# Section 1: Course Introduction

• Introduction to the course
• What is Data Science?
• What is Machine learning?
• Environment setup and Installation.
• Jupyter Notebook Overview

# Section 2. Python Crash Course

• Datatypes in Python (List / Dictionaries / Tuples)
• Functions, Procedure, lambda expressions, string slicing and dicing.
• Python comparison operators
• Module Outline Loops and Conditionals statements.
• Datatypes in Python (List / Dictionaries / Tuples)
• Python Crash course exercise {mini project}

## Section 3. Statistics, Probability and Python Practice

• Types of Data
• Mean, Median, Mode
• Using mean, median, and mode in Python
• Variation and Standard Deviation.
• Percentiles and Moments.
• Probability Density Function; Probability Mass Function
• Covariance and Correlation
• Conditional Probability
• Bayes’ Theorem
• Exercise Solution: Conditional Probability of Purchase by Age

## Section 4: Python Data Analysis

• Introduction to Numpy.
• Numpy array Indexing, Operations.
• Exercise on Numpy.
• Introduction to Pandas
• Pandas Series usage
• Dataframes in pandas and its usage
• Missing data treatment using pandas
• Groupby merging joining and concatenation operations using pandas.
• Introduction to Scipy library
• Matrix Operations using Scipy.
• Project1: Ecommerce Purchase order evaluations.
• Project 2: SF salary

## Section 5: Python for Data Visualizations

• Introduction to Data Visualization section
• A Crash Course in matplotlib.
• Implementation of matplotlib on various datasets.
• Exercise on Matplotlib.
• A Crash course in Seaborn.
• Distribution plot
• Categorical plot
• Matrix plot
• Grids
• Regression plots
• Exercise on Seaborn’s lib.
• Pandas Built in Data Visualization.
• Plotly and Cufflinks
• Geographical plotting using Choropleth.

## Section 6: Introduction to Machine Learning

• Introduction to machine learning.
• Supervised, Unsupervised and reinforcement learning.
• Classification vs clustering algorithms.
• Linear Regression model theory with mathematical Implementation.
• Linear regression with Python.
• Exercise on Linear regression implementation using Scikit learn library.
• Project: Customer Analysis (Comparing the company website vs mobile application)
• Cross Validation and Bias variance Trade off.
• Logistic Regression model theory + mathematical implementation
• Where to use logistic regression, dataset analysis
• Logistic regression with Python.
• Exercise on Logistic regression implementation using Scikit learn library.
• Multivariate Regression, and Predicting Car Prices

• K-Nearest Neighbors + theory + Implementation with python
• Mathematics behind K-Nearest Neighbor
• Exercise on K-Nearest Neighbors implementation using Scikit learn library.

• Decision Tree and Random forest + theory + Implementation with python
• Mathematics behind Decision tree and
• What is Ensemble method?
• Exercise on Decision Tree and Random forest classifier its implementation using Scikit learn library.

• Support Vector Machines + maths behind Support vector machines.
• SVM kernels
• Linearly separable data
• Non Linearly separable data.
• SVM project Overview

• K-Means clustering + hierarchical agglomerative clustering + mathematical implementation.
• Exercise on K-means clustering

• Dimensionality reduction using Principal component analysis.
• PCA with python
• Exercise on PCA.

## Section 7: Recommender system

• What is recommender system?
• Application of recommender system in real time applications.
• Types of recommender system (User based and Item based recommender system)
• Techniques to implement recommender system.
• Exercise on recommender system with python.
• Projectâ€‹: Movie recommendation for users.
• Restaurant recommender system.

## Section 8: Natural Language processing. With Deep Learning

• Introduction to Natural Language processing
• NLTK Python library.
• Exercise on NLTK

• Neural Net and Deep Learning
• What is tensorflow?
• Tensorflow Installation.
• Tensorflow basics.
• MNIST with Multilayer perceptron
• Tensorflow with Contrib Learn
• TensorFlow Exercise

• What is Keras?
• Keras Basics.
• Pipeline implementation using keras.
• MNIST implementation with Keras.

## Section 9: Spark and Scala Introduction

• Introduction to Spark
• Introduction to Scala
• Scala Oops Concept
• Spark RDDs
• Types of RDDs
• Transformations and Actions
• Spark Advanced Functions for Machine Learning

## Section 10: Spark SQL

• Spark SQL
• Data Set
• Data Frame
• Differentiation between Spark RDDs, Transformations and Actions
• Usage of Spark SQL in Data Science
• Scala Library for Machine Learning
• Scala Application in Log and email Processing

## Section 11: Spark Streaming

• Spark Streaming Introduction
• Spark Streaming Library
• Large Dataset processing using Scala with Spark

## Section 12: Spark and Scala Machine Learning

• Scala Application in Machine Learning
• Linear Regression
• Advanced Analytics using Spark
• Machine Learning and Graph Data Processing

## Section 13: Data Science and Machine Learning Projects

• End to End KT on real-time application of Data Science and Machine Learning
• How to use Spar, Scala and Python Libraries in different use case
• Comparison of different libraries for different domains
• Real-time use cases discussion
• Profile preparation
• Mock Interviews
We Provide Project and Course COmpletion Certificate
Training
Project
Course and Project Completion Certificate
There are total four modules in this course:
Data Science source systems including Hadoop and Data warehouse Storage systems
Statistics
Machine Learning
Deep Learning

We also Cover Project with real time usecases part of this training
We are getting very good response based on previous Batches feedback about Trainer and course content

We start a new Batch every Monnt
Batch Schedule : Every Saturday Sunday (Weekend Batch)

We Have multiple projects based on below mentioned domains:
Banking
Healthcare
Insurance