Post Graduate Program in Data Science - Job Guarantee Program

Programming is an increasingly important skill. This course will establish your proficiency in handling basic programming concepts. This program will help you to gain basic programming concepts like data types, variables, strings, loops, functions, and software engineering concepts like multithreading and multitasking.

• Introduction to Programming

What is Programming?
Need to learn programming
Front-end vs Backend developer

• Understanding Different Programming Tools
Which Programming language High / low level programming Java, Python, Cpp, Ruby, C#, Machine Coding and JQuery PHP vs Ruby vs Python
• HTML and CSS (web)
Sublime Introduction and Basics of HTML Introduction and Basics of CSS Basics of JavaScript
• CPP
Introduction to C++ Installation and Basics of C++ Data Structures and Variables Operators in C++ Loop and If-Else Statements Pointers and its use

Statistics is the discipline of allocating a prospect through the classification, collection, and analysis of data. A substructure part of Data Science, this Course helps you in defining the statistical terms. The Course explains measures of central tendency and dispersion and comprehended skewness, correlation, regression, and distribution. It will enable you to make data-driven predictions through statistics and its essential applications of it.

• Introduction to Statistics and Data Science
Introduction to Statistics Introduction to Data Science
• Fundamentals of Descriptive Statistics
Measures of Central Tendency, Asymmetry, and Variability Practical Example: Descriptive Statistics Distributions
• Advanced Studies
Estimators and Estimates Hypothesis Testing: Introduction Practical Examples for Hypothesis Testing
• Regression Analysis
Fundamentals of Regression Analysis Assumptions of Regression Analysis Dealing with Categorical Data Practical Examples for Regression Analysis

The next step to becoming a data scientist is learning R—the most in-demand open source technology. R is the most powerful Data Science and analytics language, which has a steep learning curve and vigorous community. Data Science with R is becoming the technology of choice for organizations that are adopting the power of analytics for competitive expedience.

• Introduction to Business Analytics
Analytics Definition and Applications Data Science, Data Mining, Statistics Supervised vs Unsupervised Learning
• Introduction to R Programming
About R: R Installation Data Import and Export Operators in R
• Data Structures
Data Types Data Structures
• Data Management in R
Apply Family in R Aggregate and Table Commands Data Manipulation in R Managing Missing values in R
• Advanced Data Visualization
Introduction to basic graphs Introduction to GGPlot Library Plots: Scatter Plot, Histogram, Bar Plot, Box Plot, Heatmap, etc
• Descriptive Statistics in R
Applying function of Statistics in R
• Regression Analysis
Introduction to Regression analysis Building models for analysis Linear Regression Multi-Linear Regression Logistic Regression Assumptions of the model
• Decision Tree: Classification
Introduction to Decision Tree Building models for analysis CART Approach
• Clustering: K-means and Hierarchical
Introduction to Cluster Analysis Building models for analysis K-means Clustering Hierarchical Clustering
• Association Rule Analysis
Introduction to Association Rule Analysis Understanding requirements for ARA: Support, Confidence, and Lift Building models for analysis

This Data Science with Python course will set up your mastery of Data Science and analytics techniques using Python. In this Python for Data Science course, you will learn the essential concepts of Python programming and gain in-depth knowledge in data analytics, Machine Learning, data visualization, web scraping, and natural language processing

• Data Science Overview
Data Science, Data Mining, Statistics Supervised vs Unsupervised Learning
• Data Analytics and Business Application
Analytics Definition and Applications Why Analytics and Roles (Application and Roles in various domains..) Tools and Techniques in Analytics
• Python Environment Setup and Essentials
Anaconda - Download & Setup IDEs - Jupyter, Spyder, PyCharm Git - Setup and Configuration with IDEs Creating and Managing Analytics/ ML Projects
• Mathematical Computing with Python
Understanding NumPy Library Managing and manipulating data
• Scientific Computing with Python
Understanding SciPy Library Managing and manipulating data
• Data Manipulation with Pandas
Group Summaries Crosstab, Pivot and Reshape data Managing Missing Values Outliers Detection Managing indexes in pandas
• Data Visualization in Python using Matplotlib
Selection of Grap Libraries (matplotlib, seaborn, plotnine) Basic Graphs (histogram, barplot, boxplot, pie, etc) Managing plot parameters(size, title, axis, legend, etc) Advanced Graphs (correlation, heatmap, mosaic, etc) Exporting graphs

This Natural Language Processing course will give you a comprehensive detail of the science behind applying Machine Learning algorithms to process large amounts of natural language data. Learn the concepts of statistical machine translation and neural models, deep semantic similarity model (DSSM), neural knowledge base embedding, deep reinforcement learning technique, neural models applied in image captioning, and visual question answering using Python’s Natural Language Toolkit (NLTK).

• Introduction to NLP
Introduction to Natural Language Processing Components of NLP Applications of NLP Challenges and scope Data formats Text Processing Assisted Practice: Implement Text Processing Using Stemming and Regular Expression after Noise Removal and Convert It into List of Phrases Preprocessing in NLP-Tokenization, Lemmatization, Stemming, Normalisation, Stop Tweets Cleanup and Analysis Using Regular Expressions
• Feature Engineering on Text Data
N-Gram Bag of Words Document Term Matrix TF-IDF Levenshtein Distance Word Embedding(Word2Vec) Doc2vec PCA Word Analogies Dense Encoding Topic Modelling Assisted Practice: Word2vec Model Creation Assisted Practice: Word Analogies Demo Assisted Practice: Identify Topics from News Items Build Your Own News Search Engine
• Natural Language Understanding Techniques
Parts of Speech Tagging Dependency Parsing Constituency Parsing Morphological Parsing Named Entity Recognition Coreference Resolution Word Sense Disambiguation Fuzzy Search Document and Sentence Similarity Document Indexing Sentiment Analysis Assisted Practice: Analyzing the Disease and Instrument Name with the Action Performed Assisted Practice: Analyzing the Sentiments Assisted Practice: Extract City and Person Name from Text Identifying Top Product Feature from User Reviews
• Natural Language Generation
Retrieval based model Generative based model AIML Language Modelling Sentence Correction Assisted Practice: Create AIML Patterns for QnA on Mental Wellness Assisted Practice: To Predict the Next Word in a Sentence Create your Own Spell Checker
• NLP Libraries
Spacy NLTK Gensim TextBlob Stanford NLP LUIS Assisted Practice: Simplilearn Review Analysis Create your Own NLP Module
• NLP with Machine Learning & Deep Learning
Neural Machine Translation Introduction to RNN, LSTM LSTM Forward Pass LSTM Backprop through time Applications of LSTM Advanced LSTM Structures Encoder Decoder Attention Text Classification and Summarization Document Clustering Attention Mechanism Question Answering Engine Assisted Practice: Target Spam Words and Patterns Assisted Practice: Summarization of News Document Clustering for BBC News
• Speech Recognition Techniques
Basic concepts for voice/sound Sequential models Creating speech model Saving model Implementation/use cases Speech libraries Assisted Practice: Translation from Speech to Text Speech to Text: Extract Keywords from Audio Reviews

This Tableau training will help you master the various aspects of the program and gain skills such as building visualization, organizing data, and designing dashboards. You will also learn concepts of statistics, mapping, and data connection. Tableau is an essential asset to those wishing to succeed in Data Science.

• Introduction to Data Visualization and Power of Tableau
Comparison and benefits against reading raw numbers Real use cases from various business domains Some quick and powerful examples using Tableau without going into the technical details of Tableau Installing Tableau Tableau interface Connecting to Data Source Tableau data types Data preparation
• Architecture of Tableau
Installation of Tableau Desktop Architecture of Tableau Interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane, etc.) How to start with Tableau The ways to share and export the work done in Tableau
• Working with Metadata and Data Blending
Connection to Excel Cubes and PDFs Management of metadata and extracts Data preparation Joins (Left, Right, Inner, and Outer) and Union Dealing with NULL values, cross-database joining, data extraction, data blending, refresh extraction, incremental extraction, how to build extract, etc.
• Creation of Sets
Mark, highlight, sort, group, and use sets (creating and editing sets, IN/OUT, sets in hierarchies) Constant sets Computed sets, bins, etc
• Working with Filters
Filters (Addition and removal) Filtering continuous dates, dimensions, and measures Interactive Filters, marks card, and hierarchies How to create folders in Tableau Sorting in Tableau Types of sorting Filtering in Tableau Types of filters Filtering the order of operations
• Organizing Data and Visual Analytics
Using Formatting Pane to work with menu, fonts, alignments, settings, and copy-paste Formatting data using labels and tooltips Edit axes and annotations K-means cluster analysis Trend and reference lines Visual analytics in Tableau Forecasting, confidence interval, reference lines, and bands
• Working with Mapping Preview
Working on coordinate points Plotting longitude and latitude Editing unrecognized locations Customizing geocoding, polygon maps, WMS: web mapping services Working on the background image, including add image Plotting points on images and generating coordinates from them Map visualization, custom territories, map box, WMS map How to create map projects in Tableau Creating dual axes maps and editing locations
• Working with Calculations and Expressions
Calculation syntax and functions in Tableau Various types of calculations, including Table, String, Date, Aggregate, Logic, and Number LOD expressions, including concept and syntax Aggregation and replication with LOD expressions Nested LOD expressions Levels of details: fixed level, lower level, and higher level Quick table calculations The creation of calculated fields Predefined calculations How to validate
• Working with Parameters Preview
Creating parameters Parameters in calculations Using parameters with filters Column selection parameters Chart selection parameters How to use parameters in the filter session How to use parameters in calculated fields How to use parameters in the reference line
• Charts and Graphs
Dual axes graphs Histograms Single and dual axes Box plot Charts: motion, Pareto, funnel, pie, bar, line, bubble, bullet, scatter, and waterfall charts Maps: tree and heat maps Market basket analysis (MBA) Using Show me
• Dashboards and Stories
Building and formatting a dashboard using size, objects, views, filters, and legends Best practices for making creative as well as interactive dashboards using the actions Creating stories, including the intro of story points Creating as well as updating the story points Adding catchy visuals in stories Adding annotations with descriptions; dashboards and stories What is a dashboard? Highlight actions, URL actions, and filter actions Selecting and clearing values Best practices to create dashboards Dashboard examples; using Tableau workspace and Tableau interface Learning about Tableau joins Types of joins Tableau field types Saving as well as publishing data source Live vs extract connection Various file types
• Tableau Prep
Introduction to Tableau Prep How Tableau Prep helps quickly combine join, shape, and clean data for analysis Creation of smart examples with Tableau Prep Getting deeper insights into the data with great visual experience Making data preparation simpler and accessible Integrating Tableau Prep with Tableau analytical workflow Understanding the seamless process from data preparation to analysis with Tableau Prep
• Integration of Tableau with R & Hadoop
Introduction to R language Applications and use cases of R applications and use cases of R Learning R functions in Tableau The integration of Tableau with Hadoop

The Business Intelligence course curriculum is composed of modules which impart the essential knowledge along with developing interest and curiosity. You will earn a Henry Harvin certificate when you have put your learnings to the test and come out a winner. Once you have completed all requirements about the Business Intelligence course, our trainers would be honored to hand over the Course completion certificate to you, in person!

• Business Intelligence (BI) Concepts
Introduction to Business Intelligence The importance of Business Intelligence The relation between Business Intelligence and Data Warehouse Tools and Technologies in Business Intelligence area
• Microsoft Power BI (MSPBI) Introduction
Power BI Architecture Power BI introduction and overview Introduction Power BI desktop and Power BI in Excel
• Connecting Power BI with Different Data Sources
Connect to CSV files in Power BI Desktop Connect to Excel in Power BI Desktop Connect to text in Power BI Desktop Connect to SQL Server in Power BI Desktop Connect to a Web page from Power BI Desktop Connect to Direct SQL Query in Power BI Desktop
• Power Query for Data Transformation
Power Query Introduction Query Editor Query Editor Interface Column Transformations Data Type Adding Column Text Transformations Number Column Calculations Date and Time Calculations M built-in functions
• Data Modeling in Power BI
Introduction to DAX Calculated Columns Measures Calculated Tables Row Context vs Set Context Advanced calculations using Calculate functions Time Intelligence Functions
• Reports in Power BI
Create a new Power BI report The report editor in Power BI Add a page to a Power BI report Add a filter to a report in Power BI Save a report in Power BI About filters and highlighting in Power BI reports How to use report filters Analyze in Excel Change how visuals interact in a report Open a Power BI report in Reading View Go from Reading View to Editing View in Power BI Interact with a report in Editing View in Power BI Aggregates (sum, average, maximum, etc.) in Power BI Rename a report in Power BI Page display settings in a Power BI report Duplicate a report page in Power BI Delete a page from a Power BI report Rename a report page
• Reports & Visualization Types in Power BI
Types of visualization in a Power BI report Custom visualization to a Power BI report Download a custom visual from the gallery Getting started with color formatting and axis properties Change how a chart is sorted in a Power BI report Move, resize, and pop out a visualization in a Power BI report Drill down in a visualization in Power BI Histograms Basic Area chart Combo Chart in Power BI Customize visualization title, background, and legend Customize X-axis and Y-axis properties Doughnut charts in Power BI Enhanced Scatter charts in Power BI Funnel charts in Power BI KPI Visuals Radial Gauge charts in Power BI Scatter Charts in Power BI Slicers in Power BI Tree Maps in Power BI Waterfall charts in Power BI
• Dashboards in Power BI
Create a Power BI dashboard Dashboard tiles in Power BI Pin a tile to a Power BI dashboard from a report Power BI publisher for Excel Pin an entire report page to a Power BI dashboard Data alerts in Power BI service Add an image, text box, video, or web code to your dashboard Edit a tile — resize, move, rename, pin, delete, add hyperlink Tips for designing a great Power BI dashboard Print a dashboard, print a dashboard tile, print a report page Display dashboards and reports in Full Screen mode (TV mode) Display a dashboard tile in Focus mode Featured dashboards in Power BI Create a phone view of a dashboard Add an image to a dashboard
• Data Refresh in Power BI
Configure scheduled refresh Refresh a dataset created from a Power BI Desktop file – local Refresh a dataset created from a Power BI Desktop file – cloud Refresh a dataset created from an Excel workbook – local Disable privacy settings
• Projects — End to End Data Modeling & Visualization
Project 1 Project 2

SQL is the Leading Programming Language for relational databases. Manage and Code relational databases and database-driven applications. Create MySql database and database design. Master the Core Concepts. American National Standard Institute considers SQL as the most standard database

• SQL Overview
Introduction to SQL Installation and Getting Started
• SQL Manipulation
Relational Databases SQL Statements SQL commands
• JOIN
Introduction to JOIN Inner JOIN Left JOIN Right JOIN Full Outer JOIN Cross JOIN
• String Functions
Length Upper Lower Replace Trim, L Trim, R Trim Concatenation Substring List Aggregation
• Mathematical Functions
Ceil Random Setseed Round Power
• Data-Time Functions
Current Date and Time Age Extract
• Tuning Tips
Soft Delete vs Hard Delete Update vs Case String Functions Joins Schema

• Retail
• E-commerce
• Web & Social Media
• Banking
• Supply Chain
• Healthcare
• Insurance
• Entrepreneurship /Start-Ups
• Finance & Accounts

The candidate will be provided with two projects which will check the candidate’s understanding of the topics practically

• Project 1
• Project 2

• HR: Analyze the Attrition rate of Employees
• Sales: Predicting Department wise Sales
• Multi-Domain: Business Analytics Optimization
• Marketing: Website Trend Analysis
• Financial Analysis: Stock Market Prediction
• Finance: Analyze ETF Trends

• Neural Network
This module will equip the candidate with the knowledge of Nural Networks. Gain Comprehensive knowledge about the Activation Functions and feedforward neural network. Learn about backpropagation and gradient descent. Know about the full connected layer forward and backward pass. Get acquainted with Data Preprocessing, Data Augmentation, weight initialization, working with google collab, and more
• Computer Vision
This module will help the candidate to gain knowledge of Computer Vision. Learn to work with images. Gain knowledge about Convolutions 2D for images. Know about the CNN architectures. Get acquainted with the knowledge of Semantic segmentation using UNet, and more
• Natural Language Programming (NLP)
This module will equip the candidate with the knowledge of Natural Language Processing. Learn the Preprocessing in NLP-Tokenization, Lemmatization, Stemming, Normalisation, Stop words, BOW, TF-IDF. Know about Word embedding, POS Tagging, LSTM application, Encoder-Decoder attention, and more

The Machine Learning course will make you a Master in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will learn concepts and techniques, including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for your role with advanced Machine Learning knowledge.

- Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modeling

- Acquire practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach that includes working on four

major end-to-end projects and 25+ hands-on exercises

- Acquire thorough knowledge of the statistical and heuristic aspects of Machine Learning

- Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-n

means clustering, and more in Python

- Validate Machine Learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithms, which

include Boosting & Bagging techniques

- Comprehend the theoretical concepts and how they relate to the practical aspects of Machine Learning

- Gain expertise in Machine Learning using the Scikit-Learn package

- Use the Scikit-Learn package for natural language processing

Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides.