The Best Data Science / Data Analytics / Business Analytics Training Course in Kolkata
- 160 hours of exhaustive training covering all areas of Data Science
- Taught by working Data Scientists, alumni of IIT, IIM, ISB, etc.
- Record placements so far!
- Live Projects / Internships
Career Opportunities for Data Science Professionals
We are sure you all know that with the data revolution taking the world by storm, Data Science professionals have become the most sought after all over the world as there is a huge skill-gap. More and more organizations are turning data-centric and data professionals are in demand for a variety of roles. Some of them are as follows:
Data Science Course in kolkata Overview
With the aim to provide the best in Data Science course in kolkata, we have designed a course that can be said to be one of the most comprehensive Data Science courses in Kolkata. Your search for the best Data Science course should end here as what we offer covers the complete Data Science life-cycle concepts. We will teach you how to obtain data from all available sources, clean it to suit your purpose and explore it to get significant variables for testing by extracting features in your data sets. We will also teach you how to use data visualization techniques. Then we teach you data modelling and also interpreting those models and data in layman’s terms so that your customer understands your solutions when you present it to them.
We cover the full range of skills and tools needed by data professionals to work in today’s industry. Check the course details given below to get an idea of the things you will learn. The course will be taught by well-known data scientists (all IIT, ISB alumni) from the industry with years of experience providing data science solutions as well as training.
We also provide placement assistance as part of the Data Science training program in kolkata. Our students have been placed successfully in various national and multinational companies.
You too can be an accomplished data professional. Join us now!
Things You Will Learn
- SQL Command
- Data Query Language (DQL)
- Data Manipulation Language (DML)
- Data Definition Language (DDL)
- Data Control Language (DCL)
- SQL RDBMS Concept – Features & Advantages
- Record or a Row
- RDBMS – Database Normalization
- Primary and Foreign Keys
- Index in RDBMS
- SQL Data Types | Data Types in SQL Server
- Clause in SQL
- WITH Clause
- SELECT Clause
- FROM Clause
- WHERE clause
- GROUP BY clause
- HAVING Clause
- ORDER BY Clause
- SQL Operators – Arithmetic, Comparison, & Logical
- Operators in SQL – Alias, IN and Between
- Create Database | SQL Drop & Select Database
- SQL Join – Inner, Left, Right & Full Joins
- SQL Index – Create, Unique, Composite Index
- SQL Functions
- Stored Procedure in SQL
- Triggers in SQL
Introduction to R Programming
- Introduction to R
- Data Types in R
How To Install R & R Studio
Data Structures in R
- Variable in R
- Operators in R
- Conditiional Statement
- Decision Making<
- IF Statement
- IF-Else Statement
- Nested IF-Else Statement
- Switch Statement
- While Loop
- Repeat Loop
- For Loop
- User-defined Function
- Calling a Function
- Calling a Function without an Argument
- Calling a Function with an Argument
- Decision Making<
- Box Plots
- Bar Charts
- Pareto Chart
- Pie Chart
- Line Chart
How to Import Dataset in R
- Read CSV Files
- Read Excel Files
- Read SAS Files
- Read STATA Files
- Read SPSS Files
- Read JSON Files
- Read Text Files
- Hmisc or mise
- Data Table
How to Integrate R and SQL
How to Get Data From SQL to R
Description: You will get an introduction to the Python programming language and understand the importance of it. How to download and work with Python along with all the basics of Anaconda will be taught. You will also get a clear idea of downloading the various Python libraries and how to use them.
- About ExcelR Solutions and Innodatatics
- Do’s and Don’ts as a participant
- Introduction to Python
- Installation of Anaconda Python
- Difference between Python2 and Python3
- Python Environment
- Exception Handling (Error Handling)
Description: In this module, you will learn the basics such as assigning a variable, differences between dictionary, sets, tuple, and some decision making statements. Also, you will learn about working with different loops, data types and its usage.
- Data Types
- Conditional Statements
Description: This module helps you to learn and understand the different libraries used in Python. You will also get a clear idea about the NumPy library and how you can use it. NumPy is a Numeric Python library which helps in dealing with the numeric calculations with data frames.
- NumPy Introduction
- Array Indexing
- NumPy Data Types
- Treating Missing and NA’s
- Reshaping and combining Arrays
Description: In this module, you will learn how to download the Pandas package and syntax for the same. Pandas is also a library similar to Numpy which predominantly helps in working with series data and data frames. You will learn how to impute the data in the place of missing values called the missing value treatment done in the Pandas package itself.
- Pandas Introduction
- Basic Operations on Series
- Working with Text Data
- Working with Missing Data
- Indexing and Selecting Data
- Merge, Join and Concatenate
Description: In this module, you will learn where, how and when to use the Matplotlib library. This library is used to visualize the data. You will get an in-depth understanding of the importance of this library.
- Introduction to Matplotlib
- Matplotlib design and different visualizations
Description: This module will help you to understand the importance of Seaborn package and downloading the library Just like the Matplotlib library, the Seaborn library is also used in visualizing the data allowing high-level visualizations with categorical data.
- Introduction to Seaborn Library
- Visualizing the Distribution of the Datasets
- Plotting the Categorical Data
- Visualizing Linear Relationships
- Visualizing Statistical Relationships
Description: In this module, you will understand the importance of both Scipy and Sklearn libraries which are predominantly used in building Machine LearningAlgorithms working with Linear Equations. Sklearn also known as Scikit-learn, is a machine learning library for the Python programming language. You will get a clear idea of where you can use these libraries along with some examples.
- Installing both SciPy and Sklearn Libraries
- Introduction to SciPy (Mathematical Algorithms)
- Introduction to Sklearn (Machine Learning Algorithms)
Description: Learn about High-level overview of Data Science project management methodology, Statistical Analysis using examples, understand Statistics and Statistics 101. Also, learn about exploratory data analysis, data cleansing, data preparation, feature engineering.
- High-Level overview of Data Science / Machine Learning project management methodology
- Videos for Data Collection – Surveys and Design of Experiments will be provided
- The various Data Types namely continuous, discrete, categorical, count, qualitative, quantitative and its identification and application. Further classification of data in terms of Nominal, Ordinal, Interval and Ratio types
- Random Variable and its definition
- Probability and Probability Distribution – Continuous probability distribution / Probability density function and Discrete probability distribution / Probability mass function
Description: Continue with the discussion on understanding Statistics, the various Moments of business decision and other Basic Statistics Concepts. Also, learn about some graphical techniques in Analytics.
- Balanced vs Imbalanced datasets
- Various sampling techniques for handling balanced vs imbalanced datasets
- Videos for handling imbalanced data will be provided
- What is Sampling Funnel, its application and its components
- Sampling frame
- Simple random sampling
- Measure of central tendency
- Mean / Average
- Measure of Dispersion
- Standard Deviation
- Expected value of probability distribution
Description: Learn about the other moments of business decision as part of Statistical Analysis. Learn more about Visual data representation and graphical techniques. Learn about Python, R programming with respect to Data Science and Machine Learning. Understand how to work with different Python IDE and Python programming examples.
- Measure of Skewness
- Measure of Kurtosis
- Various graphical techniques to understand data
- Bar plot
- Box plot
- Scatter plot
- Introduction to R and RStudio
- Installation of Python IDE
- Anaconda and Spyder
- Working with Python and R with some basic commands
Description: Learn about Normal Distribution and Standard Normal Distribution. Rules and Principles of Normal distribution. And how to check for normality by QQ normal distribution Plot.
- Normal Distribution
- Standard Normal Distribution / Z distribution
- Z scores and Z table
- QQ Plot / Quantile-Quantile plot
Description: Under this last topic on Basics of statistics, learn some higher statistical concepts and gain understanding on interval estimates.
- Sampling Variation
- Central Limit Theorem
- Sample size calculator
- T-distribution / Student’s-t distribution
- Confidence interval
- Population parameter – Standard deviation known
- Population parameter – Standard deviation unknown
Description: Get introduced to Hypothesis testing, various Hypothesis testing Statistics, understand what is Null Hypothesis, Alternative hypothesis and types of hypothesis testing.
- Parametric vs Non-parametric tests
- Formulating a Hypothesis
- Choosing Null and Alternative hypothesis
- Type I and Type II errors
- Comparative study of sample proportions using Hypothesis testing
- 2 sample t test
Description: Learn about the various types of tests in Hypothesis testing. Get introduced to the prerequisites and conditions needed to proceed with a Hypothesis test. Understand the interpretation of the results of a Hypothesis testing and probabilities of Alpha error.
- 1 sample t test
- 1 sample z test
- 2 Proportion test
- Chi-Square test
- Non-Parametric test
Description: Continuing the discussion on Hypothesis testing, learn more about non-parametric tests. Perform tests using R and interpret the results.
- Non-Parametric test continued
- Hypothesis testing using Python and R
Description: Learn about Linear Regression, components of Linear Regression viz regression line, Linear Regression calculator, Linear Regression equation. Get introduced to Linear Regression analysis, Multiple Linear Regression and Linear Regression examples.
- Scatter diagram
- Correlation Analysis
- Correlation coefficient
- Ordinary least squares
- Principles of regression
- Splitting the data into training, validation and testing datasets
- Understanding Overfitting (Variance) vs Underfitting (Bias)
- Generalization error and Regularization techniques
- Introduction to Simple Linear Regression
- Heteroscedasticity / Equal Variance
Description: In the second part of the tutorial, you will learn about the Models and Assumptions for building Linear Regression Models, build Multiple Linear Regression Models and evaluate the results of the Linear Regression Analysis.
- LINE assumption
- Collinearity (Variance Inflation Factor)
- Multiple Linear Regression
- Model Quality metrics
- Deletion diagnostics
Description: Learn to analyse Attribute Data, understand the principles of Logistic Regression, Logit Model. Learn about Regression Statistics and Logistic Regression Analysis.
- Principles of Logistic Regression
- Types of Logistic Regression
- Assumption and Steps in Logistic Regression
- Analysis of Simple Logistic Regression result
Description: Learn about the Multiple Logistic Regression and understand the Regression Analysis, Probability measures and its interpretation. Know what is a confusion matrix and its elements. Get introduced to “Cut off value” estimation using ROC curve. Work with gain chart and lift chart.
- Multiple Logistic Regression
- Confusion matrix
- False Positive, False Negative
- True Positive, True Negative
- Sensitivity, Recall, Specificity, F1
- Receiver operating characteristics curve (ROC curve)
- Lift charts and Gain charts
Description: Learn about the Discrete probability distribution. Types of Discrete probability distribution viz Binomial distribution, Poisson distribution and working with the probability distribution formula.
- Binomial Distribution
- Negative Binomial Distribution
- Poisson Distribution
Description: Get introduced to various advanced regression techniques, especially regression analysis of count data namely Poisson Regression, Negative binomial regression. Learn when to use Poisson regression and Negative binomial regression for predicting count data.
- Poisson Regression
- Poisson Regression with Offset
- Negative Binomial regression
- Treatment of data with excessive zeros
- Zero-inflated Poisson
- Zero-inflated Negative Binomial
- Hurdle model
Description: Get introduced to Multinomial regression, or otherwise known as multinomial logistic regression, learn about multinomial logit models and multinomial logistic regression examples.
- Logit and Log Likelihood
- Category Baselining
- Modeling Nominal categorical data
- Additional videos are provided on Lasso / Ridge regression for identifying the most significant variables
Description: As part of Data Mining Unsupervised get introduced to various clustering algorithms, learn about Hierarchal clustering, K means clustering using clustering examples and know what clustering machine learning is all about.
- Supervised vs Unsupervised learning
- Data Mining Process
- Measure of distance
- Numeric – Euclidean, Manhattan, Mahalanobis
- Categorical – Binary Euclidean, Simple Matching Coefficient, Jaquard’s Coefficient
- Mixed – Gower’s General Dissimilarity Coefficient
- Types of Linkages
- Single Linkage / Nearest Neighbour
- Complete Linkage / Farthest Neighbour
- Average Linkage
- Centroid Linkage
- Hierarchical Clustering / Agglomerative Clustering
Description: In this continuation lecture learn about K means Clustering, Clustering ratio and various clustering metrics. Get introduced to methods of making optimum clusters.
- K-Means Clustering
- Measurement metrics of clustering – Within Sum of Squares, Between Sum of Squares, Total Sum of Squares
- Choosing the ideal K value using Scree plot / Elbow Curve
- Additional videos are provided to understand K-Medians, K-Medoids, K-Modes, Clustering Large Applications (CLARA), Partitioning Around Medoids (PAM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS)
Description: Learn to apply data reduction in data mining using dimensionality reduction techniques. Gain knowledge about the advantages of dimensionality reduction using PCA and SVD.
- Why dimension reduction
- Advantages of PCA
- Calculation of PCA weights
- 2D Visualization using Principal components
- Basics of Matrix algebra
- SVD – Decomposition of matrix data
Description: Under data mining unsupervised techniques, learn about Network Analytics and the measures used. Get introduced to Network Analysis tools like NodeXL.
- Definition of a network (the LinkedIn analogy)
- Measure of Node strength in a Network
- Degree centrality
- Closeness centrality
- Eigenvector centrality
- Adjacency matrix
- Betweenness centrality
- Cluster coefficient
- Introduction to Google Page Ranking
Description: Learn one of the most important topic Association rules in data mining. Understand how the Apriori algorithm works, and the association rule mining algorithm.
- What is Market Basket / Affinity Analysis
- Measure of association
- Lift Ratio
- Apriori Algorithm
- Sequential Pattern Mining
Description: Learn how online recommendations are made. Get insights about online Recommender System, Content-Based Recommender Systems, Content-Based Filtering and various recommendation engine algorithms. Get to know about people to people collaborative filtering and Item to item collaborative filtering.
- User-based collaborative filtering
- Measure of distance / similarity between users
- Driver for recommendation
- Computation reduction techniques
- Search based methods / Item to item collaborative filtering
- SVD in recommendation
- Vulnerability of recommender systems
- Deciding the K value
- Building a KNN model by splitting the data
- Understanding the various generalization and regulation techniques to avoid overfitting and underfitting
Description: The aim of this course is to understand what Data Visualization is all about. You will understand what are the best practices of Data Visualization, creating data visualization charts and understanding which visualization tools can be considered. Further, you will look into why you need to consider Tableau. You will also get an understanding of products in Tableau You will get an understanding as to what Data Visualization Principles are. Our course content is designed as per Tableau Certification. Edward Tufte considered as the father of Data Visualization came up with Data Integrity rules that need to be followed to get beautiful pieces of evidence.
- Why visualization came into Picture
- Importance of Visualizing Data
- How Data is getting generated
- Poor Visualizations Vs.Perfect Visualizations
- Principles of Visualizations
- Examples of Perfect Visualization
- Tufte’s Graphical Integrity Rule
- Tufte’s Principles for Analytical Design Visual
Description: You will get to know about Tableau Desktop, Tableau Server, Tableau Online, Tableau Prep and get an understanding on 14-day trial option i.e. the free version of Tableau – Tableau Public and how Tableau Public login works. You will also look into Tableau Public vs Tableau Desktop and glance on Tableau free download for Students. Get an understanding of Tableau Interactive Dashboards. You will also see what Tableau Reader is all about. You will see different types of Data. You will see what Tableau Architecture is and how it works. What Tableau Data source page is and how to customize the data is learnt here. You will understand what Discrete data and Continuous data are and their differences. You will also see how Data Interpretation works and what exactly happens to the data after interpretation. You will also get to understand the user interface on Tableau.
- Products of Tableau
- Tableau Public in detail
- About Viz of the day, Viz of the week
- Start Page on Tableau Desktop Professional
- Tableau Architecture
- Connecting to Data Source
- Understanding on Data Source Page
- Pivot Tables
- Difference between Discrete data and Continuous data
- Data Interpretation
- Tableau User Interface
Description: You will get a know around of the Charts in Tableau from the Show me Panel that is available in the Tableau work area. Text Table, hands-on understanding is given. Get an understanding of how Heat map for websites works. Check the Highlight tables in Tableau. You will see how Pie Charts are created. Also, see some Pie chart example like which do we use these pie chart. You will also see how the Bar charts are created. You will also see what are the other bar charts. Eg bar chart stacked, Side by side bar chart.
- Text Tables, Totals
- Highlight Tables
- Heat Maps
- Copy and Exporting the Data
- Pie Chart
- Bar Chart
- Arbitrary Formatting of Colors
- Conditional Formatting
- Stacked Bars
- Side by Sidebars
- Tree Chart
- Circle Chart
- Side by side Circle chart
Description: If say the data is present in different sheets or different Data sources, then the need to learn how joins, Unions, Cross database joins tableau and data blending in tableau help us in connecting them.
- Cross-Database Connections
- Data Blending
Description: Learn how to create Filters in Tableau. You will find out the types of filters in Tableau and understand the hierarchy of filters, other filters like quick filters and context filters in Tableau. Get an exposure to how Extracts, Extracts Filters and Live data works in Tableau.
- Extract Filters
- Extract and Live Connections
- Data Source Filters
- Dimension Filters
- Measures Filters
- Date Filters
- Various Options on Filters
Description: Understand what are Sets and Groups in Tableau. You will also get an understanding of sets vs groups in Tableau. Understand about folders and Tableau hierarchy. Get hands-on exposure on creating Folders, Groups, Sets Hierarchy in Tableau. You will be able to understand the parameters in Tableau that makes the visualization dynamic. Hands-on exposure to how parameters in tableau help come up with dynamic or interactive Dashboards.
Description: Time series charts can also be created. Listed below are some of the Intermediate level charts that can be analysed. This helps you to work with multiple dimensions and multiple measures on the view area.
- Time Series Charts (Line Chart)
- Area Chart
- Dual Line Chart
- Dual Combination
- Combination Chart
Description: You will understand about maps in Tableau. Also, see what is tableau map layers are and how to see latitude and longitude on google maps and customize geocoding. You will also understand what symbol maps and filled maps are in Tableau.
- Symbol Maps
- Filled Maps
- Background images
- Polygon Maps
- Connecting to WMS Server
- Lasso, Radial and Rectangular selection
Description: You will learn about the box and whisker plot. We generally refer this as box plots. You will learn about scatter plots and then see what is trend analysis and different models. You will understand what is predictive analysis and see how you can use predictive analysis in forecasting in Tableau. Learn how to create Histogram. Get an understanding of Funnel chart. We will learn about donut chart and how to create donut chart in Tableau. You will also learn about the waterfall chart. The other name of the waterfall model is the Gantt Chart. You will get a look into how Pareto chart is created, get an understanding of what Pareto analysis is before you get into the working. Understand the concepts of bullet charts.
- Scatter Plot
- Box Plot
- Bullet Chart
- Packed Bubble
- Funnel Chart
- Donut Chart
- Waterfall Chart / Gantt Chart
- Pareto Chart
Description: Look at how the calculations can be done using “Create Calculated Fields” option. Understand of how various Logical, String, Numerical, Ad-hoc Calculations and Quick Table Calculations can be done here. You will work on LoD in Tableau. Which means Level of Detail. This helps in build little more advanced calculations.
- Logical Calculations
- String Calculations
- Numerical Calculations
- Quick Table Calculations
- Ad-hoc Calculation
- LOD Expressions
Description: You will learn about Actions in Tableau and different actions like Filters in Tableau Dashboards. You will get an understanding of the Tableau server. You will also learn the concepts done on R tool implement in Tableau and see how the integration between these tools take place.
- Integration between R and Tableau
- Integration between Hadoop and Tableau
- Dashboards and Actions
- Connecting Data to Tableau Server
Description: The key to any successful project accomplishment including analytics consulting projects would be to understand the business problem. Also, you should understand the initial activities to be performed in Data Science projects for solving business problems using Data Analytics.
- Business Objectives
- Business Constraints
- Creating a Business Case
- Components of Business Case
- Creating Project Charter
- Components of Business Case
Description: Understanding the various forms of collecting data and collecting the right data is of paramount importance for developing interesting insights in solving analytics problems. Deciding on the various market research techniques and ways of collecting data is pivotal to the success of Data Science projects.
- Market Research using Secondary Data Sciences
- Data Collection from Primary Data Sources
- Performing Surveys and Questionnaire
- Performing Experiments
- Validating Data Quality
Description: Gathering the data alone is not sufficient, Data Scientists need to ensure that it is in a clean format. Exploring the data while performing data cleansing consumes a significant amount of time and allocating the right amount of effort towards these activities is very important.
- Data cleansing including Outlier Analysis, Imputation, etc.
- EDA to bring interesting Descriptive Analytics for actioning
- Feature Engineering to get Derived Variables
- Applying Domain Knowledge
- Getting the final data for Predictive Modeling
Description: Determine whether Data Mining supervised learning or unsupervised learning is applicable for solving the business problem or do you need to implement a combination of both to solve the problem. Understanding what process has to be followed from selecting the right variables and algorithms required for solving a problem is learnt in this module.
- Decide statistically on what are the most important variables
- How to decide on which is the right technique / algorithm
- Deciding on how to deal with balanced / imbalanced dataset
- Deciding on highest accuracy model and high-performance model
Description: Learn how to close a Data Science project or Artificial Intelligence project and determine whether the purpose of the project success criteria is met or not. Deciding on how to deploy the solution at the client side is very important because all the hard work will be meaningless if customers do not get an easy way of viewing the solution and results.
- Decide on the model deployment strategy – Web / Mobile / Etc.
- How to gauge the project closure criteria
- Performing Review and Retrospection
- Deciding upon model maintenance and upgradation strategy
Description: Learn about how data is playing a key role in an organization. Data is the new oil that is the driving force for all industry, sectors and domains. With big data in the current world, organizations need to take leverage from Data to gain a competitive edge in real-time. Understand the need for Big Data tools, various components of Big Data, the architecture and the Big Data tools for processing.
- Introduction to Big Data
- Data, Data, Data Everywhere
- 3 V’s of Big Data (Volume, Variety and Velocity)
- Challenges with Big data
- Need and significance of innovative technologies
- What is Hadoop
- History of Hadoop and its uses
- Different components of Hadoop
- Various Hadoop Distributions
Description: Learn about the three main components of Big Data Hadoop. Understand the Master / Slave architecture of Hadoop. Learn about the Demons of Storage component – HDFS and Processing component – MapReduce and finally learn about the resource manager which manages all the operations in the Hadoop Cluster.
- Significance of HDFS in Hadoop
- HDFS Features
- Daemons of Hadoop and functionalities
- Data Storage in HDFS
- Accessing HDFS
- Data Flow
- HDFS commands hands-on
- Introduction to MapReduce
- MapReduce Architecture
- Data Types
- Input Splits and Records
- Basic MapReduce Program
- The MapReduce Web UI
Description: Learn about the first multi-user operating system – Linux and file system of Linux OS, Kernel, Interactive Shells, etc. Understand the usage of the Terminal and its commands. Learn about virtualization softwares like VMware and VirtualBox. Creation of a virtual Linux machine for Pseudo Hadoop Cluster setup
- VMware Workstation
- Setup of Linux Virtual Machine
- What is Linux OS
- Flavours of Linux Os
- Linux File System
- Advantages of Linux Os
- Hands-on Linux Terminal Commands
Description: Introduction to SQL like programming language on Big Data Hadoop over MapReduce. Components of the Hive execution engine and the flow of the execution. Learn how different Data Warehousing tool – Apache Hive is with respect to SQL language.
- Hive Engine and its Components
- RDBMS Hive Metastore
- Comparison with Traditional Databases
- Hive Tables
- Querying Data
- User-Defined Functions
Description: Introduction to traditional Database system – RDBMS and its SQL programming language. Learn about NoSQL database (HBase) and its advantages. Learn how to move data from traditional Database to Big Data Hadoop system and vice versa using Apache Sqoop.
- Introduction to MySQL
- Basics of traditional RDBMS concepts
- Difference between SQL and NoSQL (HBase)
- Introduction to Sqoop
- Benefits of Sqoop
- Sqoop Architecture and Internals
- MySQL client and server installation
- How to connect to the relational database using Sqoop
- Sqoop Commands
Description: Introduction to super-fast, memory based, cluster computing framework – Apache Spark. Components of the Unified Stack Apache Spark. Learn how Spark attains super speed over the Big Data residing in HDFS. Comparison between distributed frameworks – Hadoop and Spark. Learn what is RDD and its creation. Difference between Dataframe, Datasets and RDD in Apache Spark 2.X and their applications. Start writing Spark functions using multiple programming languages.
- Introduction to Apache Spark
- Apache Spark vs Hadoop
- Spark Architecture
- Spark Execution Environment – SparkContext, SQLContext, SparkSession
- RDD and Operations on RDD’s
- Spark Unified Stack
- Spark Core
- Spark SQL
- Spark MLlib
- Spark Streaming
- PySpark (Spark using Python)
- Broadcast & Accumulator
- Understand the core Azure architectural components
- describe Regions
- describe Availability Zones
- describe Resource Groups
- describe Azure Resource manager
- describe the benefits and usage of core Azure architectural components
- Core products available in Azure
- products available for Compute such as Virtual Machines, Virtual Machine Scale Sets, App Service and Functions
- products available for Storage such as Blob Storage, Disk Storage, File Storage, and Archive Storage
- products available for Databases such as CosmosDB, Azure SQL Database, Azure Database Migration service, and Azure SQL Data Warehouse
- solutions available on Azure
- Big Data and Analytics and products that are available for Big Data and Analytics such as SQL Data Warehouse, HDInsight and Data Lake Analytics
- Artificial Intelligence (AI) and products that are available for AI such as Azure Machine Learning Service and Studio
- Azure management tools
- Azure CLI, PowerShell, and the Azure Portal
- Overview of Azure Machine Learning studio
Description: Under the Naive Bayes classifier tutorial, learn how the classification modeling is done using Bayesian classification, understand the same using Naive Bayes example. Learn about Naive Bayes through the example of text mining.
- Probability – Recap
- Bayes Rule
- Naive Bayes Classifier
- Text Classification using Naive Bayes
Description: Bagging and Boosting is an ensemble technique which is a part of the random forest algorithm. Learn about Bagging and Boosting examples under this tutorial.
- Boosting / Bootstrap Aggregating
- AdaBoost / Adaptive Boosting
- Gradient Boosting
- Extreme Gradient Boosting (XGB)
Description: Decision Tree and Random Forest are one of the most powerful classifier algorithms today. Under this tutorial, learn about Decision Tree Analysis, Decision Tree examples and Random Forest algorithms.
- Elements of Classification Tree – Root node, Child Node, Leaf Node, etc.
- Greedy algorithm
- Measure of Entropy
- Attribute selection using Information Gain
- Ensemble techniques
- Decision Tree C5.0 and understanding various arguments
- Random Forest and understanding various arguments
Description: Text mining or Text data mining is one of the wide spectrum of tools for analyzing unstructured data. As a part of this course, learn about Text analytics, the various text mining techniques, its application, text mining algorithms and sentiment analysis.
- Sources of data
- Bag of words
- Pre-processing, corpus Document-Term Matrix (DTM) and TDM
- Word Clouds
- Corpus level word clouds
- Sentiment Analysis
- Positive Word clouds
- Negative word clouds
- Unigram, Bigram, Trigram
- Semantic network
Description: Learn how to extract data from Social Media, download user reviews from E-commerce and Travel websites. Generate various visualizations using the downloaded data.
- Extract Tweets from Twitter
- Extract user reviews of the products from Amazon, Snapdeal and TripAdvisor
Description: Learn how to perform text analytics using Python and work with various libraries that aid in data extraction, text mining, sentiment analysis and
- Install Libraries from Shell
- Extraction and text analytics in Python
Description: Natural language processing applications are in great demand now and various natural language processing projects are being taken up. As part of this tutorial, learn about Natural language and ‘Natural language understanding’.
- Topic Modeling
- Sentiment Extraction
- Lexicons and Emotion Mining
Description: Forecasting or Time Series Analysis is an important component in analytics. Here, get to know the various forecasting methods, forecasting techniques and business forecasting techniques. Get introduced to the time series components and the various time series analysis using time series examples.
- Introduction to time series data
- Steps of forecasting
- Components of time series data
- Scatter plot and Time Plot
- Lag Plot
- ACF – Auto-Correlation Function / Correlogram
- Visualization principles
- Naive forecast methods
- Errors in forecast and its metrics
- Model Based approaches
- Linear Model
- Exponential Model
- Quadratic Model
- Additive Seasonality
- Multiplicative Seasonality
- Model-Based approaches
- AR (Auto-Regressive) model for errors
- Random walk
- ARMA (Auto-Regressive Moving Average), Order p and q
- ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q
- Data-driven approach to forecasting
- Smoothing techniques
- Moving Average
- Exponential Smoothing
- Holts / Double Exponential Smoothing
- Winters / HoltWinters
- De-seasoning and de-trending
- Econometric Models
- Forecasting Best Practices
- Forecasting using Python
- Forecasting using R
- DECISION TREE
- NAIVE BAYES
- RANDOM FOREST
- NEURAL NETWORKS
- TEXT MINING
- NEGATIVE BINOMIAL REGRESSION
- POISSONS REGRESSION
- MULTINOMIAL REGRESSION
- RECOMMENDATION SYSTEM
- WEB EXTRACTIONS:
- Amazon Review Extraction
- IMDB Review Extraction
- Snapdeal Review Extraction
Project 1: How To Identify Fraudulent And Ilegal Transactions Due To Insider Trading
Project Related To: Finance Service Insurance
Problem Description: In spite of the mature regulatory norms, the act of insider trading is on the rise. More robust the regulatory norms become, more intelligent the insider traders become. This is forcing the firms to always be on toes and keep developing better ways of identifying the fraud. Insider trading gives away the secrets of the organizations, which are strictly not to be disclosed outside the boardroom. The ugly politics of companies, which cannot get head-on with the ethical businesses are heavily resorting to these ways of mending the rules to make this way to success. How do you identify the sheep in wolves’ clothing?
Project 2: Learn On How To Predict The Deposits Churn And Reduce The Risk Of Losing Customers
Project Related To: Finance Service
Problem Description: Considerably, alongside growing the customer base, not maintaining sufficient funds as deposit amounts could lead to levying penalty and this could, in turn, lead to customer churn.
a) How to devise strategies in retaining customers and also ensuring that they maintain required funds in deposits or increase the funds in deposits
b) How to predict on who is the most probable customer to churn
c) How to find out about customers who will continue to stay despite levying penalty for maintaining an amount below the par in the deposit accounts
d) How to segment customers and devise business strategies for each of these segments
e) These are the challenges for which banks need an immediate solution.
Project 3: Want To Know On How Sentiment Analysis Is Performed From Twitter’s Unstructured Data
Project Related To: Social Media Analytics
Project Description: With the increase in digitization, the amount of accessibility to social media for a common person has increased manifold. The Advent of technology not only comes with the advantages but also the disadvantages. Many people who have access to the internet do not restrain from giving to-the-point feedbacks and are not at all shying away. But sometimes, these reviews and feedbacks are given only because of the unhealthy competition.
At times, this is creating a lot of trouble to the genuine products and manufacturers, risking them to drop the plans of manufacturing those products. It also results in dropping of rating of those products.
Project 4: How To Increase The Probability Of ‘Click-Through Rate’ Of Ads Posted On Social Media
Project Related To: Social Media Analytics
Problem Description: The world is now experiencing the highest internet penetration ever. Companies without proper online presence hardly survive. In this context, increasing online visibility, especially when netizens perform a search on search engines is at its prime. There is fierce competition among companies to feature on the first page and being on the top of the search results; this is because people hardly ever move to pages beyond the first page to explore the results. Both top-line and bottom-line of companies are now greatly dependent on Social Media Presence.
a) How prominently your website appears in search results.
b) What should be done to be on the first page
Project 5: Analytics On Political Party Representatives
Project Related To: Social Media Analytics
Project Description: Citizens are resorting to posting messages on social media and the web to vent out the frustration or happiness associated with the daily activities going around. There is no transparency on how many promises were done by political party members at the time of the election. Lack of clarity on the performance of the elected representatives leading to some sections misguiding the society with false claims.
Project 6: HR ANALYTICS
Project Related To: Retail Organizations
Project Description: There is an ever-increasing focus on effective recruitment. An organization invests a lot of its time and resources in search of the potential candidates. The investment become loses if the selected candidates do not join the organization in the end.
Project 7: Warranty Claims
Project Related To: Retail sector
Project Description: Analysis to predict an item when sold, what is the probability that a customer would file for warranty and to understand important factors associated with them.
Project 8: Performance Prediction For Teachers and Students
Project Related To: Retail Sector
Project Description: Educational Data Mining (EDM) aims at knowledge discovery by applying mining techniques to identify hidden knowledge and patterns about students and teachers performance. The idea is to help improve performance by taking appropriate action based on the prediction. Early prediction helps in devising appropriate solutions to draw better results for both students and teachers.
Project 9: Students School Dropouts
Project Related To: Retail Sectors
Project Description: Educational Data Mining (EDM) aims at knowledge discovery by applying mining techniques to identify hidden knowledge and patterns about students dropouts from primary schools. The idea is to help improve the overall quality of primary education by taking appropriate action based on the prediction in school dropouts. Early prediction helps in devising appropriate solutions to help schools address students dropout.
Project 10: Chat Bot
Project Related To: Retail Sector
Project Description: Digitization is penetrating into the remote parts of even the third world in recent times. With the advent of advanced technology and digitization, the data that is being generated is very huge and the number of hands asking for queries on customer product and services is increasing at a rapid pace. Keeping the current and future demand in mind, it will and is becoming a challenging task for the clients to satisfy their customers in responding to their queries.
Project 11: How To Bring Data From Varied Sources To Generate Reports For Businesses To Draw Insights To Devise Strategies
Project Related To: Business Intelligence and Reporting
- Analytical capability of the reporting tool (Tableau) helps in drawing significant insights to make swift decisions
- Aesthetic visual pop coupled with analytics feature helps in knowing the potential of the data
- The extremely easy-to-integrate feature of reporting and analytical tool helps in collaborating data from varied sources, giving scope for robust visualization.
- The development of visually attractive reports of dashboard combines many sheets in single place giving room for faster analysis
Project 12: How To Generate A Single Report Personalized To Various Departments Using View Security Settings On Server End
Project Related To: Business Intelligence and Reporting
- Increased efficiency among departments
- Reduced Data leakages resulted in huge cost savings
- Parallel reports enhanced the resolution capability at a low time
- Actionable Insights are derived at a faster rate, resulting in profit generation
Project 13: How To Connect Big Data Source Engines To Tableau And Establish Dashboard Reporting Through Streaming Data
Project Related To: Business Intelligence and Reporting
Project Description: Want to know how to connect big data source engines to Tableau and establish dashboard reporting through streaming data.