Best DATA SCIENCE training in Pune

Module 1: Fundamentals of Statistics & Data Science
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
oVectors
oMatrices
Optimization
oTheory Of optimization
oGradients 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
oNormal Distribution
oBinomial Distribution

Module 2: RDBMS: SQL
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.

Module 3: Python for Data Science
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

oSeaborn Installation
oIntroduction to Seaborn
oBasics of Plotting
oPlots Generation
oVisualizing the Distribution of a Dataset
oSelection 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
. Data understanding
Observations, variables, and data matrices
Types of variables
Measures of Central Tendency
Arithmetic Mean / Average
oMerits & Demerits of Arithmetic Mean and Mode
oMerits & Demerits of Mode and Median
oMerits & 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

Module 4: Machine Learning
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
Na ve Bays Algorithm
K-Nearest Neighbor Algorithm
Decision Tress
1.SingleTree
2.Random Forest
Support Vector Machines
Model Ensemble
Model Evaluation and performance
oK-Fold Cross Validation
oROC, AUC etc
Hyper parameter tuning
oRegression
oclassification
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
7. Recommendation Systems
collaborative filtering model
content-based filtering model.
Hybrid collaborative system.

Module 5: Git
Introduction to Git and Distributed version control
Life Cycle
Create clone & commit Operations
Push & Update Operations
Stash, Move, Rename & Delete Operations

Module 6: AI & Deep Learning
1.Artificial Intelligence
oAn Introduction to Artificial Intelligence
oHistory of Artificial Intelligence
oFuture and Market Trends in Artificial Intelligence
oIntelligent Agents Perceive-Reason-Act Loop
oSearch and Symbolic Search
oConstraint-based Reasoning
oSimple Adversarial Search (Game-Playing)
oNeural Networks and Perceptions
oUnderstanding Feedforward Networks
oBoltzmann Machines and Autoencoders
oExploring Backpropagation
2.Deep Networks and Structured Knowledge
oUnderstanding Sensor Processing
oNatural Language Processing
oStudying Neural Elements
oConvolutional Networks
oRecurrent Networks
oLong Short-Term Memory (LSTM) Networks
3.Natural Language Processing
oNatural Language Processing
oNatural Language Processing in Python
oStudying Deep Learning
oArtificial Neural Networks
oANN Intuition
oPlan of Attack
oStudying the Neuron
oThe Activation Function
oWorking of Neural Networks
oExploring Gradient Descent
oStochastic Gradient Descent
oExploring Backpropagation
4.Artificial and Conventional Neural Network
oUnderstanding Artificial Neural Network
oBuilding an ANN
oBuilding Problem Description
oEvaluation the ANN
oImproving the ANN
oTuning 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.

Module 7: Machine Learning in Cloud
Machine Learning Features and Services
Using python in Cloud
How to access Machine Learning Services
Lab on accessing Machine learning services
Uploading Data
Preparation of Data
Deployment by Publishing Models using
AWS or other cloud computing

Module 9: Data Visualization with Tableau
1.Introduction to Data Visualization and the 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

Module 10: Project Work and Case Studies
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
Amazon-Recommender
Image Classification
Sentiment Analysis