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Topic

What does it mean?

Introduction to business analytics

  • What is analytics and why is it so important?
  • Applications of analytics
  • Different kinds of analytics
  • Various analytics tools
  • Analytics project methodology
  • Case study
In this section we shall provide you an overview into the world of analytics. You will learn about the various applications of analytics, how companies are using analytics to prosper and study the analytics cycle.

Fundamentals of R

  • Installation of R & R Studio
  • Getting started with R
  • Basic and Advanced Data types in R
  • Variable operators in R
  • Working with R data frames
  • Reading and writing data files to R
  • R functions and loops
  • Special utility functions
  • Merging and sorting data
  • Case study on data management using R
  • Practice assignment
R is the most popular software/language for data management & statistical analysis of data. It is free and open source. This module is all about learning how to manage and manipulate data and datasets, the very first step of analytics. We shall teach you how to use the R language to work with data using a case study.

Univariate statistics in R

  • Summarizing data, measures of central tendency
  • Measures of data variability & distributions
  • Using R language to summarize data
  • Practice assignment
This is where you shall learn how to start understanding the story your data is narrating by summarizing the data, checking its variability and shape. We shall take you through various ways of doing this using the R language and also solve a real-world case study

Data visualization in R

  • Need for data visualization
  • Components of data visualization
  • Utility and limitations
  • Introduction to grammar of graphics
  • Using the ggplot2 package in R to create visualizations
  • Practice assignment
Data visualization is extremely important to understand what the data is saying and gain insights in just one glance. Visualization of data is a strong point of the R software and you will learn the same in this module.

Hypothesis testing and ANOVA in R

  • Introducing statistical inference
  • Estimators and confidence intervals
  • Central Limit theorem
  • Parametric and non-parametric statistical tests
  • Analysis of variance (ANOVA)
  • Conducting statistical test
  • Case study
With 95% confidence we can say that there is a 75% chance, people visiting this site thrice will enroll for the course :). In this module, you learn how to create a hypothesis, statistically test it and validate it through data and present it with clear and formal numbers to support decision making.

Data preparation using R

  • Needs & methods of data preparation
  • Handling missing values
  • Outlier treatment
  • Transforming variables
  • Derived variables
  • Binning data
  • Modifying data with Base R
  • Data processing with dplyr package
  • Reshaping data in R
  • Practice assignment
Real world data is rarely going to be given to you perfect on a platter. It will always be dirty with missing data points, incorrect data, variables needing to be changed or created in order to analyze etc. A typical analytics project will have 60% of its time spent on preparing data for analysis. This is a crucial process as properly cleaned data will result in more accurate and stable analysis. We shall teach you all the techniques required to be successful in this aspect.

Predictive analytics in R

1. Correlation and Linear regression

  • Correlation
  • Simple linear regression
  • Multiple linear regression
  • Model diagnostics and validation
  • Case study
A statistical model is the core of predictive analytics and regression is one of the most powerful tools for making predictions by finding patterns in data. You shall learn the basic of regression modelling hands-on through real world cases

2. Logistic regression

  • Moving from linear to logistic regression
  • Model assumptions and Odds ratio
  • Model assessment and gains table
  • ROC curve and KS statistic
  • Case study
Logistic regression is the work-horse of the predictive analytics world. It is used to make predictions in cases where the outcomes are dual in nature i.e. an X or Y scenario where we need to predict if X will be the case or will Y, given some data. This is a must-know technique and we shall make you comfortable with it through real world problems.

3. Segmentation for marketing analytics

  • Need for segmentation
  • Criterion of segmentation
  • Types of distances
  • Clustering algorithms
    • Hierarchical clustering
    • K-means clustering
    • DBSCAN clustering
  • Deciding number of clusters
  • Case study
Learn why and how to statistically divide a broad customer market into various segments of customers who are similar to each other so as to be able to better target and meet their needs in a cost effective manner. This is one of the most essential techniques in marketing analytics.

4. Time series forecasting

  • What are time-series?
  • Need for forecasting
  • Smoothing techniques
  • ARIMA/SARIMA models
  • Case Study
The ability to forecast into the future is very important for any business and it is necessary to have as accurate a forecasting as possible for corporate planning for finance, sales, marketing, strategy etc. In this module learn the techniques of forecasting without being mis-led by seasonal and cyclical impacts.

5. Decision Trees, Random Forests & Boosting Machines

  • What are decision trees?
  • Entropy & Gini impurity index
  • Decision Trees (CART)
  • Random Forests
  • Extra Trees
  • Boosting & XGBoost algorithms
Decision trees are one of the most popular classification and prediction methods for helping in decision making. Learn the various decision tree algorithms and learn how to create a decision tree model and then extend to a random forest model. Also learn the very popular boosting algorithms.
Solving an actual business problem through analytics – Simulating an analytics project Simulation of an actual analytics project where you shall be completely hands-on and you will understand how everything you have learnt so far comes together to solve a business problem through analytics

Data Science & Machine Learning in Python

Introduction to Data Science

  • What is data science and why is it so important?
  • Applications of data science
  • Various data science tools
  • Data Science project methodology
  • Tool of choice-Python: what & why?
  • Case study
In this section we shall provide you an overview into the world of data science & machine learning. You will learn about the various applications of data science, how companies from all sort of domains are solving their day to day to long term business problems. We’ll learn about required skill sets of a data scientist which make them capable of filling up this vital role. Once the stage is set and we understand where we are heading we discuss why Python is the tool of choice in data science.

Introduction to Python

  • Installation of Python framework and packages: Anaconda & pip
  • Writing/Running python programs using Spyder Command Prompt
  • Working with Jupyter notebooks
  • Creating Python variables
  • Numeric , string and logical operations
  • Data containers : Lists , Dictionaries, Tuples & sets
  • Practice assignment
Python is one of the most popular & powerful languages for data science used by most top companies like Facebook, Amazon, Google, Yahoo etc. It is free and open source. This module is all about learning how to start working with Python. We shall teach you how to use the Python language to work with data.

Iterative Operations & Functions in Python

  • Writing for loops in Python
  • While loops and conditional blocks
  • List/Dictionary comprehensions with loops
  • Writing your own functions in Python
  • Writing your own classes and functions
  • Practice assignment
This is where you shall learn the functionalities and powerful capabilities of Python that will make it easy for you to work with data and set the stage for using Python for machine learning & data science.

Data summary & visualization in Python

  • Need for data summary & visualization
  • Summarising numeric data in pandas
  • Summarising categorical data
  • Group wise summary of mixed data
  • Basics of visualisation with ggplot & Seaborn
  • Inferential visualisation with Seaborn
  • Visual summary of different data combinations
  • Practice assignment
Data visualization is extremely important to understand what the data is saying and gain insights in just one glance. Visualization of data is a strong point of the Python software using the latest ggplot & Seaborn packages and you will learn the same in this module.

Data Handling in Python using NumPy & Pandas

  • Introduction to NumPy arrays, functions & properties
  • Introduction to Pandas & data frames
  • Importing and exporting external data in Python
  • Feature engineering using Python
Python is a very versatile language and in this module we expand on its capabilities related to data handling. Focusing on packages numpy and pandas we learn how to manipulate data which will be eventually useful in converting raw data suitable for machine learning algorithms.

Machine Learning Basics

  • Converting business problems to data problems
  • Understanding supervised and unsupervised learning with examples
  • Understanding biases associated with any machine learning algorithm
  • Ways of reducing bias and increasing generalisation capabilites
  • Drivers of machine learning algorithms
  • Cost functions
  • Brief introduction to gradient descent
  • Importance of model validation
  • Methods of model validation
  • Cross validation & average error
In this module we understand how we can transform our business problems to data problems so that we can use machine learning algos to solve them. We will further get into discovering what categories of business problems and subsequently machine learning algos are there. Then we will get updated on methodologies associated with solving such problems. These methodologies will form basis of techniques we learn ahead in the course. We’ll wrap up this module with discussion on importance and methods of validation of our results.

Generalised Linear Models in Python

  • Linear Regression
  • Regularisation of Generalised Linear Models
  • Ridge and Lasso Regression
  • Logistic Regression
  • Methods of threshold determination and performance measures for classification score models
  • Case Study
We start with implementing machine learning algorithms in this module. We also get exposed to some important concepts related to regression and classification which we will be using in the later modules as well. Also this is where we get introduced to scikit-learn, the legendary python library famous for its machine learning prowess.

Case Studies:
  1. Automate lender & borrower matching through prediction of loan interest rates - In this case study, we try to automate the process of lender and borrower matching for a fintech company by predicting interest rates offered.
  2. Classify customers based on revenue potential for a wealth management firm- In this classification case study, we help a financial institution to predict which one of their customers are going to fall in high revenue grid so that they can be given selective discounts for customer acquisition in a highly competitive industry of wealth management.

Tree Models using Python

  • Introduction to decision trees
  • Tuning tree size with cross validation
  • Introduction to bagging algorithm
  • Random Forests
  • Grid search and randomized grid search
  • ExtraTrees (Extremely Randomised Trees)
  • Partial dependence plots
  • Case Study & Assignment
In this module you will learn a very popular class of machine learning models which are rule based tree structures also known as Decision Trees. We'll examine the biased nature of these models and learn how to use bagging methodologies to arrive at a new technique known as Random Forest to analyse data.

Case Studies: In the class we continue with the case studies taken in previous module of simple linear models and see how the tree based models compare in terms of performance in comparison to the linear models. In take home exercises we have two case studies:
  1. Capture risks associated with micro loans: In the 1st exercise you will work on micro loans. Its inherently risky to hand out micro loans because of lack of checks in the natural process of micro loans. and in this case study we try to capture risk associated with these micro loans.
  2. How do the tech specifications of a vehicle impact its emissions? In the 2nd case study we find out effect of technical design specification of a vehicle on average emission and thus its environmental impact.

Boosting Algorithms using Python

  • Concept of weak learners
  • Introduction to boosting algorithms
  • Adaptive Boosting
  • Extreme Gradient Boosting (XGBoost)
  • Case Study & assignment
Want to win data science contest on Kaggle or data hackathons or be known as a top data scientist? Then learning boosting algorithms is a must as they provide a very powerful way of analysing data and solving hard to crack problems.

Case Studies:
  1. Save lives by predicting health issues in diabetics: A health care system in a state is struggling with poor detection of severity of health issues in diabetic people. This results in need for re-hospitalisation and many unfortunately not in time. Find out if boosting algos can save lives!
  2. Predicting annual income based on census data: In the take home exercise, find out whether someone is going to have annual income higher than a certain amount just by simple census data and thus identifying potential fraud cases when it comes to filing their taxes.

Support Vector Machines (SVM) & kNN in Python

  • Introduction to idea of observation based learning
  • Distances and similarities
  • k Nearest Neighbours (kNN) for classification
  • Brief mathematical background on SVM/li>
  • Regression with kNN & SVM
  • Case Study
We step in a powerful world of “observation based algorithms” which can capture patterns in the data which otherwise go undetected. We start this discussion with KNN which is fairly simple. After that we move to SVM which is very powerful at capturing non-linear patterns in the data.

Case Study: Since KNN and SVM take a lot of processing time, we have kept the class discussion case study simple. Same implementation steps can be used to work on any complex business problem as well.

Unsupervised learning in Python

  • Need for dimensionality reduction
  • Principal Component Analysis (PCA)
  • Difference between PCAs and Latent Factors
  • Factor Analysis
  • Hierarchical, K-means & DBSCAN Clustering
  • Case study
Many machine learning algos become difficult to work with when dealing with many variables in the data. We will learn methods which help solve this problem and also clustering techniques. Case Studies:
  1. Understanding impact of cash assistance programs in New York: To understand PCA, we take up data of cash assistance programs in New York. This has more than 60 variables. We’ll see how can we reduce the size of the data.
  2. Car Survey Data: We take up car survey data which contains technical & price detail of vehicles through 11 numeric variables. We’ll see if these 11 variables represent any hidden factors representing different properties of a vehicle.
  3. Pricing wines based on chemical properties: For K-Means we take data containing chemical properties of 4000+ white wines and examine whether we can find segments of wines based on their chemical compositions.
  4. Customer spend data at a retail chain: For DBSCAN we see how DBSCAN can be used for anomaly detection using expense data of customers from a retail chain.

Neural Networks

  • Introduction to Neural Networks
  • Single layer neural network
  • Multiple layer neural network
  • Backpropagation algorithm
  • Implementation in Python
Artificial Neural Networks are the building blocks of artificial intelligence. Learn the techniques which replicate how the human brain works and create machines which can solve problems like humans. Case Studies:
  1. Predicting annual income based on census data. find out whether someone is going to have annual income higher than a certain amount just by simple census data and thus identifying potential fraud cases when it comes to filing their taxes.

Text Mining in Python

  • Gathering text data using web scraping with urllib
  • Processing raw web data with BeautifulSoup
  • Interacting with Google search using urllib with custom user agent
  • Collecting twitter data with Twitter API
  • Naive Bayes Algorithm
  • Feature Engineering with text data
  • Sentiment analysis
  • Case study
Text data forms a big chunk of data available in the world today. Analysing text data can give a business very powerful insights to take advantage of. Python provides very useful ways to scrape data from the web or extract data from social media sites using APIs and then analyse the data. Case Studies:
  1. Live demonstrations of web scraping and data cleaning
  2. Making a portfolio tracking tool using Yahoo finance with Python
  3. Tagging an SMS as SPAM or NON-SPAM based on its content algorithmically with Naive Bayes

Ensemble methods

  • Making use of multiple ML models taken together
  • Simple Majority vote and weighted majority vote
  • Blending
  • Stacking
  • Case study
Individual machine learning models extract pattern from the data in different ways , which at times results in them extracting different patterns from the data. Rather than sticking to just one algorithm and not making use of other’s results is what we move past in this module. We learn to make use of multiple ML models taken together to make our predictive modelling solutions even more powerful.

Bokeh

  • Introduction to Bokeh charts and plotting
We introduce you to Bokeh, an evolving library in python which has all the tools that you’ll need to make small prototypes of data products which can be scaled later.

Artificial Intelligence in Tensorflow & Keras

Introduction to AI

  • What is AI and how will it change the world?
  • What is deep learning?
  • Uses of deep learning
  • Examples and applications
Get introduced to the world of Artificial Intelligence which is poised to change the entire world/ Understand what is deep learning and how it is used in AI

Getting started with Tensorflow

  • Setting up Tensorflow and GPU instances on GCP
  • Understanding computation graph and basics of tensorflow
  • Implementing simple preceptron in Tensorflow
  • Implementing multi-layer neural network in Tensorflow
  • Visualising training with Tensorboard
TensorFlow™ is an open source software library in Python for high performance numerical computation. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning.

Deep Feed Forward & Convolutional Neural Networks

  • Implementing deep neural net for image classification
  • Understanding convolutions, strides, padding, filters etc
  • Implementing CNN with tensor flow
  • Regularizing with dropout
  • Learning rate decay and its effects
  • Batch normalisation and its effects
A feedforward neural network is an artificial neural network wherein connections between the nodes do not form a cycle and information only flows in one direction. a convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks, most commonly applied to analyzing visual imagery. Learn these techniques for classifying images.

Introduction to Keras

  • Basics of Keras
  • Composing various models in Keras
  • Parameter tuning in Keras with previous examples
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow. It was developed with a focus on enabling fast experimentation and allows for easy and fast prototyping.

Recurrent Neural Networks, Long-Short Term Memory and Gated Recurrent Unit

  • Introduction to RNN architecture
  • Modeling sequence
  • Limitation of RNNs
  • Introduction to LSTM and use cases with implementation (text data)
  • Introduction to GRU and implementation (text data)
A recurrent neural network (RNN) is a class of artificial neural network where connections between nodes form a directed graph along a sequence. Long Short Term Memory networks – usually just called “LSTMs” – are a special kind of RNN, capable of learning long-term dependencies. Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks. These techniques are very popular for Natural Language Processing.

Autoencoders, Generative Adverserial Networks, Hopfield networks

  • Autoencoders and dimensionality reduction
  • GANs and their implementation
  • Hopfield networks
  • Variational auto encoders
  • Word2vec & Glove
An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction. Generative adversarial networks (GANs) are, implemented by a system of two neural networks contesting with each other. The purpose of a Hopfield net is to store 1 or more patterns and to recall the full patterns based on partial input. These are techniques used in computer vision problems.

Big Data Analysis in Hadoop & Spark

Introduction to Big Data & Hadoop

  • What is Big Data?
  • Traditional data management systems and their limitations
  • Business applications of big data
  • What is Hadoop and why is it used?
  • The Hadoop eco-system
  • Hadoop use-cases
In this module, you will understand the meaning of big data, how traditional systems are limited in their ability to handle big data and how the Hadoop eco-system helps in solving this problem. You will learn about the various parts of the Hadoop eco-system and their roles.

HDFS (Hadoop Distributed File System)

  • HDFS Architecture and internals
  • HDFS Daemons
  • Files and blocks
  • Namenode Memory concerns
  • Secondary namenode
  • HDFS access options
  • Installing and conifguring Hadoop
  • Hadoop daemons and commands
  • HDFS Federation
After this module you will learn the various basic Hadoop shell commands. You will also learn about the distributed file storage system of Hadoop, HDFS, why it is used and how is it different and how files are read and written in the storage system. You will work hands-on in implementing what is taught in this module.

HBase concepts

  • Architecture and role of HBase
  • Characteristics of HBase schema design
  • Implement basic programming for HBase
  • Combine best capabilities of HDFS and HBase
HBase is a distributed, versioned, column-oriented, multidimensional storage system, designed for high performance and high availability. Learn all about HBase in this module.

Mapreduce

  • MapReduce basics
  • Functional programming concepts
  • List processing
  • Mapping and reducing lists< and putting it together
  • Word Count example application
  • Understanding the driver, mapper and reducer
  • Closer look at MapReduce data flow
  • Build iterative Mapreduce applications
  • Understand combiners & partitioners
In this module, you will understand the MapReduce framework, how it works on HDFS and learn the basics of MapReduce programming and data flow (Basic Java knowledge will be required in the MapReduce modules for which videos will be provided)

Analyzing data with Pig and Hive

  • Pig architecture, program structure and execution process
  • Introduction to Pig Latin
  • Joins & filtering using Pig
  • Group & co-group
  • Schema merging and redefining functions
  • Pig functions
  • Introduction to Hive architecture
  • Using Hive command line interface
  • Create & execute Hive queries
  • Data types, operators & functions in Hive
  • Basic DDL operations
  • Data manipulation using Hive
  • Join operations & advanced querying in Hive
Pig is a platform to analyse large data sets through a high level language. In this module you will focus on learning both to query and analyse large amounts of data stored in distributed storage systems. Hive is a data warehouse software for managing and querying large scale datasets. It uses a SQL like language, HiveQL to query the data. Learn Hive in-depth in this module.

Transferring data using Sqoop & Flume

  • Basics of Sqoop & Sqoop architecture
  • Import data into Hive using Sqoop
  • Export data from HDFS using Sqoop
  • Drivers and connectors in Sqoop
  • Importing and exporting data in Sqoop
  • Flume architecture
  • Use Flume configuration file
  • Configure & build Flume for data aggregation
Sqoop is a tool designed to transfer data between Hadoop and relational databases. Learn how to use Sqoop in this module. In this module learn to work with Flume which is a service for efficiently collecting, aggregating, and moving large amounts of streaming data into HDFS.

Scala & Spark

  • Scala environment setup
  • Scala REPL
  • Scala classes and Objects
  • Scala variables
  • Scala functions, anonymous functions and methods
  • Scala closures
  • Scala Collections & Traits
  • Apache Spark and Spark Core Programming
  • Difference between Spark & Hadoop frameworks
  • Key components of Spark eco-system
  • Initialize a Spark application
  • Run a Spark job on YARN
  • Create an RDD from a file or directory in HDFS
  • Persist an RDD in memory or on disk
  • Perform Spark transformations & actions on an RDD
  • Create Spark DataFrames from an existing RDD
  • Write a Spark SQL application
Scala is a modern multi-paradigm programming language designed to express common programming patterns in a concise, elegant, and type-safe way. Apache Spark is developed in Scala. We will learn functional programming language Scala in this module. Apache Spark is a new cluster computing platform, designed for fast and general purpose Big Data processing. Spark is faster than Mapreduce. Spark programs can be written in Java, Scala, or Python. Because Spark is written in JVM language Scala, therefore Scala is the primary choice of language. Learn the highly in-demand technologies of Spark and Scala in this module.
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  • It was a great experience and pleasure to learn from Edvancer.  The online class room is as good as a real class room. It was highly interactive with brainstorming on many ideas. The course content also depicts real life scenarios. Altogether it was a great learning experience.

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  • The data science course provides an in-depth understanding of analytics with hands-on experience on R & Python using case studies from varied domains. You get all one needs for excelling in the field of analytics. The faculty have a very good grasp of all the concepts and the Edvancer team is very supportive.

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  • I got a great job as Sr. Analyst with a 75% pay hike post this course! The course is a perfect blend of analytics tools and techniques. If you want to learn real stuff in analytics and not just the theoretical concepts, this course is for you.

    Ashish Kumar – B.Tech, IIT Madras

Benefits of taking the Artificial Intelligence specialization course

  • Be recognized in the industry with a TCS iON ProCert certificate
  • Learn artificial intelligence from the basics including predictive modeling, machine learning, deep learning and big data processing through 12 real world projects.
  • Learn R, Python, Tensorflow, Keras, Hadoop, Spark & SQL hands-on to manage, manipulate, cleanse and analyze data
  • You will not just learn the techniques and tools in isolation but will combine and apply them to derive business insights from raw data and automate decision making to human levels
  • AI talent demand is much more than the available skilled supply. Become employable in this fast growing new age field by demonstrating the skills learnt through this course
  • Use these new-age skills in your existing role to become more efficient and effective

AI Corporate Training

Your employees can be trained on this artificial intelligence course in Mumbai, Hyderabad, Bangalore, Delhi, Kolkata, Pune, Chennai or anywhere else as per your requirements. Kindly contact us on +9180809289 48 or on info@edvancer,in to know more about the artificial intelligence training for your team.


Who should take this course

This course is for students pursuing their graduation/post-graduation and for working professionals who have completed their graduation in any technical or engineering field. There are no other prerequisites but you do need to have a quantitative bent of mind. For those who hate maths or numbers or coding, while we shall try to make you as comfortable as possible, the AI field itself may prove to be a challenge for you.