Data Science Training in Noida

1. Fundamentals of Data Science and Machine Learning

• Introduction to Data Science
• Need of Data Science
• BigData and Data Science’
• Data Science and machine learning
• Data Science Life Cycle
• Data Science Platform
• Data Science Use Cases
• Skill Required for Data Science

2. Mathematics For Data Science

• Linear Algebra
  • Vectors
  • Matrices
• Optimization
  • Theory Of optimization
  • Gradients Descent

3. Introduction to Statistics

• Descriptive vs. Inferential Statistics
• Types of data
• Measures of central tendency and dispersion
• Hypothesis & inferences
• Hypothesis Testing
• Confidence Interval
• Central Limit Theorem

4. Probability and Probability Distributions

• Probability Theory
• Conditional Probability
• Data Distribution
• Distribution Functions
  • Normal Distribution
  • Binomial Distribution

• 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
• Installing Anaconda
• Understanding the Jupyter notebook
• Python Identifiers, Keywords
• Discussion about installed module s and packages

2. Conditional Statement ,Loops and File Handling

• Python Data Types and Variable
• Condition and Loops in Python
• Decorators
• Python Modules & Packages
• Python Files and Directories manipulations
• Use various files and directory functions for OS operations

3. Python Core Objects and Functions

• Built in modules (Library Functions)
• Numeric and Math’s Module
• String/List/Dictionaries/Tuple
• Complex Data structures in Python
• Python built in function
• Python user defined functions

4. Introduction to NumPy

• Array Operations
• Arrays Functions
• Array Mathematics
• Array Manipulation
• Array I/O
• Importing Files with Numpy

5. Data Manipulation with Pandas

• Data Frames
• I/O
• Selection in DFs
• Retrieving in DFs
• Applying Functions
• Reshaping the DFs – Pivot
• Combining DFs
  Merge
  Join
• Data Alignment 

6. SciPy

• Matrices Operations
• Create matrices
  Inverse, Transpose, Trace,   Norms , Rank etc
• Matrices Decomposition
  Eigen Values & vectors
  SVDs

7.Visualization with Seaborn

• Seaborn Installation
• Introduction to Seaborn
• Basics of Plotting
• Plots Generation
• Visualizing the Distribution of a
  Dataset
• Selection color palettes

8. Visualization with Matplotlib

• Matplotlib Installation

• Matplotlib Basic Plots & it’
  s Containers

• Matplotlib components and
  properties

• Pylab & Pyplot

• Scatter plots

• 2D Plots

• Histograms
• Bar Graphs
• Pie Charts
• Box Plots
• Customization
• Store Plots

9.SciKit Learn

• Basics
• Data Loading
• Train/Test Data generation
• Preprocessing
  Generate Model
• Evaluate Models

10. Descriptive Statistics

• Observations, variables, and data matrices
• Types of variables
• Measures of Central Tendency
• Arithmetic Mean / Average
  • Merits & Demerits of Arithmetic Mean and Mode
  • Merits & Demerits of Mode and Median
  • Merits & Demerits of Median Variance

11. Probability Basics

• Notation and Terminology
• Unions and Intersections
• Conditional Probability and Independence

12. Probability Distributions

• Random Variable
• Probability Distributions
• Probability Mass Function
• Parameters vs. Statistics
• Binomial Distribution
• Poisson Distribution
• Normal Distribution
• Standard Normal Distribution
• Central Limit Theorem
• Cumulative Distribution function

13.  Tests of Hypothesis

• Large Sample Test
• Small Sample Test
• One Sample: Testing Population Mean
• Hypothesis in One Sample z-test
• Two Sample: Testing Population Mean
• One Sample t-test – Two Sample t-test
• Paired t-test
• Hypothesis in Paired Samples t-test
• Chi-Square test

14. Data Analysis

• Case study- Netflix
• Deep analysis on Netflix data

1. Exploratory Data Analysis

• Data Exploration
• Missing Value handling
• Outliers Handling
• Feature Engineering

2. Feature Selection

• Importance of Feature Selection in Machine Learning
• Filter Methods
• Wrapper Methods
• Embedded Methods

3. Machine Learning: Supervised Algorithms Classification

• Introduction to Machine Learning
• Logistic Regression
• Naïve Bays Algorithm
• K-Nearest Neighbor Algorithm
• Decision Tress
  1. SingleTree
  2. Random Forest
• Support Vector Machines
• Model Ensemble
• Model Evaluation and performance
  • K-Fold Cross Validation
  • ROC, AUC etc…
• Hyper parameter tuning
  • Regression
  • classification

4. Machine Learning: Regression

• Simple Linear Regression
• Multiple Linear Regression
• Decision Tree and Random Forest Regression

5. Machine Learning: Unsupervised Learning Algorithms

• Similarity Measures
• Cluster Analysis and Similarity Measures

6. Ensemble algorithms

• Bagging
• Boosting
• Voting
• Stacking
• K-means Clustering
• Hierarchical Clustering
• Principal Components Analysis
• Association Rules Mining & Market Basket Analysis

1.Artificial Intelligence

• An Introduction to Artificial Intelligence
• History of Artificial Intelligence
• Future and Market Trends in AI
• Intelligent Agents – Perceive-Reason-

Act Loop

• Search and Symbolic Search
• Constraint-based Reasoning
• Simple Adversarial Search (Game-

Playing)

• Neural Networks and Perceptions
• Understanding Feedforward Networks
• Boltzmann Machines and Autoencoders
• Exploring Backpropagation

2.Deep Networks & Structured
Knowledge

• Understanding Sensor Processing
• Natural Language Processing
• Studying Neural Elements
  Convolutional Networks
• Recurrent Networks
• Long Short-Term Memory (LSTM)
  Networks

3.Natural Language Processing

• Natural Language Processing

• NLP in Python

• Studying Deep Learning

• Artificial Neural Networks

• ANN Intuition

• Plan of Attack

• Studying the Neuron

• The Activation Function

• Working of Neural Networks

• Exploring Gradient Descent

• Stochastic Gradient Descent

• Exploring Back propagation

4.Artificial and Conventional Neural
Network

• Understanding Artificial Neural Network
• Building an ANN
• Building Problem Description
• Evaluation the ANN
• Improving the ANN
• Tuning the ANN

5.Image Processing / Machine
Vision

• Image basics
• Loading and saving images
• Thresholding
• Bluring
• Masking
• Image Augmentation

6.Conventional Neural Networks

• CNN Intuition
• Convolution Operation
• ReLU Layer
• Pooling and Flattening
• Full Connection
• Softmax and Cross-Entropy
• Building a CNN
• Evaluating the CNN
• Improving the CNN
• Tuning the CNN

7.Recurrent Neural Network

• Recurrent Neural Network
• RNN Intuition
• The Vanishing Gradient Problem
• LSTMs and LSTM Variations
• Practical Intuition
• Building an RNN
• Evaluating the RNN
• Improving the RNN
• Tuning the RNN

8.Time Series Data

• Introduction to Time series data
  Data
• Cleaning in time series
• Pre-Processing Time-series Data
• Predictions in Time Series using
  ARIMA, Facebook Prophet models

GIT: Complete Overview

• Introduction to Git & Distributed
  Version Control
• Life Cycle
• Create clone & commit Operatons
• Push & Update Operations
• Stash, Move, Rename & Delete
  Operations

Machine Learning Features & Services

• Using python in Cloud
• How to access Machine Learning Services
• Lab on accessing Machine learning
  services
• Uploading Data
• Preparation of Data
• Applying Machine Learning Model
• Deployment by Publishing Models using
  AWS or other cloud computing

1.Introduction to Data Visualization
& Power of Tableau

• Architecture of Tableau
• Product Components
• Working with Metadata and Data Blending
• Data Connectors
• Data Model
• File Types
• Dimensions & Measures
• Data Source Filters
• Creation of Sets

2.Scatter Plot

• Gantt Chart
• Funnel Chart
• Waterfall Chart
• Working with Filters
• Organizing Data and Visual Analytics
• Working with Mapping
• Working with Calculations and
  Expressions
• Working with Parameters
• Charts and Graphs
• Dashboards and Stories

• Machine Learning end to end Project blueprint
• Case study on real data after each model.
• Regression predictive modeling – E-commerce
• Classification predictive modeling – Binary Classification
• Case study on Binary Classification – Bank Marketing
• Case study on Sales Forecasting and market analysis
• Widespread coverage for each Topic
• Various Approaches to Solve Data Science Problem
• Pros and Cons of Various Algorithms and approaches