array of values
    • Diagonal of a Matrix
    • Trace of a Matrix
    • Parsing, Adding and Subtracting Matrices
    • "Statistical Functions: numpy.mean()
    • Numpy.median()
    • Numpy.std()
    • Numpy.sum()
    • Numpy.min()"
    • Filter in Numpy
  • Matplotlib
    • Introduction
    • Pyplot
    • Figure Class
    • Axes Class
    • Setting Limits and Tick Labels
    • Multiple Plots
    • Legend
    • Different Types of Plots
    • Line Graph
    • Bar Chart
    • Histograms
    • Scatter Plot
    • Pie Chart
    • 3D Plots
    • Working with Images
    • Customizing Plots
  • Seaborn
    • Catplot() function
    • Stripplot() function
    • boxplot() function
    • violinplot() function
    • pointplot() function
    • barplot() function
    • Visualizing statistical relationship with Seaborn relplot() function
    • scatterplot() function
    • regplot() function
    • lmplot() function
    • Seaborn Facetgrid() function
    • Multi-plot grids
    • Statistical Plots
    • Color Palettes
    • Faceting
    • Regression Plots
    • Distribution Plots
    • Categorical Plots
    • Pair Plots
  • Scipy
    • Signal and Image Processing (scipy.signal, scipy.ndimage)
    • Linear Algebra (scipy.linalg)
    • Integration (scipy.integrate)
    • Statistics (scipy.stats)
    • Spatial Distance and Clustering (scipy.spatial)
  • Statsmodels
    • Linear Regression (statsmodels.regression)
    • Time Series Analysis (statsmodels.tsa)
    • Statistical Tests (statsmodels.stats)
    • Anova (statsmodels.stats.anova)
    • Datasets (statsmodels.datasets)

  • Set Theory
    • Data Representation & Database Operations
  • Combinatorics
    • Feature Selection
    • Permutations and Combinations for Sampling
    • Hyperparameter Tuning
    • Experiment Design
    • Data Partitioning and Cross-Validation
  • Probability
    • Basics
    • Theoretical Probability
    • Empirical Probability
    • Addition Rule
    • Multiplication Rule
    • Conditional Probability
    • Total Probability
    • Probability Decision Tree
    • Bayes Theorem
    • Sensitivity & Specificity in Probability
    • Bernouli Naïve Bayes, Gausian Naïve Bayes, Multinomial Naïve Bayes
  • Distributions
    • Binomial, Poisson, Normal Distribution, Standard Normal Distribution
    • Guassian Distribution, Uniform Distribution
    • Z Score
    • Skewness
    • Kurtosis
    • Geometric Distribution
    • Hyper Geometric Distribution
    • Markov Chain
  • Linear Algebra
    • Linear Equations
    • Matrices(Matrix Algebra: Vector Matrix Vector matrix multiplication Matrix matrix multiplication)
    • Determinant
    • Eigen Value and Eigen Vector
  • Euclidean Distance & Manhattan Distance
  • Calculus
    • Differentiation
    • Partial Differentiation
    • Max & Min
  • Indices & Logarithms

  • Introduction
    • Population & Sample
    • Reference & Sampling technique
  • Types of Data
    • Qualitative or Categorical – Nominal & Ordinal
    • Quantitative or Numerical – Discrete & Continuous
    • Cross Sectional Data & Time Series Data
  • Measures of Central Tendency
    • Mean, Mode & Median – Their frequency distribution
  • Descriptive statistic Measures of symmetry
    • skewness (positive skew, negative skew, zero skew)
    • kurtosis (Leptokurtic, Mesokurtic, Platrykurtic)
  • Measurement of Spread
    • Range, Variance, Standard Deviation
  • Measures of variability
    • Interquartile Range (IQR)
    • Mean Absolute Deviation (MAD)
    • Coefficient of variation
    • Covariance
  • Levels of Data Measurement
    • Nominal, Ordinal, Interval, Ratio
  • Variable
    • Types of Variables
    • Categorical Variables - Nomial variable & ordinal variables
    • Numerical Variables: discreate & continuous
    • Dependent Variable
    • Independent Variable
    • Control Moderating & Mediating
  • Frequency Distribution Table
    • Nominal, Ordinal, Interval, Ratio
  • Types of Variables
    • Categorical Variables - Nomial variable & ordinal variables
    • Numerical Variables: discreate & continuous
    • Dependent Variable
    • Independent Variable
    • Control Moderating & Mediating
  • Frequency Distribution Table
    • Relative Frequency, Cumulative Frequency
    • Histogram
    • Scatter Plots
    • Range
    • Calculate Class Width
    • Create Intervals
    • Count Frequencies
    • Construct the Table
  • Correlation, Regression & Collinearity
    • Pearson & Spearman Correlation Methods
    • Regression Error Metrics
  • Others
    • Percentiles, Quartiles, Inner Quartile Range
    • Different types of Plots for Continuous, Categorical variable
    • Box Plot, Outliers
    • Confidence Intervals
    • Central Limit Theorem
    • Degree of freedom
  • Bias and Variance in ML
  • Entropy in ML
  • Information Gain
  • Surprise in ML
  • Loss Function & Cost Function
    • Mean Squared Error, Mean Absolute Error – Loss Function
    • Huber Loss Function
    • Cross Entropy Loss Function
  • Inferential Statistics
    • Hypothesis Testing: One tail, two tail and p-value
    • Formulation of Null & Alternate Hypothesis
    • Type-I error & Type-II error
    • Statistical Tests
    • Sample Test
    • ANOVA Test
    • Chi-square Test
    • Z-Test & T-Test

  • Introduction
    • DBMS vs RDBMS
    • Intro to SQL
    • SQL vs NoSQL
    • MySQL Installation
  • Keys
    • Primary Key
    • Foreign Key
  • Constraints
    • Unique
    • Not NULL
    • Check
    • Default
    • Auto Increment
  • CRUD Operations
    • Create
    • Retrieve
    • Update
    • Delete
  • SQL Languages
    • Data Definition Language (DDL)
    • Data Query Language
    • Data Manipulation Language (DML)
    • Data Control Language
    • Transaction Control Language
  • SQL Commands
    • Create
    • Insert
    • Alter, Modify, Rename, Update
    • Delete, Truncate, Drop
    • Grant, Revoke
    • Commit, Rollback
    • Select
  • SQL Clause
    • Where
    • Distinct
    • OrderBy
    • GroupBy
    • Having
    • Limit
  • Operators
    • Comparison Operators
    • Logical Operators
    • Membership Operators
    • Identity Operators
  • Wild Cards
  • Aggregate Functions
  • SQL Joins
    • Inner Join & Outer Join
    • Inner Join & Outer Join
    • Self & Cross Join
    • Natural Join

  • EDA
    • Univariate Analysis
    • Bivariate Analysis
    • Multivariate Analysis
  • Data Visualisation
    • Various Plots on different datatypes
    • Plots for Continuous Variables
    • Plots for Discrete Variables
    • Plots for Time Series Variables
  • ML Introduction
    • What is Machine Learning?
    • Types of Machine Learning Methods
    • Classification problem in general
    • Validation Techniques: CV,OOB
    • Different types of metrics for Classification
    • Curse of dimensionality
    • Feature Transformations
    • Feature Selection
    • Imabalanced Dataset and its effect on Classification
    • Bias Variance Tradeoff
  • Important Element of Machine Learning
  • Multiclass Classification
    • One-vs-All
    • Overfitting and Underfitting
    • Error Measures
    • PCA learning
    • Statistical learning approaches
    • Introduce to SKLEARN FRAMEWORK
  • Data Processing
    • Creating training and test sets, Data scaling and Normalisation
    • Feature Engineering – Adding new features as per requirement, Modifying the data
    • Data Cleaning – Treating the missing values, Outliers
    • Data Wrangling – Encoding, Feature Transformations, Feature Scaling
    • Feature Selection – Filter Methods, Wrapper Methods, Embedded Methods
    • Dimension Reduction – Principal Component Analysis (Sparse PCA & Kernel PCA), Singular Value Decomposition
    • Non Negative Matrix Factorization
  • Regression
    • Introduction to Regression
    • Mathematics involved in Regression
    • Regression Algorithms
    • Simple Linear Regression
    • Multiple Linear Regression
    • Polynomial Regression
    • Lasso Regression
    • Ridge Regression
    • Elastic Net Regression
  • Evaluation Metrics for Regression
    • Mean Absolute Error (MAE)
    • Mean Squared Error (MSE)
    • Root Mean Squared Error (RMSE)
    • Adjusted R²
  • Classification
    • Introduction
    • K-Nearest Neighbors
    • Logistic Regression
    • Support Vector Machines (Linear SVM)
    • Linear Classification
    • Kernel-based classification
    • Non-linear examples
    • 2 features forms straight line & 3 features forms plane
    • Hyperplane and Support vectors
    • Controlled support vector machines
    • Support vector Regression
    • Kernel SVM (Non-Linear SVM)
    • Naives Bayes
    • Decision Trees
    • Random Forest / Bagging
    • Ada Boost
    • Gradient Boost
    • XG Boost
    • Evaluation Metrics for Classification
  • Clustering
  • Introduction
  • K-Means Clustering
    • Finding the optimal number of clusters
    • Optimizing the inertia
    • Cluster instability
    • Elbow method
  • Hierarchical Clustering
  • Agglomerative clustering
  • DBSCAN Clustering
  • Association Rules
    • Market Basket Analysis
    • Apriori Algorithm
  • Recommendation Engines
    • Collaborative Filtering
    • User based collaborative filtering
    • Item based collaborative filtering
    • Recommendation Engines
  • Time Series & Forecasting
    • What is Time series data
    • Different components of time series data
    • Stationary of time series data
    • ACF, PACF
    • Time Series Models
    • AR
    • ARMA
    • ARIMA
    • SARIMAX
  • Model Selection & Evaluation
  • Over Fitting & Under Fitting
    • Biance-Variance Tradeoff
    • Hyper Parameter Tuning
    • Joblib And Pickling
  • Others
    • Dummy Variable, Onehotencoding
    • gridsearchcv vs randomizedsearchcv
  • ML Pipeline
  • ML Model Deployment in Flask

  • Introduction
    • Power BI for Data scientist
    • Types of reports
    • Data source types
    • Installation
  • Basic Report Design
    • Data sources and Visual types
    • Canvas and fields
    • Table and Tree map
    • Format button and Data Labels
    • Legend,Category and Grid
    • CSV and PDF Exports
  • Visual Sync, Grouping
    • Slicer visual
    • Orientation,selection process
    • Slicer:Number,Text,slicer list
    • Bin count,Binning
  • Hierarchies, Filters
    • Creating Hierarchies
    • Drill Down options
    • Expand and show
    • Visual filter,Page filter,Report filter
    • Drill Thru Reports
  • Power Query
    • Power Query transformation
    • Table and Column Transformations
    • Text and time transformations
    • Power query functions
    • Merge and append transformations
  • DAX Functions
    • DAX Architecture,Entity Sets
    • DAX Data types,Syntax Rules
    • DAX measures and calculations
    • Creating measures
    • Creating Columns

  • Deep learning at Glance
    • Introduction to Neural Network
    • Biological and Artificial Neuron
    • Introduction to perceptron
    • Perceptron and its learning rule and drawbacks
    • Multilayer Perceptron, loss function
    • Neural Network Activation function
  • Training MLP: Backpropagation
  • Cost Function
  • Gradient Descent Backpropagation - Vanishing and Exploding Gradient Problem
  • Introduce to Py-torch
  • Regularization
  • Optmizers
  • Hyperparameters and tuning of the same
  • TensorFlow Framework
    • Introduction to TensorFlow
    • TensorFlow Basic Syntax
    • TensorFlow Graphs
    • Variables and Placeholders
    • TensorFlow Playground
  • ANN (Artificial Neural Network)
    • ANN Architecture
    • Forward & Backward Propagation, Epoch
    • Introduction to TensorFlow, Keras
    • Vanishing Gradient Descend
    • Fine-tuning neural network hyperparameter
    • Number of hidden layers, Number of neurons per hidden layer
    • Activation function
    • INSTALLATION OF YOLO V8, KERAS, THEANO
  • PY-TORCH Library
  • RNN (Recurrent Neural Network)
    • Introduction to RNN
    • Back Propagation through time
    • Input and output sequences
    • RNN vs ANN
    • LSTM (Long Short-Term Memory)
    • Different types of RNN: LSTM, GRU
    • Biirectional RNN
    • Sequential-to-sequential architecture (Encoder Decoder)
    • BERT Transformers
    • Text generation and classification using Deep Learning
    • Generative-AI (Chat-GPT)
  • Basics of Image Processing
    • Histogram of images
    • Basic filters applied on the images
  • Convolutional Neural Networks (CNN)
    • ImageNet Dataset
    • Project: Image Classification
    • Different types of CNN architectures
    • Recurrent Neural Network (RNN)
    • Using pre-trained model: Transfer Learning

  • Natural Language Processing (NLP)
    • Text Cleaning
    • Texts, Tokens
    • Basic text classification based on Bag of Words
  • Document Vectorization
    • Bag of Words
    • TF-IDF Vectorizer
    • n-gram: Unigram, Bigram
    • Word vectorizer basics, One Hot Encoding
    • Count Vectorizer
    • Word cloud and gensim
    • Word2Vec and Glove
    • Text classification using Word2Vec and Glove
    • Parts of Speech Tagging (PoS Tagging or POST)
    • Topic Modelling using LDA
    • Sentiment Analysis
  • Twitter Sentiment Analysis Using Textblob
    • TextBlob
    • Installing textblob library
    • Simple TextBlob Sentiment Analysis Example
    • Using NLTK’s Twitter Corpus
  • Spacy Library
    • Introduction, What is a Token, Tokenization
    • Stop words in spacy library
    • Stemming
    • Lemmatization
    • Lemmatization through NLTK
    • Lemmatization using spacy
    • Word Frequency Analysis
    • Counter
    • Part of Speech, Part of Speech Tagging
    • Pos by using spacy and nltk
    • Dependency Parsing
    • Named Entity Recognition(NER)
    • NER with NLTK
    • NER with spacy

  • Human vision vs Computer vision
    • CNN Architecture
    • CONVOLUTION – MAX POOLING – FLATTEN LAYER – FULLY CONNECTED LAYER
    • CNN Architecture
    • Striding and padding
    • Max pooling
    • Data Augmentation
    • Introduction to OpenCV & YoloV3 Algorithm
  • Image Processing with OpenCV
    • Image basics with OpenCV
    • Opening Image Files with OpenCV
    • Drawing on Images, Image files with OpenCV
    • Face Detection with OpenCV
  • Video Processing with OpenCV
    • Introduction to Video Basics, Object Detection
    • Object Detection with OpenCV
  • Reinforcement Learning
    • Introduction to Reinforcement Learning
    • Architecture of Reinforcement Learning
    • Reinforcement Learning with Open AI
    • Policy Gradient Theory
  • OPEN AI
    • Introduction to Open AI
    • Generative AI
    • Chat Gpt (3.5)
    • LLM (Large Language Model)
    • Classification Tasks with Generative AI
    • Content Generation and Summarization with Generative AI
    • Information Retrieval and Synthesis workflow with Gen AI
  • Time Series and Forecasting
    • Time Series Forecasting using Deep Learning
    • Seasonal-Trend decomposition using LOESS (STL) models.
    • Bayesian time series analysis
  • MakerSuite Google
    • PaLM API
    • MUM models
  • Azure ML
Who can learn this course

This course is suitable for:

  • Students and recent graduates interested in pursuing a career in data science or AI.
  • Working professionals seeking to transition into the field of data science or AI.
  • Data analysts, statisticians, and business intelligence professionals looking to enhance their analytical skills.
  • Software engineers and developers interested in incorporating machine learning and AI techniques into their applications.
  • Anyone with a passion for data and a desire to explore the fascinating world of data science and artificial intelligence.
 

General Certificate (NASSCOM Approved Courses)



  • Accredited by NASCCOM
  • Industry-recognized
  • Developed in consultation with industry experts
  • Focus on future skills
  • Aligned with industry demands
  • Comprehensive curriculum
  • Hands-on training
  • Career-oriented
  • Enhances employability
  • Endorsed by NASCCOM

Average package of course (Full Stack Data Science Program)

90% Avg
salary hike
5L Avg
Package
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